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Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of Surgery and Possible Associations with Cancer Relapse

Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of... applied sciences Article Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of Surgery and Possible Associations with Cancer Relapse 1 , 2 , † 3 , † 3 3 , 4 5 Alessia Vignoli , Elena Mori , Samantha Di Donato , Luca Malorni , Chiara Biagioni , 5 3 6 3 , 7 6 Matteo Benelli , Vanessa Calamai , Stefano Cantafio , Annamaria Parnofiello , Maddalena Baraghini , 6 3 5 4 1 , 2 Alessia Garzi , Francesca Del Monte , Dario Romagnoli , Ilenia Migliaccio , Claudio Luchinat , 1 , 2 , 3 , Leonardo Tenori * and Laura Biganzoli * Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy; vignoli@cerm.unifi.it (A.V.); luchinat@cerm.unifi.it (C.L.) Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy Department of Medical Oncology, New Hospital of Prato-S. Stefano, 59100 Prato, Italy; elena2.mori@uslcentro.toscana.it (E.M.); samantha.didonato@uslcentro.toscana.it (S.D.D.); luca.malorni@uslcentro.toscana.it (L.M.); vanessa.calamai@uslcentro.toscana.it (V.C.); annamaria.parnofiello@uslcentro.toscana.it (A.P.); francesca.delmonte@uslcentro.toscana.it (F.D.M.) “Sandro Pitigliani” Translational Research Unit, New Hospital of Prato-S. Stefano, 59100 Prato, Italy; ilenia.migliaccio@uslcentro.toscana.it Bioinformatics Unit, Medical Oncology Department, New Hospital of Prato-S. Stefano, 59100 Prato, Italy; chiara.biagioni@uslcentro.toscana.it (C.B.); matteo.benelli@uslcentro.toscana.it (M.B.); dario.romagnoli@uslcentro.toscana.it (D.R.) Citation: Vignoli, A.; Mori, E.; Di Department of Surgery, New Hospital of Prato-S. Stefano, 59100 Prato, Italy; Donato, S.; Malorni, L.; Biagioni, C.; stefano.cantafio@uslcentro.toscana.it (S.C.); maddalena.baraghini@uslcentro.toscana.it (M.B.); Benelli, M.; Calamai, V.; Cantafio, S.; alessia.garzi@uslcentro.toscana.it (A.G.) Parnofiello, A.; Baraghini, M.; et al. Department of Medicine (DAME), University of Udine, 33100 Udine, Italy Exploring Serum NMR-Based * Correspondence: tenori@cerm.unifi.it (L.T.); laura.biganzoli@uslcentro.toscana.it (L.B.); Tel.: +39-0554574281 (L.T.); +39-0574802520 (L.B.) Metabolomic Fingerprint of † Co-first authors. Colorectal Cancer Patients: Effects of Surgery and Possible Associations Abstract: Background: Colorectal cancer (CRC) is the fourth most commonly diagnosed and third with Cancer Relapse. Appl. Sci. 2021, most deadly cancer worldwide. Surgery is the main treatment option for early disease; however, a 11, 11120. https://doi.org/10.3390/ relevant proportion of CRC patients relapse. Here, variations among preoperative and postoperative app112311120 serum metabolomic fingerprint of CRC patients were studied, and possible associations between metabolic variations and cancer relapse were explored. Methods: A total of 41 patients with stage Academic Editor: John Patrick Alao I–III CRC, planned for radical resection, were enrolled. Serum samples, collected preoperatively Received: 21 October 2021 (t0) and 4–6 weeks after surgery before the start of any treatment (t1), were analyzed via NMR Accepted: 18 November 2021 spectroscopy. NMR data were analyzed using multivariate and univariate statistical approaches. Published: 23 November 2021 Results: Serum metabolomic fingerprints show differential clustering between t0 and t1 (82–85% accuracy). Pyruvate, HDL-related parameters, acetone, and 3-hydroxybutyrate appear to be the Publisher’s Note: MDPI stays neutral major players in this discrimination. Eight out of the 41 CRC patients enrolled developed cancer with regard to jurisdictional claims in relapse. Postoperative, relapsed patients show an increase of pyruvate and HDL-related parameters, published maps and institutional affil- and a decrease of Apo-A1 Apo-B100 ratio and VLDL-related parameters. Conclusions: Surgery iations. significantly alters the metabolomic fingerprint of CRC patients. Some metabolic changes seem to be associated with the development of cancer relapse. These data, if validated in a larger cohort, open new possibilities for risk stratification in patients with early-stage CRC. Copyright: © 2021 by the authors. Keywords: metabolomics; colorectal cancer; nuclear magnetic resonance; surgery; relapse Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons 1. Introduction Attribution (CC BY) license (https:// Colorectal cancer (CRC) is the third most frequently diagnosed cancer and the sec- creativecommons.org/licenses/by/ ond leading cause of cancer death worldwide [1–3]. A total of 80% of colon cancers are 4.0/). Appl. Sci. 2021, 11, 11120. https://doi.org/10.3390/app112311120 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, 11120 2 of 12 diagnosed at early stage (stage 1 to 3), and surgery is the primary treatment option with curative intent for this type of disease [4]. Unfortunately, about 35% of these patients develop cancer relapse, which, in the majority of cases, occurs within the first 2–3 years after surgery [5,6]. TNM staging at diagnosis, based on depth of tumor wall invasion (T), lymph node involvement (N), and presence of distant metastasis (M), is currently the principal instrument available to predict risk of relapse, and thus to identify patients who may have potential benefits from adjuvant treatment [7,8]. Colorectal cancer is a heterogeneous disease, even within the same pathological stage, with different characteristics of clinical onset and different individual response to treatment. Moreover, patients with stage II and III CRC are shown to have different prognoses, particularly those who receive adjuvant chemotherapy, with 5-year overall survival (OS) ranging between 50% and 90% [9]. Adjuvant chemotherapy is strongly indicated in stage III disease, which is associated with a reduction of the relative risk of death of 33%, and an absolute survival benefit of 5–10% [10]. In stage III, the use of oxaliplatin in addition to fluoropyrimidines yields a further significant advantage of about 5% in terms of disease-free survival (DFS) and OS. Conversely, the therapeutic indication in patients with stage II CRC is controversial, as treatment with 5-Fluorouracil has an absolute benefit of 3–4% [11,12]. In patients with clinicopathologically high-risk stage II disease [13], decision-making around adjuvant chemotherapy treatment needs to be carefully evaluated and discussed, considering also recurrence risk factors such as baseline carcinoembryonic antigen and vascular invasion [7]. There is no evidence to support the use of adjuvant chemotherapy in stage I disease. Con- sidering all the above mentioned data, identifying patients who are most likely to benefit from adjuvant chemotherapy and preventing the other patients from futile treatments and unnecessary exposure to toxicity is crucial in stage II disease. Early detection of disease relapse is extremely relevant in CRC, as radical surgical intervention in patients with oligometastatic CRC can achieve a proven survival benefit. Therefore, early detection of relapse could potentially increase cure rates. Postoperative surveillance with clinical, radiological, and markers examination is often unable to identify early metastatic disease and/or postoperative minimal residual disease. Based on these considerations, improved risk stratification tools are required to reduce the number of patients treated unnecessarily. Metabolomics is defined as the comprehensive measurement of the ensemble of metabolites present in a biological specimen, the so-called metabolome [14]. Metabolites represent, at the same time, the downstream output of the genome, transcriptome, and pro- teome, as well as the upstream input from various exogenous factors such as environment, lifestyle, diet, and drug administration [15]. In contrast to genomics, which indicates what might happen, metabolomic profiling/phenotyping captures what is actually happening in the body, and for this reason, in the last few years, metabolomics has been extensively applied in biomedical research [16–22]. Several relevant efforts to improve risk stratification in CRC have been made in the past years, considering mismatch repair (MMR) status, as well as BRAF and KRAS muta- tions, and the presence of tumor-derived circulating DNA [23,24]. Metabolomics has also emerged as a technique capable of contributing significantly in this setting [25–31]. Some of us have shown, in a cohort of elderly patients, that nuclear magnetic resonance (NMR)- based metabolomics can discriminate between early and metastatic CRC. This approach may be a useful tool to build a prognostic model capable of assessing the likelihood of cancer relapse, based on the degree to which a serum fingerprint derived from a patient with early disease resembles that of a metastatic patient [13]. The study presented here explores the variations among preoperative and postoper- ative metabolomic serum fingerprints of CRC patients, obtained via NMR spectroscopy (Figure 1); moreover, for the first time, to the best of our knowledge, possible associations between pre/post-surgery metabolic variations and cancer relapse are examined. Appl. Sci. 2021, 11, x FOR PEER REVIEW 3 of 12 Appl. Sci. 2021, 11, 11120 3 of 12 (Figure 1); moreover, for the first time, to the best of our knowledge, possible associations between pre/post-surgery metabolic variations and cancer relapse are examined. Figure 1. Study design. Figure 1. Study design. 2. Materials and Methods 2. Materials and Methods 2.1. Study Cohort 2.1. Study Cohort From June 2017 to August 2018, we prospectively enrolled 41 patients with histo- From June 2017 to August 2018, we prospectively enrolled 41 patients with histolog- logically diagnosed CRC, who were treated as per standard clinical practice at the Prato ically diagnosed CRC, who were treated as per standard clinical practice at the Prato Hos- Hospital. All patients enrolled met the following inclusion criteria: (i) female or male pa- pital. All patients enrolled met the following inclusion criteria: (i) female or male patients tients with radically operable heteroplasia of the colon/rectum (stage I, II, III); (ii) Eastern with radically operable heteroplasia of the colon/rectum (stage I, II, III); (ii) Eastern Coop- Cooperative Oncology Group Scale of Performance Status (ECOG PS) 0–1; (iii) patients of erative Oncology Group Scale of Performance Status (ECOG PS) 0–1; (iii) patients of age age  18 years. For all patients enrolled, the following data were collected: (i) demographic ≥18 years. For all patients enrolled, the following data were collected: (i) demographic data; (ii) clinical and histological characterization of the tumor; (iii) any other clinical data; (ii) clinical and histological characterization of the tumor; (iii) any other clinical in- information useful for the study (i.e., comorbidities, drug treatments). formation useful for the study (i.e., comorbidities, drug treatments). All patients signed informed consent before entry into the study. The present study All patients signed informed consent before entry into the study. The present study complies with the 1964 Declaration of Helsinki and its later amendments and received complies with the 1964 Declaration of Helsinki and its later amendments and received the the approval by the local ethics committee (Comitato Etico Regione Toscana—Area Vasta approval by the local ethics committee (Comitato Etico Regione Toscana—Area Vasta Centro, study number: 10208_bio). Centro, study number: 10208_bio). 2.2. Samples Collection 2.2. Samples Collection Serum samples were collected and stored following standard operating procedures Serum samples were collected and stored following standard operating procedures validated at international level [32]. Two  10 mL of overnight fasting peripheral blood validated at international level [32]. Two × 10 mL of overnight fasting peripheral blood were collected for each patient at the two timepoints (t0: before the radical tumor resection; were collected for each patient at the two timepoints (t0: before the radical tumor resec- t1: 4–6 weeks after surgery before the start of any adjuvant treatment) in serum vacutainer tion; t1: 4–6 weeks after surgery before the start of any adjuvant treatment) in serum vacu- and processed within one hour from phlebotomy. After clot formation at room temper- tainer and processed within one hour from phlebotomy. After clot formation at room tem- ature, tubes were centrifuged at 1600 RCF for 10 min at 4 C. Then, serum aliquots of perature, tubes were centrifuged at 1600 RCF for 10 min at 4 °C. Then, serum aliquots of 1 mL (labelled with an anonymized code) were immediately frozen at 80 C, pending 1 mL (labelled with an anonymized code) were immediately frozen at −80 °C, pending NMR analysis. NMR analysis. 2.3. NMR Analysis 2.3. NMR Analysis 2.3.1. Acquisition of NMR Data 2.3.1. Acquisition of NMR Data All NMR spectra were acquired using a Bruker 600 MHz spectrometer (Bruker BioSpin, Rheinstetten, Germany) operating at 600.13 MHz proton Larmor frequency, equipped with All NMR spectra were acquired using a Bruker 600 MHz spectrometer (Bruker Bio- Sp anin, automatic Rheinstreefrigerated tten, Germ(6 any) op C) sample eratinchanger g at 600 (SampleJet, .13 MHz p Brrot uker on Larm BioSpin). or fr Temperatur equency, e stabilization (approximately 0.1 K at the sample) was obtained using a BTO 2000 thermo- equipped with an automatic refrigerated (6 °C) sample changer (SampleJet, Bruker Bio- Sp couple. in). Tem Befor perat e ure NMR stab acquisition, ilization (ap to prequilibrate oximately 0.temperatur 1 K at the sam e at ple 310 ) was ob K, each taine sample d usinwas g maintained inside the NMR probe head for at least 300 s. The spectrometer was calibrated a BTO 2000 thermocouple. Before NMR acquisition, to equilibrate temperature at 310 K, each dailysamp , befor le ewas m any measur aintained in ement, side following the NMR pr strict obe h standar ead for d at operation least 300 s. pr The ocedur spect esr[ om 33]- to ensure high spectral quality and reproducibility. eter was calibrated daily, before any measurement, following strict standard operation procedure Serum s [samples 33] to ens contain ure high low spect molecular ral qualitweight y and re metabolites producibility. as well as high molecular weight macromolecules; for this reason, three different pulse sequences were used to en- able the selective detection of the different serum molecular components: a 1D spin echo Carr–Purcell–Meiboom–Gill sequence (CPMG) was used to selectively detect signals of low molecular weight metabolites, and a 1D diffusion-edited pulse sequence was used to selectively acquire the signals of high molecular weight components (i.e., lipids, lipopro- Appl. Sci. 2021, 11, 11120 4 of 12 teins, proteins). Moreover, a 1D nuclear Overhauser effect spectroscopy pulse sequence (NOESY) was applied to detect signals of all molecules present in concentrations above the NMR detection limit. A detailed description of sample preparation procedures, instrument configuration, and NMR parameters setting can be retrieved from our previous publication [16]. 2.3.2. Spectral Processing Before applying Fourier transform, free induction decays were multiplied by an exponential function equivalent to a 0.3 Hz line-broadening factor. Using automated routine of TopSpin 3.6 (Bruker BioSpin), Fourier-transformed spectra were corrected for phase and baseline distortions, and NOESY and CPMG spectra were also calibrated at the anomeric glucose H doublet at  5.24 ppm. Each 1D spectrum in the range between 0.2 and 10.0 ppm was segmented into chemical shift bins of 0.02 ppm, and the corresponding spectral areas were integrated using AssureNMR software (Bruker BioSpin). The spectral region containing residual water signal ( 5.12–4.38 ppm) was removed, and the dimension of the system was reduced to 453 bins. 2.4. Statistical Analysis All data analysis was executed in the “R” statistical environment [34]. Multivariate analysis was performed on binned spectra without any a priori knowledge of the metabo- lites present. Multilevel partial least square analysis (mPLS) [35,36] was performed to obtain data reduction (R script developed in-house). Support vector machine [37] applied on the first nine mPLS components was used for classification purposes. Models were evaluated by means of 100 cycles of a Monte Carlo cross-validation scheme (in-house- developed R script). In brief, 90% of the pairs of data, selected at random at each iteration, were used as a training set to build the model. Then, the remaining 10% was tested, and sensitivity, specificity, and accuracy (calculated according to the standard definitions) were assessed. Univariate analysis was conducted directly on the spectral regions associated with the metabolites/lipoproteins present in all serum samples at concentrations above the detection limit (>1 M). Metabolites and lipoprotein-related parameters were identified and quan- tified using the Bruker IVDr quantification platform [38]. Metabolites whose levels were lower than the limit of quantification (LOQ) were imputed with half the LOQ (Table S1). Nonparametric Wilcoxon signed-rank test was used to infer intraindividual differences between the two timepoints. The p-values were adjusted for multiple testing using the false discovery rate (FDR) procedure with Benjamini–Hochberg [39] correction at = 0.05. Wilcoxon rank-sum test was used to infer differences between metabolites/lipoproteins of free-from-disease and relapsed CRC patients. The p-values were not adjusted for multiple testing because the group of relapse patients is small, and therefore the correction would be too severe, increasing the risk of missing promising biomarkers. However, we are aware that this could increase the risk of a type I error. Univariate analysis on clinical data was performed using the Fisher test for categorical variables and the ANOVA test for continuous variables. Polyserial correlations between ordinal clinical variables (pT, N, grade, stage, ECOG PS) and metabolites were calculated using the function “polyserial” (R package “polycor”). Point-biserial correlations between dichotomous clinical variables (tumor localization, sex) and metabolites were calculated using the function “biserial.cor” (R package “ltm”). 3. Results 3.1. Characteristics of Enrolled Patients Forty-one patients were enrolled in the study (21 female and 20 male). The median age was 73 years (Table 1). Appl. Sci. 2021, 11, 11120 5 of 12 Table 1. Descriptive statistics of enrolled CC patients at the time of analysis. Stratified by Stratified by Progression Status Chemotherapy Treatment Whole Sample Not (N = 41) Relapsed Capecitabine XELOX No CT Relapsed p-Value p-Value (N = 8) (N = 9) (N = 10) (N = 22) (N = 33) Age at study Median (min; 73 (51;92) 71 (51;92) 78 (68;86) 0.032 77 (68;86) 65 (51;72) 78 (54;92) 0.001 entry max) F 21 (51%) 18 (55%) 3 (38%) 2 (22%) 7 (70%) 12 (55%) Sex 0.454 0.109 M 20 (49%) 15 (45%) 5 (62%) 7 (78%) 3 (30%) 10 (45%) PS 0 29 (71%) 23 (70%) 6 (75%) 7 (78%) 10 (100%) 12 (55%) ECOG PS 1 0.102 PS 1 8 (20%) 7 (21%) 1 (12%) 1 (11%) 0 (0%) 7 (32%) PS 2 4 (10%) 3 (9%) 1 (12%) 1 (11%) 0 (0%) 3 (14%) pT1 6 (15%) 6 (18%) 0 (0%) 0 (0%) 1 (10%) 5 (23%) pT2 8 (20%) 8 (24%) 0 (0%) 1 (11%) 1 (10%) 6 (27%) pT 0.086 0.477 pT3 23 (56%) 17 (52%) 6 (75%) 7 (78%) 7 (70%) 9 (41%) pT4 4 (10%) 2 (6%) 2 (25%) 1 (11%) 1 (10%) 2 (9%) N0 24 (59%) 23 (70%) 1 (12%) 3 (33%) 3 (30%) 18 (82%) N 0.005 0.005 N+ 17 (41%) 10 (30%) 7 (88%) 6 (67%) 7 (70%) 4 (18%) Stage I 11 (27%) 11 (33%) 0 (0%) 0 (0%) 0 (0%) 11 (50%) Stage II Low risk 2 (5%) 2 (6%) 0 (0%) 0 (0%) 0 (0%) 2 (9%) Stage risk 0.035 0.002 Stage II High risk 11 (27%) 10 (30%) 1 (12%) 3 (33%) 3 (30%) 5 (23%) Stage III 17 (41%) 10 (30%) 7 (88%) 6 (67%) 7 (70%) 4 (18%) G1 2 (5%) 1 (3%) 1 (12%) 1 (11%) 1 (10%) 0 (0%) G2 19 (48%) 17 (53%) 2 (25%) 2 (22%) 4 (40%) 13 (62%) Grading 0.168 0.205 G3 17 (42%) 13 (41%) 4 (50%) 5 (56%) 5 (50%) 7 (33%) G4 2 (5%) 1 (3%) 1 (12%) 1 (11%) 0 (0%) 1 (5%) NA 1 1 0 0 0 1 Left-sided 13 (32%) 12 (36%) 1 (12%) 3 (33%) 6 (60%) 4 (18%) 0.07 Localization 0.398 Right-sided 28 (68%) 21 (64%) 7 (88%) 6 (67%) 4 (40%) 18 (82%) No com. 13 (32%) 8 (24%) 5 (62%) 4 (44%) 3 (30%) 6 (27%) Comorbidities No vascular com. 8 (20%) 8 (24%) 0 (0%) 0.111 0 (0%) 3 (30%) 5 (23%) 0.519 Vascular com. 20 (49%) 17 (52%) 3 (38%) 5 (56%) 4 (40%) 11 (50%) Instable 1 (11%) 1 (14%) 0 (0%) 0 (0%) 0 (0%) 1 (33%) MSI 1 (11%) 1 (14%) 0 (0%) 0 (0%) 0 (0%) 1 (33%) MSI 1 0.278 Stable 7 (78%) 5 (71%) 2 (100%) 2 (100%) 4 (100%) 1 (33%) NA 32 26 6 7 6 19 Positive 1 (100%) 0 (0%) 1 (100%) 1 (100%) 0 (0%) 0 (0%) - - CDX2 NA 40 33 7 8 10 22 Mutated 5 (29%) 1 (11%) 4 (50%) 3 (50%) 0 (0%) 2 (67%) KRAS WT 12 (71%) 8 (89%) 4 (50%) 0.131 3 (50%) 8 (100%) 1 (33%) 0.042 NA 24 24 0 3 2 19 WT 13 (100%) 8 (100%) 5 (100%) 4 (100%) 8 (100%) 1 (100%) - - NRAS NA 28 25 3 5 2 21 Mutated 4 (24%) 3 (33%) 1 (12%) 1 (17%) 3 (38%) 0 (0%) BRAF WT 13 (76%) 6 (67%) 7 (88%) 0.576 5 (83%) 5 (62%) 3 (100%) 0.461 NA 24 24 0 3 2 19 ECOG PS: Eastern Cooperative Oncology Group Performance Status; pT: primary tumor size; N: regional lymph nodes; MSI: microsatel- lite instability. Most of the enrolled patients had a good Eastern Cooperative Oncology Group (ECOG) performance status (PS), with 29 patients (71%) having a PS 0. However, over one half of the patients (n = 38; 69%) had comorbidity, of which 20 patients had vascular comorbidity. By inclusion criteria, all patients have early-stage disease: 11 patients (27%) with stage I, 13 patients (32%) stage II, and 17 patients (41%) stage III. In particular, six patients had a Appl. Sci. 2021, 11, x FOR PEER REVIEW 6 of 12 Most of the enrolled patients had a good Eastern Cooperative Oncology Group (ECOG) performance status (PS), with 29 patients (71%) having a PS 0. However, over one Appl. Sci. 2021, 11, 11120 6 of 12 half of the patients (n = 38; 69%) had comorbidity, of which 20 patients had vascular comorbidity. By inclusion criteria, all patients have early-stage disease: 11 patients (27%) with stage I, 13 patients (32%) stage II, and 17 patients (41%) stage III. In particular, six patients pT1 (5 N0 e 1 N+), eight patients had a pT2 (6 N0 and 2 N+), 23 patients had a pT3 (18 N+), had a pT1 (5 N0 e 1 N+), eight patients had a pT2 (6 N0 and 2 N+), 23 patients had a pT3 and four patients had a pT4 (3 N0 and 1 N+). (18 N+), and four patients had a pT4 (3 N0 and 1 N+). Regarding the 13 patients with stage II, two were at low risk and 11 at high risk for Regarding the 13 patients with stage II, two were at low risk and 11 at high risk for the presence of lymphovascular invasion, T4 or G3–4. the presence of lymphovascular invasion, T4 or G3–4. The majority of tumors had intermediate (G2; 48%: N = 19) or high (G3–G4; 47% The majority of tumors had intermediate (G2; 48%: N = 19) or high (G3–G4; 47% N = N = 19) histologic grading, while G1 accounted for 5% of tumors in this population (N = 2). 19) histologic grading, while G1 accounted for 5% of tumors in this population (N = 2). A A total of 13 patients had left CRC and 28 right CRC (Table 1). total of 13 patients had left CRC and 28 right CRC (Table 1). Half of the patients (46%; n = 19) received adjuvant chemotherapy, in accordance Half of the patients (46%; n = 19) received adjuvant chemotherapy, in accordance with with clinical stage of disease. Nine patients received fluoropyrimidine monotherapy and clinical stage of disease. Nine patients received fluoropyrimidine monotherapy and 10 10 patients received polychemotherapy with oxaliplatin and fluoropyrimidine. Six out of patients received polychemotherapy with oxaliplatin and fluoropyrimidine. Six out of eleven patients at stage II at high risk received adjuvant treatment; the rest of them did not eleven patients at stage II at high risk received adjuvant treatment; the rest of them did receive chemotherapy for age or comorbidity not receive chemotherapy for age or comorbidity Thirteen out of the 17 patients with stage III disease received adjuvant treatment Thirteen out of the 17 patients with stage III disease received adjuvant treatment ac- according to tumor stage. At the last follow-up, 19% (n = 8) of patients had disease relapse cording to tumor stage. At the last follow-up, 19% (n = 8) of patients had disease relapse (Table 1). As expected, the patients with relapse had a history of stage III disease or stage II (Table 1). As expected, the patients with relapse had a history of stage III disease or stage at high risk. II at high risk. 3.2. Effects of Surgery on the Metabolome of CRC Patients 3.2. Effects of Surgery on the Metabolome of CRC Patients The mPLS analysis was performed to assess intraindividual variations between t0 The mPLS analysis was performed to assess intraindividual variations between t0 and t1 in the metabolomic fingerprints of CRC patients. The results obtained show sig- and t1 in the metabolomic fingerprints of CRC patients. The results obtained show signif- nificant differential clustering, with optimal separation of the two timepoints using each icant differential clustering, with optimal separation of the two timepoints using each type type of NMR spectra acquired, namely CPMG, NOESY, and DIFFUSION (Figure 2). All of NMR spectra acquired, namely CPMG, NOESY, and DIFFUSION (Figure 2). All models models classify t classify 0 and t1 sample t0 and t1 s with an samples accur with acy an inaccuracy the range 82– in the 85%, and the best resul range 82–85%, and ts were the best results obtained u weresin obtained g NOESY using specNOESY tra. These d spectra. ata indic These ate t data hat bot indicate h low molecu that both lar w low eight molecular me- tabolites and high molecular weight macromolecules (i.e., lipoproteins, proteins) contrib- weight metabolites and high molecular weight macromolecules (i.e., lipoproteins, proteins) contribute ute to the discri to themi discrimination. nation. Figure 2. Score plots of the first two components of the mPLS models calculated using each of the three typologies of NMR Figure 2. Score plots of the first two components of the mPLS models calculated using each of the three typologies of spectra acquired: (A) CPMG; (B) NOESY; (C) diffusion-edited. Discrimination accuracy of each model is reported. Each NMR spectra acquired: CPMG; NOESY; diffusion-edited. Discrimination accuracy of each model is reported. Each dot dot represents an NMR spectrum; dots are colored as follows: t0—orange, t1—turquoise. The first component mainly represents an NMR spectrum; dots are colored as follows: t0—orange, t1—turquoise. The first component mainly describes describes the differences between t0 and t1. The second component mainly reports the within-subject variation. the differences between t0 and t1. The second component mainly reports the within-subject variation. From univariate analysis emerges that after surgery there is a significant increase of From univariate analysis emerges that after surgery there is a significant increase of pyruvate, HDL cholesterol, HDL phospholipids, HDL Apo-A1, and HDL Apo-A2 (Figure pyruvate, HDL cholesterol, HDL phospholipids, HDL Apo-A1, and HDL Apo-A2 (Figure 3). Moreover, after surgery we observed a significant decrement of acetone, 3-hydroxybutyrate, LDL-Chol/HDL-Chol ratio, and Apo-A1/Apo-B100 ratio (Figure 3). Furthermore, several lipoprotein-related subfractions were shown to be significantly altered between t0 and t1 (Figure S1). These data point to a relevant rearrangement of the metabolic pathways related to lipoproteins, ketone bodies, and energy metabolism. Appl. Sci. 2021, 11, x FOR PEER REVIEW 7 of 12 3). Moreover, after surgery we observed a significant decrement of acetone, 3-hydroxy- butyrate, LDL-Chol/HDL-Chol ratio, and Apo-A1/Apo-B100 ratio (Figure 3). Further- more, several lipoprotein-related subfractions were shown to be significantly altered be- Appl. Sci. 2021, 11, 11120 7 of 12 tween t0 and t1 (Figure S1). These data point to a relevant rearrangement of the metabolic pathways related to lipoproteins, ketone bodies, and energy metabolism. Figure 3. Boxplots of the statistically significant metabolites and lipoproteins-related parameters discriminating CRC Figure 3. Boxplots of the statistically significant metabolites and lipoproteins-related parameters discriminating CRC pa- tients at t0 ( patients at t0 orange) and t1 (turquoise); (orange) and t1 (turquoise); p-vapl-values ues obtained u obtained sing using Wilcox Wilcoxon on signsigned-rank ed-rank test and adjuste test and adjusted d for FDR are re- for FDR are ported. *** p < 0.001; ** p < 0.01; * p < 0.05. reported. *** p < 0.001; ** p < 0.01; * p < 0.05. 3.3. Associations between Metabolome Variations after Surgery and Cancer Relapse 3.3. Associations between Metabolome Variations after Surgery and Cancer Relapse Eight out of the 41 CRC patients enrolled in the present study developed cancer Eight out of the 41 CRC patients enrolled in the present study developed cancer re- relapse in the three years after diagnosis. We hypothesized that different changes in lapse in the three years after diagnosis. We hypothesized that different changes in pre- preoperative and postoperative metabolomic serum profiles could be predictive of patients’ operative and postoperative metabolomic serum profiles could be predictive of patients’ prognosis. To explore this hypothesis, the difference between each metabolite/lipoprotein- prognosis. To explore this hypothesis, the difference between each metabolite/lipoprotein- related parameter at t1 and t0 was calculated, and each resulting difference analyzed via related parameter at t1 and t0 was calculated, and each resulting difference analyzed via univariate approaches to underline possible divergent behavior in free-from-disease and univariate approaches to underline possible divergent behavior in free-from-disease and relapsed CRC patients. Postoperative, relapsed CRC patients show a significant increase of relapsed CRC patients. Postoperative, relapsed CRC patients show a significant increase pyruvate, HDL Apo-A1, HDL Apo-A2, HDL cholesterol, HDL free cholesterol, and HDL of pyruvate, HDL Apo-A1, HDL Apo-A2, HDL cholesterol, HDL free cholesterol, and phospholipids, and a significant decrease of Apo-A1 Apo-B100 ratio, VLDL-5 cholesterol, VLDL-5 free cholesterol, and VLDL-5 phospholipids (Figure 4). Appl. Sci. 2021, 11, x FOR PEER REVIEW 8 of 12 Appl. Sci. 2021, 11, 11120 8 of 12 HDL phospholipids, and a significant decrease of Apo-A1 Apo-B100 ratio, VLDL-5 cho- lesterol, VLDL-5 free cholesterol, and VLDL-5 phospholipids (Figure 4). Figure 4. Boxplots of the differences between t1 and t0 discriminating free-from-disease (green) and relapsed (red) patients, Figure 4. Boxplots of the differences between t1 and t0 discriminating free-from-disease (green) and relapsed (red) pa- tients, only statistically significant metabolites and lipoproteins-related parameters are reported; p-values obtained using only statistically significant metabolites and lipoproteins-related parameters are reported; p-values obtained using Wilcoxon Wilcoxon signed-rank test are reported. ** p < 0.01; * p < 0.05. signed-rank test are reported. ** p < 0.01; * p < 0.05. 3.4. 3.4. Associations Associations betwee between n Metabolites Metabolites an and d CClinical linical Variables Variables Possible associations between metabolites/lipoproteins (main fractions) and clinical Possible associations between metabolites/lipoproteins (main fractions) and clinical variables were investigated. Results are reported in Table S2. variables were investigated. Results are reported in Table S2. Glycine and histidine showed statistically significant correlations with tumor size. Glycine and histidine showed statistically significant correlations with tumor size. Tyrosine correlates with tumor stage and regional lymph nodal spread (N). N also corre- Tyrosine correlates with tumor stage and regional lymph nodal spread (N). N also correlates lates with isoleucine, Apo-A1, and Apo-A2. Tumor localization (left or right colon) shows with isoleucine, Apo-A1, and Apo-A2. Tumor localization (left or right colon) shows correlations with acetone, cholesterol, LDL cholesterol, and Apo-B100. Interestingly a correlations with acetone, cholesterol, LDL cholesterol, and Apo-B100. Interestingly a panel panel of eight metabolic variables (N,N-Dimethylglycine, valine, dimethylsulfone, triglyc- of eight metabolic variables (N,N-Dimethylglycine, valine, dimethylsulfone, triglycerides, cholesterol, LDL cholesterol, Apo-A2, Apo-B100) correlates with the Eastern Cooperative Oncology Group Scale of Performance Status. Moreover, as expected, sex shows significant correlations with several metabolites/lipoproteins: creatine, creatinine, glutamine, glycine, isoleucine, leucine, formic acid, cholesterol, LDL cholesterol, HDL cholesterol, Apo-A1, Apo-A2, and Apo-B100. Of note, none of the examined metabolic features show significant correlation with tumor grade. Appl. Sci. 2021, 11, 11120 9 of 12 4. Discussion The primary option for the treatment of colorectal cancer is surgery. Adjuvant chemotherapy is strongly indicated in stage III disease and in stage II patients at high risk of relapse. Whereas, in low-risk stage II disease decision-making around adjuvant chemotherapy must be carefully evaluated. At present, postoperative surveillance via clinical, radiological and biomarkers examination often cannot identify early metastatic disease and/or postoperative micrometastatic residual disease. Based on these considerations, especially in stage II disease, improved risk-stratification tools are required to identify those patients who are most likely to benefit from adjuvant chemotherapy and need to be followed up more closely after surgery to timely detect systemic recurrence. On the other hand, accurate stratification instruments could prevent low-risk patients from unnecessary treatment and possible mild-to-severe adverse reactions. The analysis described in the present research article shows for the first time, to the best of our knowledge, the metabolomic variations among preoperative and postoperative NMR-based serum fingerprint of CRC patients. Furthermore, metabolomics as novel ap- proach for risk-stratification in CRC setting was evaluated by studying differences between pre- and postoperative serum samples of each patient enrolled. With this innovative ap- proach, each patient in the study population acts as his/her own control, thus eliminating noise from interindividual variability. Our data demonstrate that metabolomics profiles are influenced by the presence or absence of the cancerous mass. Indeed, the mPLS models calculated using each of the three NMR spectra acquired (namely, CPMG, NOESY, and DIFFUSION) show high discrimination accuracies (range 82–85%). This evidence poses an important question in terms of future study design, since sample collection when the tumor was still in place or after resection can significantly impact on metabolomic data. From the univariate analysis, it emerges that after surgery, there is a significant increase of pyruvate, HDL cholesterol, HDL phospholipids, HDL Apo-A1, and HDL Apo-A2. Moreover, we observed, postoperative, a significant decrement of acetone, 3-hydroxybutyrate, LDL- Chol/HDL-Chol ratio, and Apo-A1/Apo-B100 ratio. These data point to a relevant rewiring of the metabolic pathways associated to lipoproteins, ketone bodies, and energy metabolism. Depletion of pyruvate and increase of ketone bodies has been observed in sera of metastatic CRC patients with respect to healthy controls, and this evidence has been associ- ated with an altered energy metabolism, probably reflecting an increased gluconeogenesis and fatty acid oxidation [31]. It is interesting to note that in our dataset, these three metabolites show trend inversions after surgery. Our data show an increase of several HDL-Chol and a decrease of LDL-Chol lipoprotein- related parameters post-surgery. This may be explained by the fact that, after cancer resection, an improvement in the inflammatory status of the gut is achieved, allowing for an improved lipid metabolism and lipid assimilation in the absence of the tumor. Strikingly, despite the low number of recurrence events registered, it is peculiar that the difference in HDL-Chol is particularly marked in relapsed patients and is coupled with a decrease of VLDL-Chol. It has been observed that in colorectal cancerous tissue, the levels of cholesterol and triglycerides were reduced and HDL-Cholesterol level increased, indicating that CRC development destroys the physiological balance of lipids and lipopro- teins, leading to lipid metabolic disorders [40]. Preclinical and clinical studies have already investigated the role of cholesterol in CRC progression; however, a clear understanding of the molecular mechanism linking these two entities is still lacking [40,41]. In conclusion, our results show that surgery can affect the metabolomic and lipidomic profiles of CRC patients and they point to possible associations between these metabolic changes and cancer recurrence. This study is based on a small population of CRC patients in which a very limited number of recurrence events are present; therefore, at present, results are only speculative and require further confirmation. In order to validate these findings in a general population, we are conducting a multicentric prospective trial focused on high-risk stage disease, the LIquid BIopsy and METabolomics in colon cancer (LIBIMET) Appl. Sci. 2021, 11, 11120 10 of 12 study. LIBIMET aims primarily at redefining the risk of relapse in patients with high-risk, early-stage colon cancer by combining of ctDNA and serum metabolomics. 5. Conclusions Taken together, the data here presented highlight the notion that CRC can induce metabolic changes that are reflected at a systemic level and can be detected in serum. This evidence suggests that our approach aimed at detecting micrometastatic CRC by assessing its metabolomic fingerprint in serum is correct, and that this may be exploited for biomarker-oriented research to contribute towards better management of colorectal cancer. Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/app112311120/s1. Table S1: Data completeness for the different metabolites quantified in the serum samples analyzed via NMR. LOQ = limit of quantification. Table S2: Correlation between clinical data and metabolites. Correlation coefficients and p-values are reported in table. Figure S1: Boxplots of the statistically significant lipoproteins-related parameters discriminating of CRC patients at t0 (orange) and t1 (turquoise); p-values obtained using Wilcoxon signed-rank test and adjusted for FDR are reported. Author Contributions: Study conception and design, E.M., S.D.D., C.L., L.T. and L.B.; patient enrolment and management: E.M., S.D.D., V.C., S.C., M.B. (Maddalena Baraghini) and A.G.; collection of clinical data and serum samples: E.M., S.D.D., C.B., M.B. (Maddalena Baraghini), V.C., A.P., S.C., M.B. (Matteo Benelli), A.G. and F.D.M.; NMR analysis: A.V.; statistical analysis, biostatistics, and computational analysis, A.V., C.B., M.B. (Matteo Benelli), D.R. and L.T.; results interpretation, A.V., E.M., S.D.D., L.M., I.M., C.L., L.T. and L.B.; writing—original draft preparation: A.V. and E.M.; writing—review and editing: A.V., E.M., S.D.D., L.M., C.B., M.B. (Matteo Benelli), V.C., A.P., S.C., M.B. (Maddalena Baraghini), A.G., F.D.M., D.R., I.M., C.L., L.T. and L.B.; supervision, C.L, L.T. and L.B. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the local ethics committee (Comitato Etico Regione Toscana—Area Vasta Centro, study number: 10208_bio). Informed Consent Statement: Written informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data and R script are available from the corresponding authors upon reasonable request. Acknowledgments: In memoriam of Angelo Di Leo who passed away on 13 June 2021, while this work was being completed. The authors acknowledge the Fondazione Pitigliani per la lotta contro i tumori ONLUS for its support. The authors acknowledge Instruct-ERIC, a Landmark ESFRI project, and specifically the CERM/CIRMMP Italy Centre. Alessia Vignoli was supported by an AIRC fellowship for Italy. Conflicts of Interest: The authors declare no conflict of interest. References 1. NCCN Guidelines for Colon Cancer 2021. Available online: https://www.nccn.org/guidelines/guidelines-detail (accessed on 5 November 2021). 2. Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer Statistics, 2020. CA Cancer J. Clin. 2020, 70, 7–30. [CrossRef] [PubMed] 3. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBO- CAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [CrossRef] [PubMed] 4. AIOM: Linee Guida Tumori del Colon 2020. Available online: https://www.aiom.it/linee-guida-aiom-2020-tumori-del-colon/ (accessed on 5 November 2021). 5. Reinert, T.; Schøler, L.V.; Thomsen, R.; Tobiasen, H.; Vang, S.; Nordentoft, I.; Lamy, P.; Kannerup, A.-S.; Mortensen, F.V.; Stribolt, K.; et al. Analysis of Circulating Tumour DNA to Monitor Disease Burden Following Colorectal Cancer Surgery. Gut 2016, 65, 625–634. [CrossRef] [PubMed] Appl. Sci. 2021, 11, 11120 11 of 12 6. Guraya, S.Y. Pattern, Stage, and Time of Recurrent Colorectal Cancer after Curative Surgery. Clin. Colorectal Cancer 2019, 18, e223–e228. [CrossRef] 7. Hall, M.J.; Morris, A.M.; Sun, W. Precision Medicine Versus Population Medicine in Colon Cancer: From Prospects of Prevention, Adjuvant Chemotherapy, and Surveillance. Am. Soc. Clin. Oncol. Educ. Book 2018, 38, 220–230. [CrossRef] [PubMed] 8. Dienstmann, R.; Mason, M.J.; Sinicrope, F.A.; Phipps, A.I.; Tejpar, S.; Nesbakken, A.; Danielsen, S.A.; Sveen, A.; Buchanan, D.D.; Clendenning, M.; et al. Prediction of Overall Survival in Stage II and III Colon Cancer beyond TNM System: A Retrospective, Pooled Biomarker Study. Ann. Oncol. 2017, 28, 1023–1031. [CrossRef] 9. SEER Cancer Statistics Review, 1975–2017. Available online: https://seer.cancer.gov/csr/1975_2017/index.html (accessed on 30 March 2021). 10. Renfro, L.A.; Grothey, A.; Xue, Y.; Saltz, L.B.; André, T.; Twelves, C.; Labianca, R.; Allegra, C.J.; Alberts, S.R.; Loprinzi, C.L.; et al. ACCENT-Based Web Calculators to Predict Recurrence and Overall Survival in Stage III Colon Cancer. J. Natl. Cancer Inst. 2014, 106, dju333. [CrossRef] 11. Benson, A.B.; Schrag, D.; Somerfield, M.R.; Cohen, A.M.; Figueredo, A.T.; Flynn, P.J.; Krzyzanowska, M.K.; Maroun, J.; McAllister, P.; Van Cutsem, E.; et al. American Society of Clinical Oncology Recommendations on Adjuvant Chemotherapy for Stage II Colon Cancer. J. Clin. Oncol. 2004, 22, 3408–3419. [CrossRef] 12. Kumar, A.; Kennecke, H.F.; Renouf, D.J.; Lim, H.J.; Gill, S.; Woods, R.; Speers, C.; Cheung, W.Y. Adjuvant Chemotherapy Use and Outcomes of Patients with High-Risk versus Low-Risk Stage II Colon Cancer. Cancer 2015, 121, 527–534. [CrossRef] 13. Di Donato, S.; Vignoli, A.; Biagioni, C.; Malorni, L.; Mori, E.; Tenori, L.; Calamai, V.; Parnofiello, A.; Di Pierro, G.; Migliaccio, I.; et al. A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting. Cancers 2021, 13, 2762. [CrossRef] [PubMed] 14. Nicholson, J.K.; Lindon, J.C. Systems Biology: Metabonomics. Nature 2008, 455, 1054–1056. [CrossRef] 15. Vignoli, A.; Risi, E.; McCartney, A.; Migliaccio, I.; Moretti, E.; Malorni, L.; Luchinat, C.; Biganzoli, L.; Tenori, L. Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer. Int. J. Mol. Sci. 2021, 22, 4687. [CrossRef] 16. Vignoli, A.; Ghini, V.; Meoni, G.; Licari, C.; Takis, P.G.; Tenori, L.; Turano, P.; Luchinat, C. High-Throughput Metabolomics by 1D NMR. Angew. Chem. Int. Ed. 2019, 58, 968–994. [CrossRef] [PubMed] 17. Wishart, D.S. Emerging Applications of Metabolomics in Drug Discovery and Precision Medicine. Nat. Rev. Drug Discov. 2016, 15, 473–484. [CrossRef] [PubMed] 18. Vignoli, A.; Tenori, L.; Giusti, B.; Valente, S.; Carrabba, N.; Baizi, D.; Barchielli, A.; Marchionni, N.; Gensini, G.F.; Marcucci, R.; et al. Differential Network Analysis Reveals Metabolic Determinants Associated with Mortality in Acute Myocardial Infarction Patients and Suggests Potential Mechanisms Underlying Different Clinical Scores Used To Predict Death. J. Proteome Res. 2020, 19, 949–961. [CrossRef] [PubMed] 19. Zhang, L.; Zhu, B.; Zeng, Y.; Shen, H.; Zhang, J.; Wang, X. Clinical Lipidomics in Understanding of Lung Cancer: Opportunity and Challenge. Cancer Lett. 2020, 470, 75–83. [CrossRef] [PubMed] 20. Pietzner, M.; Stewart, I.D.; Raffler, J.; Khaw, K.-T.; Michelotti, G.A.; Kastenmüller, G.; Wareham, N.J.; Langenberg, C. Plasma Metabolites to Profile Pathways in Noncommunicable Disease Multimorbidity. Nat. Med. 2021, 27, 471–479. [CrossRef] [PubMed] 21. Vignoli, A.; Paciotti, S.; Tenori, L.; Eusebi, P.; Biscetti, L.; Chiasserini, D.; Scheltens, P.; Turano, P.; Teunissen, C.; Luchinat, C.; et al. Fingerprinting Alzheimer ’s Disease by 1H Nuclear Magnetic Resonance Spectroscopy of Cerebrospinal Fluid. J. Proteome Res. 2020, 19, 1696–1705. [CrossRef] [PubMed] 22. Vignoli, A.; Tenori, L.; Giusti, B.; Takis, P.G.; Valente, S.; Carrabba, N.; Balzi, D.; Barchielli, A.; Marchionni, N.; Gensini, G.F.; et al. NMR-Based Metabolomics Identifies Patients at High Risk of Death within Two Years after Acute Myocardial Infarction in the AMI-Florence II Cohort. BMC Med. 2019, 17, 3. [CrossRef] 23. Auclin, E.; Zaanan, A.; Vernerey, D.; Douard, R.; Gallois, C.; Laurent-Puig, P.; Bonnetain, F.; Taieb, J. Subgroups and Prognostica- tion in Stage III Colon Cancer: Future Perspectives for Adjuvant Therapy. Ann. Oncol. 2017, 28, 958–968. [CrossRef] 24. Copija, A.; Waniczek, D.; Witkos, ´ A.; Walkiewicz, K.; Nowakowska-Zajdel, E. Clinical Significance and Prognostic Relevance of Microsatellite Instability in Sporadic Colorectal Cancer Patients. Int. J. Mol. Sci. 2017, 18, 107. [CrossRef] [PubMed] 25. Nannini, G.; Meoni, G.; Amedei, A.; Tenori, L. Metabolomics Profile in Gastrointestinal Cancers: Update and Future Perspectives. World J. Gastroenterol. 2020, 26, 2514–2532. [CrossRef] [PubMed] 26. Ma, Y.; Zhang, P.; Wang, F.; Liu, W.; Yang, J.; Qin, H. An Integrated Proteomics and Metabolomics Approach for Defining Oncofetal Biomarkers in the Colorectal Cancer. Ann. Surg. 2012, 255, 720–730. [CrossRef] 27. Nishiumi, S.; Kobayashi, T.; Ikeda, A.; Yoshie, T.; Kibi, M.; Izumi, Y.; Okuno, T.; Hayashi, N.; Kawano, S.; Takenawa, T.; et al. A Novel Serum Metabolomics-Based Diagnostic Approach for Colorectal Cancer. PLoS ONE 2012, 7, e40459. [CrossRef] 28. Qiu, Y.; Cai, G.; Zhou, B.; Li, D.; Zhao, A.; Xie, G.; Li, H.; Cai, S.; Xie, D.; Huang, C.; et al. A Distinct Metabolic Signature of Human Colorectal Cancer with Prognostic Potential. Clin. Cancer Res. 2014, 20, 2136–2146. [CrossRef] 29. Farshidfar, F.; Weljie, A.M.; Kopciuk, K.; Buie, W.D.; Maclean, A.; Dixon, E.; Sutherland, F.R.; Molckovsky, A.; Vogel, H.J.; Bathe, O.F. Serum Metabolomic Profile as a Means to Distinguish Stage of Colorectal Cancer. Genome Med. 2012, 4, 42. [CrossRef] [PubMed] 30. Farshidfar, F.; Weljie, A.M.; Kopciuk, K.A.; Hilsden, R.; McGregor, S.E.; Buie, W.D.; MacLean, A.; Vogel, H.J.; Bathe, O.F. A Validated Metabolomic Signature for Colorectal Cancer: Exploration of the Clinical Value of Metabolomics. Br. J. Cancer 2016, 115, 848–857. [CrossRef] Appl. Sci. 2021, 11, 11120 12 of 12 31. Bertini, I.; Cacciatore, S.; Jensen, B.V.; Schou, J.V.; Johansen, J.S.; Kruhøffer, M.; Luchinat, C.; Nielsen, D.L.; Turano, P. Metabolomic NMR Fingerprinting to Identify and Predict Survival of Patients with Metastatic Colorectal Cancer. Cancer Res. 2012, 72, 356–364. [CrossRef] [PubMed] 32. ISO/DIS 23118 Molecular In Vitro Diagnostic Examinations—Specifications for Pre-Examination Processes in Metabolomics in Urine, Venous Blood Serum and Plasma. Available online: https://www.iso.org/obp/ui/#iso:std:iso:23118:ed-1:v1:en (accessed on 4 June 2021). 33. Bruzzone, C.; Bizkarguenaga, M.; Gil-Redondo, R.; Diercks, T.; Arana, E.; García de Vicuña, A.; Seco, M.; Bosch, A.; Palazón, A.; San Juan, I.; et al. SARS-CoV-2 Infection Dysregulates the Metabolomic and Lipidomic Profiles of Serum. iScience 2020, 23, 101645. [CrossRef] 34. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2014. 35. van Velzen, E.J.J.; Westerhuis, J.A.; van Duynhoven, J.P.M.; van Dorsten, F.A.; Hoefsloot, H.C.J.; Jacobs, D.M.; Smit, S.; Draijer, R.; Kroner, C.I.; Smilde, A.K. Multilevel Data Analysis of a Crossover Designed Human Nutritional Intervention Study. J. Proteome Res. 2008, 7, 4483–4491. [CrossRef] 36. Westerhuis, J.A.; van Velzen, E.J.; Hoefsloot, H.C.; Smilde, A.K. Multivariate Paired Data Analysis: Multilevel PLSDA versus OPLSDA. Metabolomics 2010, 6, 119–128. [CrossRef] 37. Cortes, C.; Vapnik, V. Support-Vector Networks. J. Mach. Learn. Res. 1995, 20, 273–297. [CrossRef] 38. Jiménez, B.; Holmes, E.; Heude, C.; Tolson, R.F.; Harvey, N.; Lodge, S.L.; Chetwynd, A.J.; Cannet, C.; Fang, F.; Pearce, J.T.M.; et al. Quantitative Lipoprotein Subclass and Low Molecular Weight Metabolite Analysis in Human Serum and Plasma by 1H NMR Spectroscopy in a Multilaboratory Trial. Anal. Chem. 2018, 90, 11962–11971. [CrossRef] 39. Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B 1995, 57, 289–300. [CrossRef] 40. Zhang, X.; Zhao, X.-W.; Liu, D.-B.; Han, C.-Z.; Du, L.-L.; Jing, J.-X.; Wang, Y. Lipid Levels in Serum and Cancerous Tissues of Colorectal Cancer Patients. World J. Gastroenterol. 2014, 20, 8646–8652. [CrossRef] [PubMed] 41. Mayengbam, S.S.; Singh, A.; Pillai, A.D.; Bhat, M.K. Influence of Cholesterol on Cancer Progression and Therapy. Transl. Oncol. 2021, 14, 101043. 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Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of Surgery and Possible Associations with Cancer Relapse

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applied sciences Article Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of Surgery and Possible Associations with Cancer Relapse 1 , 2 , † 3 , † 3 3 , 4 5 Alessia Vignoli , Elena Mori , Samantha Di Donato , Luca Malorni , Chiara Biagioni , 5 3 6 3 , 7 6 Matteo Benelli , Vanessa Calamai , Stefano Cantafio , Annamaria Parnofiello , Maddalena Baraghini , 6 3 5 4 1 , 2 Alessia Garzi , Francesca Del Monte , Dario Romagnoli , Ilenia Migliaccio , Claudio Luchinat , 1 , 2 , 3 , Leonardo Tenori * and Laura Biganzoli * Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy; vignoli@cerm.unifi.it (A.V.); luchinat@cerm.unifi.it (C.L.) Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy Department of Medical Oncology, New Hospital of Prato-S. Stefano, 59100 Prato, Italy; elena2.mori@uslcentro.toscana.it (E.M.); samantha.didonato@uslcentro.toscana.it (S.D.D.); luca.malorni@uslcentro.toscana.it (L.M.); vanessa.calamai@uslcentro.toscana.it (V.C.); annamaria.parnofiello@uslcentro.toscana.it (A.P.); francesca.delmonte@uslcentro.toscana.it (F.D.M.) “Sandro Pitigliani” Translational Research Unit, New Hospital of Prato-S. Stefano, 59100 Prato, Italy; ilenia.migliaccio@uslcentro.toscana.it Bioinformatics Unit, Medical Oncology Department, New Hospital of Prato-S. Stefano, 59100 Prato, Italy; chiara.biagioni@uslcentro.toscana.it (C.B.); matteo.benelli@uslcentro.toscana.it (M.B.); dario.romagnoli@uslcentro.toscana.it (D.R.) Citation: Vignoli, A.; Mori, E.; Di Department of Surgery, New Hospital of Prato-S. Stefano, 59100 Prato, Italy; Donato, S.; Malorni, L.; Biagioni, C.; stefano.cantafio@uslcentro.toscana.it (S.C.); maddalena.baraghini@uslcentro.toscana.it (M.B.); Benelli, M.; Calamai, V.; Cantafio, S.; alessia.garzi@uslcentro.toscana.it (A.G.) Parnofiello, A.; Baraghini, M.; et al. Department of Medicine (DAME), University of Udine, 33100 Udine, Italy Exploring Serum NMR-Based * Correspondence: tenori@cerm.unifi.it (L.T.); laura.biganzoli@uslcentro.toscana.it (L.B.); Tel.: +39-0554574281 (L.T.); +39-0574802520 (L.B.) Metabolomic Fingerprint of † Co-first authors. Colorectal Cancer Patients: Effects of Surgery and Possible Associations Abstract: Background: Colorectal cancer (CRC) is the fourth most commonly diagnosed and third with Cancer Relapse. Appl. Sci. 2021, most deadly cancer worldwide. Surgery is the main treatment option for early disease; however, a 11, 11120. https://doi.org/10.3390/ relevant proportion of CRC patients relapse. Here, variations among preoperative and postoperative app112311120 serum metabolomic fingerprint of CRC patients were studied, and possible associations between metabolic variations and cancer relapse were explored. Methods: A total of 41 patients with stage Academic Editor: John Patrick Alao I–III CRC, planned for radical resection, were enrolled. Serum samples, collected preoperatively Received: 21 October 2021 (t0) and 4–6 weeks after surgery before the start of any treatment (t1), were analyzed via NMR Accepted: 18 November 2021 spectroscopy. NMR data were analyzed using multivariate and univariate statistical approaches. Published: 23 November 2021 Results: Serum metabolomic fingerprints show differential clustering between t0 and t1 (82–85% accuracy). Pyruvate, HDL-related parameters, acetone, and 3-hydroxybutyrate appear to be the Publisher’s Note: MDPI stays neutral major players in this discrimination. Eight out of the 41 CRC patients enrolled developed cancer with regard to jurisdictional claims in relapse. Postoperative, relapsed patients show an increase of pyruvate and HDL-related parameters, published maps and institutional affil- and a decrease of Apo-A1 Apo-B100 ratio and VLDL-related parameters. Conclusions: Surgery iations. significantly alters the metabolomic fingerprint of CRC patients. Some metabolic changes seem to be associated with the development of cancer relapse. These data, if validated in a larger cohort, open new possibilities for risk stratification in patients with early-stage CRC. Copyright: © 2021 by the authors. Keywords: metabolomics; colorectal cancer; nuclear magnetic resonance; surgery; relapse Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons 1. Introduction Attribution (CC BY) license (https:// Colorectal cancer (CRC) is the third most frequently diagnosed cancer and the sec- creativecommons.org/licenses/by/ ond leading cause of cancer death worldwide [1–3]. A total of 80% of colon cancers are 4.0/). Appl. Sci. 2021, 11, 11120. https://doi.org/10.3390/app112311120 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, 11120 2 of 12 diagnosed at early stage (stage 1 to 3), and surgery is the primary treatment option with curative intent for this type of disease [4]. Unfortunately, about 35% of these patients develop cancer relapse, which, in the majority of cases, occurs within the first 2–3 years after surgery [5,6]. TNM staging at diagnosis, based on depth of tumor wall invasion (T), lymph node involvement (N), and presence of distant metastasis (M), is currently the principal instrument available to predict risk of relapse, and thus to identify patients who may have potential benefits from adjuvant treatment [7,8]. Colorectal cancer is a heterogeneous disease, even within the same pathological stage, with different characteristics of clinical onset and different individual response to treatment. Moreover, patients with stage II and III CRC are shown to have different prognoses, particularly those who receive adjuvant chemotherapy, with 5-year overall survival (OS) ranging between 50% and 90% [9]. Adjuvant chemotherapy is strongly indicated in stage III disease, which is associated with a reduction of the relative risk of death of 33%, and an absolute survival benefit of 5–10% [10]. In stage III, the use of oxaliplatin in addition to fluoropyrimidines yields a further significant advantage of about 5% in terms of disease-free survival (DFS) and OS. Conversely, the therapeutic indication in patients with stage II CRC is controversial, as treatment with 5-Fluorouracil has an absolute benefit of 3–4% [11,12]. In patients with clinicopathologically high-risk stage II disease [13], decision-making around adjuvant chemotherapy treatment needs to be carefully evaluated and discussed, considering also recurrence risk factors such as baseline carcinoembryonic antigen and vascular invasion [7]. There is no evidence to support the use of adjuvant chemotherapy in stage I disease. Con- sidering all the above mentioned data, identifying patients who are most likely to benefit from adjuvant chemotherapy and preventing the other patients from futile treatments and unnecessary exposure to toxicity is crucial in stage II disease. Early detection of disease relapse is extremely relevant in CRC, as radical surgical intervention in patients with oligometastatic CRC can achieve a proven survival benefit. Therefore, early detection of relapse could potentially increase cure rates. Postoperative surveillance with clinical, radiological, and markers examination is often unable to identify early metastatic disease and/or postoperative minimal residual disease. Based on these considerations, improved risk stratification tools are required to reduce the number of patients treated unnecessarily. Metabolomics is defined as the comprehensive measurement of the ensemble of metabolites present in a biological specimen, the so-called metabolome [14]. Metabolites represent, at the same time, the downstream output of the genome, transcriptome, and pro- teome, as well as the upstream input from various exogenous factors such as environment, lifestyle, diet, and drug administration [15]. In contrast to genomics, which indicates what might happen, metabolomic profiling/phenotyping captures what is actually happening in the body, and for this reason, in the last few years, metabolomics has been extensively applied in biomedical research [16–22]. Several relevant efforts to improve risk stratification in CRC have been made in the past years, considering mismatch repair (MMR) status, as well as BRAF and KRAS muta- tions, and the presence of tumor-derived circulating DNA [23,24]. Metabolomics has also emerged as a technique capable of contributing significantly in this setting [25–31]. Some of us have shown, in a cohort of elderly patients, that nuclear magnetic resonance (NMR)- based metabolomics can discriminate between early and metastatic CRC. This approach may be a useful tool to build a prognostic model capable of assessing the likelihood of cancer relapse, based on the degree to which a serum fingerprint derived from a patient with early disease resembles that of a metastatic patient [13]. The study presented here explores the variations among preoperative and postoper- ative metabolomic serum fingerprints of CRC patients, obtained via NMR spectroscopy (Figure 1); moreover, for the first time, to the best of our knowledge, possible associations between pre/post-surgery metabolic variations and cancer relapse are examined. Appl. Sci. 2021, 11, x FOR PEER REVIEW 3 of 12 Appl. Sci. 2021, 11, 11120 3 of 12 (Figure 1); moreover, for the first time, to the best of our knowledge, possible associations between pre/post-surgery metabolic variations and cancer relapse are examined. Figure 1. Study design. Figure 1. Study design. 2. Materials and Methods 2. Materials and Methods 2.1. Study Cohort 2.1. Study Cohort From June 2017 to August 2018, we prospectively enrolled 41 patients with histo- From June 2017 to August 2018, we prospectively enrolled 41 patients with histolog- logically diagnosed CRC, who were treated as per standard clinical practice at the Prato ically diagnosed CRC, who were treated as per standard clinical practice at the Prato Hos- Hospital. All patients enrolled met the following inclusion criteria: (i) female or male pa- pital. All patients enrolled met the following inclusion criteria: (i) female or male patients tients with radically operable heteroplasia of the colon/rectum (stage I, II, III); (ii) Eastern with radically operable heteroplasia of the colon/rectum (stage I, II, III); (ii) Eastern Coop- Cooperative Oncology Group Scale of Performance Status (ECOG PS) 0–1; (iii) patients of erative Oncology Group Scale of Performance Status (ECOG PS) 0–1; (iii) patients of age age  18 years. For all patients enrolled, the following data were collected: (i) demographic ≥18 years. For all patients enrolled, the following data were collected: (i) demographic data; (ii) clinical and histological characterization of the tumor; (iii) any other clinical data; (ii) clinical and histological characterization of the tumor; (iii) any other clinical in- information useful for the study (i.e., comorbidities, drug treatments). formation useful for the study (i.e., comorbidities, drug treatments). All patients signed informed consent before entry into the study. The present study All patients signed informed consent before entry into the study. The present study complies with the 1964 Declaration of Helsinki and its later amendments and received complies with the 1964 Declaration of Helsinki and its later amendments and received the the approval by the local ethics committee (Comitato Etico Regione Toscana—Area Vasta approval by the local ethics committee (Comitato Etico Regione Toscana—Area Vasta Centro, study number: 10208_bio). Centro, study number: 10208_bio). 2.2. Samples Collection 2.2. Samples Collection Serum samples were collected and stored following standard operating procedures Serum samples were collected and stored following standard operating procedures validated at international level [32]. Two  10 mL of overnight fasting peripheral blood validated at international level [32]. Two × 10 mL of overnight fasting peripheral blood were collected for each patient at the two timepoints (t0: before the radical tumor resection; were collected for each patient at the two timepoints (t0: before the radical tumor resec- t1: 4–6 weeks after surgery before the start of any adjuvant treatment) in serum vacutainer tion; t1: 4–6 weeks after surgery before the start of any adjuvant treatment) in serum vacu- and processed within one hour from phlebotomy. After clot formation at room temper- tainer and processed within one hour from phlebotomy. After clot formation at room tem- ature, tubes were centrifuged at 1600 RCF for 10 min at 4 C. Then, serum aliquots of perature, tubes were centrifuged at 1600 RCF for 10 min at 4 °C. Then, serum aliquots of 1 mL (labelled with an anonymized code) were immediately frozen at 80 C, pending 1 mL (labelled with an anonymized code) were immediately frozen at −80 °C, pending NMR analysis. NMR analysis. 2.3. NMR Analysis 2.3. NMR Analysis 2.3.1. Acquisition of NMR Data 2.3.1. Acquisition of NMR Data All NMR spectra were acquired using a Bruker 600 MHz spectrometer (Bruker BioSpin, Rheinstetten, Germany) operating at 600.13 MHz proton Larmor frequency, equipped with All NMR spectra were acquired using a Bruker 600 MHz spectrometer (Bruker Bio- Sp anin, automatic Rheinstreefrigerated tten, Germ(6 any) op C) sample eratinchanger g at 600 (SampleJet, .13 MHz p Brrot uker on Larm BioSpin). or fr Temperatur equency, e stabilization (approximately 0.1 K at the sample) was obtained using a BTO 2000 thermo- equipped with an automatic refrigerated (6 °C) sample changer (SampleJet, Bruker Bio- Sp couple. in). Tem Befor perat e ure NMR stab acquisition, ilization (ap to prequilibrate oximately 0.temperatur 1 K at the sam e at ple 310 ) was ob K, each taine sample d usinwas g maintained inside the NMR probe head for at least 300 s. The spectrometer was calibrated a BTO 2000 thermocouple. Before NMR acquisition, to equilibrate temperature at 310 K, each dailysamp , befor le ewas m any measur aintained in ement, side following the NMR pr strict obe h standar ead for d at operation least 300 s. pr The ocedur spect esr[ om 33]- to ensure high spectral quality and reproducibility. eter was calibrated daily, before any measurement, following strict standard operation procedure Serum s [samples 33] to ens contain ure high low spect molecular ral qualitweight y and re metabolites producibility. as well as high molecular weight macromolecules; for this reason, three different pulse sequences were used to en- able the selective detection of the different serum molecular components: a 1D spin echo Carr–Purcell–Meiboom–Gill sequence (CPMG) was used to selectively detect signals of low molecular weight metabolites, and a 1D diffusion-edited pulse sequence was used to selectively acquire the signals of high molecular weight components (i.e., lipids, lipopro- Appl. Sci. 2021, 11, 11120 4 of 12 teins, proteins). Moreover, a 1D nuclear Overhauser effect spectroscopy pulse sequence (NOESY) was applied to detect signals of all molecules present in concentrations above the NMR detection limit. A detailed description of sample preparation procedures, instrument configuration, and NMR parameters setting can be retrieved from our previous publication [16]. 2.3.2. Spectral Processing Before applying Fourier transform, free induction decays were multiplied by an exponential function equivalent to a 0.3 Hz line-broadening factor. Using automated routine of TopSpin 3.6 (Bruker BioSpin), Fourier-transformed spectra were corrected for phase and baseline distortions, and NOESY and CPMG spectra were also calibrated at the anomeric glucose H doublet at  5.24 ppm. Each 1D spectrum in the range between 0.2 and 10.0 ppm was segmented into chemical shift bins of 0.02 ppm, and the corresponding spectral areas were integrated using AssureNMR software (Bruker BioSpin). The spectral region containing residual water signal ( 5.12–4.38 ppm) was removed, and the dimension of the system was reduced to 453 bins. 2.4. Statistical Analysis All data analysis was executed in the “R” statistical environment [34]. Multivariate analysis was performed on binned spectra without any a priori knowledge of the metabo- lites present. Multilevel partial least square analysis (mPLS) [35,36] was performed to obtain data reduction (R script developed in-house). Support vector machine [37] applied on the first nine mPLS components was used for classification purposes. Models were evaluated by means of 100 cycles of a Monte Carlo cross-validation scheme (in-house- developed R script). In brief, 90% of the pairs of data, selected at random at each iteration, were used as a training set to build the model. Then, the remaining 10% was tested, and sensitivity, specificity, and accuracy (calculated according to the standard definitions) were assessed. Univariate analysis was conducted directly on the spectral regions associated with the metabolites/lipoproteins present in all serum samples at concentrations above the detection limit (>1 M). Metabolites and lipoprotein-related parameters were identified and quan- tified using the Bruker IVDr quantification platform [38]. Metabolites whose levels were lower than the limit of quantification (LOQ) were imputed with half the LOQ (Table S1). Nonparametric Wilcoxon signed-rank test was used to infer intraindividual differences between the two timepoints. The p-values were adjusted for multiple testing using the false discovery rate (FDR) procedure with Benjamini–Hochberg [39] correction at = 0.05. Wilcoxon rank-sum test was used to infer differences between metabolites/lipoproteins of free-from-disease and relapsed CRC patients. The p-values were not adjusted for multiple testing because the group of relapse patients is small, and therefore the correction would be too severe, increasing the risk of missing promising biomarkers. However, we are aware that this could increase the risk of a type I error. Univariate analysis on clinical data was performed using the Fisher test for categorical variables and the ANOVA test for continuous variables. Polyserial correlations between ordinal clinical variables (pT, N, grade, stage, ECOG PS) and metabolites were calculated using the function “polyserial” (R package “polycor”). Point-biserial correlations between dichotomous clinical variables (tumor localization, sex) and metabolites were calculated using the function “biserial.cor” (R package “ltm”). 3. Results 3.1. Characteristics of Enrolled Patients Forty-one patients were enrolled in the study (21 female and 20 male). The median age was 73 years (Table 1). Appl. Sci. 2021, 11, 11120 5 of 12 Table 1. Descriptive statistics of enrolled CC patients at the time of analysis. Stratified by Stratified by Progression Status Chemotherapy Treatment Whole Sample Not (N = 41) Relapsed Capecitabine XELOX No CT Relapsed p-Value p-Value (N = 8) (N = 9) (N = 10) (N = 22) (N = 33) Age at study Median (min; 73 (51;92) 71 (51;92) 78 (68;86) 0.032 77 (68;86) 65 (51;72) 78 (54;92) 0.001 entry max) F 21 (51%) 18 (55%) 3 (38%) 2 (22%) 7 (70%) 12 (55%) Sex 0.454 0.109 M 20 (49%) 15 (45%) 5 (62%) 7 (78%) 3 (30%) 10 (45%) PS 0 29 (71%) 23 (70%) 6 (75%) 7 (78%) 10 (100%) 12 (55%) ECOG PS 1 0.102 PS 1 8 (20%) 7 (21%) 1 (12%) 1 (11%) 0 (0%) 7 (32%) PS 2 4 (10%) 3 (9%) 1 (12%) 1 (11%) 0 (0%) 3 (14%) pT1 6 (15%) 6 (18%) 0 (0%) 0 (0%) 1 (10%) 5 (23%) pT2 8 (20%) 8 (24%) 0 (0%) 1 (11%) 1 (10%) 6 (27%) pT 0.086 0.477 pT3 23 (56%) 17 (52%) 6 (75%) 7 (78%) 7 (70%) 9 (41%) pT4 4 (10%) 2 (6%) 2 (25%) 1 (11%) 1 (10%) 2 (9%) N0 24 (59%) 23 (70%) 1 (12%) 3 (33%) 3 (30%) 18 (82%) N 0.005 0.005 N+ 17 (41%) 10 (30%) 7 (88%) 6 (67%) 7 (70%) 4 (18%) Stage I 11 (27%) 11 (33%) 0 (0%) 0 (0%) 0 (0%) 11 (50%) Stage II Low risk 2 (5%) 2 (6%) 0 (0%) 0 (0%) 0 (0%) 2 (9%) Stage risk 0.035 0.002 Stage II High risk 11 (27%) 10 (30%) 1 (12%) 3 (33%) 3 (30%) 5 (23%) Stage III 17 (41%) 10 (30%) 7 (88%) 6 (67%) 7 (70%) 4 (18%) G1 2 (5%) 1 (3%) 1 (12%) 1 (11%) 1 (10%) 0 (0%) G2 19 (48%) 17 (53%) 2 (25%) 2 (22%) 4 (40%) 13 (62%) Grading 0.168 0.205 G3 17 (42%) 13 (41%) 4 (50%) 5 (56%) 5 (50%) 7 (33%) G4 2 (5%) 1 (3%) 1 (12%) 1 (11%) 0 (0%) 1 (5%) NA 1 1 0 0 0 1 Left-sided 13 (32%) 12 (36%) 1 (12%) 3 (33%) 6 (60%) 4 (18%) 0.07 Localization 0.398 Right-sided 28 (68%) 21 (64%) 7 (88%) 6 (67%) 4 (40%) 18 (82%) No com. 13 (32%) 8 (24%) 5 (62%) 4 (44%) 3 (30%) 6 (27%) Comorbidities No vascular com. 8 (20%) 8 (24%) 0 (0%) 0.111 0 (0%) 3 (30%) 5 (23%) 0.519 Vascular com. 20 (49%) 17 (52%) 3 (38%) 5 (56%) 4 (40%) 11 (50%) Instable 1 (11%) 1 (14%) 0 (0%) 0 (0%) 0 (0%) 1 (33%) MSI 1 (11%) 1 (14%) 0 (0%) 0 (0%) 0 (0%) 1 (33%) MSI 1 0.278 Stable 7 (78%) 5 (71%) 2 (100%) 2 (100%) 4 (100%) 1 (33%) NA 32 26 6 7 6 19 Positive 1 (100%) 0 (0%) 1 (100%) 1 (100%) 0 (0%) 0 (0%) - - CDX2 NA 40 33 7 8 10 22 Mutated 5 (29%) 1 (11%) 4 (50%) 3 (50%) 0 (0%) 2 (67%) KRAS WT 12 (71%) 8 (89%) 4 (50%) 0.131 3 (50%) 8 (100%) 1 (33%) 0.042 NA 24 24 0 3 2 19 WT 13 (100%) 8 (100%) 5 (100%) 4 (100%) 8 (100%) 1 (100%) - - NRAS NA 28 25 3 5 2 21 Mutated 4 (24%) 3 (33%) 1 (12%) 1 (17%) 3 (38%) 0 (0%) BRAF WT 13 (76%) 6 (67%) 7 (88%) 0.576 5 (83%) 5 (62%) 3 (100%) 0.461 NA 24 24 0 3 2 19 ECOG PS: Eastern Cooperative Oncology Group Performance Status; pT: primary tumor size; N: regional lymph nodes; MSI: microsatel- lite instability. Most of the enrolled patients had a good Eastern Cooperative Oncology Group (ECOG) performance status (PS), with 29 patients (71%) having a PS 0. However, over one half of the patients (n = 38; 69%) had comorbidity, of which 20 patients had vascular comorbidity. By inclusion criteria, all patients have early-stage disease: 11 patients (27%) with stage I, 13 patients (32%) stage II, and 17 patients (41%) stage III. In particular, six patients had a Appl. Sci. 2021, 11, x FOR PEER REVIEW 6 of 12 Most of the enrolled patients had a good Eastern Cooperative Oncology Group (ECOG) performance status (PS), with 29 patients (71%) having a PS 0. However, over one Appl. Sci. 2021, 11, 11120 6 of 12 half of the patients (n = 38; 69%) had comorbidity, of which 20 patients had vascular comorbidity. By inclusion criteria, all patients have early-stage disease: 11 patients (27%) with stage I, 13 patients (32%) stage II, and 17 patients (41%) stage III. In particular, six patients pT1 (5 N0 e 1 N+), eight patients had a pT2 (6 N0 and 2 N+), 23 patients had a pT3 (18 N+), had a pT1 (5 N0 e 1 N+), eight patients had a pT2 (6 N0 and 2 N+), 23 patients had a pT3 and four patients had a pT4 (3 N0 and 1 N+). (18 N+), and four patients had a pT4 (3 N0 and 1 N+). Regarding the 13 patients with stage II, two were at low risk and 11 at high risk for Regarding the 13 patients with stage II, two were at low risk and 11 at high risk for the presence of lymphovascular invasion, T4 or G3–4. the presence of lymphovascular invasion, T4 or G3–4. The majority of tumors had intermediate (G2; 48%: N = 19) or high (G3–G4; 47% The majority of tumors had intermediate (G2; 48%: N = 19) or high (G3–G4; 47% N = N = 19) histologic grading, while G1 accounted for 5% of tumors in this population (N = 2). 19) histologic grading, while G1 accounted for 5% of tumors in this population (N = 2). A A total of 13 patients had left CRC and 28 right CRC (Table 1). total of 13 patients had left CRC and 28 right CRC (Table 1). Half of the patients (46%; n = 19) received adjuvant chemotherapy, in accordance Half of the patients (46%; n = 19) received adjuvant chemotherapy, in accordance with with clinical stage of disease. Nine patients received fluoropyrimidine monotherapy and clinical stage of disease. Nine patients received fluoropyrimidine monotherapy and 10 10 patients received polychemotherapy with oxaliplatin and fluoropyrimidine. Six out of patients received polychemotherapy with oxaliplatin and fluoropyrimidine. Six out of eleven patients at stage II at high risk received adjuvant treatment; the rest of them did not eleven patients at stage II at high risk received adjuvant treatment; the rest of them did receive chemotherapy for age or comorbidity not receive chemotherapy for age or comorbidity Thirteen out of the 17 patients with stage III disease received adjuvant treatment Thirteen out of the 17 patients with stage III disease received adjuvant treatment ac- according to tumor stage. At the last follow-up, 19% (n = 8) of patients had disease relapse cording to tumor stage. At the last follow-up, 19% (n = 8) of patients had disease relapse (Table 1). As expected, the patients with relapse had a history of stage III disease or stage II (Table 1). As expected, the patients with relapse had a history of stage III disease or stage at high risk. II at high risk. 3.2. Effects of Surgery on the Metabolome of CRC Patients 3.2. Effects of Surgery on the Metabolome of CRC Patients The mPLS analysis was performed to assess intraindividual variations between t0 The mPLS analysis was performed to assess intraindividual variations between t0 and t1 in the metabolomic fingerprints of CRC patients. The results obtained show sig- and t1 in the metabolomic fingerprints of CRC patients. The results obtained show signif- nificant differential clustering, with optimal separation of the two timepoints using each icant differential clustering, with optimal separation of the two timepoints using each type type of NMR spectra acquired, namely CPMG, NOESY, and DIFFUSION (Figure 2). All of NMR spectra acquired, namely CPMG, NOESY, and DIFFUSION (Figure 2). All models models classify t classify 0 and t1 sample t0 and t1 s with an samples accur with acy an inaccuracy the range 82– in the 85%, and the best resul range 82–85%, and ts were the best results obtained u weresin obtained g NOESY using specNOESY tra. These d spectra. ata indic These ate t data hat bot indicate h low molecu that both lar w low eight molecular me- tabolites and high molecular weight macromolecules (i.e., lipoproteins, proteins) contrib- weight metabolites and high molecular weight macromolecules (i.e., lipoproteins, proteins) contribute ute to the discri to themi discrimination. nation. Figure 2. Score plots of the first two components of the mPLS models calculated using each of the three typologies of NMR Figure 2. Score plots of the first two components of the mPLS models calculated using each of the three typologies of spectra acquired: (A) CPMG; (B) NOESY; (C) diffusion-edited. Discrimination accuracy of each model is reported. Each NMR spectra acquired: CPMG; NOESY; diffusion-edited. Discrimination accuracy of each model is reported. Each dot dot represents an NMR spectrum; dots are colored as follows: t0—orange, t1—turquoise. The first component mainly represents an NMR spectrum; dots are colored as follows: t0—orange, t1—turquoise. The first component mainly describes describes the differences between t0 and t1. The second component mainly reports the within-subject variation. the differences between t0 and t1. The second component mainly reports the within-subject variation. From univariate analysis emerges that after surgery there is a significant increase of From univariate analysis emerges that after surgery there is a significant increase of pyruvate, HDL cholesterol, HDL phospholipids, HDL Apo-A1, and HDL Apo-A2 (Figure pyruvate, HDL cholesterol, HDL phospholipids, HDL Apo-A1, and HDL Apo-A2 (Figure 3). Moreover, after surgery we observed a significant decrement of acetone, 3-hydroxybutyrate, LDL-Chol/HDL-Chol ratio, and Apo-A1/Apo-B100 ratio (Figure 3). Furthermore, several lipoprotein-related subfractions were shown to be significantly altered between t0 and t1 (Figure S1). These data point to a relevant rearrangement of the metabolic pathways related to lipoproteins, ketone bodies, and energy metabolism. Appl. Sci. 2021, 11, x FOR PEER REVIEW 7 of 12 3). Moreover, after surgery we observed a significant decrement of acetone, 3-hydroxy- butyrate, LDL-Chol/HDL-Chol ratio, and Apo-A1/Apo-B100 ratio (Figure 3). Further- more, several lipoprotein-related subfractions were shown to be significantly altered be- Appl. Sci. 2021, 11, 11120 7 of 12 tween t0 and t1 (Figure S1). These data point to a relevant rearrangement of the metabolic pathways related to lipoproteins, ketone bodies, and energy metabolism. Figure 3. Boxplots of the statistically significant metabolites and lipoproteins-related parameters discriminating CRC Figure 3. Boxplots of the statistically significant metabolites and lipoproteins-related parameters discriminating CRC pa- tients at t0 ( patients at t0 orange) and t1 (turquoise); (orange) and t1 (turquoise); p-vapl-values ues obtained u obtained sing using Wilcox Wilcoxon on signsigned-rank ed-rank test and adjuste test and adjusted d for FDR are re- for FDR are ported. *** p < 0.001; ** p < 0.01; * p < 0.05. reported. *** p < 0.001; ** p < 0.01; * p < 0.05. 3.3. Associations between Metabolome Variations after Surgery and Cancer Relapse 3.3. Associations between Metabolome Variations after Surgery and Cancer Relapse Eight out of the 41 CRC patients enrolled in the present study developed cancer Eight out of the 41 CRC patients enrolled in the present study developed cancer re- relapse in the three years after diagnosis. We hypothesized that different changes in lapse in the three years after diagnosis. We hypothesized that different changes in pre- preoperative and postoperative metabolomic serum profiles could be predictive of patients’ operative and postoperative metabolomic serum profiles could be predictive of patients’ prognosis. To explore this hypothesis, the difference between each metabolite/lipoprotein- prognosis. To explore this hypothesis, the difference between each metabolite/lipoprotein- related parameter at t1 and t0 was calculated, and each resulting difference analyzed via related parameter at t1 and t0 was calculated, and each resulting difference analyzed via univariate approaches to underline possible divergent behavior in free-from-disease and univariate approaches to underline possible divergent behavior in free-from-disease and relapsed CRC patients. Postoperative, relapsed CRC patients show a significant increase of relapsed CRC patients. Postoperative, relapsed CRC patients show a significant increase pyruvate, HDL Apo-A1, HDL Apo-A2, HDL cholesterol, HDL free cholesterol, and HDL of pyruvate, HDL Apo-A1, HDL Apo-A2, HDL cholesterol, HDL free cholesterol, and phospholipids, and a significant decrease of Apo-A1 Apo-B100 ratio, VLDL-5 cholesterol, VLDL-5 free cholesterol, and VLDL-5 phospholipids (Figure 4). Appl. Sci. 2021, 11, x FOR PEER REVIEW 8 of 12 Appl. Sci. 2021, 11, 11120 8 of 12 HDL phospholipids, and a significant decrease of Apo-A1 Apo-B100 ratio, VLDL-5 cho- lesterol, VLDL-5 free cholesterol, and VLDL-5 phospholipids (Figure 4). Figure 4. Boxplots of the differences between t1 and t0 discriminating free-from-disease (green) and relapsed (red) patients, Figure 4. Boxplots of the differences between t1 and t0 discriminating free-from-disease (green) and relapsed (red) pa- tients, only statistically significant metabolites and lipoproteins-related parameters are reported; p-values obtained using only statistically significant metabolites and lipoproteins-related parameters are reported; p-values obtained using Wilcoxon Wilcoxon signed-rank test are reported. ** p < 0.01; * p < 0.05. signed-rank test are reported. ** p < 0.01; * p < 0.05. 3.4. 3.4. Associations Associations betwee between n Metabolites Metabolites an and d CClinical linical Variables Variables Possible associations between metabolites/lipoproteins (main fractions) and clinical Possible associations between metabolites/lipoproteins (main fractions) and clinical variables were investigated. Results are reported in Table S2. variables were investigated. Results are reported in Table S2. Glycine and histidine showed statistically significant correlations with tumor size. Glycine and histidine showed statistically significant correlations with tumor size. Tyrosine correlates with tumor stage and regional lymph nodal spread (N). N also corre- Tyrosine correlates with tumor stage and regional lymph nodal spread (N). N also correlates lates with isoleucine, Apo-A1, and Apo-A2. Tumor localization (left or right colon) shows with isoleucine, Apo-A1, and Apo-A2. Tumor localization (left or right colon) shows correlations with acetone, cholesterol, LDL cholesterol, and Apo-B100. Interestingly a correlations with acetone, cholesterol, LDL cholesterol, and Apo-B100. Interestingly a panel panel of eight metabolic variables (N,N-Dimethylglycine, valine, dimethylsulfone, triglyc- of eight metabolic variables (N,N-Dimethylglycine, valine, dimethylsulfone, triglycerides, cholesterol, LDL cholesterol, Apo-A2, Apo-B100) correlates with the Eastern Cooperative Oncology Group Scale of Performance Status. Moreover, as expected, sex shows significant correlations with several metabolites/lipoproteins: creatine, creatinine, glutamine, glycine, isoleucine, leucine, formic acid, cholesterol, LDL cholesterol, HDL cholesterol, Apo-A1, Apo-A2, and Apo-B100. Of note, none of the examined metabolic features show significant correlation with tumor grade. Appl. Sci. 2021, 11, 11120 9 of 12 4. Discussion The primary option for the treatment of colorectal cancer is surgery. Adjuvant chemotherapy is strongly indicated in stage III disease and in stage II patients at high risk of relapse. Whereas, in low-risk stage II disease decision-making around adjuvant chemotherapy must be carefully evaluated. At present, postoperative surveillance via clinical, radiological and biomarkers examination often cannot identify early metastatic disease and/or postoperative micrometastatic residual disease. Based on these considerations, especially in stage II disease, improved risk-stratification tools are required to identify those patients who are most likely to benefit from adjuvant chemotherapy and need to be followed up more closely after surgery to timely detect systemic recurrence. On the other hand, accurate stratification instruments could prevent low-risk patients from unnecessary treatment and possible mild-to-severe adverse reactions. The analysis described in the present research article shows for the first time, to the best of our knowledge, the metabolomic variations among preoperative and postoperative NMR-based serum fingerprint of CRC patients. Furthermore, metabolomics as novel ap- proach for risk-stratification in CRC setting was evaluated by studying differences between pre- and postoperative serum samples of each patient enrolled. With this innovative ap- proach, each patient in the study population acts as his/her own control, thus eliminating noise from interindividual variability. Our data demonstrate that metabolomics profiles are influenced by the presence or absence of the cancerous mass. Indeed, the mPLS models calculated using each of the three NMR spectra acquired (namely, CPMG, NOESY, and DIFFUSION) show high discrimination accuracies (range 82–85%). This evidence poses an important question in terms of future study design, since sample collection when the tumor was still in place or after resection can significantly impact on metabolomic data. From the univariate analysis, it emerges that after surgery, there is a significant increase of pyruvate, HDL cholesterol, HDL phospholipids, HDL Apo-A1, and HDL Apo-A2. Moreover, we observed, postoperative, a significant decrement of acetone, 3-hydroxybutyrate, LDL- Chol/HDL-Chol ratio, and Apo-A1/Apo-B100 ratio. These data point to a relevant rewiring of the metabolic pathways associated to lipoproteins, ketone bodies, and energy metabolism. Depletion of pyruvate and increase of ketone bodies has been observed in sera of metastatic CRC patients with respect to healthy controls, and this evidence has been associ- ated with an altered energy metabolism, probably reflecting an increased gluconeogenesis and fatty acid oxidation [31]. It is interesting to note that in our dataset, these three metabolites show trend inversions after surgery. Our data show an increase of several HDL-Chol and a decrease of LDL-Chol lipoprotein- related parameters post-surgery. This may be explained by the fact that, after cancer resection, an improvement in the inflammatory status of the gut is achieved, allowing for an improved lipid metabolism and lipid assimilation in the absence of the tumor. Strikingly, despite the low number of recurrence events registered, it is peculiar that the difference in HDL-Chol is particularly marked in relapsed patients and is coupled with a decrease of VLDL-Chol. It has been observed that in colorectal cancerous tissue, the levels of cholesterol and triglycerides were reduced and HDL-Cholesterol level increased, indicating that CRC development destroys the physiological balance of lipids and lipopro- teins, leading to lipid metabolic disorders [40]. Preclinical and clinical studies have already investigated the role of cholesterol in CRC progression; however, a clear understanding of the molecular mechanism linking these two entities is still lacking [40,41]. In conclusion, our results show that surgery can affect the metabolomic and lipidomic profiles of CRC patients and they point to possible associations between these metabolic changes and cancer recurrence. This study is based on a small population of CRC patients in which a very limited number of recurrence events are present; therefore, at present, results are only speculative and require further confirmation. In order to validate these findings in a general population, we are conducting a multicentric prospective trial focused on high-risk stage disease, the LIquid BIopsy and METabolomics in colon cancer (LIBIMET) Appl. Sci. 2021, 11, 11120 10 of 12 study. LIBIMET aims primarily at redefining the risk of relapse in patients with high-risk, early-stage colon cancer by combining of ctDNA and serum metabolomics. 5. Conclusions Taken together, the data here presented highlight the notion that CRC can induce metabolic changes that are reflected at a systemic level and can be detected in serum. This evidence suggests that our approach aimed at detecting micrometastatic CRC by assessing its metabolomic fingerprint in serum is correct, and that this may be exploited for biomarker-oriented research to contribute towards better management of colorectal cancer. Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/app112311120/s1. Table S1: Data completeness for the different metabolites quantified in the serum samples analyzed via NMR. LOQ = limit of quantification. Table S2: Correlation between clinical data and metabolites. Correlation coefficients and p-values are reported in table. Figure S1: Boxplots of the statistically significant lipoproteins-related parameters discriminating of CRC patients at t0 (orange) and t1 (turquoise); p-values obtained using Wilcoxon signed-rank test and adjusted for FDR are reported. Author Contributions: Study conception and design, E.M., S.D.D., C.L., L.T. and L.B.; patient enrolment and management: E.M., S.D.D., V.C., S.C., M.B. (Maddalena Baraghini) and A.G.; collection of clinical data and serum samples: E.M., S.D.D., C.B., M.B. (Maddalena Baraghini), V.C., A.P., S.C., M.B. (Matteo Benelli), A.G. and F.D.M.; NMR analysis: A.V.; statistical analysis, biostatistics, and computational analysis, A.V., C.B., M.B. (Matteo Benelli), D.R. and L.T.; results interpretation, A.V., E.M., S.D.D., L.M., I.M., C.L., L.T. and L.B.; writing—original draft preparation: A.V. and E.M.; writing—review and editing: A.V., E.M., S.D.D., L.M., C.B., M.B. (Matteo Benelli), V.C., A.P., S.C., M.B. (Maddalena Baraghini), A.G., F.D.M., D.R., I.M., C.L., L.T. and L.B.; supervision, C.L, L.T. and L.B. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the local ethics committee (Comitato Etico Regione Toscana—Area Vasta Centro, study number: 10208_bio). Informed Consent Statement: Written informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data and R script are available from the corresponding authors upon reasonable request. Acknowledgments: In memoriam of Angelo Di Leo who passed away on 13 June 2021, while this work was being completed. The authors acknowledge the Fondazione Pitigliani per la lotta contro i tumori ONLUS for its support. The authors acknowledge Instruct-ERIC, a Landmark ESFRI project, and specifically the CERM/CIRMMP Italy Centre. Alessia Vignoli was supported by an AIRC fellowship for Italy. Conflicts of Interest: The authors declare no conflict of interest. References 1. NCCN Guidelines for Colon Cancer 2021. Available online: https://www.nccn.org/guidelines/guidelines-detail (accessed on 5 November 2021). 2. Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer Statistics, 2020. CA Cancer J. Clin. 2020, 70, 7–30. [CrossRef] [PubMed] 3. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBO- CAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [CrossRef] [PubMed] 4. AIOM: Linee Guida Tumori del Colon 2020. Available online: https://www.aiom.it/linee-guida-aiom-2020-tumori-del-colon/ (accessed on 5 November 2021). 5. Reinert, T.; Schøler, L.V.; Thomsen, R.; Tobiasen, H.; Vang, S.; Nordentoft, I.; Lamy, P.; Kannerup, A.-S.; Mortensen, F.V.; Stribolt, K.; et al. Analysis of Circulating Tumour DNA to Monitor Disease Burden Following Colorectal Cancer Surgery. Gut 2016, 65, 625–634. [CrossRef] [PubMed] Appl. Sci. 2021, 11, 11120 11 of 12 6. Guraya, S.Y. Pattern, Stage, and Time of Recurrent Colorectal Cancer after Curative Surgery. Clin. Colorectal Cancer 2019, 18, e223–e228. [CrossRef] 7. Hall, M.J.; Morris, A.M.; Sun, W. Precision Medicine Versus Population Medicine in Colon Cancer: From Prospects of Prevention, Adjuvant Chemotherapy, and Surveillance. Am. Soc. Clin. Oncol. Educ. Book 2018, 38, 220–230. [CrossRef] [PubMed] 8. Dienstmann, R.; Mason, M.J.; Sinicrope, F.A.; Phipps, A.I.; Tejpar, S.; Nesbakken, A.; Danielsen, S.A.; Sveen, A.; Buchanan, D.D.; Clendenning, M.; et al. Prediction of Overall Survival in Stage II and III Colon Cancer beyond TNM System: A Retrospective, Pooled Biomarker Study. Ann. Oncol. 2017, 28, 1023–1031. [CrossRef] 9. SEER Cancer Statistics Review, 1975–2017. Available online: https://seer.cancer.gov/csr/1975_2017/index.html (accessed on 30 March 2021). 10. Renfro, L.A.; Grothey, A.; Xue, Y.; Saltz, L.B.; André, T.; Twelves, C.; Labianca, R.; Allegra, C.J.; Alberts, S.R.; Loprinzi, C.L.; et al. ACCENT-Based Web Calculators to Predict Recurrence and Overall Survival in Stage III Colon Cancer. J. Natl. Cancer Inst. 2014, 106, dju333. [CrossRef] 11. Benson, A.B.; Schrag, D.; Somerfield, M.R.; Cohen, A.M.; Figueredo, A.T.; Flynn, P.J.; Krzyzanowska, M.K.; Maroun, J.; McAllister, P.; Van Cutsem, E.; et al. American Society of Clinical Oncology Recommendations on Adjuvant Chemotherapy for Stage II Colon Cancer. J. Clin. Oncol. 2004, 22, 3408–3419. [CrossRef] 12. Kumar, A.; Kennecke, H.F.; Renouf, D.J.; Lim, H.J.; Gill, S.; Woods, R.; Speers, C.; Cheung, W.Y. Adjuvant Chemotherapy Use and Outcomes of Patients with High-Risk versus Low-Risk Stage II Colon Cancer. Cancer 2015, 121, 527–534. [CrossRef] 13. Di Donato, S.; Vignoli, A.; Biagioni, C.; Malorni, L.; Mori, E.; Tenori, L.; Calamai, V.; Parnofiello, A.; Di Pierro, G.; Migliaccio, I.; et al. A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting. Cancers 2021, 13, 2762. [CrossRef] [PubMed] 14. Nicholson, J.K.; Lindon, J.C. Systems Biology: Metabonomics. Nature 2008, 455, 1054–1056. [CrossRef] 15. Vignoli, A.; Risi, E.; McCartney, A.; Migliaccio, I.; Moretti, E.; Malorni, L.; Luchinat, C.; Biganzoli, L.; Tenori, L. Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer. Int. J. Mol. Sci. 2021, 22, 4687. [CrossRef] 16. Vignoli, A.; Ghini, V.; Meoni, G.; Licari, C.; Takis, P.G.; Tenori, L.; Turano, P.; Luchinat, C. High-Throughput Metabolomics by 1D NMR. Angew. Chem. Int. Ed. 2019, 58, 968–994. [CrossRef] [PubMed] 17. Wishart, D.S. Emerging Applications of Metabolomics in Drug Discovery and Precision Medicine. Nat. Rev. Drug Discov. 2016, 15, 473–484. [CrossRef] [PubMed] 18. Vignoli, A.; Tenori, L.; Giusti, B.; Valente, S.; Carrabba, N.; Baizi, D.; Barchielli, A.; Marchionni, N.; Gensini, G.F.; Marcucci, R.; et al. Differential Network Analysis Reveals Metabolic Determinants Associated with Mortality in Acute Myocardial Infarction Patients and Suggests Potential Mechanisms Underlying Different Clinical Scores Used To Predict Death. J. Proteome Res. 2020, 19, 949–961. [CrossRef] [PubMed] 19. Zhang, L.; Zhu, B.; Zeng, Y.; Shen, H.; Zhang, J.; Wang, X. Clinical Lipidomics in Understanding of Lung Cancer: Opportunity and Challenge. Cancer Lett. 2020, 470, 75–83. [CrossRef] [PubMed] 20. Pietzner, M.; Stewart, I.D.; Raffler, J.; Khaw, K.-T.; Michelotti, G.A.; Kastenmüller, G.; Wareham, N.J.; Langenberg, C. Plasma Metabolites to Profile Pathways in Noncommunicable Disease Multimorbidity. Nat. Med. 2021, 27, 471–479. [CrossRef] [PubMed] 21. Vignoli, A.; Paciotti, S.; Tenori, L.; Eusebi, P.; Biscetti, L.; Chiasserini, D.; Scheltens, P.; Turano, P.; Teunissen, C.; Luchinat, C.; et al. Fingerprinting Alzheimer ’s Disease by 1H Nuclear Magnetic Resonance Spectroscopy of Cerebrospinal Fluid. J. Proteome Res. 2020, 19, 1696–1705. [CrossRef] [PubMed] 22. Vignoli, A.; Tenori, L.; Giusti, B.; Takis, P.G.; Valente, S.; Carrabba, N.; Balzi, D.; Barchielli, A.; Marchionni, N.; Gensini, G.F.; et al. NMR-Based Metabolomics Identifies Patients at High Risk of Death within Two Years after Acute Myocardial Infarction in the AMI-Florence II Cohort. BMC Med. 2019, 17, 3. [CrossRef] 23. Auclin, E.; Zaanan, A.; Vernerey, D.; Douard, R.; Gallois, C.; Laurent-Puig, P.; Bonnetain, F.; Taieb, J. Subgroups and Prognostica- tion in Stage III Colon Cancer: Future Perspectives for Adjuvant Therapy. Ann. Oncol. 2017, 28, 958–968. [CrossRef] 24. Copija, A.; Waniczek, D.; Witkos, ´ A.; Walkiewicz, K.; Nowakowska-Zajdel, E. Clinical Significance and Prognostic Relevance of Microsatellite Instability in Sporadic Colorectal Cancer Patients. Int. J. Mol. Sci. 2017, 18, 107. [CrossRef] [PubMed] 25. Nannini, G.; Meoni, G.; Amedei, A.; Tenori, L. Metabolomics Profile in Gastrointestinal Cancers: Update and Future Perspectives. World J. Gastroenterol. 2020, 26, 2514–2532. [CrossRef] [PubMed] 26. Ma, Y.; Zhang, P.; Wang, F.; Liu, W.; Yang, J.; Qin, H. An Integrated Proteomics and Metabolomics Approach for Defining Oncofetal Biomarkers in the Colorectal Cancer. Ann. Surg. 2012, 255, 720–730. [CrossRef] 27. Nishiumi, S.; Kobayashi, T.; Ikeda, A.; Yoshie, T.; Kibi, M.; Izumi, Y.; Okuno, T.; Hayashi, N.; Kawano, S.; Takenawa, T.; et al. A Novel Serum Metabolomics-Based Diagnostic Approach for Colorectal Cancer. PLoS ONE 2012, 7, e40459. [CrossRef] 28. Qiu, Y.; Cai, G.; Zhou, B.; Li, D.; Zhao, A.; Xie, G.; Li, H.; Cai, S.; Xie, D.; Huang, C.; et al. A Distinct Metabolic Signature of Human Colorectal Cancer with Prognostic Potential. Clin. Cancer Res. 2014, 20, 2136–2146. [CrossRef] 29. Farshidfar, F.; Weljie, A.M.; Kopciuk, K.; Buie, W.D.; Maclean, A.; Dixon, E.; Sutherland, F.R.; Molckovsky, A.; Vogel, H.J.; Bathe, O.F. Serum Metabolomic Profile as a Means to Distinguish Stage of Colorectal Cancer. Genome Med. 2012, 4, 42. [CrossRef] [PubMed] 30. Farshidfar, F.; Weljie, A.M.; Kopciuk, K.A.; Hilsden, R.; McGregor, S.E.; Buie, W.D.; MacLean, A.; Vogel, H.J.; Bathe, O.F. A Validated Metabolomic Signature for Colorectal Cancer: Exploration of the Clinical Value of Metabolomics. Br. J. Cancer 2016, 115, 848–857. [CrossRef] Appl. Sci. 2021, 11, 11120 12 of 12 31. Bertini, I.; Cacciatore, S.; Jensen, B.V.; Schou, J.V.; Johansen, J.S.; Kruhøffer, M.; Luchinat, C.; Nielsen, D.L.; Turano, P. Metabolomic NMR Fingerprinting to Identify and Predict Survival of Patients with Metastatic Colorectal Cancer. Cancer Res. 2012, 72, 356–364. [CrossRef] [PubMed] 32. ISO/DIS 23118 Molecular In Vitro Diagnostic Examinations—Specifications for Pre-Examination Processes in Metabolomics in Urine, Venous Blood Serum and Plasma. Available online: https://www.iso.org/obp/ui/#iso:std:iso:23118:ed-1:v1:en (accessed on 4 June 2021). 33. Bruzzone, C.; Bizkarguenaga, M.; Gil-Redondo, R.; Diercks, T.; Arana, E.; García de Vicuña, A.; Seco, M.; Bosch, A.; Palazón, A.; San Juan, I.; et al. SARS-CoV-2 Infection Dysregulates the Metabolomic and Lipidomic Profiles of Serum. iScience 2020, 23, 101645. [CrossRef] 34. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2014. 35. van Velzen, E.J.J.; Westerhuis, J.A.; van Duynhoven, J.P.M.; van Dorsten, F.A.; Hoefsloot, H.C.J.; Jacobs, D.M.; Smit, S.; Draijer, R.; Kroner, C.I.; Smilde, A.K. Multilevel Data Analysis of a Crossover Designed Human Nutritional Intervention Study. J. Proteome Res. 2008, 7, 4483–4491. [CrossRef] 36. Westerhuis, J.A.; van Velzen, E.J.; Hoefsloot, H.C.; Smilde, A.K. Multivariate Paired Data Analysis: Multilevel PLSDA versus OPLSDA. Metabolomics 2010, 6, 119–128. [CrossRef] 37. Cortes, C.; Vapnik, V. Support-Vector Networks. J. Mach. Learn. Res. 1995, 20, 273–297. [CrossRef] 38. Jiménez, B.; Holmes, E.; Heude, C.; Tolson, R.F.; Harvey, N.; Lodge, S.L.; Chetwynd, A.J.; Cannet, C.; Fang, F.; Pearce, J.T.M.; et al. Quantitative Lipoprotein Subclass and Low Molecular Weight Metabolite Analysis in Human Serum and Plasma by 1H NMR Spectroscopy in a Multilaboratory Trial. Anal. Chem. 2018, 90, 11962–11971. [CrossRef] 39. Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B 1995, 57, 289–300. [CrossRef] 40. Zhang, X.; Zhao, X.-W.; Liu, D.-B.; Han, C.-Z.; Du, L.-L.; Jing, J.-X.; Wang, Y. Lipid Levels in Serum and Cancerous Tissues of Colorectal Cancer Patients. World J. Gastroenterol. 2014, 20, 8646–8652. [CrossRef] [PubMed] 41. Mayengbam, S.S.; Singh, A.; Pillai, A.D.; Bhat, M.K. Influence of Cholesterol on Cancer Progression and Therapy. Transl. Oncol. 2021, 14, 101043. [CrossRef] [PubMed]

Journal

Applied SciencesMultidisciplinary Digital Publishing Institute

Published: Nov 23, 2021

Keywords: metabolomics; colorectal cancer; nuclear magnetic resonance; surgery; relapse

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