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Discovery and Validation of Circulating EVL mRNA as a Prognostic Biomarker in Pancreatic Cancer

Discovery and Validation of Circulating EVL mRNA as a Prognostic Biomarker in Pancreatic Cancer Hindawi Journal of Oncology Volume 2021, Article ID 6656337, 11 pages https://doi.org/10.1155/2021/6656337 Research Article Discovery and Validation of Circulating EVL mRNA as a Prognostic Biomarker in Pancreatic Cancer 1 2 3 2 1 4 2,4 YanDu , KaiYao , QingboFeng, FeiyuMao, ZechangXin, PengXu, andJieYao Clinic Medical College, Dalian Medical University, Dalian 116000, China Clinic Medical College, Yangzhou University, Yangzhou 225000, China Department of Liver Surgery, West China Hospital Sichuan University, Chengdu 610000, China Department of Hepatobiliary and Pancreatic Surgery, Northern Jiangsu People’s Hospital, Clinic Medical College, Yangzhou University, Yangzhou 225000, China Correspondence should be addressed to Jie Yao; docyao@hotmail.com Received 12 December 2020; Revised 25 March 2021; Accepted 4 April 2021; Published 20 April 2021 Academic Editor: Pierfrancesco Franco Copyright © 2021 Yan Du et al. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Circulating plasma mRNAs can be analyzed to identify putative cancer biomarkers. )is study was conducted in an effort to detect plasma mRNA biomarkers capable of predicting pancreatic cancer (PACA) patient prognosis. Material and Methods. First, prognostic mRNAs that were differentially expressed in PACA in )e Cancer Genome Atlas (TCGA) were established, after which microarray expression profiles from PACA patient plasma samples were utilized to specifically identify potential prognostic plasma mRNA biomarkers associated with this cancer type. In total, plasma samples were then collected from 79 PACA patients and 19 healthy controls to confirm differential mRNA expression via qPCR, while Kaplan–Meier analyses were used to examine the link between mRNA expression and patient overall survival. Results. In total, three prognostic differentially expressed genes were identified in PACA patient plasma samples, including SMAP2, PTPN6, and EVL (Ena/VASP-like). Plasma EVL levels were confirmed via qPCR to be correlated with tumor pathology (p<0.01), while the overall survival of patients with low plasma EVL levels was poor (p<0.01). Multivariate Cox regression analyses further confirmed that plasma EVL levels were independent predictors of PACA patient prognosis. Conclusion. We found that PACA is associated with the downregulation of plasma EVL mRNA levels, indicating that this mRNA may be a viable biomarker associated with patient prognosis. promising candidates capable of guiding the diagnosis and 1. Introduction monitoring of many cancers, including PACA [4–7]. For Pancreatic cancer (PACA) is among the deadliest forms of example, in one clinical study, galectin-9 was shown to be cancer globally [1], accounting for the fourth-highest rate of highly expressed in human PACA and to be correlated with cancer-related mortality in the USA with a survival rate of patient prognosis through an analysis of galectin-9 levels in below 9% [2, 3]. )e prognosis of PACA is generally very serum samples from 70 PACA patients, 36 patients with poor in part because tumors are asymptomatic during their benign pancreatic diseases, and 28 healthy controls [8]. One early stages such that they are rarely detected until after they database study found serum LAMC2 levels to exhibit a have metastasized, at which time patients have generally significantly improved diagnostic utility relative to CA199 poor outcomes and a 5-year survival rate of below 3% [1]. when discriminating between PACA patients, healthy Owing to the poor outcomes associated with this disease, controls, and individuals with benign diseases [9]. In a there is an urgent need for the identification of novel bio- separate study, researchers identified a 25-component markers of localized PACA that can be used to predict tumor PACA serum biomarker signature through gene expression progression and guide timely treatment efforts. Hemato- analyses of serum samples from 34 pancreatic cancer pa- logical biomarkers have been identified in recent years as tients and 30 healthy controls [10]. 2 Journal of Oncology Herein, we utilized )e Cancer Genome Atlas (TCGA) validated using all 79 PACA patient and control samples. )ese database and microarray sequencing analyses of clinical samples were collected between January 2015 and September 2019 from pancreatic ductal cell carcinoma patients under- patient plasma samples to identify mRNAs associated with PACA patient prognosis. In total, we identified three going postoperative pathological evaluation. Patient clinico- mRNAs that were downregulated in PACA patient plasma pathological characteristics were collected, and staging was and correlated with patient survival outcomes. We subse- performed as per the American Joint Committee on Cancer quently confirmed the prognostic relevance of plasma EVL (AJCC) criteria. )e most recent patient follow-up was con- mRNA expression levels in PACA patients by analyzing ducted on September 30, 2020, and patient survival was cal- plasma samples from 79 PACA patients and 19 healthy culated from the date of surgery to the date of death or most controls. recent follow-up. All patients provided written informed consent, and the Ethics Committee of Northern Jiangsu People’s Hospital approved this study. 2. Materials and Methods 2.1. Bioinformatics Analysis. RNA sequencing data per- 2.5. qPCR. A StepOnePlus Real-Time PCR System (Applied taining to PACA tumor tissue samples and paracancerous Biosystems, NY, USA) was used to conduct qPCR assays. control tissues were downloaded from the TCGA database Briefly, cDNA was prepared with a PrimeScript RT Reagent (https://portal.gdc.cancer.gov/accessed August 20, 2020). Kit. All qPCR reactions were conducted in a 20ul volume Information related to patient overall survival (OS) was containing 10ng of cDNA based on provided instructions. obtained for all patients. )e DESeq R package was then used −ΔΔCt Relative EVL expression was assessed via the 2 ap- to standardize this PACA RNA transcriptomic dataset, after proach, and primers used were as follows: 5′- which R v. x64 3.6.4 and the edgeR package were used to CTCAAAGTCCGATGCCAACC-3′ (forward) and 5′- identify differentially expressed (DE) mRNAs associated with PACA using the following screening criteria: FC>2 TCTTGGCCAGCAGTTTGTTC-3′ (reverse) for EVL and 5′-CTCGCTTCGGCAGCACA-3′ (forward) and 5′- and FDR<0.05. Kaplan–Meier analyses were used to evaluate the link between mRNA expression and patient OS AACGCTTCACGAATTTGCGT-3′ (reverse) for U6. All qPCR analyses were conducted in triplicate. using the R Survival package. 2.2. mRNA Expression Profiling. TRIzol (Takara, Japan) was 2.6. Statistical Analysis. SPSS 24.0 (Chicago, IL, USA) and used to extract total RNA from patient plasma samples, after Prism 7 (GraphPad Software, Inc., CA, USA) were used for which human protein-coding transcripts in these samples statistical testing. Categorical data are given as frequencies were profiled with Affymetrix Human mRNA Array 2.0 and percentages. )e link between EVL expression and (HTA 2.0) GeneChips (Affymetrix, CA, USA) by QiMing patient clinicopathological characteristics was assessed via a Biotech (Shanghai, China). Briefly, rRNA was removed from chi-squared test, while Spearman’s rank correlation coeffi- plasma samples, followed by transcription and amplification cients were used to gauge bivariate correlations. to prepare full-length fluorescent cRNAs without 3′ bias. Kaplan–Meier survival curves and log-rank tests were used Each cRNA was then hybridized to the Affymetrix Human to assess patient survival. p<0.05 was the significance mRNA Array, and sample labeling and hybridization were threshold. conducted with the Affymetrix Microarray-Based Gene Expression Analysis protocol. 3. Results 3.1. Identification of PACA-Related DE mRNAs in the TCGA 2.3. Microarray Data Analysis. For microarray analyses, Database. We began by comparing mRNA expression differentially expressed genes were identified using the profiles between PACA patient tumor and paracancerous following criteria: |FC|>1.5, p<0.05, and FDR<0.05. Gene tissue samples in the TCGA database. In total, 823 DE Ontology (GO) enrichment analyses (http://www. mRNAs were identified in PACA tumors when comparing geneontology.org) were employed to assess the relation- these two tissue types (p<0.05 and FC≥2.0), of which 34 ship between these differentially expressed genes and specific were upregulated and 789 were downregulated (Figure 1(a)). biological processes (BPs), cellular components (CCs), and Hierarchical clustering analyses clearly demonstrated that molecular functions (MFs). Kyoto Encyclopedia of Genes we were able to differentiate between tumor and para- and Genomes (KEGG, http://www.genome.jp/kegg) en- cancerous tissue samples based upon these DE mRNA ex- richment analyses were also conducted to establish the pression profiles (Figure 1(b)). enrichment of these genes in specific biological pathways. 2.4. Sample Collection. In total, 79 PACA patients and 19 3.2. Identification of Prognostic DE mRNAs in Patient Tissue healthy controls were included in this study. Plasma mRNA Samples. Next, the relationship between identified DE samples from 7 PACA patients and 3 healthy controls were mRNAs and PACA patient OS was evaluated using subjected to plasma mRNA array profiling, after which the Kaplan–Meier curves and the log-rank test based upon PACA observed differences in plasma mRNA expression were patient survival data in the TCGA database. In total, 94 DE Journal of Oncology 3 –5 0 510 log2 (fold change) (a) (b) Figure 1: Continued. –log10 (q-value) 4 Journal of Oncology 7.5 5.0 2.5 0.0 –2 0 2 log2 (fold change) (c) (d) Figure 1: Continued. –log10 (q-value) Journal of Oncology 5 Diffgene sig GO RNA binding Structural constituent of ribosome Ubiquitin-like protein ligase binding Cadherin binding Ubiquitin protein ligase binding Enzyme binding Nucleic acid binding GTPase activity Hydrogen ion transmembrane transporter activity Structural molecule activity Macromolecular complex Cytosol Extracellular exosome Extracellular organelle Extracellular vesicle Protein complex Vesicle Intracellular ribonucleoprotein complex Ribonucleoprotein complex Ribosomal subunit Interspecies interaction between organisms Symbiosis, encompassing mutualism through parasitism Viral process RNA catabolic process Translational initiation mRNA catabolic process Cotranslational protein targeting to membrane Establishment of protein localization to endoplasmic reticulum Protein targeting to ER SRP-dependent cotranslational protein targeting to membrane 0 20 40 60 (–LgP) Biotype Biological process Cellular component Molecular function (e) Figure 1: Continued. Gene ontology category 6 Journal of Oncology Diffgene sig pathway Viral myocarditis Viral carcinogenesis Ubiquitin mediated proteolysis Thermogenesis Systemic lupus erythematosus Shigellosis RNA transport Ribosome Retrograde endocannabinoid signaling Regulation of actin cytoskeleton Protein export Proteasome Phagosome Pathogenic escherichia coli infection Parkinson disease Oxidative phosphorylation Non-alcoholic fatty liver disease (NAFLD) Kaposi sarcoma-associated herpesvirus infection Huntington disease Human T-cell leukemia virus 1 infection Human immunodeficiency virus 1 infection Human cytomegalovirus infection Ferroptosis Epstein-Barr virus infection Endocytosis Cellular senescence Cardiac muscle contraction Antigen processing and presentation Alzheimer disease Alcoholism 510 15 20 25 –LgP Diffgene_count –LgP (f) Figure 1: Identification of mRNAs that are differentially regulated in the TCGA database and in microarray-based plasma mRNA ex- pression profiles from PACA patients. (a), (b) Volcano plots and hierarchical clustering analyses were used to identify mRNAs that were differentially expressed between pancreatic tumor tissue and control samples in the TCGA dataset. (c), (d) Volcano plots and hierarchical clustering analyses were used to detect mRNAs that were differentially expressed between pancreatic tumor tissue and control samples in our microarray dataset. (e) Differentially expressed mRNAs were subjected to GO enrichment analyses of key biological processes, cellular components, and molecular functions. (f) Top enriched KEGG pathways for differentially expressed mRNAs in the present microarray dataset. )e size of the circle represents the number of genes enriched in the pathway. Circle colors correspond to p values. TCGA, )e Cancer Genome Atlas; DE mRNA, differentially expressed mRNA; and KEGG, Kyoto Encyclopedia of Genes and Genomes. Pathway Journal of Oncology 7 predictors of poorer PACA patient OS. A subsequent mRNAs were found to be correlated with PACA patient prognosis. multivariate Cox regression analysis revealed that EVL ex- pression, T classification, and M classification were all in- dependent predictors of PACA patient postoperative OS 3.3. Identification of PACA-Related DE mRNAs in Patient duration (all p<0.05) (Table 3). Plasma Tissue Samples. DE mRNAs in plasma samples from PACA and control patients were next identified using a 4. Discussion microarray-based approach. In total, this analysis led to the identification of 2240 DE mRNAs in the plasma of PACA PACA is a deadly cancer type that is forecast to become the patients (p<0.05 and FC≥1.5), of which 152 and 2088 were second leading cancer-associated cause of mortality in the up- and downregulated, respectively (Figures 1(c) and 1(d)). future [11]. As such, novel diagnostic and prognostic bio- GO and KEGG enrichment analyses were conducted to markers associated with this disease must be identified in an assess the enrichment of these DE mRNAs in specific bio- effort to improve patient treatment and survival outcomes. logical pathways, compartments, and functional classifica- Prior research has shown that genes that are dysregulated in tions, with the most enriched GO and KEGG terms being PACA may offer value as prognostic or diagnostic bio- shown in Figures 1(e) and 1(f), respectively. markers for patients with this cancer type [12–16]. Plasma biomarkers are particularly attractive targets for patient 3.4. Identification of Prognostic DE mRNAs in Patient Plasma diagnosis, staging, and monitoring as they can be assessed Samples. In our TCGA analysis, we had identified 94 via a relatively noninvasive liquid biopsy approach. After prognosis-related DE mRNAs. Using a Venn diagram being released from cells, RNA molecules form complexes package, we determined which of these 94 mRNAs over- with lipids that protect these RNAs from nuclease-mediated lapped with our list of 2240 DE mRNAs detected in PACA degradation [17–19]. In general, cancer patients exhibit patient plasma samples (Figure 2(a)), ultimately leading to higher levels of circulating RNA than do healthy individuals the identification of three prognostic DEGs in patient owing to the higher rates of tumor cell proliferation and plasma samples: PTPN6, EVL, and SMAP2. As EVL apoptotic death in the former cohort [20]. As such, in the exhibited the strongest prognostic correlation of these three present study, we sought to identify candidate plasma genes in the TCGA patient cohort, we selected it as a target mRNA biomarkers capable of predicting PACA patient for further study. survival outcomes. We began by employing a bioinformatics approach to assess PACA-related mRNA expression profiles in the 3.5. EVL Downregulation Is Linked to PACA Patient Clini- TCGA database as a means of detecting potential prognostic copathological Features. We determined that EVL mRNA biomarkers in these cancer patients. However, mRNAs that expression was significantly decreased in the majority of are differentially expressed in tumor tissues may not nec- tested PACA patient plasma samples (n �79) relative to essarily be differentially expressed in patient plasma samples, normal control patient plasma samples (n �19; p<0.001; given that normal tissues also contribute to plasma RNA Figure 2(b)). To explore the clinical significance of EVL profiles and have the potential to mask tumor-derived expression (Table 1), we next interrogated the link between mRNA signals in circulation [21]. By comparing our TCGA its expression and PACA patient clinicopathological char- findings to the results of a microarray analysis of PACA acteristics. We determined that EVL expression was nega- patient plasma samples, we identified just three prognosis- tively correlated with PACA pathological stage (p<0.01) related DE mRNAs in these plasma samples: PTPN6, EVL, and patient age (p<0.05), but was unrelated to patient sex, and SMAP2. clinical stage, TNM classification, vascular invasion status, )rough further validation experiments, we confirmed or nerve invasion status. Spearman’s correlation analyses of that EVL mRNA expression was decreased in PACA patient the relationship between EVL and these parameters yielded plasma samples relative to samples from healthy controls. comparable results (Table 2). Decreased EVL mRNA expression was associated with poor OS and with tumor pathological stage and was an inde- 3.6. Decreased EVL Expression Is Predictive of Poor Prognosis. pendent predictor of PACA patient prognosis. EVL is an Using the TCGA database, we evaluated the prognostic Ena/VASP (enabled/vasodilator-stimulated phosphopro- significance of EVL expression levels in PACA. )e tein) family member protein involved in actin cytoskeleton Kaplan–Meier survival curve revealed that a low EVL ex- regulation [22, 23]. Alterations in cytoskeletal composition pression level was associated with a poorer patient prognosis can influence cellular motility, ultimately driving or sup- (p<0.0001, Figure 2(c)). In order to further verify the pressing tumor cell invasion and migration. Mouneimne prognostic value of plasma EVL levels, we collected follow- et al. suggested that EVL downregulation was capable of up data for these 79 PACA patients, and the results of suppressing tumor cell migration and invasion in vitro and survival analyses showed EVL low expression to be asso- in vivo, and decreased EVL expression in human tumor cells ciated with poorer OS in plasma samples (p<0.01 has been shown to be associated with high invasive activity, Figure 2(d)). Univariate Cox regression analyses identified increased protrusion, decreased contractility, and reduced EVL expression (p<0.01), T classification (p<0.01), M adhesion [24]. Grady et al. found EVL to be commonly classification (p<0.01), and nerve invasion (p<0.05) as downregulated in human colorectal cancer through a 8 Journal of Oncology 2.0 ∗∗∗ 1.5 PTPN PTPN66 91 22237 237 SM SMA APP22 EV EVLL 1.0 0.5 0.0 Normal Cancer (a) (b) 0 10 20 30 0 1000 2000 3000 Months Days High expression High expression Low expression Low expression Log-rank <0.01 Log-rank <0.0001 HR (high) = 0.525 HR (high) = 0.44 (c) (d) Figure 2: Plasma EVL levels are decreased in patients with pancreatic cancer. (a) Overlapping genes between the TCGA prognostic gene set and the differentially expressed pancreatic-cancer-associated plasma microarray gene set. (b) Plasma EVL levels were significantly decreased in plasma samples from 79 pancreatic cancer patients relative to 19 normal controls as determined via qPCR. (c), (d) Kaplan–Meier survival curves revealed that elevated EVL was associated with better overall pancreatic cancer patient survival in both the TCGA database ((c), p<0.0001) and the present clinical dataset ((d), p<0.01); p values were calculated via the log-rank test. EVL, Ena/VASP-like; qPCR, real- time quantitative polymerase chain reaction; and TCGA, )e Cancer Genome Atlas. mechanism associated with altered CpG methylation up- that EVL downregulation in PACA patients promotes dis- stream of EVL [25]. Li et al. found EVL mRNA expression to ease progression via driving tumor invasion and metastasis, be decreased in cervical cancer [26]. As such, we hypothesize ultimately leading to poor patient outcomes. Overall survival (%) Overall survival (%) Relative EVL mRNA expression Journal of Oncology 9 Table 1: )e expression of EVL and clinicopathologic features in 79 pancreatic cancer patients. EVL Characteristics p value (χ2 test) Low expression High expression Age 0.010 ≦60 6 16 >60 34 23 Gender 0.556 Male 18 15 Female 22 24 T classification 0.417 I, II 21 24 III, IV 19 15 N classification 0.516 No 26 28 Yes 14 11 Metastasis 1.000 No 37 37 Yes 3 2 Clinical stage 0.260 I, II 31 34 III, IV 9 5 Pathological differentiation 0.007 1, 2 19 30 3, 4 21 9 Vessel invasion 0.106 No 33 26 Yes 7 13 Nerve invasion 0.406 No 9 12 Yes 31 27 Table 2: Spearman analysis of the correlations between EVL and clinicopathological variables. EVL expression level Variables Spearman correlation p value Age (year) −0.290 0.009 Gender 0.066 0.562 T classification −0.091 0.424 N classification −0.073 0.522 Metastasis −0.049 0.670 Clinical stage −0.127 0.266 Pathological differentiation −0.303 0.007 Venous invasion 0.182 0.108 Nervous invasion −0.094 0.412 Table 3: Univariate and multivariate Cox regression analyses of prognostic parameters in pancreatic cancer patients. Univariate analysis Multivariate analysis p value Regression coefficient (SE) p value Relative risk 95% confidence interval T classification 0.009 2.065 (0.276) 0.037 1.795 1.036–3.110 Metastasis 0.002 5.374 (0.541) 0.001 6.785 2.253–20.428 EVL expression 0.010 0.508 (0.264) 0.010 0.491 0.286–0.841 10 Journal of Oncology [10] C. Wingren, A. Sandstrom, ¨ R. Segersvard ¨ et al., “Identification 5. Conclusions of serum biomarker signatures associated with pancreatic In summary, we validated the prognostic value of EVL in cancer,” Cancer Research, vol. 72, no. 10, pp. 2481–2490, 2012. [11] L. Rahib, B. D. Smith, R. Aizenberg, A. B. Rosenzweig, patient plasma samples, revealing that reduced plasma EVL J. M. Fleshman, and L. M. Matrisian, “Projecting cancer in- expression is an independent predictor of PACA patient cidence and deaths to 2030: the unexpected burden of thyroid, prognosis. liver, and pancreas cancers in the United States,” Cancer Research, vol. 74, no. 11, pp. 2913–2921, 2014. 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Proteomics, vol. 19, Article ID e1800489, 2019. [15] W.-W. Zhang, Y. Rong, Q. Liu, C.-L. Luo, Y. Zhang, and Authors’ Contributions F.-B. Wang, “Integrative diagnosis of cancer by combining CTCs and associated peripheral blood cells in liquid biopsy,” Yan Du and Kai Yao contributed equally to this work. Clinical and Translational Oncology, vol. 21, no. 7, pp. 828– 835, 2019. [16] J. Cheng, Q. Tang, X. Cao, and B. Burwinkel, “Cell-Free Acknowledgments circulating DNA integrity based on peripheral blood as a biomarker for diagnosis of cancer: a systematic review,” )is work was supported by a grant from the National Cancer Epidemiology Biomarkers & Prevention, vol. 26, no.11, Natural Science Foundation of China (No. 81772516). pp. 1595–1602, 2017. [17] H. Ren, Z. Chen, L. Yang et al., “Apolipoprotein C1 (APOC1) References promotes tumor progression via MAPK signaling pathways in colorectal cancer,” Cancer Management and Research, vol. 11, [1] R. L. Siegel, K. D. Miller, and A. 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Discovery and Validation of Circulating EVL mRNA as a Prognostic Biomarker in Pancreatic Cancer

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Hindawi Publishing Corporation
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Copyright © 2021 Yan Du et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

Hindawi Journal of Oncology Volume 2021, Article ID 6656337, 11 pages https://doi.org/10.1155/2021/6656337 Research Article Discovery and Validation of Circulating EVL mRNA as a Prognostic Biomarker in Pancreatic Cancer 1 2 3 2 1 4 2,4 YanDu , KaiYao , QingboFeng, FeiyuMao, ZechangXin, PengXu, andJieYao Clinic Medical College, Dalian Medical University, Dalian 116000, China Clinic Medical College, Yangzhou University, Yangzhou 225000, China Department of Liver Surgery, West China Hospital Sichuan University, Chengdu 610000, China Department of Hepatobiliary and Pancreatic Surgery, Northern Jiangsu People’s Hospital, Clinic Medical College, Yangzhou University, Yangzhou 225000, China Correspondence should be addressed to Jie Yao; docyao@hotmail.com Received 12 December 2020; Revised 25 March 2021; Accepted 4 April 2021; Published 20 April 2021 Academic Editor: Pierfrancesco Franco Copyright © 2021 Yan Du et al. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Circulating plasma mRNAs can be analyzed to identify putative cancer biomarkers. )is study was conducted in an effort to detect plasma mRNA biomarkers capable of predicting pancreatic cancer (PACA) patient prognosis. Material and Methods. First, prognostic mRNAs that were differentially expressed in PACA in )e Cancer Genome Atlas (TCGA) were established, after which microarray expression profiles from PACA patient plasma samples were utilized to specifically identify potential prognostic plasma mRNA biomarkers associated with this cancer type. In total, plasma samples were then collected from 79 PACA patients and 19 healthy controls to confirm differential mRNA expression via qPCR, while Kaplan–Meier analyses were used to examine the link between mRNA expression and patient overall survival. Results. In total, three prognostic differentially expressed genes were identified in PACA patient plasma samples, including SMAP2, PTPN6, and EVL (Ena/VASP-like). Plasma EVL levels were confirmed via qPCR to be correlated with tumor pathology (p<0.01), while the overall survival of patients with low plasma EVL levels was poor (p<0.01). Multivariate Cox regression analyses further confirmed that plasma EVL levels were independent predictors of PACA patient prognosis. Conclusion. We found that PACA is associated with the downregulation of plasma EVL mRNA levels, indicating that this mRNA may be a viable biomarker associated with patient prognosis. promising candidates capable of guiding the diagnosis and 1. Introduction monitoring of many cancers, including PACA [4–7]. For Pancreatic cancer (PACA) is among the deadliest forms of example, in one clinical study, galectin-9 was shown to be cancer globally [1], accounting for the fourth-highest rate of highly expressed in human PACA and to be correlated with cancer-related mortality in the USA with a survival rate of patient prognosis through an analysis of galectin-9 levels in below 9% [2, 3]. )e prognosis of PACA is generally very serum samples from 70 PACA patients, 36 patients with poor in part because tumors are asymptomatic during their benign pancreatic diseases, and 28 healthy controls [8]. One early stages such that they are rarely detected until after they database study found serum LAMC2 levels to exhibit a have metastasized, at which time patients have generally significantly improved diagnostic utility relative to CA199 poor outcomes and a 5-year survival rate of below 3% [1]. when discriminating between PACA patients, healthy Owing to the poor outcomes associated with this disease, controls, and individuals with benign diseases [9]. In a there is an urgent need for the identification of novel bio- separate study, researchers identified a 25-component markers of localized PACA that can be used to predict tumor PACA serum biomarker signature through gene expression progression and guide timely treatment efforts. Hemato- analyses of serum samples from 34 pancreatic cancer pa- logical biomarkers have been identified in recent years as tients and 30 healthy controls [10]. 2 Journal of Oncology Herein, we utilized )e Cancer Genome Atlas (TCGA) validated using all 79 PACA patient and control samples. )ese database and microarray sequencing analyses of clinical samples were collected between January 2015 and September 2019 from pancreatic ductal cell carcinoma patients under- patient plasma samples to identify mRNAs associated with PACA patient prognosis. In total, we identified three going postoperative pathological evaluation. Patient clinico- mRNAs that were downregulated in PACA patient plasma pathological characteristics were collected, and staging was and correlated with patient survival outcomes. We subse- performed as per the American Joint Committee on Cancer quently confirmed the prognostic relevance of plasma EVL (AJCC) criteria. )e most recent patient follow-up was con- mRNA expression levels in PACA patients by analyzing ducted on September 30, 2020, and patient survival was cal- plasma samples from 79 PACA patients and 19 healthy culated from the date of surgery to the date of death or most controls. recent follow-up. All patients provided written informed consent, and the Ethics Committee of Northern Jiangsu People’s Hospital approved this study. 2. Materials and Methods 2.1. Bioinformatics Analysis. RNA sequencing data per- 2.5. qPCR. A StepOnePlus Real-Time PCR System (Applied taining to PACA tumor tissue samples and paracancerous Biosystems, NY, USA) was used to conduct qPCR assays. control tissues were downloaded from the TCGA database Briefly, cDNA was prepared with a PrimeScript RT Reagent (https://portal.gdc.cancer.gov/accessed August 20, 2020). Kit. All qPCR reactions were conducted in a 20ul volume Information related to patient overall survival (OS) was containing 10ng of cDNA based on provided instructions. obtained for all patients. )e DESeq R package was then used −ΔΔCt Relative EVL expression was assessed via the 2 ap- to standardize this PACA RNA transcriptomic dataset, after proach, and primers used were as follows: 5′- which R v. x64 3.6.4 and the edgeR package were used to CTCAAAGTCCGATGCCAACC-3′ (forward) and 5′- identify differentially expressed (DE) mRNAs associated with PACA using the following screening criteria: FC>2 TCTTGGCCAGCAGTTTGTTC-3′ (reverse) for EVL and 5′-CTCGCTTCGGCAGCACA-3′ (forward) and 5′- and FDR<0.05. Kaplan–Meier analyses were used to evaluate the link between mRNA expression and patient OS AACGCTTCACGAATTTGCGT-3′ (reverse) for U6. All qPCR analyses were conducted in triplicate. using the R Survival package. 2.2. mRNA Expression Profiling. TRIzol (Takara, Japan) was 2.6. Statistical Analysis. SPSS 24.0 (Chicago, IL, USA) and used to extract total RNA from patient plasma samples, after Prism 7 (GraphPad Software, Inc., CA, USA) were used for which human protein-coding transcripts in these samples statistical testing. Categorical data are given as frequencies were profiled with Affymetrix Human mRNA Array 2.0 and percentages. )e link between EVL expression and (HTA 2.0) GeneChips (Affymetrix, CA, USA) by QiMing patient clinicopathological characteristics was assessed via a Biotech (Shanghai, China). Briefly, rRNA was removed from chi-squared test, while Spearman’s rank correlation coeffi- plasma samples, followed by transcription and amplification cients were used to gauge bivariate correlations. to prepare full-length fluorescent cRNAs without 3′ bias. Kaplan–Meier survival curves and log-rank tests were used Each cRNA was then hybridized to the Affymetrix Human to assess patient survival. p<0.05 was the significance mRNA Array, and sample labeling and hybridization were threshold. conducted with the Affymetrix Microarray-Based Gene Expression Analysis protocol. 3. Results 3.1. Identification of PACA-Related DE mRNAs in the TCGA 2.3. Microarray Data Analysis. For microarray analyses, Database. We began by comparing mRNA expression differentially expressed genes were identified using the profiles between PACA patient tumor and paracancerous following criteria: |FC|>1.5, p<0.05, and FDR<0.05. Gene tissue samples in the TCGA database. In total, 823 DE Ontology (GO) enrichment analyses (http://www. mRNAs were identified in PACA tumors when comparing geneontology.org) were employed to assess the relation- these two tissue types (p<0.05 and FC≥2.0), of which 34 ship between these differentially expressed genes and specific were upregulated and 789 were downregulated (Figure 1(a)). biological processes (BPs), cellular components (CCs), and Hierarchical clustering analyses clearly demonstrated that molecular functions (MFs). Kyoto Encyclopedia of Genes we were able to differentiate between tumor and para- and Genomes (KEGG, http://www.genome.jp/kegg) en- cancerous tissue samples based upon these DE mRNA ex- richment analyses were also conducted to establish the pression profiles (Figure 1(b)). enrichment of these genes in specific biological pathways. 2.4. Sample Collection. In total, 79 PACA patients and 19 3.2. Identification of Prognostic DE mRNAs in Patient Tissue healthy controls were included in this study. Plasma mRNA Samples. Next, the relationship between identified DE samples from 7 PACA patients and 3 healthy controls were mRNAs and PACA patient OS was evaluated using subjected to plasma mRNA array profiling, after which the Kaplan–Meier curves and the log-rank test based upon PACA observed differences in plasma mRNA expression were patient survival data in the TCGA database. In total, 94 DE Journal of Oncology 3 –5 0 510 log2 (fold change) (a) (b) Figure 1: Continued. –log10 (q-value) 4 Journal of Oncology 7.5 5.0 2.5 0.0 –2 0 2 log2 (fold change) (c) (d) Figure 1: Continued. –log10 (q-value) Journal of Oncology 5 Diffgene sig GO RNA binding Structural constituent of ribosome Ubiquitin-like protein ligase binding Cadherin binding Ubiquitin protein ligase binding Enzyme binding Nucleic acid binding GTPase activity Hydrogen ion transmembrane transporter activity Structural molecule activity Macromolecular complex Cytosol Extracellular exosome Extracellular organelle Extracellular vesicle Protein complex Vesicle Intracellular ribonucleoprotein complex Ribonucleoprotein complex Ribosomal subunit Interspecies interaction between organisms Symbiosis, encompassing mutualism through parasitism Viral process RNA catabolic process Translational initiation mRNA catabolic process Cotranslational protein targeting to membrane Establishment of protein localization to endoplasmic reticulum Protein targeting to ER SRP-dependent cotranslational protein targeting to membrane 0 20 40 60 (–LgP) Biotype Biological process Cellular component Molecular function (e) Figure 1: Continued. Gene ontology category 6 Journal of Oncology Diffgene sig pathway Viral myocarditis Viral carcinogenesis Ubiquitin mediated proteolysis Thermogenesis Systemic lupus erythematosus Shigellosis RNA transport Ribosome Retrograde endocannabinoid signaling Regulation of actin cytoskeleton Protein export Proteasome Phagosome Pathogenic escherichia coli infection Parkinson disease Oxidative phosphorylation Non-alcoholic fatty liver disease (NAFLD) Kaposi sarcoma-associated herpesvirus infection Huntington disease Human T-cell leukemia virus 1 infection Human immunodeficiency virus 1 infection Human cytomegalovirus infection Ferroptosis Epstein-Barr virus infection Endocytosis Cellular senescence Cardiac muscle contraction Antigen processing and presentation Alzheimer disease Alcoholism 510 15 20 25 –LgP Diffgene_count –LgP (f) Figure 1: Identification of mRNAs that are differentially regulated in the TCGA database and in microarray-based plasma mRNA ex- pression profiles from PACA patients. (a), (b) Volcano plots and hierarchical clustering analyses were used to identify mRNAs that were differentially expressed between pancreatic tumor tissue and control samples in the TCGA dataset. (c), (d) Volcano plots and hierarchical clustering analyses were used to detect mRNAs that were differentially expressed between pancreatic tumor tissue and control samples in our microarray dataset. (e) Differentially expressed mRNAs were subjected to GO enrichment analyses of key biological processes, cellular components, and molecular functions. (f) Top enriched KEGG pathways for differentially expressed mRNAs in the present microarray dataset. )e size of the circle represents the number of genes enriched in the pathway. Circle colors correspond to p values. TCGA, )e Cancer Genome Atlas; DE mRNA, differentially expressed mRNA; and KEGG, Kyoto Encyclopedia of Genes and Genomes. Pathway Journal of Oncology 7 predictors of poorer PACA patient OS. A subsequent mRNAs were found to be correlated with PACA patient prognosis. multivariate Cox regression analysis revealed that EVL ex- pression, T classification, and M classification were all in- dependent predictors of PACA patient postoperative OS 3.3. Identification of PACA-Related DE mRNAs in Patient duration (all p<0.05) (Table 3). Plasma Tissue Samples. DE mRNAs in plasma samples from PACA and control patients were next identified using a 4. Discussion microarray-based approach. In total, this analysis led to the identification of 2240 DE mRNAs in the plasma of PACA PACA is a deadly cancer type that is forecast to become the patients (p<0.05 and FC≥1.5), of which 152 and 2088 were second leading cancer-associated cause of mortality in the up- and downregulated, respectively (Figures 1(c) and 1(d)). future [11]. As such, novel diagnostic and prognostic bio- GO and KEGG enrichment analyses were conducted to markers associated with this disease must be identified in an assess the enrichment of these DE mRNAs in specific bio- effort to improve patient treatment and survival outcomes. logical pathways, compartments, and functional classifica- Prior research has shown that genes that are dysregulated in tions, with the most enriched GO and KEGG terms being PACA may offer value as prognostic or diagnostic bio- shown in Figures 1(e) and 1(f), respectively. markers for patients with this cancer type [12–16]. Plasma biomarkers are particularly attractive targets for patient 3.4. Identification of Prognostic DE mRNAs in Patient Plasma diagnosis, staging, and monitoring as they can be assessed Samples. In our TCGA analysis, we had identified 94 via a relatively noninvasive liquid biopsy approach. After prognosis-related DE mRNAs. Using a Venn diagram being released from cells, RNA molecules form complexes package, we determined which of these 94 mRNAs over- with lipids that protect these RNAs from nuclease-mediated lapped with our list of 2240 DE mRNAs detected in PACA degradation [17–19]. In general, cancer patients exhibit patient plasma samples (Figure 2(a)), ultimately leading to higher levels of circulating RNA than do healthy individuals the identification of three prognostic DEGs in patient owing to the higher rates of tumor cell proliferation and plasma samples: PTPN6, EVL, and SMAP2. As EVL apoptotic death in the former cohort [20]. As such, in the exhibited the strongest prognostic correlation of these three present study, we sought to identify candidate plasma genes in the TCGA patient cohort, we selected it as a target mRNA biomarkers capable of predicting PACA patient for further study. survival outcomes. We began by employing a bioinformatics approach to assess PACA-related mRNA expression profiles in the 3.5. EVL Downregulation Is Linked to PACA Patient Clini- TCGA database as a means of detecting potential prognostic copathological Features. We determined that EVL mRNA biomarkers in these cancer patients. However, mRNAs that expression was significantly decreased in the majority of are differentially expressed in tumor tissues may not nec- tested PACA patient plasma samples (n �79) relative to essarily be differentially expressed in patient plasma samples, normal control patient plasma samples (n �19; p<0.001; given that normal tissues also contribute to plasma RNA Figure 2(b)). To explore the clinical significance of EVL profiles and have the potential to mask tumor-derived expression (Table 1), we next interrogated the link between mRNA signals in circulation [21]. By comparing our TCGA its expression and PACA patient clinicopathological char- findings to the results of a microarray analysis of PACA acteristics. We determined that EVL expression was nega- patient plasma samples, we identified just three prognosis- tively correlated with PACA pathological stage (p<0.01) related DE mRNAs in these plasma samples: PTPN6, EVL, and patient age (p<0.05), but was unrelated to patient sex, and SMAP2. clinical stage, TNM classification, vascular invasion status, )rough further validation experiments, we confirmed or nerve invasion status. Spearman’s correlation analyses of that EVL mRNA expression was decreased in PACA patient the relationship between EVL and these parameters yielded plasma samples relative to samples from healthy controls. comparable results (Table 2). Decreased EVL mRNA expression was associated with poor OS and with tumor pathological stage and was an inde- 3.6. Decreased EVL Expression Is Predictive of Poor Prognosis. pendent predictor of PACA patient prognosis. EVL is an Using the TCGA database, we evaluated the prognostic Ena/VASP (enabled/vasodilator-stimulated phosphopro- significance of EVL expression levels in PACA. )e tein) family member protein involved in actin cytoskeleton Kaplan–Meier survival curve revealed that a low EVL ex- regulation [22, 23]. Alterations in cytoskeletal composition pression level was associated with a poorer patient prognosis can influence cellular motility, ultimately driving or sup- (p<0.0001, Figure 2(c)). In order to further verify the pressing tumor cell invasion and migration. Mouneimne prognostic value of plasma EVL levels, we collected follow- et al. suggested that EVL downregulation was capable of up data for these 79 PACA patients, and the results of suppressing tumor cell migration and invasion in vitro and survival analyses showed EVL low expression to be asso- in vivo, and decreased EVL expression in human tumor cells ciated with poorer OS in plasma samples (p<0.01 has been shown to be associated with high invasive activity, Figure 2(d)). Univariate Cox regression analyses identified increased protrusion, decreased contractility, and reduced EVL expression (p<0.01), T classification (p<0.01), M adhesion [24]. Grady et al. found EVL to be commonly classification (p<0.01), and nerve invasion (p<0.05) as downregulated in human colorectal cancer through a 8 Journal of Oncology 2.0 ∗∗∗ 1.5 PTPN PTPN66 91 22237 237 SM SMA APP22 EV EVLL 1.0 0.5 0.0 Normal Cancer (a) (b) 0 10 20 30 0 1000 2000 3000 Months Days High expression High expression Low expression Low expression Log-rank <0.01 Log-rank <0.0001 HR (high) = 0.525 HR (high) = 0.44 (c) (d) Figure 2: Plasma EVL levels are decreased in patients with pancreatic cancer. (a) Overlapping genes between the TCGA prognostic gene set and the differentially expressed pancreatic-cancer-associated plasma microarray gene set. (b) Plasma EVL levels were significantly decreased in plasma samples from 79 pancreatic cancer patients relative to 19 normal controls as determined via qPCR. (c), (d) Kaplan–Meier survival curves revealed that elevated EVL was associated with better overall pancreatic cancer patient survival in both the TCGA database ((c), p<0.0001) and the present clinical dataset ((d), p<0.01); p values were calculated via the log-rank test. EVL, Ena/VASP-like; qPCR, real- time quantitative polymerase chain reaction; and TCGA, )e Cancer Genome Atlas. mechanism associated with altered CpG methylation up- that EVL downregulation in PACA patients promotes dis- stream of EVL [25]. Li et al. found EVL mRNA expression to ease progression via driving tumor invasion and metastasis, be decreased in cervical cancer [26]. As such, we hypothesize ultimately leading to poor patient outcomes. Overall survival (%) Overall survival (%) Relative EVL mRNA expression Journal of Oncology 9 Table 1: )e expression of EVL and clinicopathologic features in 79 pancreatic cancer patients. EVL Characteristics p value (χ2 test) Low expression High expression Age 0.010 ≦60 6 16 >60 34 23 Gender 0.556 Male 18 15 Female 22 24 T classification 0.417 I, II 21 24 III, IV 19 15 N classification 0.516 No 26 28 Yes 14 11 Metastasis 1.000 No 37 37 Yes 3 2 Clinical stage 0.260 I, II 31 34 III, IV 9 5 Pathological differentiation 0.007 1, 2 19 30 3, 4 21 9 Vessel invasion 0.106 No 33 26 Yes 7 13 Nerve invasion 0.406 No 9 12 Yes 31 27 Table 2: Spearman analysis of the correlations between EVL and clinicopathological variables. EVL expression level Variables Spearman correlation p value Age (year) −0.290 0.009 Gender 0.066 0.562 T classification −0.091 0.424 N classification −0.073 0.522 Metastasis −0.049 0.670 Clinical stage −0.127 0.266 Pathological differentiation −0.303 0.007 Venous invasion 0.182 0.108 Nervous invasion −0.094 0.412 Table 3: Univariate and multivariate Cox regression analyses of prognostic parameters in pancreatic cancer patients. 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