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CDCA3 Predicts Poor Prognosis and Affects CD8<sup>+</sup> T Cell Infiltration in Renal Cell Carcinoma

CDCA3 Predicts Poor Prognosis and Affects CD8+ T Cell Infiltration in Renal Cell... Hindawi Journal of Oncology Volume 2022, Article ID 6343760, 12 pages https://doi.org/10.1155/2022/6343760 Research Article CDCA3 Predicts Poor Prognosis and Affects CD8 T Cell Infiltration in Renal Cell Carcinoma Yuanyuan Bai , Shangfan Liao , Zhenjie Yin , Bingyong You , Dongming Lu , Yongmei Chen , Daoxun Chen , and Yongyang Wu Department of Urology, Affiliated Sanming First Hospital, Fujian Medical University, Sanming, 365100 Fujian, China Correspondence should be addressed to Yongyang Wu; wuyyfj@fjmu.edu.cn Received 25 May 2022; Revised 5 July 2022; Accepted 7 September 2022; Published 2 September 2022 Academic Editor: Federica Tomao Copyright © 2022 Yuanyuan Bai 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. Background. Cell division cycle associated 3 (CDCA3) mediates the ubiquitination WEE1 kinase at G2/M phase. However, its contribution to cancer immunity remains uncertain. Methods.We first evaluated the effect of CDCA3 on the prognosis of patients with renal cell carcinoma (RCC). The results of bioinformatics analysis were verified by the tissue microarray, immunofluorescence (IF) staining, CCK-8 assay, colony formation, cell cycle, and Western blot. Results. Bioinformatics analysis predicated CDCA3 was an independent predictor of poor prognosis in RCC and was associated with poor TNM stage and grade. CDCA3 was related to the infiltration of CD8 T cells and Tregs. Tissue microarray demonstrated that CDCA3 was strongly associated with poor prognosis and positively relevant to CD8 Tinfiltration. In vitro experiments showed that exgenomic interference of CDCA3 could attenuate cellular proliferation, arrest cell cycle, and blockade accumulation of CDK4, Bub3, and Cdc20 in mitosis process. Conclusion. CDCA3 presents as a good biomarker candidate to predict the prognosis of RCC patients and potentiates the immune tumor microenvironment (TME) of RCC. Cell division malfunctions trigger tumor development 1. Introduction and antitumor immune response [7]. CDCA3 has been Renal cell carcinoma (RCC) is a malignancy from the kidney shown to be a poor prognostic factor for renal papillary cell epithelium and the mobility has steadily increased globally carcinoma, nonsmall cell lung cancer, etc. [8–10]. Scholars in recent years [1]. The first-line antiangiogenic therapies reveal that CDCA3 was upregulated in RCC and promote such as tyrosine kinase inhibitors (TKI) have presented the tumor progression and sunitinib resistance [11] via activat- certain effect for RCC patients, however, the response is dis- ing the NF-κB/cyclin D1 signaling axis [12]. There are data continued in short time for the majorities [2]. Immune indicating that CDCA3 can serve as an important biomarker checkpoint inhibitors (ICIs) usher a new time of cancer ther- to evaluate the therapeutic sensitivity of TKI and therefore it apeutic strategies via sparking anticancer immunity [3]. would be appropriate to underline this aspect also in light of CD8 T cells serve as an essential effector and partially rele- the possible associations of immunological therapies and vant to the effect of ICI [4]. Traditionally, RCC is considered TKI in various types of malignant tumors [13]. Moreover as an immunogenic cancer, and immunotherapy has shown it should be very interesting to test the role of CDCA3 as a a certain effect of RCC [5, 6]. In clinical practices, we observe predictive biomarker of toxicity related to a prolonged use the effect of ICIs is diversified, however, scholars fail to find of these novel agents in combination therapy of RCC [14]. a good candidate to predicate the response and adverse However, the immune impact of CDCA3 has also not been effects (AEs) of ICI in RCC treatment. The biomarkers will well reported. also help identify subgroups that respond to immunotherapy In this paper, we try to evaluate the predicable perfor- and avoid severe AEs. mance of CDCA3 in RCC and figure out the attribution of 2 Journal of Oncology 1.00 Log-rank P=1.18e-12 HR(High grups)=2.784 95%CI(2.099, 3.693) 0.75 4 0.50 0.25 RiskType Median time:5.7 0.00 High groups Low groups Groups = High groups 440 143 26 1 1 Groups = Low groups 441 196 52 3 0 04 8 12 16 Time (years) Groups Groups = High groups Groups = Low groups 1.00 0.75 0 Status Alive Dead 0.50 z-score of expression –2 –1 0.25 CDCA3 1 0.00 0.00 0.25 0.50 0.75 1.00 False positive fraction Type 1-Year, AUC = 0.729.95% CI (0.671-0.787) 3-Year, AUC = 0.689.95% CI (0.644-0.734) 5-Year, AUC = 0.729,95% CI (0.688-0.77) (a) (b) ∗ ∗ C2 1.45( ) 0 C2 9.94( ) C1 C1 0 9.94( ) 0 1.45( ) 100 100 50 50 High Low High Low T1 T3 FEMALE T2 MALE T4 (c) (d) ∗ ∗ 5.73( ) 0 0 C2 C2 6.75( ) ∗ ∗ C1 6.75( ) C1 0 5.73( ) 0 75 75 50 50 25 25 0 0 High Low High Low M0 N0 N1 M1 N2 (e) (f) ∗ ∗ 0 0 C2 11.88( ) C2 6.33( ) ∗ ∗ 6.33( ) C1 0 11.88( ) C1 0 75 75 50 50 25 25 0 0 High Low High Low III G1 G3 I IV G2 G4 II (g) (h) Figure 1: Continued. Groups True positive fraction Overall survival probability Percentage (%) Percentage(%) Percentage (%) Percentage(%) Percentage(%) Percentage (%) Log2(TPM+1) Time Journal of Oncology 3 0 10 20 30 40 50 60 70 80 90 100 1.0 Points G2 G4 Grade 0.8 G1 C-index: 0.754(0.701-1) G3 p-value = p<0.001 Total points 0.6 0 10 20 30 40 50 60 70 80 90 100 Linear predictor 0.4 –4.5 –4 –3.5 –3 –2.5 –2 –1.5 –1 –0.5 0 0.5 1 1.5 1-year survival Pro 0.95 0.9 0.8 0.7 0.2 2-year survival Pro 0.95 0.9 0.8 0.7 0.6 0.0 3-year survival Pro 0.95 0.9 0.8 0.7 0.6 0.5 0.4 0.0 0.2 0.4 0.6 0.8 1.0 Nomogram-prediced(%) n = 512 d = 169 p = 3,512 subjects per group X resampling optimism added, B = 200 5-year survival Pro 0.95 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Gray:ideal Based on observed-predicted 1-year 3-year 2-year 5-year (i) (j) 1.00 0.75 0.50 0.25 p=0.003 0.00 0 1224364860 72 84 96 Time (months) High exp 27 23 21 21 19 17 7 3 0 Low exp 123 120 117 114 109 106 76 38 0 0 1224364860 72 84 96 Time (months) CDCA3 High exp Low exp (k) Figure 1: CDCA3 may be an independent prognostic factor for RCC. Survival curves, ROC curves (a), and mortality risk curves (b) of patients with different CDCA3 expression levels. (c–h) Comparison of distribution of gender, T, N, M stage, clinical stage, and grade among patients with different CDCA3 expression. CDCA3-related nomogram (i) and calibration curve (j) predict OS of the RCC ∗ ∗∗ ∗∗∗ patients. (k) Survival curve of patients with different CDCA3 expression in tissue microarray.( p <0:05, p <0:01, p <0:001). Table 1: Univariate and multivariate COX regression analysis. Uni-COX p value Hazard ratio (95% CI) Multi-COX p value Hazard ratio (95% CI) CDCA3 < 0.0001 2.34698 (2.0353, 2.70639) CDCA3 < 0.0001 1.71293 (1.41056, 2.08011) Age < 0.0001 1.028 (1.01694, 1.03918) Age < 0.0001 1.03039 (1.01629, 1.04468) Gender 0.4682 0.9037 (0.68738, 1.1881) Race 0.5979 1.10877 (0.75541, 1.62743) Clinical stage < 0.0001 2.01609 (1.79567, 2.26356) Clinical stage < 0.0001 1.52842 (1.31228, 1.78016) Grade < 0.0001 2.29073 (1.86981, 2.80639) Grade 0.0029 1.41708 (1.12665, 1.78237) CDCA3 to TME of RCC. Finally, we endorse that targeting group and low expression group based on the median of CDCA3 would be a potential therapeutic way to flight RCC. gene expression. We first drew Kaplan-Meier (KM) survival curve, receiver operating characteristic (ROC) curve, and risk curve to study the prognosis of patients in terms of over- 2. Methods and Materials all survival (OS). Next, we analyzed the differences in clinical 2.1. Data Collection and Preprocessing. The RNA-seq data, data including gender, clinical stage, TNM stage, and grade clinical information, somatic mutation data, and microsatel- among different expression groups of CDCA3. In addition, lite instability (MSI) status of 881 RCC were all from The we used univariate and multivariate COX regression to ana- Cancer Genome Atlas (TCGA, https://portal.gdc.cancer lyze the prognostic significance of CDCA3 expression and .gov/) portal. Patients were divided into high expression clinical data. At the same time, we drew a nomogram CDCA3 Survival probability Observed (%) 4 Journal of Oncology Table 2: CDCA3 expression and demographic and culture was maintained in a humidified incubator with clinicopathological characteristics. 37 C, 5% CO . CDCA3 knockdown lentivirus was designed by Obio Technology Corp (Shanghai, China). Then, Caki-1 CDCA3 and 786-O were transfected with the lentivirus, according N p value Low High to the manufacturer’s instructions. Two days later, puromy- Age cin was added for screening. Knockdown efficiencies of ≥57 58 16 74 CDCA8 were assessed by Western blot. 0.666 <57 65 11 76 2.4. Western Blotting. Cultured cell lysates were prepared Gender using a Column Tissue & Cell Protein Extraction Kit (Epi- Female 33 10 43 0.408 zyme, Shanghai, China; # PC201PLUS). Then total proteins Male 90 17 107 were then separated on 10% SDS polyacrylamide gels. After Size(cm ) overnight incubation with various primary antibodies, ≤175 62 13 75 including anti-CDCA3 (Proteintech, 15594-1-AP), CDK4 1.000 >175 61 14 75 (Proteintech, 11026-1-AP), Cdc20 (Proteintech, 10252-1- T AP), Bub3 (Proteintech, 27073-1-AP), and anti-GADPH (CST, #5174) at 4 C, membranes were washed thrice for T1-2 116 23 139 0.215 5 min each time, using TBST (in 0.1% Tween20). Then, they T3 7 4 11 were incubated in the presence of a secondary rabbit anti- body (1 : 1000, LF102, Epizyme) for 1 h and washed thrice N0 121 26 147 using TBST for 5 min each time. Signals were detected using 1.000 N1-2 2 1 3 the chemiluminescence system. 2.5. Cell Proliferation Assay. The cells were seeded in 96-well diagram and calibration curve to better interpret the prog- plates (1,000 cells/well) and cultured for 1, 2, and 3 days. nostic significance of CDCA3. Moreover, fold change = 2 After adding 10 μl CCK-8 (Dojindo, Japan) to each well was used to compare the differences of gene expression and incubating at 37 C for 2 h, the absorbance at 450 nm among different expression groups of CDCA3, and a heat was measured by the Rayto-6000 system (Rayto, China). map of differentially expressed genes was drawn to show the expression trend in different groups. Finally, considering 2.6. Colony Formation Assay. For cell proliferation, we that CDCA3 can be used as an oncogene to affect the pro- seeded 200 cells to each well of 6-well plates for 14 days, then gression of tumor, we performed Gene Ontology (GO) and fixed with 4% paraformaldehyde (PFA) and stained with Kyoto Encyclopedia of Genes and Genomes (KEGG) enrich- crystal violet. The cells were photographed, and the numbers ment analysis on the upregulated genes of CDCA3 in differ- of colonies were counted. ent expression groups to identify CDCA3 functional pathway localization in tumors. 2.7. Flow Cytometry. Cell cycle analysis was performed using a Cell Cycle Staining Kit (MultiSciences, Hangzhou, China), 2.2. Correlation between Tumor Immune Cell Infiltration as instructed by the manufacturer. Cells were washed using and CDCA3 Gene Expression. Cell type Identification By PBS, after which 1 ml of DNA staining solution and 10 μl Estimating Relative Subsets Of RNA Transcripts (CIBER- of permeate were added to the cell suspension and vortexed SORT) algorithm was used to estimate the infiltration pro- to mix. Finally, cells were stained in the dark at 4 C for portion of 22 kinds of immune cells in normal kidney and 30 min and analyzed by flow cytometry. The stained cells RCC samples to describe the profile of immune cell infiltra- were assessed by flow cytometry (BD FACSCanto [TM] II, tion in RCC. The abundance of immune cells infiltration and USA), and analysed by FlowJo vX.0.7 software. the expression of 8 important immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT, and 2.8. Tissue Microarray. The RCC tissue microarray was pur- SIGLEC15) among different CDCA3 expression groups were chased from Outdo (Shanghai, China) and contains 150 compared. Finally, we also analyzed the correlation between RCC tissues and 30 paired paracancer tissues along with CDCA3 expression with tumor mutation burden (TMB) and their survival, clinical information, etc. Samples were col- MSI. Tumor Immune Single-cell Hub (TISCH, http://tisch lected from the National Human Genetic Resources Sharing .comp-genomics.org/) is a scRNA-seq database focusing on Service platform (2005DKA21300). All points on the chip TME. We obtained the relationship between CDCA3 and were detected by Immunofluorescence (IF). The expression RCC TME at single-cell level in TISCH. of CDCA3, CD8, CD4, CD68, FOXP3, and PD-1 was detected by intensity and positive number of IF. We divided 2.3. Cell Culture and Transfection of Lentivirus. Caki-1 and 150 RCC patients into two groups based on the optimal 786-O were purchased from the Type Culture Collection CDCA3 cut-off value and plotted survival curves to identify (Chinese Academy of Sciences, Shanghai, China). Cells were their prognostic significance. Finally, we analyzed the corre- cultured in RPMI-1640 medium (HyClone, USA) with 10% lation between CDCA3 and CD8, CD4, FOXP3, CD68, and fetal bovine serum (Gibco, Grand Island, NY, USA). The PD-1. Journal of Oncology 5 Groups 3.23e-01 Macrophage M1 7.48e-09 Ty Typ pe e T cell follicular helper*** 1.16e-04 T cell CD8+*** B cell memo B cell memor ry y Myeloid dendritic cell resting 9.88e-01 T cell gamma delta B cell na B cell nai iv ve e Neutrophil 1.88e-01 B cell plasma 2.18e-02 B cell p B cell pl lasma asma 1 NK cell activated Mast cell resting* 4.08e-03 Eosino Eosinop phi hil l NK cell resting Macrophage M0** 2.72e-04 B cell memory*** M Macr acrp ph hag age e M0 M0 T cell CD4+ memory activated 1.34e-02 Myeloid dendritic cell activated* T cell CD4+ memory resting 1.77e-01 M Macr acrp ph hag age e M1 M1 Neutrophil 7.33e-03 M Macr acrp ph hag age e M2 M2 T cell CD4+ native Myeloid dendritic cell resting** 7.33e-03 0 M Mast cell ac ast cell act ti iv va at ted ed T cell CD8 T cell CD4+ memory activated 4.89e-01 T cell CD4+ naive T cell follicular helper M Mast cell r ast cell re es st ti in ng g 7.02e-01 Eosinophil 2.16e-01 Mo Mon no oc cy y y yt te e T cell gaamma delta T cell regulatory (Tregs) 1.95e-15 T cell regulatory (Tregs) M M My ye elo loid de id dendr ndri it tic cell ac ic cell act ti iv va at te ed d d d NK cell activated* 3.75e-02 –1 Macrophage M2*** 1.85e-06 Monocyte*** 6.28e-05 B cell naive*** 3.76e-07 NK cell resting* 1.08e-02 T cell CD4+ memory resting 4.85e-01 Mast cell activated 1.62e-01 –2 Groups High Low (a) (b) loge(S)=17.56, p=9e–10, 𝜌 =0.23, CI95% [0.16, 0.30], n =690 log (S)=17.56, p=90.046, 𝜌 =–0.08, CI [–0.15, 0.00], n =685 Spearman pairs e Spearman 95% pairs 0.7 0.6 0.5 0.4 0 12345 12 345 Log2 (CDCA3 expression) Log2 (CDCA3 expression) (c) (d) ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎ ⁎⁎ ⁎ KIRC_GSE139555 Celltype (minor-lineage) B Mast CD4Teff Moncyte CD4Tn NK CD8Teff Plasma CD8Tex Th1 Endothelial Tprolif M1 cDC1 M2 pDC Group High Low (e) (f) Figure 2: Continued. Percent (%) Immune checkpoint CD274 CTLA4 TMB score HAVCR2 LAG3 PDCD1 MSI score PDCDILG2 TIGIT SIGLEC15 6 Journal of Oncology HALLMARK_G2M_CHECKPOINT colour CDCA3 level 1.6 2.0 1.2 1.5 0.8 1.0 –5 –10 0.4 0.5 –15 0 –10 –5 0 5 10 0.0 UMAP_1 level 0.4 0.8 1.2 1.6 (g) (h) Figure 2: CDCA3 affects immune infiltration in RCC. (a) The distribution of immune cells infiltration in RCC. (b) Comparison of immune cells infiltration between different CDCA3 expression groups. (c) Correlation analysis between CDCA3 and TMB. (d) Correlation analysis between CDCA3 expression and MSI. (e) Comparison of 8 immune checkpoints in different expression groups of CDCA3. (f) Single-cell level distribution of immune cells in RCC. (g) Expression of CDCA3 in immune cells. (h) The relationship between CDCA3 and the G2/ ∗ ∗∗ ∗∗∗ M checkpoint in immune cells. ( p <0:05, p <0:01, p <0:001). 2.9. Immunofluorescence Staining. Tissue microarray were curve showed higher mortality in high-CDCA3 patients deparaffinized by graded alcohol and then washed three than low-CDCA3 patients (Figure 1(b)). Among the patients times with phosphate-buffered saline (PBS), permeabilized with different CDCA3 expression groups, gender, TNM with 0.4% Triton X-100 for 30 min, and blocked with goat stage, clinical stage, and grade showed differences in distri- serum working liquid (Wuhan Boster Biological Technol- bution (Figures 1(c)–1(h)). Univariate and multivariate ogy, Wuhan, China) for 2 hours after antigen retrieval. The COX analysis showed that CDCA3, age, TNM stage, and sections were then incubated overnight with mixed primary grade could be used as prognostic factors of RCC, and antibodies at 4 C, washed in PBS to remove unbound pri- CDCA3 could independently predict the prognosis of RCC mary antibodies, and incubated with secondary antibodies (Table 1). We also constructed the prognostic nomogram in the dark at room temperature (RT) for 1 hour. The sec- and calibration curve of RCC, and the 5-year overall survival rate could be estimated according to the total score tions were counterstained with 4 , 6 diamidino-2- (C − index = 0:754, Figures 1(i) and 1(j)). Demographic phenylindole (Sigma-Aldrich) for 5 minutes and washed characteristics and pathological baseline of tissue microarray with PBS. The primary antibodies included CDCA3 (Pro- were listed in Table 2, showing that high CDCA3 expression teintech, 15594-1-AP). The fluorophore-conjugated second- levels predicted shorter survival (p =0:003, Figure 1(k)), ary antibodies used were goat anti-rabbit Alexa Fluor 488 (1: which proves the bioinformatics analysis. In summary, 500; Abbkine, Wuhan, China) and goat anti-mouse Alexa CDCA3 can be an independent prognostic factor and reflect Fluor 549 (1: 500; Abbkine, Wuhan, China). Images were the rate of tumor progression tumor progression in RCC. captured by confocal laser scanning microscopy (Nikon A1 + R, Japan). The fluorescence intensity was analyzed by 3.2. CDCA3 Is Related to Immune Infiltration. Figure 2(a) using the ImageJ software. showed the infiltration of immune cells in RCC. On this basis, we further analyzed the different abundance of 2.10. Statistical Analysis. In this study, R (version 4.0.2), immune cell infiltration among different CDCA3 expression GraphPad Prism 8, and SPSS 20.0 software were used to analyze the data. Survival, survminer, timeROC, rms, groups (Figure 2(b)). The infiltration of CD8 T cell (p <0:001), Tregs (p <0:001), memory B cell (p <0:001), Limma, ggplot2, pheatmap, and ClusterProfiler R package were used in this study. The significance of differences follicular helper T cell (p <0:001), activated NK cell between groups was assessed by the student T test. Chi- (p <0:05), and M0 macrophage (p <0:01) was upregulated in the patients with high expression of CDCA3, while naive square test was used for categorical variables, and Wilcoxon test was used for continuous data. Survival differences were B cell (p <0:001), resting NK cell (p <0:05), Monocyte (p <0:001), and M2 macrophage (p <0:001) was downregu- calculated using Kaplan-Meier and logarithmic rank tests. lated. TMB and MSI levels reflect tumor surface neoantigen abundance and can stimulate antitumor immune response. 3. Results CDCA3 was also positively correlated with TMB (p <0:001 3.1. Prognostic Significance of CDCA3 in RCC. First, KM sur- , r =0:23, Figure 2(c)) and negatively correlated with MSI vival analysis of TCGA-RCC revealed a shorter survival time (p =0:046, r = −0:08, Figure 2(d)). CD274 (PD-L1, p < in the high-CDCA3 expression group versus the low- 0:001), PDCD1LG2 (PD-L2, p <0:01), and SIGLEC15 CDCA3 expression group (p <0:001, n = 881). ROC curves (p <0:05) were downregulated in patients with high expres- suggested a good accuracy of CDCA3 expression in predict- sion of CDCA3, while CTLA4 (p <0:001), LAG3 (p <0:001), ing RCC prognosis (AUC =0:729, Figure 1(a)). The risk PDCD1 (PD-1, p <0:001), and TIGIT (p <0:01) were UMAP_2 Journal of Oncology 7 Low immune infiltration High CDCA3 expression High immune infiltration Low CDCA3 expression 20× 400× CDCA3 CD68 DAPI FOXP3 PD-1 CD8 CD4 (a) r = 0.266, p = 0.001 r = –0.013, p = 0.874 5 5 0 0 0 510 0 510 CDCA3 CDCA3 (b) (c) r = 0.098, p = 0.233 r = 0.032, p = 0.698 0 0 0 510 0 510 CDCA3 CDCA3 (d) (e) r = –0.073, p = 0.376 0 510 CDCA3 (f) Figure 3: The relationship between CDCA3 and immune cell infiltration in tissue microarray. (a) Representative images of IF staining in tissues microarray. (b–f) Correlation between CDCA3 and CD8, CD4, FOXP3, CD68, FOXP3, and PD-1. FOXP3 CD8 PD-1 CD68 CD4 8 Journal of Oncology group AURKB MYBL2 BIRC5 UBE2C –1 PABPCIL RCAN2 PRAME IL20RB TSPAN7 PGGHG RHEX PTGER3 KRT19 FOSB C10orf99 –2 C1QL1 ATP6V0D2 SAA1 SLPI PVALB –0.584962500721156 0 0.584962500721156 –3 group Log (fold change) Up Down Down-regulation None Up-regulation (a) (b) –log10(p.adjust) KEGG pathway (Up) GO (Up) –log10(p.adjust) p53 signalling pathway spindle organization Viral protein interaction with spindle assembly cytokine and cytokine receptor sister chromatid segregation Viral carcinogenesis regulation of sister chromatid segregation TGF-beta signaling pathway Staphylococcus aureus infection regulation of nuclear divistion Progesterone-mediated oocyte maturation regulation of mitotic sister chromatid separation Oocyte meisis regulation of mitotic nuclear division 14 NF-kappa B signaling pathway regulation of chromosome separation JAk-STAT signaling pathway regulation of chromosome segregation IL-17 signaling pathway organelle fission nuclear division Human T-cell leukemia virus 1 infection Homologous recombination nuclear chromosome segregation mitotic spindle organization Glycerophospholipid metabolism Fanconi anemia pathway mitotic sister chromatid segregation Ether lipid metabolism mitotic nuclear division microtubule cytoskeleton organization Cytkine-cytokine receptor interaction involved in mitosis Coronavirus disease-COVID-19 metaphase/anaphase transitionof cell cycle Complement and coagulation cascades meiotic cell cycle Cellular senescence chromosome separation chromosome segregation 10 Cell cycle 0.02 0.04 0.06 0.08 0.10 0.04 0.06 0.08 0.10 0.12 Enrichment Ratio Enrichment Ratio Count Count 5 20 (c) (d) Figure 4: Enrichment analysis of CDCA3 positively correlated genes. (a) Distribution of differential genes with different CDCA3 expression levels. (b) The heat map of the differential genes expression in CDCA3 high and low expression groups. KEGG enrichment analysis (c) and GO enrichment analysis (d) of differential genes in CDCA3 high and low expression groups. upregulated (Figure 2(e)). The distribution of immune cells (Figure 3(b)). However, our study did not observe the corre- in KIRC is shown in Figure 2(f). Figure 2(g) shows immune lation between CDCA3 and CD4, FOXP3, CD68, and PD-1 cells hardly express CDCA3. CDCA3 can regulate G2/M (Figures 3(c)–3(f)). In conclusion, CDCA3 was closely phase, so we analyzed the relationship between CDCA3 related to tumor immune cells infiltration and antitumor and immune cells G2/M checkpoint. Our results show a immunity. And CDCA3 may be important for RCC risk broad association of CDCA3 with immune cell G2/M check- stratification and immunotherapy guidance. points (Figure 2(h)). Further, we conducted tissue microarray to try to prove 3.3. Identification of Molecular Mechanism of CDCA3. The the above results. Figure 3(a) shows that we performed IF distribution of different genes among patients with different staining in RCC tissue microarray. There was a significant CDCA3 expression groups was shown in the volcano map positive correlation between CDCA3 and CD8 (Figure 4(a)). The heat map showed the expression trend –Log P-value 10 Journal of Oncology 9 786-O Caki-1 ⁎⁎⁎ CON NC RNAi CON NC RNAi ⁎⁎⁎ ⁎⁎⁎ CDK4 BUB3 024 48 72 96 Time (h) CON-786-0 NC-786-0 CDC20 RNAi-786-0 CDCA3 GAPDH (a) (b) ⁎⁎⁎ ⁎⁎⁎ ⁎⁎ 024 48 72 96 Time (h) CON-Caki-1 NC-Caki-1 RNAi-Caki-1 (c) CON NC RNAi ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ (d) Figure 5: Continued. Caki-1 786-O OD Value OD Value Colony Formation Number Colony Formation Number CON-Caki-1 CON-786-O NC-Caki-1 NC-786-O RNAi-Caki-1 RNAi-786-O 10 Journal of Oncology RNAi NC 80 ⁎⁎ 300 PI-A subset-2 30.8% PI-A subset PI-A subset 52.5% PI-A subset-1 ⁎ 200 65.9% 8.98% PI-A subset-1 6.46% PI-A subset-2 23.2% 100 ⁎ 0 0 G0/G1 S G2/M 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K PI-A PI-A NC-786-O RNAi-786-O PI-A subset 67.7% PI-A subset-1 600 PI-A subset 11.0% 53.9% PI-A subset-1 PI-A subset-2 14.1% 15.5% 400 ⁎ PI-A subset-2 23.8% ⁎⁎ 200 20 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K G0/G1 S G2/M PI-A PI-A NC-Caki-1 RNAi-Caki-1 (e) Figure 5: The function of CDCA3 was confirmed by in vitro experiments. (a) Western blot was used to detect the expression of CDCA3, CDK4, Bub3, and Cdc20 in different groups of cells. CCK-8 array to detect (b) 786-O and (c) Caki-1 proliferation. (d) Representative images ∗ ∗∗ ∗∗∗ of crystal violet stain on day 15. (e) Representative images of flow cytometry.( p <0:05, p <0:01, p <0:001). of 50 upregulated genes and 50 downregulated genes with 4. Discussion the greatest difference (Figure 4(b)). KEGG enrichment analysis showed that the related pathways were mainly con- Our study suggested that CDCA3 can independently predict centrated in p53 signal pathway, TGF-β signal pathway, NF- prognosis and affect tumor progression in RCC. CDCA3 κB signal pathway, and JAK-STAT signal pathway. may also be involved in the regulation of immune-related (Figure 4(c)). GO enrichment analysis showed that its bio- pathways, and stimulated the infiltration of immune cells, logical function was mainly enriched in spindle organiza- such as CD8 T cells and Tregs. Importantly, we verified tion, regulation of sister chromatid segregation, and our results in vitro. nuclear division (Figure 4(d)). These results suggest that Scholars revealed that CDCA3 influence many tumor CDCA3 mainly affects cell cycle in RCC and may regulate progression and treatment through a variety of pathways antitumor immune response through NF-κB axis and other and is associated with poorer prognosis [8, 15]. Our results important immune-related pathways. also showed consistency. Patients with high CDCA3 expres- sion had significantly worse survival and clinical stage, 3.4. CDCA3 Knockdown Attenuated RCC Cell Proliferation which was confirmed by our results and public databases. and Arrested Cell Cycle. To further understand the effect of One important reason is that dysregulation of cell cycle is CDCA3 on the biological behavior of RCC, we constructed the basis of abnormal proliferation of tumor cells. We also CDCA3-knockdown cell lines for functional experiments. confirmed that downregulation of CDCA3 blocked the G2/ Lentiviruses carrying CDCA3 shRNA were used to obtain M phase of cells and reduced cell proliferation ability. This CDCA3-knockdown Caki-1 and 786-O. The Western blot directly proved that the functional localization of CDCA3 results showed that the expression of CDCA3 was signifi- was a key regulatory protein in the cell cycle, and its abnor- cantly decreased in the RNAi group, indicating that the mal expression can affect tumor progression and prognosis. CDCA3-knockdown cell lines were successfully constructed Infiltrating immune cells directly affect the occurrence, (Figure 5(a)). Meanwhile, the expression of CDK4, BUB3, development, and treatment of tumors. It has been reported and Cdc20 was decreased (Figure 5(a)), which indicating cell that CDCA3 is closely related to immune infiltration in cycle arrest. The CCK-8 assay showed that CDCA3 knock- hepatocellular carcinoma [16]. Our results showed that down remarkably attenuated the cell proliferation CDCA3 affected tumor infiltration of various immune cells, (Figures 5(b) and 5(c)). The ability of colony formation including CD8 T cells in endogenous and exogenous data. was notably impaired after knockdown of CDCA3 gene Previous studies have shown that CD8 T cells can recognize (Figure 5(d)). The flow cytometric indicated that CDCA3 tumor-specific antigens and played a role in tumor control knockdown cause G1, S, and G2/M phase arrest [17]. The high density of tumor infiltrating CD8 T cells (Figure 5(e)). In general, CDCA3 expression affects cell cycle has been proved to be associated with a good prognosis of operation and cell proliferation. most cancers [18], but the infiltration of CD8 T cells in Caki-1 786-O Count Count Count Count Cell cycle distribution (%) Cell cycle distribution (%) Journal of Oncology 11 RCC was associated with a poor prognosis [19], this is con- cate the immune status and curative effect of patients, which sistent with our survival outcomes. Since immune cells provides an important reference for immunotherapy of hardly express CDCA3, antitumor therapy targeting CDCA3 RCC. When we focus on CDCA3, the problem seems to may not cause damage to immune cells, which is a potential become transparent. CDCA3 has the potential to evaluate treatment. Our study was firstly proved that CDCA3 may be prognosis and TME and helps to hierarchically label patients involved in the regulation of immune cell infiltration and at high risk. Then apply medical intervention in advance, tumorigenesis in RCC. But more importantly, the specific select appropriate treatment strategies, and improve the pathway through which CDCA3 affects immune infiltration prognosis. However, our study has its limitations. First, the needs further study. specific mechanism of CDCA3 on CD8 T cells and its influ- As we know, immune checkpoint is a key molecule in ence on immunotherapy of renal cell carcinoma need to be tumor immune escape pathway. There were a lot of evi- further explored; second, we have proved that CDCA3 can dences showed that immune checkpoints were related to block the cell cycle, but there is no further study on the bio- the benefit degree of ICIs treatment, which can be used as logical mechanism. biomarkers for ICIs treatment [20–22]. Our results showed that patients with high expression of CDCA3 also expressed 5. Conclusion high levels of CTLA4 and PD-1. This initially showed that there was a close relationship between CDCA3 and immune CDCA3 can be used as an oncogene to affect the prognosis checkpoints and further suggested that CDCA3 may partic- of RCC patients. Downregulation of CDCA3 causes cell ipate in the immune pathway of RCC by regulating immune stagnation in G2/M phase, promotes cell apoptosis, and regulatory factors, which may be a potential target for reduces proliferation ability. More importantly, the immu- immunotherapy. Moreover, findings suggested that TMB nological implications of CDCA3 have also been preliminar- may predict clinical response to ICIs [23]. The neoantigen ily evaluated. CDCA3 may participate in the regulation of produced by TMB may be an important reason for stimulat- immune infiltration in tumor microenvironment by affect- ing antitumor response. In our study, we found that there ing the expression of many immune regulatory factors and was a positive correlation between CDCA3 and TMB, also TMB, which is expected to provide valuable reference for suggesting that CDCA3 may predict the benefits of clinical ICIs treatment. Overall, CDCA3 can be used as a immunotherapy. biomarker to evaluate prognosis and CD8 T cell infiltration In summary, reactive TME is the key to immunotherapy, in RCC. Targeted therapy against CDCA3 is a promising and CDCA3 helps to evaluate this phenomenon. Further- new therapeutic modality, and focusing on it may help to more, enrichment analysis was performed to evaluate the improve the management of therapeutic resistance in the actual molecular mechanism of CDCA3 in RCC. CDCA3 combination of ICI and TKI, but this needs further research has been suggested to influence the NF-κB pathway to medi- to confirm. ate tumor progression [12]. Our results supported this point. CDCA3 is also involved in P53 and TGF pathways. NF-κBis involved in the regulation of inflammation and innate Data Availability immunity in tumor development. P53 also plays an impor- tant role in immune system. P53 mutation in cancer triggers All public data access addresses are visible in the manuscript. B cell antibody response and CD8 killing T cell response Data archiving will be made available on reasonable request [24]. TGF-β can inhibit the proliferation, activation, and and all of the authors are responsible to the data. effector function of T cells. In addition, TGF-β further enhances immunosuppression in TME by promoting Tregs differentiation and destroying T cell immunity [25]. These Conflicts of Interest evidences suggest that CDCA3 has a reasonable influence The authors declare no conflict of interest. on TME, but more specific studies are needed to uncover the regulatory mechanisms. TME is recognized as a complex dynamic ecosystem, Authors’ Contributions which is composed of malignant tumor cells, various infiltra- tion immune cells, fibroblasts, and a variety of cytokines. In All authors read and approved the final manuscript. Yua- this ecosystem, immune response plays an important role in nyuan Bai and Yongyang Wu designed the experiments, per- tumorigenesis and development. RCC has always been formed the experiments, analyzed the data, and wrote the regarded as an immunogenic malignant tumor [26–28], paper. Zhenjie Yin, Bingyong You, Yongmei Chen, and and it is usually insensitive to chemotherapy and radiother- Daoxun Chen reviewed and revised the manuscript. apy. Immunotherapy is regarded as another therapeutic tar- get in addition to chemotherapy and radiotherapy [29]. Clinicians are focusing on immunotherapy to create a new Acknowledgments era of RCC treatment, trying to break through the traditional barrier [30]. The first thing to use immunotherapy is to eval- The study is supported by the Natural Science Foundation of uate the immune status, which is the premise of personalized Fujian Province, China (No. 2019 J01590, treatment. Therefore, find a biomarker that can better indi- No.2022 J01122348). 12 Journal of Oncology References [16] Z. Wang, S. Chen, G. Wang, and S. Li, “CDCA3 is a novel prognostic biomarker associated with immune infiltration in [1] A. Znaor, J. Lortet-Tieulent, M. Laversanne, A. Jemal, and hepatocellular carcinoma,” BioMed Research International, F. Bray, “International variations and trends in renal cell carci- vol. 2021, Article ID 6622437, 19 pages, 2021. noma incidence and mortality,” European Urology, vol. 67, [17] D. S. Chen and I. Mellman, “Oncology meets immunology: the no. 3, pp. 519–530, 2015. cancer-immunity cycle,” Immunity, vol. 39, no. 1, pp. 1–10, [2] D. Y. Heng, W. Xie, M. M. Regan et al., “Prognostic factors for overall survival in patients with metastatic renal cell carcinoma [18] W. H. Fridman, F. Pagès, C. Sautès-Fridman, and J. Galon, treated with vascular endothelial growth factor-targeted “The immune contexture in human tumours: impact on clini- agents: results from a large, multicenter study,” Journal of Clin- cal outcome,” Nature Reviews. Cancer, vol. 12, no. 4, pp. 298– ical Oncology, vol. 27, no. 34, pp. 5794–5799, 2009. 306, 2012. [3] F.-J. Hsueh and Y. Tsai, “Current and future aspect of immu- [19] Q. Pan, L. Wang, S. Chai, H. Zhang, and B. Li, “The immune notherapy for advanced renal cell carcinoma,” Urological Sci- infiltration in clear cell renal cell carcinoma and their clinical ence, vol. 31, no. 1, pp. 8–14, 2020. implications: a study based on TCGA and GEO databases,” [4] H. Raskov, A. Orhan, J. P. Christensen, and I. Gögenur, “Cyto- Journal of Cancer, vol. 11, no. 11, pp. 3207–3215, 2020. toxic CD8 T cells in cancer and cancer immunotherapy,” [20] S. Bagchi, R. Yuan, and E. G. Engleman, “Immune checkpoint British Journal of Cancer, vol. 124, no. 2, pp. 359–367, 2021. inhibitors for the treatment of cancer: clinical impact and [5] J. I. Clark, M. K. K. Wong, H. L. Kaufman et al., “Impact of mechanisms of response and resistance,” Annual Review of Sequencing Targeted Therapies With High-dose Interleukin- Pathology, vol. 16, no. 1, pp. 223–249, 2021. 2 Immunotherapy: An Analysis of Outcome and Survival of [21] G. Giannone, E. Ghisoni, S. Genta et al., “Immuno-metabolism Patients With Metastatic Renal Cell Carcinoma From an On- and microenvironment in cancer: key players for immuno- SM going Observational IL-2 Clinical Trial: PROCLAIM ,” Clin- therapy,” International Journal of Molecular Sciences, vol. 21, ical Genitourinary Cancer, vol. 15, no. 1, pp. 31–41.e4, 2017. no. 12, p. 4414, 2020. [6] D. Lavacchi, E. Pellegrini, V. E. Palmieri et al., “Immune [22] Y. Lai, F. Tang, Y. Huang et al., “The tumour microenviron- checkpoint inhibitors in the treatment of renal cancer: current ment and metabolism in renal cell carcinoma targeted or state and future perspective,” International Journal of Molecu- immune therapy,” Journal of Cellular Physiology, vol. 236, lar Sciences, vol. 21, no. 13, p. 4691, 2020. no. 3, pp. 1616–1627, 2021. [7] C. Kissling and S. Di Santo, “Tumor treating fields - behind [23] L. M. Sholl, F. R. Hirsch, D. Hwang et al., “The promises and and beyond inhibiting the cancer cell cycle,” CNS & Neurolog- challenges of tumor mutation burden as an immunotherapy ical Disorders Drug Targets, vol. 19, no. 8, pp. 599–610, 2020. biomarker: a perspective from the International Association [8] M. N. Adams, J. T. Burgess, Y. He et al., “Expression of for the Study of Lung Cancer pathology committee,” Journal CDCA3 is a prognostic biomarker and potential therapeutic of Thoracic Oncology, vol. 15, no. 9, pp. 1409–1424, 2020. target in non-small cell lung cancer,” Journal of Thoracic [24] A. J. Levine, “P53 and the immune response: 40 years of Oncology, vol. 12, no. 7, pp. 1071–1084, 2017. exploration-a plan for the future,” International Journal of [9] Y. Zhang, W. Yin, W. Cao, P. Chen, L. Bian, and Q. Ni, Molecular Sciences, vol. 21, no. 2, p. 541, 2020. “CDCA3 is a potential prognostic marker that promotes cell [25] A. Dahmani and J. S. Delisle, “TGF-β in T cell biology: impli- proliferation in gastric cancer,” Oncology Reports, vol. 41, cations for cancer immunotherapy,” Cancers, vol. 10, no. 6, no. 4, pp. 2471–2481, 2019. p. 194, 2018. [10] H. Li, M. Li, C. Yang et al., “Prognostic value of CDCA3 in kid- [26] R. Raman and D. Vaena, “Immunotherapy in metastatic renal ney renal papillary cell carcinoma,” Aging, vol. 13, no. 23, cell carcinoma: a comprehensive review,” BioMed Research pp. 25466–25483, 2021. International, vol. 2015, Article ID 367354, 8 pages, 2015. [11] Y. Liu, G. Cheng, Z. Huang et al., “Long noncoding RNA [27] C. Mazza, B. Escudier, and L. Albiges, “Nivolumab in renal cell SNHG12 promotes tumour progression and sunitinib resis- carcinoma: latest evidence and clinical potential,” Therapeutic tance by upregulating CDCA3 in renal cell carcinoma,” Cell Advances in Medical Oncology, vol. 9, no. 3, pp. 171–181, 2017. Death & Disease, vol. 11, no. 7, p. 515, 2020. [28] M. Itsumi and K. Tatsugami, “Immunotherapy for renal cell [12] P. Gu, M. Zhang, J. Zhu, X. He, and D. Yang, “Suppression of carcinoma,” Clinical & Developmental Immunology, CDCA3 inhibits prostate cancer progression via NF-κB/cyclin vol. 2010, article 284581, Article ID 284581, pp. 1–8, 2010. D1 signaling inactivation and p21 accumulation,” Oncology [29] M. Binnewies, E. W. Roberts, K. Kersten et al., “Understanding Reports, vol. 47, no. 2, 2021. the tumor immune microenvironment (TIME) for effective [13] K. B. Sahin, E. T. Shah, G. P. Ferguson et al., “Elevating therapy,” Nature Medicine, vol. 24, no. 5, pp. 541–550, 2018. CDCA3 Levels Enhances Tyrosine Kinase Inhibitor Sensitivity [30] S. Biswas and T. Eisen, “Immunotherapeutic strategies in kid- in TKI-Resistant EGFR Mutant Non-Small-Cell Lung Can- ney cancer–when TKIs are not enough,” Nature Reviews Clin- cer,” Cancers, vol. 13, no. 18, p. 4651, 2021. ical Oncology, vol. 6, no. 8, pp. 478–487, 2009. [14] L. Bianchi, L. Rossi, F. Tomao, A. Papa, F. Zoratto, and S. Tomao, “Thyroid dysfunction and tyrosine kinase inhibitors in renal cell carcinoma,” Endocrine-Related Cancer, vol. 20, no. 5, pp. R233–R245, 2013. [15] Q. Chen, L. Zhou, X. Ye, M. Tao, and J. Wu, “miR-145-5p sup- presses proliferation, metastasis and EMT of colorectal cancer by targeting CDCA3,” Pathology, Research and Practice, vol. 216, no. 4, article 152872, 2020. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Oncology Hindawi Publishing Corporation

CDCA3 Predicts Poor Prognosis and Affects CD8<sup>+</sup> T Cell Infiltration in Renal Cell Carcinoma

CDCA3 Predicts Poor Prognosis and Affects CD8<sup>+</sup> T Cell Infiltration in Renal Cell Carcinoma

Abstract

<i>Background</i>. Cell division cycle associated 3 (CDCA3) mediates the ubiquitination WEE1 kinase at G2/M phase. However, its contribution to cancer immunity remains uncertain. <i>Methods</i>. We first evaluated the effect of CDCA3 on the prognosis of patients with renal cell carcinoma (RCC). The results of bioinformatics analysis were verified by the tissue microarray, immunofluorescence (IF) staining, CCK-8 assay, colony formation, cell cycle, and Western blot. <i>Results</i>. Bioinformatics analysis predicated CDCA3 was an independent predictor of poor prognosis in RCC and was associated with poor TNM stage and grade. CDCA3 was related to the infiltration of CD8<sup>+</sup> T cells and Tregs. Tissue microarray demonstrated that CDCA3 was strongly associated with poor prognosis and positively relevant to CD8<sup>+</sup> T infiltration. In vitro experiments showed that exgenomic interference of CDCA3 could attenuate cellular proliferation, arrest cell cycle, and blockade accumulation of CDK4, Bub3, and Cdc20 in mitosis process. <i>Conclusion</i>. CDCA3 presents as a good biomarker candidate to predict the prognosis of RCC patients and potentiates the immune tumor microenvironment (TME) of RCC.

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Publisher
Hindawi Publishing Corporation
ISSN
1687-8450
eISSN
1687-8469
DOI
10.1155/2022/6343760
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Abstract

Hindawi Journal of Oncology Volume 2022, Article ID 6343760, 12 pages https://doi.org/10.1155/2022/6343760 Research Article CDCA3 Predicts Poor Prognosis and Affects CD8 T Cell Infiltration in Renal Cell Carcinoma Yuanyuan Bai , Shangfan Liao , Zhenjie Yin , Bingyong You , Dongming Lu , Yongmei Chen , Daoxun Chen , and Yongyang Wu Department of Urology, Affiliated Sanming First Hospital, Fujian Medical University, Sanming, 365100 Fujian, China Correspondence should be addressed to Yongyang Wu; wuyyfj@fjmu.edu.cn Received 25 May 2022; Revised 5 July 2022; Accepted 7 September 2022; Published 2 September 2022 Academic Editor: Federica Tomao Copyright © 2022 Yuanyuan Bai 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. Background. Cell division cycle associated 3 (CDCA3) mediates the ubiquitination WEE1 kinase at G2/M phase. However, its contribution to cancer immunity remains uncertain. Methods.We first evaluated the effect of CDCA3 on the prognosis of patients with renal cell carcinoma (RCC). The results of bioinformatics analysis were verified by the tissue microarray, immunofluorescence (IF) staining, CCK-8 assay, colony formation, cell cycle, and Western blot. Results. Bioinformatics analysis predicated CDCA3 was an independent predictor of poor prognosis in RCC and was associated with poor TNM stage and grade. CDCA3 was related to the infiltration of CD8 T cells and Tregs. Tissue microarray demonstrated that CDCA3 was strongly associated with poor prognosis and positively relevant to CD8 Tinfiltration. In vitro experiments showed that exgenomic interference of CDCA3 could attenuate cellular proliferation, arrest cell cycle, and blockade accumulation of CDK4, Bub3, and Cdc20 in mitosis process. Conclusion. CDCA3 presents as a good biomarker candidate to predict the prognosis of RCC patients and potentiates the immune tumor microenvironment (TME) of RCC. Cell division malfunctions trigger tumor development 1. Introduction and antitumor immune response [7]. CDCA3 has been Renal cell carcinoma (RCC) is a malignancy from the kidney shown to be a poor prognostic factor for renal papillary cell epithelium and the mobility has steadily increased globally carcinoma, nonsmall cell lung cancer, etc. [8–10]. Scholars in recent years [1]. The first-line antiangiogenic therapies reveal that CDCA3 was upregulated in RCC and promote such as tyrosine kinase inhibitors (TKI) have presented the tumor progression and sunitinib resistance [11] via activat- certain effect for RCC patients, however, the response is dis- ing the NF-κB/cyclin D1 signaling axis [12]. There are data continued in short time for the majorities [2]. Immune indicating that CDCA3 can serve as an important biomarker checkpoint inhibitors (ICIs) usher a new time of cancer ther- to evaluate the therapeutic sensitivity of TKI and therefore it apeutic strategies via sparking anticancer immunity [3]. would be appropriate to underline this aspect also in light of CD8 T cells serve as an essential effector and partially rele- the possible associations of immunological therapies and vant to the effect of ICI [4]. Traditionally, RCC is considered TKI in various types of malignant tumors [13]. Moreover as an immunogenic cancer, and immunotherapy has shown it should be very interesting to test the role of CDCA3 as a a certain effect of RCC [5, 6]. In clinical practices, we observe predictive biomarker of toxicity related to a prolonged use the effect of ICIs is diversified, however, scholars fail to find of these novel agents in combination therapy of RCC [14]. a good candidate to predicate the response and adverse However, the immune impact of CDCA3 has also not been effects (AEs) of ICI in RCC treatment. The biomarkers will well reported. also help identify subgroups that respond to immunotherapy In this paper, we try to evaluate the predicable perfor- and avoid severe AEs. mance of CDCA3 in RCC and figure out the attribution of 2 Journal of Oncology 1.00 Log-rank P=1.18e-12 HR(High grups)=2.784 95%CI(2.099, 3.693) 0.75 4 0.50 0.25 RiskType Median time:5.7 0.00 High groups Low groups Groups = High groups 440 143 26 1 1 Groups = Low groups 441 196 52 3 0 04 8 12 16 Time (years) Groups Groups = High groups Groups = Low groups 1.00 0.75 0 Status Alive Dead 0.50 z-score of expression –2 –1 0.25 CDCA3 1 0.00 0.00 0.25 0.50 0.75 1.00 False positive fraction Type 1-Year, AUC = 0.729.95% CI (0.671-0.787) 3-Year, AUC = 0.689.95% CI (0.644-0.734) 5-Year, AUC = 0.729,95% CI (0.688-0.77) (a) (b) ∗ ∗ C2 1.45( ) 0 C2 9.94( ) C1 C1 0 9.94( ) 0 1.45( ) 100 100 50 50 High Low High Low T1 T3 FEMALE T2 MALE T4 (c) (d) ∗ ∗ 5.73( ) 0 0 C2 C2 6.75( ) ∗ ∗ C1 6.75( ) C1 0 5.73( ) 0 75 75 50 50 25 25 0 0 High Low High Low M0 N0 N1 M1 N2 (e) (f) ∗ ∗ 0 0 C2 11.88( ) C2 6.33( ) ∗ ∗ 6.33( ) C1 0 11.88( ) C1 0 75 75 50 50 25 25 0 0 High Low High Low III G1 G3 I IV G2 G4 II (g) (h) Figure 1: Continued. Groups True positive fraction Overall survival probability Percentage (%) Percentage(%) Percentage (%) Percentage(%) Percentage(%) Percentage (%) Log2(TPM+1) Time Journal of Oncology 3 0 10 20 30 40 50 60 70 80 90 100 1.0 Points G2 G4 Grade 0.8 G1 C-index: 0.754(0.701-1) G3 p-value = p<0.001 Total points 0.6 0 10 20 30 40 50 60 70 80 90 100 Linear predictor 0.4 –4.5 –4 –3.5 –3 –2.5 –2 –1.5 –1 –0.5 0 0.5 1 1.5 1-year survival Pro 0.95 0.9 0.8 0.7 0.2 2-year survival Pro 0.95 0.9 0.8 0.7 0.6 0.0 3-year survival Pro 0.95 0.9 0.8 0.7 0.6 0.5 0.4 0.0 0.2 0.4 0.6 0.8 1.0 Nomogram-prediced(%) n = 512 d = 169 p = 3,512 subjects per group X resampling optimism added, B = 200 5-year survival Pro 0.95 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Gray:ideal Based on observed-predicted 1-year 3-year 2-year 5-year (i) (j) 1.00 0.75 0.50 0.25 p=0.003 0.00 0 1224364860 72 84 96 Time (months) High exp 27 23 21 21 19 17 7 3 0 Low exp 123 120 117 114 109 106 76 38 0 0 1224364860 72 84 96 Time (months) CDCA3 High exp Low exp (k) Figure 1: CDCA3 may be an independent prognostic factor for RCC. Survival curves, ROC curves (a), and mortality risk curves (b) of patients with different CDCA3 expression levels. (c–h) Comparison of distribution of gender, T, N, M stage, clinical stage, and grade among patients with different CDCA3 expression. CDCA3-related nomogram (i) and calibration curve (j) predict OS of the RCC ∗ ∗∗ ∗∗∗ patients. (k) Survival curve of patients with different CDCA3 expression in tissue microarray.( p <0:05, p <0:01, p <0:001). Table 1: Univariate and multivariate COX regression analysis. Uni-COX p value Hazard ratio (95% CI) Multi-COX p value Hazard ratio (95% CI) CDCA3 < 0.0001 2.34698 (2.0353, 2.70639) CDCA3 < 0.0001 1.71293 (1.41056, 2.08011) Age < 0.0001 1.028 (1.01694, 1.03918) Age < 0.0001 1.03039 (1.01629, 1.04468) Gender 0.4682 0.9037 (0.68738, 1.1881) Race 0.5979 1.10877 (0.75541, 1.62743) Clinical stage < 0.0001 2.01609 (1.79567, 2.26356) Clinical stage < 0.0001 1.52842 (1.31228, 1.78016) Grade < 0.0001 2.29073 (1.86981, 2.80639) Grade 0.0029 1.41708 (1.12665, 1.78237) CDCA3 to TME of RCC. Finally, we endorse that targeting group and low expression group based on the median of CDCA3 would be a potential therapeutic way to flight RCC. gene expression. We first drew Kaplan-Meier (KM) survival curve, receiver operating characteristic (ROC) curve, and risk curve to study the prognosis of patients in terms of over- 2. Methods and Materials all survival (OS). Next, we analyzed the differences in clinical 2.1. Data Collection and Preprocessing. The RNA-seq data, data including gender, clinical stage, TNM stage, and grade clinical information, somatic mutation data, and microsatel- among different expression groups of CDCA3. In addition, lite instability (MSI) status of 881 RCC were all from The we used univariate and multivariate COX regression to ana- Cancer Genome Atlas (TCGA, https://portal.gdc.cancer lyze the prognostic significance of CDCA3 expression and .gov/) portal. Patients were divided into high expression clinical data. At the same time, we drew a nomogram CDCA3 Survival probability Observed (%) 4 Journal of Oncology Table 2: CDCA3 expression and demographic and culture was maintained in a humidified incubator with clinicopathological characteristics. 37 C, 5% CO . CDCA3 knockdown lentivirus was designed by Obio Technology Corp (Shanghai, China). Then, Caki-1 CDCA3 and 786-O were transfected with the lentivirus, according N p value Low High to the manufacturer’s instructions. Two days later, puromy- Age cin was added for screening. Knockdown efficiencies of ≥57 58 16 74 CDCA8 were assessed by Western blot. 0.666 <57 65 11 76 2.4. Western Blotting. Cultured cell lysates were prepared Gender using a Column Tissue & Cell Protein Extraction Kit (Epi- Female 33 10 43 0.408 zyme, Shanghai, China; # PC201PLUS). Then total proteins Male 90 17 107 were then separated on 10% SDS polyacrylamide gels. After Size(cm ) overnight incubation with various primary antibodies, ≤175 62 13 75 including anti-CDCA3 (Proteintech, 15594-1-AP), CDK4 1.000 >175 61 14 75 (Proteintech, 11026-1-AP), Cdc20 (Proteintech, 10252-1- T AP), Bub3 (Proteintech, 27073-1-AP), and anti-GADPH (CST, #5174) at 4 C, membranes were washed thrice for T1-2 116 23 139 0.215 5 min each time, using TBST (in 0.1% Tween20). Then, they T3 7 4 11 were incubated in the presence of a secondary rabbit anti- body (1 : 1000, LF102, Epizyme) for 1 h and washed thrice N0 121 26 147 using TBST for 5 min each time. Signals were detected using 1.000 N1-2 2 1 3 the chemiluminescence system. 2.5. Cell Proliferation Assay. The cells were seeded in 96-well diagram and calibration curve to better interpret the prog- plates (1,000 cells/well) and cultured for 1, 2, and 3 days. nostic significance of CDCA3. Moreover, fold change = 2 After adding 10 μl CCK-8 (Dojindo, Japan) to each well was used to compare the differences of gene expression and incubating at 37 C for 2 h, the absorbance at 450 nm among different expression groups of CDCA3, and a heat was measured by the Rayto-6000 system (Rayto, China). map of differentially expressed genes was drawn to show the expression trend in different groups. Finally, considering 2.6. Colony Formation Assay. For cell proliferation, we that CDCA3 can be used as an oncogene to affect the pro- seeded 200 cells to each well of 6-well plates for 14 days, then gression of tumor, we performed Gene Ontology (GO) and fixed with 4% paraformaldehyde (PFA) and stained with Kyoto Encyclopedia of Genes and Genomes (KEGG) enrich- crystal violet. The cells were photographed, and the numbers ment analysis on the upregulated genes of CDCA3 in differ- of colonies were counted. ent expression groups to identify CDCA3 functional pathway localization in tumors. 2.7. Flow Cytometry. Cell cycle analysis was performed using a Cell Cycle Staining Kit (MultiSciences, Hangzhou, China), 2.2. Correlation between Tumor Immune Cell Infiltration as instructed by the manufacturer. Cells were washed using and CDCA3 Gene Expression. Cell type Identification By PBS, after which 1 ml of DNA staining solution and 10 μl Estimating Relative Subsets Of RNA Transcripts (CIBER- of permeate were added to the cell suspension and vortexed SORT) algorithm was used to estimate the infiltration pro- to mix. Finally, cells were stained in the dark at 4 C for portion of 22 kinds of immune cells in normal kidney and 30 min and analyzed by flow cytometry. The stained cells RCC samples to describe the profile of immune cell infiltra- were assessed by flow cytometry (BD FACSCanto [TM] II, tion in RCC. The abundance of immune cells infiltration and USA), and analysed by FlowJo vX.0.7 software. the expression of 8 important immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT, and 2.8. Tissue Microarray. The RCC tissue microarray was pur- SIGLEC15) among different CDCA3 expression groups were chased from Outdo (Shanghai, China) and contains 150 compared. Finally, we also analyzed the correlation between RCC tissues and 30 paired paracancer tissues along with CDCA3 expression with tumor mutation burden (TMB) and their survival, clinical information, etc. Samples were col- MSI. Tumor Immune Single-cell Hub (TISCH, http://tisch lected from the National Human Genetic Resources Sharing .comp-genomics.org/) is a scRNA-seq database focusing on Service platform (2005DKA21300). All points on the chip TME. We obtained the relationship between CDCA3 and were detected by Immunofluorescence (IF). The expression RCC TME at single-cell level in TISCH. of CDCA3, CD8, CD4, CD68, FOXP3, and PD-1 was detected by intensity and positive number of IF. We divided 2.3. Cell Culture and Transfection of Lentivirus. Caki-1 and 150 RCC patients into two groups based on the optimal 786-O were purchased from the Type Culture Collection CDCA3 cut-off value and plotted survival curves to identify (Chinese Academy of Sciences, Shanghai, China). Cells were their prognostic significance. Finally, we analyzed the corre- cultured in RPMI-1640 medium (HyClone, USA) with 10% lation between CDCA3 and CD8, CD4, FOXP3, CD68, and fetal bovine serum (Gibco, Grand Island, NY, USA). The PD-1. Journal of Oncology 5 Groups 3.23e-01 Macrophage M1 7.48e-09 Ty Typ pe e T cell follicular helper*** 1.16e-04 T cell CD8+*** B cell memo B cell memor ry y Myeloid dendritic cell resting 9.88e-01 T cell gamma delta B cell na B cell nai iv ve e Neutrophil 1.88e-01 B cell plasma 2.18e-02 B cell p B cell pl lasma asma 1 NK cell activated Mast cell resting* 4.08e-03 Eosino Eosinop phi hil l NK cell resting Macrophage M0** 2.72e-04 B cell memory*** M Macr acrp ph hag age e M0 M0 T cell CD4+ memory activated 1.34e-02 Myeloid dendritic cell activated* T cell CD4+ memory resting 1.77e-01 M Macr acrp ph hag age e M1 M1 Neutrophil 7.33e-03 M Macr acrp ph hag age e M2 M2 T cell CD4+ native Myeloid dendritic cell resting** 7.33e-03 0 M Mast cell ac ast cell act ti iv va at ted ed T cell CD8 T cell CD4+ memory activated 4.89e-01 T cell CD4+ naive T cell follicular helper M Mast cell r ast cell re es st ti in ng g 7.02e-01 Eosinophil 2.16e-01 Mo Mon no oc cy y y yt te e T cell gaamma delta T cell regulatory (Tregs) 1.95e-15 T cell regulatory (Tregs) M M My ye elo loid de id dendr ndri it tic cell ac ic cell act ti iv va at te ed d d d NK cell activated* 3.75e-02 –1 Macrophage M2*** 1.85e-06 Monocyte*** 6.28e-05 B cell naive*** 3.76e-07 NK cell resting* 1.08e-02 T cell CD4+ memory resting 4.85e-01 Mast cell activated 1.62e-01 –2 Groups High Low (a) (b) loge(S)=17.56, p=9e–10, 𝜌 =0.23, CI95% [0.16, 0.30], n =690 log (S)=17.56, p=90.046, 𝜌 =–0.08, CI [–0.15, 0.00], n =685 Spearman pairs e Spearman 95% pairs 0.7 0.6 0.5 0.4 0 12345 12 345 Log2 (CDCA3 expression) Log2 (CDCA3 expression) (c) (d) ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎ ⁎⁎ ⁎ KIRC_GSE139555 Celltype (minor-lineage) B Mast CD4Teff Moncyte CD4Tn NK CD8Teff Plasma CD8Tex Th1 Endothelial Tprolif M1 cDC1 M2 pDC Group High Low (e) (f) Figure 2: Continued. Percent (%) Immune checkpoint CD274 CTLA4 TMB score HAVCR2 LAG3 PDCD1 MSI score PDCDILG2 TIGIT SIGLEC15 6 Journal of Oncology HALLMARK_G2M_CHECKPOINT colour CDCA3 level 1.6 2.0 1.2 1.5 0.8 1.0 –5 –10 0.4 0.5 –15 0 –10 –5 0 5 10 0.0 UMAP_1 level 0.4 0.8 1.2 1.6 (g) (h) Figure 2: CDCA3 affects immune infiltration in RCC. (a) The distribution of immune cells infiltration in RCC. (b) Comparison of immune cells infiltration between different CDCA3 expression groups. (c) Correlation analysis between CDCA3 and TMB. (d) Correlation analysis between CDCA3 expression and MSI. (e) Comparison of 8 immune checkpoints in different expression groups of CDCA3. (f) Single-cell level distribution of immune cells in RCC. (g) Expression of CDCA3 in immune cells. (h) The relationship between CDCA3 and the G2/ ∗ ∗∗ ∗∗∗ M checkpoint in immune cells. ( p <0:05, p <0:01, p <0:001). 2.9. Immunofluorescence Staining. Tissue microarray were curve showed higher mortality in high-CDCA3 patients deparaffinized by graded alcohol and then washed three than low-CDCA3 patients (Figure 1(b)). Among the patients times with phosphate-buffered saline (PBS), permeabilized with different CDCA3 expression groups, gender, TNM with 0.4% Triton X-100 for 30 min, and blocked with goat stage, clinical stage, and grade showed differences in distri- serum working liquid (Wuhan Boster Biological Technol- bution (Figures 1(c)–1(h)). Univariate and multivariate ogy, Wuhan, China) for 2 hours after antigen retrieval. The COX analysis showed that CDCA3, age, TNM stage, and sections were then incubated overnight with mixed primary grade could be used as prognostic factors of RCC, and antibodies at 4 C, washed in PBS to remove unbound pri- CDCA3 could independently predict the prognosis of RCC mary antibodies, and incubated with secondary antibodies (Table 1). We also constructed the prognostic nomogram in the dark at room temperature (RT) for 1 hour. The sec- and calibration curve of RCC, and the 5-year overall survival rate could be estimated according to the total score tions were counterstained with 4 , 6 diamidino-2- (C − index = 0:754, Figures 1(i) and 1(j)). Demographic phenylindole (Sigma-Aldrich) for 5 minutes and washed characteristics and pathological baseline of tissue microarray with PBS. The primary antibodies included CDCA3 (Pro- were listed in Table 2, showing that high CDCA3 expression teintech, 15594-1-AP). The fluorophore-conjugated second- levels predicted shorter survival (p =0:003, Figure 1(k)), ary antibodies used were goat anti-rabbit Alexa Fluor 488 (1: which proves the bioinformatics analysis. In summary, 500; Abbkine, Wuhan, China) and goat anti-mouse Alexa CDCA3 can be an independent prognostic factor and reflect Fluor 549 (1: 500; Abbkine, Wuhan, China). Images were the rate of tumor progression tumor progression in RCC. captured by confocal laser scanning microscopy (Nikon A1 + R, Japan). The fluorescence intensity was analyzed by 3.2. CDCA3 Is Related to Immune Infiltration. Figure 2(a) using the ImageJ software. showed the infiltration of immune cells in RCC. On this basis, we further analyzed the different abundance of 2.10. Statistical Analysis. In this study, R (version 4.0.2), immune cell infiltration among different CDCA3 expression GraphPad Prism 8, and SPSS 20.0 software were used to analyze the data. Survival, survminer, timeROC, rms, groups (Figure 2(b)). The infiltration of CD8 T cell (p <0:001), Tregs (p <0:001), memory B cell (p <0:001), Limma, ggplot2, pheatmap, and ClusterProfiler R package were used in this study. The significance of differences follicular helper T cell (p <0:001), activated NK cell between groups was assessed by the student T test. Chi- (p <0:05), and M0 macrophage (p <0:01) was upregulated in the patients with high expression of CDCA3, while naive square test was used for categorical variables, and Wilcoxon test was used for continuous data. Survival differences were B cell (p <0:001), resting NK cell (p <0:05), Monocyte (p <0:001), and M2 macrophage (p <0:001) was downregu- calculated using Kaplan-Meier and logarithmic rank tests. lated. TMB and MSI levels reflect tumor surface neoantigen abundance and can stimulate antitumor immune response. 3. Results CDCA3 was also positively correlated with TMB (p <0:001 3.1. Prognostic Significance of CDCA3 in RCC. First, KM sur- , r =0:23, Figure 2(c)) and negatively correlated with MSI vival analysis of TCGA-RCC revealed a shorter survival time (p =0:046, r = −0:08, Figure 2(d)). CD274 (PD-L1, p < in the high-CDCA3 expression group versus the low- 0:001), PDCD1LG2 (PD-L2, p <0:01), and SIGLEC15 CDCA3 expression group (p <0:001, n = 881). ROC curves (p <0:05) were downregulated in patients with high expres- suggested a good accuracy of CDCA3 expression in predict- sion of CDCA3, while CTLA4 (p <0:001), LAG3 (p <0:001), ing RCC prognosis (AUC =0:729, Figure 1(a)). The risk PDCD1 (PD-1, p <0:001), and TIGIT (p <0:01) were UMAP_2 Journal of Oncology 7 Low immune infiltration High CDCA3 expression High immune infiltration Low CDCA3 expression 20× 400× CDCA3 CD68 DAPI FOXP3 PD-1 CD8 CD4 (a) r = 0.266, p = 0.001 r = –0.013, p = 0.874 5 5 0 0 0 510 0 510 CDCA3 CDCA3 (b) (c) r = 0.098, p = 0.233 r = 0.032, p = 0.698 0 0 0 510 0 510 CDCA3 CDCA3 (d) (e) r = –0.073, p = 0.376 0 510 CDCA3 (f) Figure 3: The relationship between CDCA3 and immune cell infiltration in tissue microarray. (a) Representative images of IF staining in tissues microarray. (b–f) Correlation between CDCA3 and CD8, CD4, FOXP3, CD68, FOXP3, and PD-1. FOXP3 CD8 PD-1 CD68 CD4 8 Journal of Oncology group AURKB MYBL2 BIRC5 UBE2C –1 PABPCIL RCAN2 PRAME IL20RB TSPAN7 PGGHG RHEX PTGER3 KRT19 FOSB C10orf99 –2 C1QL1 ATP6V0D2 SAA1 SLPI PVALB –0.584962500721156 0 0.584962500721156 –3 group Log (fold change) Up Down Down-regulation None Up-regulation (a) (b) –log10(p.adjust) KEGG pathway (Up) GO (Up) –log10(p.adjust) p53 signalling pathway spindle organization Viral protein interaction with spindle assembly cytokine and cytokine receptor sister chromatid segregation Viral carcinogenesis regulation of sister chromatid segregation TGF-beta signaling pathway Staphylococcus aureus infection regulation of nuclear divistion Progesterone-mediated oocyte maturation regulation of mitotic sister chromatid separation Oocyte meisis regulation of mitotic nuclear division 14 NF-kappa B signaling pathway regulation of chromosome separation JAk-STAT signaling pathway regulation of chromosome segregation IL-17 signaling pathway organelle fission nuclear division Human T-cell leukemia virus 1 infection Homologous recombination nuclear chromosome segregation mitotic spindle organization Glycerophospholipid metabolism Fanconi anemia pathway mitotic sister chromatid segregation Ether lipid metabolism mitotic nuclear division microtubule cytoskeleton organization Cytkine-cytokine receptor interaction involved in mitosis Coronavirus disease-COVID-19 metaphase/anaphase transitionof cell cycle Complement and coagulation cascades meiotic cell cycle Cellular senescence chromosome separation chromosome segregation 10 Cell cycle 0.02 0.04 0.06 0.08 0.10 0.04 0.06 0.08 0.10 0.12 Enrichment Ratio Enrichment Ratio Count Count 5 20 (c) (d) Figure 4: Enrichment analysis of CDCA3 positively correlated genes. (a) Distribution of differential genes with different CDCA3 expression levels. (b) The heat map of the differential genes expression in CDCA3 high and low expression groups. KEGG enrichment analysis (c) and GO enrichment analysis (d) of differential genes in CDCA3 high and low expression groups. upregulated (Figure 2(e)). The distribution of immune cells (Figure 3(b)). However, our study did not observe the corre- in KIRC is shown in Figure 2(f). Figure 2(g) shows immune lation between CDCA3 and CD4, FOXP3, CD68, and PD-1 cells hardly express CDCA3. CDCA3 can regulate G2/M (Figures 3(c)–3(f)). In conclusion, CDCA3 was closely phase, so we analyzed the relationship between CDCA3 related to tumor immune cells infiltration and antitumor and immune cells G2/M checkpoint. Our results show a immunity. And CDCA3 may be important for RCC risk broad association of CDCA3 with immune cell G2/M check- stratification and immunotherapy guidance. points (Figure 2(h)). Further, we conducted tissue microarray to try to prove 3.3. Identification of Molecular Mechanism of CDCA3. The the above results. Figure 3(a) shows that we performed IF distribution of different genes among patients with different staining in RCC tissue microarray. There was a significant CDCA3 expression groups was shown in the volcano map positive correlation between CDCA3 and CD8 (Figure 4(a)). The heat map showed the expression trend –Log P-value 10 Journal of Oncology 9 786-O Caki-1 ⁎⁎⁎ CON NC RNAi CON NC RNAi ⁎⁎⁎ ⁎⁎⁎ CDK4 BUB3 024 48 72 96 Time (h) CON-786-0 NC-786-0 CDC20 RNAi-786-0 CDCA3 GAPDH (a) (b) ⁎⁎⁎ ⁎⁎⁎ ⁎⁎ 024 48 72 96 Time (h) CON-Caki-1 NC-Caki-1 RNAi-Caki-1 (c) CON NC RNAi ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ (d) Figure 5: Continued. Caki-1 786-O OD Value OD Value Colony Formation Number Colony Formation Number CON-Caki-1 CON-786-O NC-Caki-1 NC-786-O RNAi-Caki-1 RNAi-786-O 10 Journal of Oncology RNAi NC 80 ⁎⁎ 300 PI-A subset-2 30.8% PI-A subset PI-A subset 52.5% PI-A subset-1 ⁎ 200 65.9% 8.98% PI-A subset-1 6.46% PI-A subset-2 23.2% 100 ⁎ 0 0 G0/G1 S G2/M 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K PI-A PI-A NC-786-O RNAi-786-O PI-A subset 67.7% PI-A subset-1 600 PI-A subset 11.0% 53.9% PI-A subset-1 PI-A subset-2 14.1% 15.5% 400 ⁎ PI-A subset-2 23.8% ⁎⁎ 200 20 0 50K 100K 150K 200K 250K 0 50K 100K 150K 200K 250K G0/G1 S G2/M PI-A PI-A NC-Caki-1 RNAi-Caki-1 (e) Figure 5: The function of CDCA3 was confirmed by in vitro experiments. (a) Western blot was used to detect the expression of CDCA3, CDK4, Bub3, and Cdc20 in different groups of cells. CCK-8 array to detect (b) 786-O and (c) Caki-1 proliferation. (d) Representative images ∗ ∗∗ ∗∗∗ of crystal violet stain on day 15. (e) Representative images of flow cytometry.( p <0:05, p <0:01, p <0:001). of 50 upregulated genes and 50 downregulated genes with 4. Discussion the greatest difference (Figure 4(b)). KEGG enrichment analysis showed that the related pathways were mainly con- Our study suggested that CDCA3 can independently predict centrated in p53 signal pathway, TGF-β signal pathway, NF- prognosis and affect tumor progression in RCC. CDCA3 κB signal pathway, and JAK-STAT signal pathway. may also be involved in the regulation of immune-related (Figure 4(c)). GO enrichment analysis showed that its bio- pathways, and stimulated the infiltration of immune cells, logical function was mainly enriched in spindle organiza- such as CD8 T cells and Tregs. Importantly, we verified tion, regulation of sister chromatid segregation, and our results in vitro. nuclear division (Figure 4(d)). These results suggest that Scholars revealed that CDCA3 influence many tumor CDCA3 mainly affects cell cycle in RCC and may regulate progression and treatment through a variety of pathways antitumor immune response through NF-κB axis and other and is associated with poorer prognosis [8, 15]. Our results important immune-related pathways. also showed consistency. Patients with high CDCA3 expres- sion had significantly worse survival and clinical stage, 3.4. CDCA3 Knockdown Attenuated RCC Cell Proliferation which was confirmed by our results and public databases. and Arrested Cell Cycle. To further understand the effect of One important reason is that dysregulation of cell cycle is CDCA3 on the biological behavior of RCC, we constructed the basis of abnormal proliferation of tumor cells. We also CDCA3-knockdown cell lines for functional experiments. confirmed that downregulation of CDCA3 blocked the G2/ Lentiviruses carrying CDCA3 shRNA were used to obtain M phase of cells and reduced cell proliferation ability. This CDCA3-knockdown Caki-1 and 786-O. The Western blot directly proved that the functional localization of CDCA3 results showed that the expression of CDCA3 was signifi- was a key regulatory protein in the cell cycle, and its abnor- cantly decreased in the RNAi group, indicating that the mal expression can affect tumor progression and prognosis. CDCA3-knockdown cell lines were successfully constructed Infiltrating immune cells directly affect the occurrence, (Figure 5(a)). Meanwhile, the expression of CDK4, BUB3, development, and treatment of tumors. It has been reported and Cdc20 was decreased (Figure 5(a)), which indicating cell that CDCA3 is closely related to immune infiltration in cycle arrest. The CCK-8 assay showed that CDCA3 knock- hepatocellular carcinoma [16]. Our results showed that down remarkably attenuated the cell proliferation CDCA3 affected tumor infiltration of various immune cells, (Figures 5(b) and 5(c)). The ability of colony formation including CD8 T cells in endogenous and exogenous data. was notably impaired after knockdown of CDCA3 gene Previous studies have shown that CD8 T cells can recognize (Figure 5(d)). The flow cytometric indicated that CDCA3 tumor-specific antigens and played a role in tumor control knockdown cause G1, S, and G2/M phase arrest [17]. The high density of tumor infiltrating CD8 T cells (Figure 5(e)). In general, CDCA3 expression affects cell cycle has been proved to be associated with a good prognosis of operation and cell proliferation. most cancers [18], but the infiltration of CD8 T cells in Caki-1 786-O Count Count Count Count Cell cycle distribution (%) Cell cycle distribution (%) Journal of Oncology 11 RCC was associated with a poor prognosis [19], this is con- cate the immune status and curative effect of patients, which sistent with our survival outcomes. Since immune cells provides an important reference for immunotherapy of hardly express CDCA3, antitumor therapy targeting CDCA3 RCC. When we focus on CDCA3, the problem seems to may not cause damage to immune cells, which is a potential become transparent. CDCA3 has the potential to evaluate treatment. Our study was firstly proved that CDCA3 may be prognosis and TME and helps to hierarchically label patients involved in the regulation of immune cell infiltration and at high risk. Then apply medical intervention in advance, tumorigenesis in RCC. But more importantly, the specific select appropriate treatment strategies, and improve the pathway through which CDCA3 affects immune infiltration prognosis. However, our study has its limitations. First, the needs further study. specific mechanism of CDCA3 on CD8 T cells and its influ- As we know, immune checkpoint is a key molecule in ence on immunotherapy of renal cell carcinoma need to be tumor immune escape pathway. There were a lot of evi- further explored; second, we have proved that CDCA3 can dences showed that immune checkpoints were related to block the cell cycle, but there is no further study on the bio- the benefit degree of ICIs treatment, which can be used as logical mechanism. biomarkers for ICIs treatment [20–22]. Our results showed that patients with high expression of CDCA3 also expressed 5. Conclusion high levels of CTLA4 and PD-1. This initially showed that there was a close relationship between CDCA3 and immune CDCA3 can be used as an oncogene to affect the prognosis checkpoints and further suggested that CDCA3 may partic- of RCC patients. Downregulation of CDCA3 causes cell ipate in the immune pathway of RCC by regulating immune stagnation in G2/M phase, promotes cell apoptosis, and regulatory factors, which may be a potential target for reduces proliferation ability. More importantly, the immu- immunotherapy. Moreover, findings suggested that TMB nological implications of CDCA3 have also been preliminar- may predict clinical response to ICIs [23]. The neoantigen ily evaluated. CDCA3 may participate in the regulation of produced by TMB may be an important reason for stimulat- immune infiltration in tumor microenvironment by affect- ing antitumor response. In our study, we found that there ing the expression of many immune regulatory factors and was a positive correlation between CDCA3 and TMB, also TMB, which is expected to provide valuable reference for suggesting that CDCA3 may predict the benefits of clinical ICIs treatment. Overall, CDCA3 can be used as a immunotherapy. biomarker to evaluate prognosis and CD8 T cell infiltration In summary, reactive TME is the key to immunotherapy, in RCC. Targeted therapy against CDCA3 is a promising and CDCA3 helps to evaluate this phenomenon. Further- new therapeutic modality, and focusing on it may help to more, enrichment analysis was performed to evaluate the improve the management of therapeutic resistance in the actual molecular mechanism of CDCA3 in RCC. CDCA3 combination of ICI and TKI, but this needs further research has been suggested to influence the NF-κB pathway to medi- to confirm. ate tumor progression [12]. Our results supported this point. CDCA3 is also involved in P53 and TGF pathways. NF-κBis involved in the regulation of inflammation and innate Data Availability immunity in tumor development. P53 also plays an impor- tant role in immune system. P53 mutation in cancer triggers All public data access addresses are visible in the manuscript. B cell antibody response and CD8 killing T cell response Data archiving will be made available on reasonable request [24]. TGF-β can inhibit the proliferation, activation, and and all of the authors are responsible to the data. effector function of T cells. In addition, TGF-β further enhances immunosuppression in TME by promoting Tregs differentiation and destroying T cell immunity [25]. These Conflicts of Interest evidences suggest that CDCA3 has a reasonable influence The authors declare no conflict of interest. on TME, but more specific studies are needed to uncover the regulatory mechanisms. TME is recognized as a complex dynamic ecosystem, Authors’ Contributions which is composed of malignant tumor cells, various infiltra- tion immune cells, fibroblasts, and a variety of cytokines. In All authors read and approved the final manuscript. Yua- this ecosystem, immune response plays an important role in nyuan Bai and Yongyang Wu designed the experiments, per- tumorigenesis and development. RCC has always been formed the experiments, analyzed the data, and wrote the regarded as an immunogenic malignant tumor [26–28], paper. Zhenjie Yin, Bingyong You, Yongmei Chen, and and it is usually insensitive to chemotherapy and radiother- Daoxun Chen reviewed and revised the manuscript. apy. Immunotherapy is regarded as another therapeutic tar- get in addition to chemotherapy and radiotherapy [29]. Clinicians are focusing on immunotherapy to create a new Acknowledgments era of RCC treatment, trying to break through the traditional barrier [30]. The first thing to use immunotherapy is to eval- The study is supported by the Natural Science Foundation of uate the immune status, which is the premise of personalized Fujian Province, China (No. 2019 J01590, treatment. Therefore, find a biomarker that can better indi- No.2022 J01122348). 12 Journal of Oncology References [16] Z. Wang, S. Chen, G. Wang, and S. Li, “CDCA3 is a novel prognostic biomarker associated with immune infiltration in [1] A. Znaor, J. Lortet-Tieulent, M. Laversanne, A. Jemal, and hepatocellular carcinoma,” BioMed Research International, F. Bray, “International variations and trends in renal cell carci- vol. 2021, Article ID 6622437, 19 pages, 2021. noma incidence and mortality,” European Urology, vol. 67, [17] D. S. Chen and I. Mellman, “Oncology meets immunology: the no. 3, pp. 519–530, 2015. cancer-immunity cycle,” Immunity, vol. 39, no. 1, pp. 1–10, [2] D. Y. Heng, W. Xie, M. M. Regan et al., “Prognostic factors for overall survival in patients with metastatic renal cell carcinoma [18] W. H. Fridman, F. Pagès, C. Sautès-Fridman, and J. Galon, treated with vascular endothelial growth factor-targeted “The immune contexture in human tumours: impact on clini- agents: results from a large, multicenter study,” Journal of Clin- cal outcome,” Nature Reviews. Cancer, vol. 12, no. 4, pp. 298– ical Oncology, vol. 27, no. 34, pp. 5794–5799, 2009. 306, 2012. [3] F.-J. Hsueh and Y. Tsai, “Current and future aspect of immu- [19] Q. Pan, L. Wang, S. Chai, H. Zhang, and B. Li, “The immune notherapy for advanced renal cell carcinoma,” Urological Sci- infiltration in clear cell renal cell carcinoma and their clinical ence, vol. 31, no. 1, pp. 8–14, 2020. implications: a study based on TCGA and GEO databases,” [4] H. Raskov, A. Orhan, J. P. Christensen, and I. Gögenur, “Cyto- Journal of Cancer, vol. 11, no. 11, pp. 3207–3215, 2020. toxic CD8 T cells in cancer and cancer immunotherapy,” [20] S. Bagchi, R. Yuan, and E. G. Engleman, “Immune checkpoint British Journal of Cancer, vol. 124, no. 2, pp. 359–367, 2021. inhibitors for the treatment of cancer: clinical impact and [5] J. I. Clark, M. K. K. Wong, H. L. Kaufman et al., “Impact of mechanisms of response and resistance,” Annual Review of Sequencing Targeted Therapies With High-dose Interleukin- Pathology, vol. 16, no. 1, pp. 223–249, 2021. 2 Immunotherapy: An Analysis of Outcome and Survival of [21] G. Giannone, E. Ghisoni, S. Genta et al., “Immuno-metabolism Patients With Metastatic Renal Cell Carcinoma From an On- and microenvironment in cancer: key players for immuno- SM going Observational IL-2 Clinical Trial: PROCLAIM ,” Clin- therapy,” International Journal of Molecular Sciences, vol. 21, ical Genitourinary Cancer, vol. 15, no. 1, pp. 31–41.e4, 2017. no. 12, p. 4414, 2020. [6] D. Lavacchi, E. Pellegrini, V. E. Palmieri et al., “Immune [22] Y. Lai, F. Tang, Y. Huang et al., “The tumour microenviron- checkpoint inhibitors in the treatment of renal cancer: current ment and metabolism in renal cell carcinoma targeted or state and future perspective,” International Journal of Molecu- immune therapy,” Journal of Cellular Physiology, vol. 236, lar Sciences, vol. 21, no. 13, p. 4691, 2020. no. 3, pp. 1616–1627, 2021. [7] C. Kissling and S. Di Santo, “Tumor treating fields - behind [23] L. M. Sholl, F. R. Hirsch, D. Hwang et al., “The promises and and beyond inhibiting the cancer cell cycle,” CNS & Neurolog- challenges of tumor mutation burden as an immunotherapy ical Disorders Drug Targets, vol. 19, no. 8, pp. 599–610, 2020. biomarker: a perspective from the International Association [8] M. N. Adams, J. T. Burgess, Y. He et al., “Expression of for the Study of Lung Cancer pathology committee,” Journal CDCA3 is a prognostic biomarker and potential therapeutic of Thoracic Oncology, vol. 15, no. 9, pp. 1409–1424, 2020. target in non-small cell lung cancer,” Journal of Thoracic [24] A. J. Levine, “P53 and the immune response: 40 years of Oncology, vol. 12, no. 7, pp. 1071–1084, 2017. exploration-a plan for the future,” International Journal of [9] Y. Zhang, W. Yin, W. Cao, P. Chen, L. Bian, and Q. Ni, Molecular Sciences, vol. 21, no. 2, p. 541, 2020. “CDCA3 is a potential prognostic marker that promotes cell [25] A. Dahmani and J. S. Delisle, “TGF-β in T cell biology: impli- proliferation in gastric cancer,” Oncology Reports, vol. 41, cations for cancer immunotherapy,” Cancers, vol. 10, no. 6, no. 4, pp. 2471–2481, 2019. p. 194, 2018. [10] H. Li, M. Li, C. Yang et al., “Prognostic value of CDCA3 in kid- [26] R. Raman and D. Vaena, “Immunotherapy in metastatic renal ney renal papillary cell carcinoma,” Aging, vol. 13, no. 23, cell carcinoma: a comprehensive review,” BioMed Research pp. 25466–25483, 2021. International, vol. 2015, Article ID 367354, 8 pages, 2015. [11] Y. Liu, G. Cheng, Z. Huang et al., “Long noncoding RNA [27] C. Mazza, B. Escudier, and L. Albiges, “Nivolumab in renal cell SNHG12 promotes tumour progression and sunitinib resis- carcinoma: latest evidence and clinical potential,” Therapeutic tance by upregulating CDCA3 in renal cell carcinoma,” Cell Advances in Medical Oncology, vol. 9, no. 3, pp. 171–181, 2017. Death & Disease, vol. 11, no. 7, p. 515, 2020. [28] M. Itsumi and K. Tatsugami, “Immunotherapy for renal cell [12] P. Gu, M. Zhang, J. Zhu, X. He, and D. Yang, “Suppression of carcinoma,” Clinical & Developmental Immunology, CDCA3 inhibits prostate cancer progression via NF-κB/cyclin vol. 2010, article 284581, Article ID 284581, pp. 1–8, 2010. D1 signaling inactivation and p21 accumulation,” Oncology [29] M. Binnewies, E. W. Roberts, K. Kersten et al., “Understanding Reports, vol. 47, no. 2, 2021. the tumor immune microenvironment (TIME) for effective [13] K. B. Sahin, E. T. Shah, G. P. Ferguson et al., “Elevating therapy,” Nature Medicine, vol. 24, no. 5, pp. 541–550, 2018. CDCA3 Levels Enhances Tyrosine Kinase Inhibitor Sensitivity [30] S. Biswas and T. Eisen, “Immunotherapeutic strategies in kid- in TKI-Resistant EGFR Mutant Non-Small-Cell Lung Can- ney cancer–when TKIs are not enough,” Nature Reviews Clin- cer,” Cancers, vol. 13, no. 18, p. 4651, 2021. ical Oncology, vol. 6, no. 8, pp. 478–487, 2009. [14] L. Bianchi, L. Rossi, F. Tomao, A. Papa, F. Zoratto, and S. Tomao, “Thyroid dysfunction and tyrosine kinase inhibitors in renal cell carcinoma,” Endocrine-Related Cancer, vol. 20, no. 5, pp. R233–R245, 2013. [15] Q. Chen, L. Zhou, X. Ye, M. Tao, and J. Wu, “miR-145-5p sup- presses proliferation, metastasis and EMT of colorectal cancer by targeting CDCA3,” Pathology, Research and Practice, vol. 216, no. 4, article 152872, 2020.

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Published: Sep 28, 2022

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