CDC20 mRNA levels in pan-cancers
Firstly, Oncomine database was applied to explore CDC20 gene expression in pan-cancer. Our results revealed that CDC20 expression was higher in bladder cancer, brain and CNS cancer, breast cancer, cervical cancer, colorectal cancer, esophageal cancer, gastric cancer, head and neck cancer, liver cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, prostate cancer and sarcoma. Only in leukemia and myeloma, CDC20 expression was lower compared to the corresponding normal tissues (Figure 1A). In order to further analyze CDC20 expression in pan-cancer, the mRNA levels of CDC20 in 33 cancer samples from TCGA database were also obtained. Our results indicated that the expression of CDC20 in 19 tumors (BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, SARC, STAD, THCA, UCEC) is higher in cancer versus adjacent normal tissues. And there was no significant difference between the expression of CDC20 and five cancers (KICH, PAAD, PCPG, SKCM and THYM) (Figure 1B). Due to the lack of normal tissue control in adrenocortical carcinoma (ACC), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), testicular germ cell tumors (TGCT), uterine carcinosarcoma (UCS), and uveal melanoma (UVM), we used the GEPIA2 to explore the expression difference between these tumor tissues and the corresponding normal tissues of the GTEx database. We found that CDC20 expression was also higher in ACC, DLBC, LAML, LGG, OV, PAAD, SKCM, THYM, UCS. (Figure 1C). In addition, cBioPortal database was utilized to further analyze the changes on CDC20 expression. As shown in Figure 1D, the highest alteration ratio was associated with amplification, followed by mutation and deep deletion and the highest alteration frequency was showed in ovarian cancer cases.
Multifaceted prognostic value of CDC20 in pan-cancers
In order to explore the correlation between the CDC20 expression and the prognosis of patients including OS, DSS, DFI, and PFI, the Kaplan-Meier (KM) survival curve and Cox proportional hazards models were utilized. The univariate cox regression analysis indicated that the upregulated CDC20 expression was negatively correlated with OS in ACC (P<0.001), KICH (P<0.001), KIRC (P<0.001), KIRP (P<0.001), LGG (P<0.001), LIHC (P<0.001), LUAD (P=0.001), MESO (P<0.001), PAAD (P<0.001), SARC (P=0.012), SKCM (P=0.009), UCEC (P=0.049) and positively with OS in THYM (P=0.03) (Figure 2A), negatively with DSS in ACC (P<0.001), KICH (P<0.001), KIRC (P<0.001), KIRP (P<0.001), LGG (P<0.001), LIHC (P<0.001), LUAD (P=0.001), MESO (P<0.001), PAAD (P<0.001), PCPG (P=0.023), PRAD (P=0.031), SARC (P=0.019), SKCM (P=0.016), THCA (P=0.049) (Figure 3A), negatively with DFI in ACC (P=0.019), BRCA (P=0.016), KIRP (P<0.001), LIHC (P<0.001), PAAD (P=0.02), PRAD (P<0.001), SARC (P=0.004), THCA (P<0.001), UCEC (P=0.043) (Figure 4A), and negatively with PFI in ACC (P<0.001), BRCA (P=0.030), KICH (P<0.001), KIRC (P<0.001), KIRP (P<0.001), LGG (P<0.001), LIHC (P<0.001), LUAD (P=0.043), MESO (P<0.001), PAAD (P<0.001), PCPG (P=0.002), PRAD (P<0.001), SARC (P=0.001), THCA (P<0.001) and UCEC (P=0.002) (Figure 5A). K-M survival analysis suggested that high expression of CDC20 was associated with poor OS time in ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO and SKCM and low expression of CDC20 was related to poor OS time only in DLBC and THYM (Figure 2B-2K). We also estimated the relationship between CDC20 expression and DSS, the K-M curve showed that the increased CDC20 expression was related with poor DSS in 7 types of cancer (ACC, KIRC, KIRP, LGG, LIHC, LUAD and MESO) and only showed that the low expression of CDC20 was associated with poor DSS time in DLBC (Figure 3B-3I). In addition, in the analysis of the relationship between DFI and CDC20 expression, the K-M survival analysis revealed that the high CDC20 expression corresponded with poor PFI in ACC, KIRP, LIHC, MESO, PRAD, SARC and THCA (Figure 4B-4H). Finally, we assessed the PFI in 33 tumors,the results from K-M analysis showed that higher levels of CDC20 mRNA was related with worse PFI in ACC, KIRC, KIRP, LGG, LIHC, MESO, PAAD, PRAD, SARC, THCA and UCEC (Figure 5B-5L).
Correlations between CDC20 expression and clinicopathology
To explore the relationship of CDC20 expression and clinicopathologic stages in diverse cancers, we obtained the CDC20 expression in stage I, II, III, and IV. As shown in Figure 6, there were significant differences in CDC20 expression between stage I and II in BRCA, LIHC, LUAD, PAAD, READ, SKCM and TGCT, between stage I and III in BRCA, KIRC, KIRP, LIHC, LUAD, SKCM and TGCT, between stage I and IV in ACC, KICH, KIRC and KIRP, between stages II and III in ACC, KIRC, KIRP and ESCA, between stages II and IV inACC, KICH, KIRC, KIRP and LIHC, between stages III and IV in KICH, KIRC and LIHC. Notably, CDC20 expression upregulated along with the increase of tumor grade in ACC, BRCA, KICH, KIRC, KIRP, LIHC, LUAD and TGCT.
Association of CDC20 expression with TMB, MSI.
TMB and MSI are closely associated with response to immunotherapy, which affect tumor prognosis[19, 20].Therefore, we calculated the TMB and MSI of each sample and investigated the relationship between CDC20 expression and TMB and MSI in pan-cancer. The results showed that the high CDC20 expression was positively correlated with theTMB in ACC, BLCA, BRCA, CESC, COAD, KICH, KIRC, LAML, LGG, LIHC, LUAD, LUSC, PAAD, PRAD, SARC, SKCM, STAD, TGCT, UCEC and UCS, while only negatively correlated with the TMB in THYM (Figure 7A).Moreover, CDC20 expression had a positive correlation with the MSI in BLCA, BRCA, COAD, HNSC, LIHC, SARC, STAD and UCEC, while only had a negative correlation with the MSI in READ (Figure 7B).
Correlation of CDC20 expression and tumor microenvironment (TME) and immune cell infiltration
The abnormal alterations of TME play a vital role in cancer cell progression and metastasis. To analyze the relationship between TME and CDC20 expression in pan-cancer, the ESTIMATE algorithm was applied to calculate stromal and immune cell scores. As shown in Figure 8A, CDC20 expression positively correlated with stromal score and immune score in LGG and THCA, and negatively in GBM, LUSC and STAD. However, in BRCA and THYM, the expression of CDC20 negatively correlated with stromal score and positively correlated with immune score.The detailed resultsare shown in Supplementary Table S1.In addition, we acquired the content of 22 specific immune cells to further explore the association between CDC20 expression and immune cell infiltration in pan-cancer. As shown in Figure 8B, the four tumors (BRCA, KIRC, THYM and LUAD) with the strongest correlation were displayed. In other cancers, the relationship of CDC20 expression and immune cell infiltrating is shown in Supplementary Table S2. Our results suggested that the expression of CDC20 was closely relevant to immune infiltration levels.
Correlations of CDC20 expression with some specific genes
In order to explore the relationship of CDC20 expression and immune checkpoint genes, we conducted gene co-expression analyses. Our result suggested that CDC20 expressionwas significantly associated with immune-related factors in most cancers (Figure 9A) (Supplementary Table S3). As an intracellular mismatch repair mechanism and epigenetic modification respectively, MMRs and DNA methylation play an important role in tumor genesis. We explored the correlations of CDC20 expression with DNA methylation and MMRs by completing the co-expression analysis of CDC20 with 5 MMRs genes (MLH1, MSH2, MSH6, PMS2 and EPCAM) and 5 methyltransferases (DNMT1, TRDMT1, DNMT3A, DNMT3B and DNMT3L). Our results indicated that the expression of CDC20 was relevant to the expression of them (Figure 9B-9C) (Supplementary Table S4-S5). m6A is a base modification behavior widely existing in mRNA and the internal modification of mRNA is used to maintain the stability of mRNA, which affects tumor development. Our results showed that the expression of CDC20 was significantly correlated with the mRNA m6A related genes (Figure 9D) (Supplementary Table S6).
To further explore the functional and pathway enrichment analyses of CDC20, GSEA was used to perform KEGG enrichment analyses. As shown in Figure 10, immune-related pathways were differently enriched in cytokine receptor interaction, Tcellreceptorsignalingpathway, antigen processing and presentation,JAK-STAT signaling pathway, regulation of autophagy, chemokine signaling pathway, RIG I like receptor signaling pathway.In addition to immune-related pathways, CDC20 also affected other pathways,includingolfactorytransduction, cell cycle, ascorbateandaldaratemetabolism, pentoseandglucuronateinterconversions and mismatchrepair.