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The timer database (TIMER2.0 (cistrome.org)) was applied to compare the differences in the expression of CHMP4C between prostate tumor samples and normal tissue samples (16). Prostate cancer transcriptome data were got from the TCGA database (https://cancergenome.nih.gov/) and used for validation of the difference analysis and paired difference analysis with “ggpubr” and “limma” packages (17).

The GEPIA2 database (http://gepia2.cancer-pku.cn/#analysis), “survival” package and “survminer” package were applied to analyze the prognosis of CHMP4C in prostate cancer (18). UALCAN database (UALCAN (uab.edu)) was applied to find the relationship between CHMP4C expression and prostate cancer Gleason score, lymph node metastasis status and TP53 mutation status (19) (20).

To further investigate the regulatory role of CHMP4C in cell proliferation, we performed co-expression gene analysis and GSEA analysis using the “limma” package and “enrichplot” package and identified important regulatory roles of CHMP4C in the cell cycle. To deepen our understanding of the biological functions of CHMP4C, we conducted grouping differences analysis of CHMP4C using the “limma” package and performed KEGG and GO analysis based on the grouping results. KEGG and GO gene sets were got from the Gene Set Enrichment Analysis (GSEA) website (GSEA (gsea-msigdb.org) (21). Then the R packages ‘enrichplot’, ‘ggplot2’, ‘clusterProfiler’ and “org.Hs.eg.db” were utilized to perform the GO and KEGG analysis.

limma package, estimate package and reshape2 package were used to analyze the immune and mesenchymal scores of CHMP4C in prostate cancer. CIBERSORT is a deconvolution algorithm that can be used to transform the normalized gene expression array into the composition of infiltrating immune cells. Based on the result of CIBERSORT, we obtained the relationship of CHMP4C with the infiltration level of 22 immune cells using “limma” packages. We then explored the relationship of CHMP4C expression with 49 immune checkpoint genes with “ggplot2” and “reshape2” R packages and set the p-value filter to 0.001.

IPS-CTLA4 blocker and IPS-PD1/PD-L1 blocker data for prostate cancer from TCGA were got in TCIA (https://tcia.at) and utilized to predict the response to ICI in patients with low and high expression of CHMP4C. External independent immune validation queue data from GEO database (https://www.ncbi.nlm.nih.gov/geo/, GSE67501). The “pRRophetic” package was applied to predict the drug sensitivity of CHMP4C.

The prostate cancer cell lines PC-3 and DU-145 were got from the Affiliated Cell Resource Center of the Chinese Academy of Medical Sciences. Culture medium was RPMI1640 medium (Biological Industries) with 10% added fetal bovine serum (Biological Industries) and 1% penicillin and streptomycin (100units/ml, Solarbio). The environmental conditions for incubation were 37 degrees, 5% CO2 humidified incubator.

Total RNA Kit (Omega Bio-Tek, USA) was used to isolate and purify total RNA from cell lines, and BIOG cDNA Synthesis Kit (BioDai, Changzhou, China) was used for reverse transcription.

In qRT-PCR, GAPDH primers were designed as follows (GAPDH-F: GGAAGGTGAAGGTCG GAGTCA, GAPDH-R: GTCATTGATGGCAACAATATATCCACT) and CHMP4C primers were designed as follows (CHMP4C-F: AGACTGAGGAGATGCTGGGCAA, CHMP4C-R: TAGTGC CTGTAATGCAGCTCGC). Relative expression differences were calculated using the 2-ΔΔCt method with the GAPDH gene as a control. si-NC and si-RNA of CHMP4C were designed as follows (si-NC: CCUCUGGCAUUAGAAUUAUTT, si- CHMP4C: CCUGCGUCUC UACAACUAU).

RIPA buffer with PMSF was used to get the total protein, and the total protein concentration was determined with the BCA method (solarbio, Beijing, China). The protein was separated on 10% SDS/PAGE gels and transferred to PVDF membranes. After transferring, the membranes were blocked with 5% skim milk and incubated overnight in primary antibody at 4°C. Bound antibodies were detected by horseradish peroxidase-labeled secondary antibody. Western blot analysis was performed with ECL luminescent reagent (solarbio, Beijing, China). The antibodies were displayed as follows (CHMP4C: Abcam, Cambridge, UK, ab168205, CDK2: Abcam, Cambridge, UK, ab32147, CCNA2: Abcam, Cambridge, UK, ab181591, GAPDH: Abcam, Cambridge, UK, ab8245).

Pathology slides from patients with prostate cancer and benign prostate hyperplasia (BPH), and we cut paraffin sections of the corresponding tissues. Immunohistochemical antibodies were obtained from Abcam, Cambridge, UK, ab272638. DAB reagent was used for staining (Zhongshan Jinqiao, ZLI-9018).

In CCK8 assay, si-NC and si-CHMP4C cells were inoculated into 96-well plates at a density of 2 × 103 cells/well, incubated at 37 degrees for 3 hours, and absorbance was measured at 450nm for 3 consecutive days. In the colony formation assay, 2 groups of cells were inoculated in 6-well plates at a density of 1000 cells/well and cultured until visible colonies were formed. Cell colonies were fixed in 4% paraformaldehyde (Solarbio, Beijing, China) for 20 min and then stained with 0.1% crystal violet solution (Solarbio, Beijing, China) for 20 min. In the wound healing assay, we inoculated cells on 6-well plates and cultured cells until fusion reached 80%-90%, using pipette tips for cell scoring and PBS for cell rinsing. Photographs were taken under the microscope at 15h, 30h, and 45h, respectively. In the transwell assay, the matrigel was melted and spread in a 24-well transwell chamber. The lower chamber was added with 10% fetal bovine serum. After 12 h of starvation, cells were transferred to transwell chambers and incubated at 37° C for 36 h. The remaining cells in the upper chamber were erased with cotton swabs and fixed with paraformaldehyde for 20 min. The cells were stained with 0.1% crystal violet solution for 20min.

Statistical analysis of bioinformatics was conducted on the R software platform and experimental data were counted using GraphPad Prism version 9.0. We adopted a t-test to compare differences between the two groups and all statistical tests were set as two-sided (P<0.05 was considered statistically significant. ***, **, * stood for P value <0.001, P value <0.01, P value <0.05, respectively).

All studies involving human tissues in this study have been reviewed and approved by the Medical Ethics Committee of the Second Hospital of Tianjin Medical University (KY2023K038), and all experiments were conducted in accordance with relevant requirements and guidelines.

We first performed immunohistochemical staining on prostate cancer tissues and benign prostatic hyperplasia tissues, revealing that CHMP4C was higher in prostate cancer tissues ( Figure 1A ). To select appropriate cell lines for a subsequent cell functional experiment, we tested the expression of CHMP4C in six prostate cancer cell lines (RWPE-1, LnCap, 22RVI, C4-2, PC-3, DU145). The results showed that the expression levels of PC-3 and DU-145 were the highest compared to RWPE-1. ( Figures 1B, C ). Then the level of CHMP4C expression was verified again by the timer database. CHMP4C was shown to be hyper-expressed among most cancer types, especially prostate cancer ( Figure 1D ). Next, we performed variance analysis and paired difference analysis using prostate cancer data from the TCGA database, with results consistent with those described above ( Figure 1E ). All of the results reveal that CHMP4C is a potential diagnostic biomarker for prostate cancer.

In this study, we utilized the GEPIA2 database and the packages “survival” and “survminer” to explore the correlation of CHMP4C expression levels with prognosis to determine whether CHMP4C can be regarded as a diagnostic biomarker for prostate cancer. We found that up-regulated CHMP4C was generally accompanied by a poor prognosis in terms of DFS and PFS ( Figure 2A ). Next, CHMP4C expression in prostate cancer was investigated in different pathological parameters using the UALCAN database. Regarding Gleason score, the expression of Gleason6/7/8/9 was significantly upregulated compared to normal controls. Although no significant difference was found in CHMP4C expression in Gleason10 samples, this may be due to the small sample size in this category (n=4). Future studies with larger sample sizes may clarify the role of CHMP4C in high-grade prostate cancer ( Figure 2B ). In lymph node metastasis, CHMP4C expression gradually increased as the number of lymph node metastases increased ( Figure 2C ). Similarly, CHMP4C expression was also upregulated in both TP53 mutant and non-mutant compared to normal control ( Figure 2D ). Taken together, the findings suggested that CHMP4C was indeed correlated with prostate cancer progression and invasion.

PC-3 and DU-145 cells were transfected with 50 nM si-CHMP4C or si-con using Lipofectamine 2000. After 48 h, the cells were harvested for analysis. The expression level of cells was detected by Western blot. The results suggested that the CHMP4C protein expression levels of PC-3 and DU-145 were significantly decreased after si-CHMP4C transfection (n=3, t-test, p<0.05) ( Figure 3A ). We then performed a cellular function experiment to investigate whether CHMP4C induces prostate cancer cell malignancy. CCK-8 cell proliferation assay results showed that CHMP4C knockdown reduced PC-3 and DU-145 proliferation viability compared to controls (n=3, t-test, p<0.05) ( Figure 3B ). Similar conclusions have also been verified in colony formation experiments. We found a significant decrease in the number of si-CHMP4C clones compared to the si-con group (n=3, t-test, p<0.01) ( Figure 3C ). In the invasiveness assay transwell, the number of invading and metastatic cells of PC-3 and DU-145 transfected with si-CHMP4C was reduced compared to the control (n=3, t-test, p<0.001) ( Figure 3D ). In the wound healing assay, knockdown of CHMP4C would further diminish the migration distance of PC-3 and DU-145 ( Figure 3E ). The above results suggested that CHMP4C is a positive factor of prostate cancer cell proliferation and invasion.

In order to further investigate the possible mechanism of action of CHMP4C in prostate cancer, we performed a co-expression analysis of CHMP4C using the TCGA database ( Supplementary Table 1 ). The results of the co-analysis suggested that CHMP4C was positively correlated with cell cycle-related genes including CCNE2, DCTN2, NSMCE2, ORC4, PRKAG1, BCCIP, CDKN3, CCNT1, CDK2, CCNB1 ( Figure 4A ). GSEA results also showed that CHMP4C was mainly involved in DNA replication in GOBP and in cell cycle regulation in KEGG ( Figure 4B ). Cyclin-dependent kinases (CDKs) and associated cell cycle chaperone proteins are the main regulators of the cell cycle. The CDK2 and cyclinA2 proteins have been shown to play a major role in the regulation of the cell cycle by regulating the transition from the G1 to the S phase. Our experimental results showed that CHMP4C knockdown resulted in a significant decrease in the expression of CDK2 and CyclinA2 ( Figure 3A ). The above findings showed that CHMP4C is likely to be involved in regulating the prostate cancer cell cycle.

We performed a grouped variance analysis of CHMP4C and presented it with a heat map ( Figure 5A ). Then GO and KEGG function enrichment analysis were conducted for these differential genes. The results suggested that the GO analysis was mostly related to the regulation of immune function ( Figure 5B ). KEGG analysis also suggested that these genes are also involved in regulating immune function. ( Figure 5C ). The above findings suggested the potential value of our CHMP4C in the mediation of immune function.

To further investigate how CHMP4C relates to immune function, we explored the correlation between the ratio of immune and mesenchymal components and the expression of CHMP4C. Next, the estimate score, stromal score, immune score of the low and high expression groups of CHMP4C were evaluated. The results suggested that low expression of CHMP4C had a higher score ( Figure 6A , p<0.01). The correlation between CHMP4C and 22 infiltrating immune cells was then analyzed ( Supplementary Table 2 ). The results revealed that immunosuppressive M2 macrophages were enriched in the high CHMP4C expression group ( Figure 6B ). We then explored the relationship between CHMP4C and 49 immune checkpoints and found that CHMP4C had a negative correlation with most immune checkpoints ( Figure 6C , p<0.001). Above findings suggested to us that the high expression level of CHMP4C in prostate cancer may represent a worse prognosis after immunotherapy.

The application of immune checkpoint inhibitors (ICI) has achieved success in tumor immunotherapy. To forecast the response to ICI, we computed scores for four subtypes based on machine learning. The results suggested that ​the group with lower expression of CHMP4C was more likely to respond to anti-PD1, anti-CTLA-4, and comprehensive therapy ( Figure 7A ). Combined with the above study, we concluded that the CHMP4C low expression group had better effect on immunotherapy and more clinical benefits for patients. The GSE67501 cohort is often used as an external independent cohort to assess immunotherapy effects (22). The predictive value of CHMP4C expression was further validated in an external cohort of 11 renal cell carcinoma patients who received PD-1/PD-L1 immunotherapy (GSE67501). The ‘stat_compare_means’ function showed that the patients with low CHMP4C expression exhibited a significantly higher response rate to the therapy ( Figure 7B ). We then utilized the R package ‘pRRophetic’ to predict vitro drug sensitivity according to the expression level of CHMP4C. The results suggested that the group with high expression of CHMP4C could benefit better when treated with paclitaxel and 5-fluorouracil ( Figures 7C, D ). Further analysis showed that bortezomib had lower IC50 values in the low expression group of CHMP4C, revealing that the bortezomib was more effective in patients with low CHMP4C ( Figure 7E ).