Aim: Prostate cancer (PCa) is the most common malignancy found in males worldwide. Although it is mostly indolent, PCa still poses a serious threat to long-term health. Materials & methods: The Can...

Background: There is significant heterogeneity in prostate cancer (PCa), but immune status can reflect its prognosis. This study aimed to explore immune-related gene-based novel subtypes and to use them to create a model predicting the risk of PCa. Methods: We downloaded the data of 487 PCa patients from The Cancer Genome Atlas (TCGA) database. We used immunologically relevant genes as input for consensus clustering and applied survival analysis and principal component analysis to determine the properties of the subtypes. We also explored differences of somatic variations, copy number variations, TMPRSS2-ERG fusion, and androgen receptor (AR) scores among the subtypes. Then, we examined the infiltration of different immune cells into the tumor microenvironment in each subtype. We next performed Gene Set Enrichment Analysis (GSEA) to illustrate the characteristics of the subtypes. Finally, based on the subtypes, we constructed a risk predictive model and verified it in TCGA, Gene Expression Omnibus (GEO), cBioPortal, and International Cancer Genome Consortium (ICGC) databases. Results: Four PCa subtypes (C1, C2, C3, and C4) were identified on immune status. Patients with the C3 subtype had the worst prognosis, while the other three groups did not differ significantly from each other in terms of their prognosis. Principal component analysis clearly distinguished high-risk (C3) and low-risk (C1 + 2 + 4) patients. Compared with the case in the low-risk subtype, the Speckle-type POZ Protein (SPOP) had a higher mutation frequency and lower transcriptional level in the high-risk subtype. In C3, there was also a higher frequency of copy number alterations (CNA) of Clusterin (CLU) and lower CLU expression. In addition, C3 had a higher frequency of TMPRSS2-ERG fusion and higher AR scores. M2 macrophages also showed significantly higher infiltration in the high-risk subtype, while CD8⁺ T cells and dendritic cells had significantly higher infiltration in the low-risk subtype. GSEA revealed that MYC, androgen, and KRAS were relatively activated and p53 was relatively suppressed in high-risk subtype, compared with the levels in the low-risk subtype. Finally, we trained a six-gene signature risk predictive model, which performed well in TCGA, GEO, cBioPortal, and ICGC databases. Conclusion: PCa can be divided into four subtypes based on immune-related genes, among which the C3 subtype is associated with a poor prognosis. Based on these subtypes, a risk predictive model was developed, which could indicate patient prognosis.

Background: Prostate cancer (PCa) is the second leading cause of cancer-related mortality among men worldwide, and its incidence has risen substantially in recent years. Therefore, there is an urgent need to identify novel biomarkers and precise therapeutic targets for managing PCa progression and recurrence. Methods: We investigated the clinical significance of NCAPG2 in PCa by exploring public datasets and our tissue microarray. Receiver operating characteristic (ROC) curve and survival analyses were performed to evaluate the correlation between NCAPG2 and PCa progression. Cell proliferation, wound healing, transwell, flow cytometry, cell cycle, tumor sphere formation, immunofluorescence (IF), co-immunoprecipitation (co-IP), and chromatin immunoprecipitation (ChIP) assays were conducted to further elucidate the molecular mechanism of NCAPG2 in PCa. Subcutaneous and orthotopic xenograft models were applied to investigate the effects of NCAPG2 on PCa proliferation in vivo. Tandem mass tag (TMT) quantitative proteomics was utilized to detect proteomic changes under NCAPG2 overexpression. Results: NCAPG2 was significantly upregulated in PCa, and its overexpression was associated with PCa progression and unfavorable prognosis. Knockdown of NCAPG2 inhibited the malignant behavior of PCa cells, whereas its overexpression promoted PCa aggressiveness. NCAPG2 depletion attenuated the development and growth of PCa in vivo. TMT quantitative proteomics analyses indicated that c-MYC activity was strongly correlated with NCAPG2 expression. The malignancy-promoting effect of NCAPG2 in PCa was mediated via c-MYC. NCAPG2 could directly bind to STAT3 and induce STAT3 occupancy on the MYC promoter, thus to transcriptionally activate c-MYC expression. Finally, we identified that NCAPG2 was positively correlated with cancer stem cell (CSC) markers and enhanced self-renewal capacity of PCa cells. Conclusions: NCAPG2 is highly expressed in PCa, and its level is significantly associated with PCa prognosis. NCAPG2 promotes PCa malignancy and drives cancer stemness via the STAT3/c-MYC signaling axis, highlighting its potential as a therapeutic target for PCa.