ABSTRACT
BACKGROUND: Prostate cancer (PCa) represents a significant health challenge for men, and the advancement of the disease often results in a grave prognosis for patients. Therefore, the identification of biomarkers associated with the diagnosis and prognosis of PCa holds paramount importance in patient health management. METHODS: The datasets pertaining to PCa were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted to investigate the modules specifically associated with the diagnosis of PCa. The hub genes were identified using the LASSO regression analysis. The expression levels of these hub genes were further validated by qRT-PCR experiments. Receiver operating characteristic (ROC) curves and nomograms were employed as evaluative measures for assessing the diagnostic value. RESULTS: The blue module identified by WGCNA exhibited a strong association with PCa. Six hub genes (SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3) were identified by LASSO regression analysis. Further verification confirmed that these six genes were significantly downregulated in tumor tissues and cells. The six hub genes and the nomogram demonstrated substantial diagnostic value, with area under the curve (AUC) values ranging from 0.754 to 0.961. Moreover, patients with low expression levels of these six genes exhibited elevated T/N pathological stage and Gleason score, implying a more advanced disease state. Meanwhile, their progression-free survival (PFS) was observed to be potentially poorer. Finally, a significant association could be observed between the expression of these genes and the dysregulation of immune cells, along with drug sensitivity. CONCLUSIONS: In summary, our study identified six hub genes, namely SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3, which can be utilized to establish a diagnostic model for PCa. The discovery may offer potential molecular targets for clinical diagnosis and treatment of PCa.
ABSTRACT
BACKGROUND: Prostate cancer (PCa) stands as the second most prevalent malignancy impacting male health, and the disease's evolutionary course presents formidable challenges in the context of patient treatment and prognostic management. Charged multivesicular body protein 4 C (CHMP4C) participates in the development of several cancers by regulating cell cycle functions. However, the role of CHMP4C in prostate cancer remains unclear. METHODS: In terms of bioinformatics, multiple PCa datasets were employed to scrutinize the expression of CHMP4C. Survival analysis coupled with a nomogram approach was employed to probe into the prognostic significance of CHMP4C. Gene set enrichment analysis (GSEA) was conducted to interrogate the functional implications of CHMP4C. In terms of cellular experimentation, the verification of RNA and protein expression levels was executed through the utilization of qRT-PCR and Western blotting. Upon the establishment of a cell line featuring stable CHMP4C knockdown, a battery of assays, including Cell Counting Kit-8 (CCK-8), wound healing, Transwell, and flow cytometry, were employed to discern the impact of CHMP4C on the proliferation, migration, invasion, and cell cycle function of PCa cells. RESULTS: The expression of CHMP4C exhibited upregulation in both PCa cells and tissues, and patients demonstrating elevated CHMP4C expression levels experienced a notably inferior prognosis. The nomogram, constructed using CHMP4C along with clinicopathological features, demonstrated a commendable capacity for prognostic prediction. CHMP4C knockdown significantly inhibited the proliferation, migration, and invasion of PCa cells (LNcaP and PC3). CHMP4C could impact the advancement of the PCa cell cycle, and its expression might be regulated by berberine. Divergent CHMP4C expression among PCa patients could induce alterations in immune cell infiltration and gene mutation frequency. CONCLUSIONS: Our findings suggest that CHMP4C might be a prognostic biomarker in PCa, potentially offering novel perspectives for the advancement of precision therapy for PCa.