Potential role of DKK3 and WIF1 in prostate cancer: bioinformatics and clinical analysis.

BACKGROUND: Prostate Cancer (PCa) is one of the most common malignant tumors in men. Some patients may progress to metastatic and Castration-Resistant Prostate Cancer (CRPC), leading to increased treatment difficulty and poor prognosis. With advancements in bioinformatics and machine learning technologies, the integration of multiple databases can efficiently identify core genes associated with the occurrence of PCa. Additionally, the Tumor Microenvironment (TME) and immune cell infiltration play crucial roles in the development and therapeutic response of PCa. Investigating the interactions between core genes and the immune microenvironment will enhance the understanding of the molecular mechanisms underlying PCa and provide new avenues for precision treatment. METHODS: This study obtained gene expression data of prostate cancer and normal tissues from the GEO (GSE69223, GSE46602) and TCGA (TCGA-PRAD) databases. Initially, each dataset was standardized, batch effects were corrected, and differentially expressed genes (DEGs) were identified, followed by GO and KEGG enrichment analyses. Subsequently, the STRING database was utilized to construct a protein-protein interaction (PPI) network, and core modules were selected using Cytoscape software. Lasso regression and Random Forest (RF) algorithms were employed to further pinpoint core genes from the key genes. In the external validation dataset (GSE46602), the diagnostic value and expression level differences of these core genes were assessed. Combining data from TCGA-PRAD, their correlations with immune cell infiltration were analyzed. The application value in molecular pathways and potential therapeutic interventions was explored through GSEA and CTRP drug sensitivity data. Finally, immunohistochemistry and Western blot experiments were conducted to confirm the expression changes of core genes in Benign Prostatic Hyperplasia (BPH) and prostate cancer tissues, alongside correlation analyses with clinical pathological parameters. RESULTS: Multi-omics integrative analysis identified a total of 339 common DEGs. The PPI network and machine learning algorithms further identified four potential core genes: DKK3, SNAI2, WIF1, and FOXA1. External validation and diagnostic value assessments revealed that DKK3 and WIF1 were significantly downregulated in PCa tissues and positively correlated with higher PSA levels and Gleason scores. Immune cell infiltration analysis indicated that the downregulation of these genes was closely associated with impaired adaptive immune function and matrix remodeling, and synergistically interacted with abnormalities in the Wnt/TGF-β pathways. Further analysis using the CTRP database showed that high expression of DKK3 increased cellular sensitivity to various anti-tumor drugs, whereas high expression of WIF1 might reduce the efficacy of certain small molecule inhibitors. Immunohistochemistry and protein level detection in clinical samples confirmed that the expression levels of DKK3 and WIF1 were significantly decreased in PCa tissues and were closely associated with patients' PSA levels and Gleason scores. CONCLUSION: Evidence from integrated databases and clinical samples indicates that WIF1 and DKK3 are significantly downregulated in PCa. Their inactivation may accelerate tumor progression and drug resistance by relieving the negative regulation of the Wnt/TGF-β pathways and affecting the immune microenvironment. Targeting the restoration or enhancement of these two tumor suppressor genes could become a new direction for the early diagnosis and precision treatment of prostate cancer. When combined with personalized medication selection, this approach is expected to significantly improve clinical outcomes for high-risk PCa patients.