Molecular Pathology of Prostate Cancer

TWIK Complex Expression in Prostate Cancer: Insights into the Biological and Therapeutic Significances of Potassium Ion Channels in Clinical Cancer

Ion channels play ubiquitous roles in the maintenance of tumour cell homeostasis and hence are attractive targets in the molecular pathogenesis and progression of prostate cancer (PCa). This study aimed to investigate the roles of the potassium ion channel complex TWIK, a member of the two-pore-domain potassium channel subfamily, in clinical PCa. The clinicopathological, gene expression, and copy number data of three clinical PCa cohorts from cancer genomics databases were analysed to determine the clinicopathological, biological, and therapeutic significances of the TWIK expression signature using statistical correlations and gene enrichment techniques. The results show that the PCa subset with high TWIK expression exhibited associations with worse pathological tumours, nodes, and overall tumour stages, as well as with high Gleason scores, high prognostic grade groups, and poorer responses to androgen deprivation therapy. Furthermore, a combination of gene set and gene ontology enrichment analyses showed that the PCa subset with high TWIK complex expression was differentially enriched for known oncogenic signalling pathways, aberrant ubiquitination and glucuronidation activities, and for gene sets of ion channel blockers and chemotherapeutic agents. The implications of these findings with respect to cancer progression, therapeutic response, and opportunities for therapeutic targeting of the TWIK complex are discussed, along with the potential of the TWIK complex as a predictive biomarker for integrated, multitargeted therapy.

Integrative multi-omics analysis and machine learning refine global histone modification features in prostate cancer

Background

Prostate cancer (PCa) is a major cause of cancer-related mortality in men, characterized by significant heterogeneity in clinical behavior and treatment response. Histone modifications play key roles in tumor progression and treatment resistance, but their regulatory effects in PCa remain poorly understood.

Methods

We utilized integrative multi-omics analysis and machine learning to explore histone modification-driven heterogeneity in PCa. The Comprehensive Machine Learning Histone Modification Score (CMLHMS) was developed to classify PCa into two distinct subtypes based on histone modification patterns. Single-cell RNA sequencing was performed, and drug sensitivity analysis identified potential therapeutic vulnerabilities.

Results

High-CMLHMS tumors exhibited elevated histone modification activity, enriched proliferative and metabolic pathways, and were strongly associated with progression to castration-resistant prostate cancer (CRPC). Low-CMLHMS tumors showed stress-adaptive and immune-regulatory phenotypes. Single-cell RNA sequencing revealed distinct differentiation trajectories related to tumor aggressiveness and histone modification patterns. Drug sensitivity analysis showed that high-CMLHMS tumors were more responsive to growth factor and kinase inhibitors (e.g., PI3K, EGFR inhibitors), while low-CMLHMS tumors demonstrated greater sensitivity to cytoskeletal and DNA damage repair-targeting agents (e.g., Paclitaxel, Gemcitabine).

Conclusion

The CMLHMS model effectively stratifies PCa into distinct subtypes with unique biological and clinical characteristics. This study provides new insights into histone modification-driven heterogeneity in PCa and suggests potential therapeutic targets, contributing to precision oncology strategies for advanced PCa.