Unveiling hormone-stimulated gene mechanisms in prostate cancer: A prognostic model, immune infiltration analysis, and drug sensitivity study.

Hormones promote the progression of prostate cancer (PRCA) through the activation of a complex regulatory network. Inhibition of hormones or modulation of specific network nodes alone is insufficient to suppress the entire oncogenic network. Therefore, it is imperative to elucidate the mechanisms underlying the occurrence and development of PRCA in order to identify reliable diagnostic markers and therapeutic targets. To this end, we used publicly available data to analyze the potential mechanisms of hormone-stimulated genes in PRCA, construct a prognostic model, and assess immune infiltration and drug sensitivity. The single-cell RNA-sequencing data of PRCA were subjected to dimensionality reduction clustering and annotation, and the cells were categorized into two groups based on hormone stimulus-related scores. The differentially expressed genes between the two groups were screened and incorporated into the least absolute shrinkage and selection operator machine learning algorithm, and a prognostic model comprising six genes (ZNF862, YIF1A, USP22, TAF7, SRSF3, and SPARC) was constructed. The robustness of the model was validation through multiple methods. Immune infiltration scores in the two risk groups were calculated using three different algorithms. In addition, the relationship between the model genes and immune cell infiltration, and that between risk score and immune cell infiltration were analyzed. Drug sensitivity analysis was performed for the model genes and risk score using public databases to identify potential candidate drugs. Our findings provide novel insights into the mechanisms of hormone-stimulated genes in PRCA progression, prognosis, and drug screening.

Genome-Wide Analyses of Prognostic and Therapeutic Alternative Splicing Signatures in Bladder Urothelial Carcinoma.

BACKGROUND: Alternative splicing (AS) is an indispensable post-transcriptional modification applied during the maturation of mRNA, and AS defects have been associated with many cancers. This study was designed to thoroughly analyze AS events in bladder urothelial carcinoma (BLCA) at the genome-wide level. METHODS: We adopted a gap analysis to screen for significant differential AS events (DASEs) associated with BLCA. DASEs with prognostic value for OS and the disease-free interval (DFI) were identified by Cox analysis. In addition, a differential AS network and AS clusters were identified using unsupervised cluster analysis. We examined differences in the sensitivity to chemotherapy and immunotherapy between BLCA patients with high and low overall survival (OS) risk. RESULTS: An extensive number of DASEs (296) were found to be clinically relevant in BLCA. A prognosis model was established based prognostic value of OS and DFI. CUGBP elav-like family member 2 (CELF2) was identified as a hub splicing factor for AS networks. We also identified AS clusters associated with OS using unsupervised cluster analysis, and we predicted that the effects of cisplatin and gemcitabine chemotherapy would be different between high- and low-risk groups based on OS prognosis. CONCLUSION: We completed a comprehensive analysis of AS events in BLCA at the genome-wide level. The present findings revealed that DASEs and splicing factors tended to impact BLCA patient survival and sensitivity to chemotherapy drugs, which may provide novel prospects for BLCA therapies.