Exploring the impact of cuproptosis on prostate cancer prognosis via RNA methylation regulation based on single cell and bulk RNA sequencing data

Background Cuproptosis, along with RNA methylation regulators, has recently come to the fore as innovative mechanisms governing cell death, exerting profound impact on the onset and progression of multiple cancers. Nonetheless, the prognostic implications and underlying regulatory mechanisms of them associated with prostate cancer (PCa) remain to be thoroughly investigated. Methods Genomic and clinical data for PCa from The Cancer Genome Atlas datasets were analyzed to identify a prognostic model through univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses that were validated utilizing external datasets. We used receiver operating characteristic curves and C-index to evaluate the accuracy of our prognostic model. In conjunction with this, we conducted single-cell RNA sequencing (scRNA-seq) analyses to investigate underlying mechanisms and evaluate the degree of immune infiltration, as well as to assess patients’ responses to diverse chemotherapy agents. Especially, qPCR assay was utilized to unveil the expression of signature genes in PCa. Results We meticulously selected six Cuproptosis-Associated RNA Methylation Regulators (CARMRs) to establish a risk prognosis model, which was further verified to obtain enhanced predictive capacity in external validation cohorts. Insights from immune infiltration and scRNA-seq analyses have elucidated the immune characteristics of PCa, and highlighted the immunosuppressive role of regulatory T cells on immune response. Additionally, drug susceptibility analysis demonstrated that patients with PCa in the low-risk category derived better benefit from bicalutamide treatment, whereas those in the high-risk group exhibited a favor response to adriamycin and docetaxel treatments. The qPCR and immunohistochemistry (IHC) staining assays also reveal the a dramatically altered expression pattern of TRDMT1 and ALYREF in PCa tissues. Conclusion In general, we established a model involving CARMRs that can better predict the risk of recurrence of PCa and have identified the possible mechanisms affecting PCa progression, thereby promoting further research in this field.