Multi-omics identification of a programmed cell death-related signature and potential target P4HB for bladder cancer based on a 101-combination machine learning and experimental validation.

Bladder cancer (BLCA) poses a significant clinical challenge due to its high mortality rates and the inadequacy of current prognostic biomarkers. Programmed cell death (PCD) is crucial in BLCA initiation, progression, and treatment, yet the interplay and specific roles of different PCD pathways in BLCA prognosis remain elusive. This study aimed to develop and validate predictive models by integrating 14 PCD patterns using comprehensive analyses of bulk RNA and single-cell RNA transcriptomic data from TCGA-BLCA and six GEO datasets. Through weighted gene co-expression network (WGCNA) analyses, 24 hub PCD-related genes (PCDGs) were identified in BLCA. Subsequently, we implemented a computational framework that integrated 10 machine learning algorithms along with 101 of their combined permutations. This framework was used to develop a programmed cell death-related signature (PCDRS). The final PCDRS consisted of 12 prognostic genes: P4HB, CHEK2, PTPN2, ATP13A2, CCT6A, TFRC, RRP12, TRAF7, POLR1B, B4GALT3, SIVA1, and TP73.The PCDRS was validated in training and external validation sets, with multivariate analysis confirming its independent prognostic value in BLCA. The PCDRS-integrated nomogram was also developed as a quantitative clinical tool. Furthermore, differences in reactive oxygen species (ROS) levels were observed in the tumor microenvironment between high- and low-risk groups based on PCDRS risk scores. Additionally, the elevated expression and tumorigenic role of P4HB in BLCA were validated through in vitro assays. In summary, P4HB may serve as a candidate gene with potential relevance to BLCA prognosis that could enhance personalized treatment strategies for patients with BLCA.