Integrated multi-optosis model for pan-cancer candidate biomarker and therapy target discovery

Regulated cell death (RCD) is fundamental to tissue homeostasis and cancer progression, influencing therapeutic responses across tumor types. Although individual RCD forms have been extensively studied, a comprehensive framework integrating multiple RCD processes has been lacking, limiting systematic biomarker discovery. To address this gap, we developed a multi-optosis model that incorporates 25 distinct RCD forms and integrates multi-omic and phenotypic data across 33 cancer types. This model enables the identification of candidate biomarkers with translational relevance through genome-wide significant associations. We analyzed 9,385 tumor samples from The Cancer Genome Atlas (TCGA) and 7,429 non-tumor samples from the Genotype-Tissue Expression (GTEx) database, accessed via UCSCXena. Our analysis involved 5,913 RCD-associated genes, spanning 62,090 transcript isoforms, 882 mature miRNAs, and 239 cancer-associated proteins. Seven omic features-protein expression, mutation, copy number variation, miRNA expression, transcript isoform expression, mRNA expression, and CpG methylation-were correlated with seven clinical phenotypic features: tumor mutation burden, microsatellite instability, tumor stemness metrics, hazard ratio contexture, prognostic survival metrics, tumor microenvironment contexture, and tumor immune infiltration contexture. We performed over 27 million pairwise correlations, resulting in 44,641 multi-omic RCD signatures. These signatures capture both unique and overlapping associations between omic and phenotypic features. Apoptosis-related genes were recurrent across most signatures, reaffirming apoptosis as a central node in cancer-related RCD. Notably, isoform-specific signatures were prevalent, indicating critical roles for alternative splicing and promoter usage in cancer biology. For example, MAPK10 isoforms showed distinct phenotypic correlations, while COL1A1 and UMOD displayed gene-level coordination in regulating tumor stemness. Notably, 879 multi-omic signatures include chimeric antigen targets currently under clinical evaluation, underscoring the translational relevance of our findings for precision oncology and immunotherapy. This integrative resource is publicly available via CancerRCDShiny (https://cancerrcdshiny.shinyapps.io/cancerrcdshiny/), supporting future efforts in biomarker discovery and therapeutic target development across diverse cancer types.