Machine learning driven multiomics analysis identifies disulfidptosis associated molecular subtypes in ovarian cancer.

ABSTRACT: Precision oncology enables molecularly guided cancer therapy through multi-omics profiling, AI-driven classification, and biomarker-targeted interventions. Disulfidptosis has emerged as a promising therapeutic target, yet no ovarian cancer classification system currently incorporates this mechanism. The sequencing data of the samples in this study were obtained from TCGA and GEO databases. We analyzed 76 genes associated with disulfidptosis and performed consensus clustering based on their expression profiles to stratify the samples into two molecular subtypes. Differentially expressed genes (DEGs) were identified by comparing ovarian cancer tissues with normal samples. LASSO regression and random forest algorithms were then applied to screen marker genes that significantly influenced the

SUBMITTER: Jin Q 

PROVIDER: S-EPMC12647703 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature