Novel Exploratory Transcriptomic Candidates as Biomarkers and Cancer Hallmark Fingerprints for Ovarian Endometroid and Clear Cell Carcinomas in Women.

BACKGROUND

Endometriosis-associated ovarian cancers (EAOCs), encompassing clear cell (CC) and endometrioid carcinomas (EC), constitute distinct biological entities yet lack robust biomarkers for precise classification, prognostication, and therapeutic decision-making in women. Therefore, we aimed to describe novel biomarkers.

METHODS

In this study, we conducted an integrated transcriptomic analysis, powered by machine learning, to discover novel consensus biomarkers and delineate cancer hallmark signatures specific to EC and CC. Drawing on gene expression profiles from EAOC specimens, we merged differential expression analysis with LASSO regression and Random Forest classification to generate a reliable biomarker panel that effectively distinguishes EC from CC. Kaplan-Meier survival analyses and mutation analyses have been performed for selected biomarker genes.

RESULTS

Novel biomarkers, among others, the genes RPS28, EPAS1, ALKBH2, and DCLRE1A, uncover extensive transcriptional alterations tied to hypoxia signaling, oxidative stress, DNA repair, and metabolic reprogramming. Gene Ontology and pathway enrichment analyses revealed synchronized upregulation of epithelial-mesenchymal transition, TNF-α/NF-κB signaling, oxidative stress, hypoxia, and KRAS signaling pathways.

CONCLUSIONS

Our work establishes novel exploratory transcriptomic candidates for innovative consensus biomarkers, yielding novel diagnostic and prognostic insights into EAOC and supporting further study of subtype-associated expression programs. The current study was designed primarily as an integrative computational investigation aimed at identifying candidate genes and molecular pathways distinguishing CC from EC.