Detecting optimal biomarkers in ovarian cancer cells from high-dimensional mRNA expression data using machine...

Reliable detection of robust biomarkers from high-dimensional transcriptomic data remains a major challenge in computational oncology. Traditional approaches often suffer from overfitting and poor generalization due to the high dimensionality of genomic data and limited sample sizes. This study aims to identify an optimal, biologically meaningful subset of mRNA biomarkers capable of distinguishing ovarian cancer samples from healthy controls using an integrated machine learning-based feature selection framework.

We analyzed mRNA expression data encompassing approximately 63,000 transcripts from ovarian cancer and control samples derived from cell lines. A hybrid feature selection pipeline combining statistical filtering, recursive elimination, and regularization was implemented under stratified cross-validation to derive stable biomarkers. Model validation was performed using Logistic Regression, Random Forest, XGBoost, and Support Vector Machine classifiers, while experimental validation was conducted through droplet digital PCR (ddPCR). Statistical analyses included ANOVA, t-tests, and pathway enrichment.

The pipeline identified 80 discriminative mRNA biomarkers with exceptionally high classification performance (accuracy = 1.00, sensitivity = 1.00, specificity = 1.00 for top models). ddPCR confirmed consistent expression patterns, with significant downregulation of ADAMTS12, FN1, and ABI3BP and overexpression of EPCAM, COX6C, and TMT1B in ovarian cancer. Pathway enrichment revealed involvement in DNA repair, RNA processing, protein translation, immune regulation, and metabolic reprogramming.

This hybrid feature selection framework applied to patient derived cell lines, effectively reduces dimensionality, enhances biomarker reliability, and uncovers biologically interpretable mRNA signatures associated with ovarian cancer, demonstrating potential for diagnostic and therapeutic applications.