Integrative Analyses of Individual Patient Genomic-Data to Discover Novel Biomarkers: Application to Cervical...

The growing availability of public genomic repositories led to increased use of integrative and meta-analytic approaches to combine multi-omics datasets for biomarker discovery. Building on this framework, our study applies a molecular discovery approach to identify diagnostic biomarkers for cervical pre-cancer in high-risk HPV-positive women using aggregated genomic evidence across populations.

This study performed a secondary analysis of publicly available DNA-methylation datasets identified through a systematic search of the Gene Expression Omnibus database to enable integrative analysis. After employing standard preprocessing methods across the selected DNA-methylation datasets, statistical analyses (specifically limma and meta-analysis with a random-effects model) were used to calculate the cumulative standardized effect sizes for all 9570 genes across the 4 selected studies.

This cross-dataset analysis led to the identification of 4 promising, non-population-specific genes, that show potential for detecting precancerous cervical lesions in high-risk HPV-positive women. Collectively, these genes reflect a molecular profile associated with Cervical Intraepithelial Neoplasia lesions undergoing malignant progression.

By leveraging aggregated data, our findings enhance the potential of generalization of biomarker signals and illustrate the strength of multi-data sets integration in advancing cervical pre-cancer triage screening strategies. Given their diagnostic promise, further experimental validation in large and diverse cohorts is warranted to evaluate their clinical applicability.