Integrative Analyses of Individual Patient Genomic-Data to Discover Novel Biomarkers: Application to Cervical Pre-Cancer DNA-Methylation Datasets.

Objective: 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. Methods: 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. Results: 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. Conclusions: 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.