Speakers - GWC2026

Abstract

Background: Breast and ovarian cancers (BC and OC) remain the most common malignancies among women globally. Delays in diagnosis leading to treatment postponement contributed to these deaths. Thus, in order to improve the noninvasive cancer detection accuracy, new diagnostic techniques are required. Methods: A blood diagnostic marker based on microRNA (miRNA) was created in this work to identify cancer types. 61000 serum samples from 19 cancer types were included in the study. Through qRT-PCR data from publications in PubMed aligned to the analytical criteria and bioinformatics analysis of serum samples from diverse cancer pathologies, a miRNA prediction model was built. For a single GSE data series, R software version 4.1.1 with the limma data analysis package was utilized; for predicting the changes in miRNA expression across several datasets, batchNormalize and robustRankAggreg were employed. These miRNAs have been linked to biological signaling pathways related to cancer, according to GO and KEGG analysis. Finally, the area under the curve analysis was used to evaluate these miRNA biomarkers' diagnostic potential. Results: 9 miRNAs were predicted to be upregulated and one miRNA to be downregulated. A particular cancer type showed a notable variation in the expression of several miRNAs. Furthermore, miRNAs controlled downstream genes that were involved in numerous biological signaling pathways linked to cancer. Conclusion: we provide an overview of our current knowledge on miRNAs in Female-Specific Cancers, with an emphasis on achieving high accuracy and cost savings compared to conventional biomarkers.

Keywords: Bioinformatics; miRNA; mRNA; cancer.