Bioinformatics-based Investigation of Autophagy-Related Biomarkers in Heritable Ovarian Carcinoma

Abstract

Abstract Objective To investigate the role of autophagy-related genes (ARGs) in Heritable Ovarian Carcinoma (HOC) and identify potential biomarkers and therapeutic targets. Methods We conducted a comprehensive bioinformatics-based analysis of gene expression patterns in 420 HOC samples and 7 normal tissues. Differential expression of 17 ARGs out of 232 candidate genes was identified. Functional annotation and pathway enrichment analyses were performed to explore the biological functions of these ARGs. A prognostic model based on 11 survival-associated ARGs was established and validated. Results Our analysis revealed differentially expressed ARGs in HOC and normal tissues, suggesting their potential as diagnostic biomarkers. GO and KEGG analyses indicated the involvement of these genes in critical biological processes and signaling pathways. The prognostic model demonstrated promising predictive capabilities for patient outcomes in HOC. Conclusion Our findings shed light on the significance of ARGs in HOC and provide potential biomarkers and therapeutic targets for improved patient outcomes in this heritable ovarian carcinoma.