Candidate genes and pathways in ovarian cancer: a systematic review and integrated bioinformatics analysis

Geraldine Ardila-Patiño, Basic Science Group CBS-FUCS, Faculty of Medicine, Fundación Universitaria de Ciencias de la Salud-FUCS, Bogotá, Colombia
Jonathan Carvajal-Veloza, Basic Science Group CBS-FUCS, Faculty of Medicine, Fundación Universitaria de Ciencias de la Salud-FUCS, Bogotá, Colombia
Carlos Maya-Aguirre, Basic Science Group CBS-FUCS, Faculty of Medicine, Fundación Universitaria de Ciencias de la Salud-FUCS, Bogotá, Colombia
Nelson E. Arenas, Department of Basic Science, Faculty of Medicine, Universidad de Cartagena, Cartagena, Colombia
Luz D. Gutiérrez-Castañeda, Research Institute, Basic Science Group CBS-FUCS, Faculty of Medicine, Fundación Universitaria de Ciencias de la Salud-FUCS, Bogotá, Colombia

Introduction: Ovarian cancer (OC) is typically diagnosed at advanced stages, leading to poor survival rates. Identifying specific biomarkers for early detection is crucial. Proteomics provides a valuable tool for discovering biomarkers that can improve diagnosis and treatment. Objectives: The objective of the study is to identify, through a systematic literature review, specific proteins differentially expressed in tissue and fluid samples from OC patients, with the goal of using them as potential clinical biomarkers for enhanced diagnostic accuracy and prognosis. Methods: A systematic review was conducted using PUBMED, SCOPUS, and Web of Science databases for studies published between 2016 and 2022. Protein-protein interaction network analysis was performed using the STRING database and Cytoscape software and plugins. Results: A total of 201 differentially expressed proteins (DEPs) were identified across 117 studies. Based on gene ontology enrichment, hub gene analysis, and validation through the GEPIA2 database, six candidate biomarkers were highlighted: ALB, CCND1, CXCL8, CTNNB1, STAT3, and TNF. Conclusion: This study provides a comprehensive proteomic analysis and highlights advances in OC biomarkers. Understanding the biological processes involved in OC progression and identifying DEPs could improve early detection and guide personalized treatment strategies.

Keywords: Bioinformatic analysis. Ovarian cancer. Protein biomarker. Differentially expressed genes. Protein-protein interactions.