Identification and validation of glycolysis-related biomarkers in ovarian cancer based on bioinformatics and machine learning
Journal Title: Journal of Air Force Medical University - Year 2023, Vol 44, Issue 9
Abstract
Objective To explore potential biomarkers and therapeutic targets with diagnostic value for ovarian cancer (OC) from genes related to glycolysis signaling pathways based on bioinformatics and machine learning. Methods The OC dataset was obtained from GEO database and TCGA database, and glycolysis-related genes were collected and collated from KEGG database and literature. Biomarkers were identified by differential expression analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms including least absolute shrinkage and selection algorithm logistic regression, support vector machine-recursive feature elimination, and random forest algorithms. Receiver operating characteristic (ROC) curves were developed to assess diagnostic value. In order to study the potential mechanism of action of the identified biomarkers in OC, enrichment analysis and drug sensitivity analysis were conducted, and three algorithms including TIMER, EPIC, and MCPCOUNTER were used to perform immunoinfiltration analysis on the biomarkers. Results A total of 67 glycolysis-related genes were obtained, 20 of which were differentially expressed in OC. Ten pivotal genes were screened by PPI network, and eight biomarkers were identified by machine learning, with six intersecting genes between them. Diagnostic value assessment showed that all the eight biomarkers had high diagnostic value ( area under the ROC curve > 0. 7 ) . In addition, they were closely associated with tumor immune cell infiltration and drug response. Conclusion Most glycolysis-related genes are aberrantly expressed in OC patients, providing diagnostic value for identifying OC, with GPI and ENO3 performing optimally. This study may provide a potential diagnostic biomarker as well as a therapeutic target for OC patients.
Authors and Affiliations
SHANG Zebin, YANG Tianhao, LIU Jian, ZHAO Binggang, ZHAO Xinchun, NIE Shanhua
Keywords
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