Identification of fatty acid metabolism signature genes in patients with pulmonary arterial hypertension using WGCNA and machine learning

• Published in: Journal of international medical research Vol. 52; no. 9; p. 3000605241277740
• Main Authors: Liu, Xibang, Wu, Dandan, Bao, Chunmiao, Huang, Zeen, Wang, Weiwei, Sun, Lili, Qiu, Lin
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.09.2024; Sage Publications Ltd; SAGE Publishing
• Subjects: Algorithms; Computational Biology - methods; Databases, Genetic; Fatty acids; Fatty Acids - metabolism; Female; Gene Expression Profiling - methods; Gene Regulatory Networks; Genes; Humans; Hypertension, Pulmonary - genetics; Machine Learning; Male; Metabolism; Observational Study; Pulmonary Arterial Hypertension - genetics; Pulmonary hypertension; Retrospective Studies; ROC Curve; Transcriptome; differentially expressed gene; weighted gene co-expression network analysis; immune cell infiltration; Fatty acid metabolism; machine learning; pulmonary arterial hypertension
• ISSN: 0300-0605; 1473-2300; 1473-2300
• DOI: 10.1177/03000605241277740

Objective To investigate the signature genes of fatty acid metabolism and their association with immune cells in pulmonary arterial hypertension (PAH). Methods Fatty acid metabolism-related genes were obtained from the GeneCards database. In this retrospective study, a PAH-related dataset was downloaded from the Gene Expression Omnibus database and analyzed to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) and machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify the signature genes. Diagnostic efficiency was assessed by receiver operating characteristic (ROC) curve analysis and a nomogram. Immune cell infiltration was subsequently classified using CIBERSORT. Results In total, 817 DEGs were screened from the GSE33463 dataset. The data were clustered into six modules via WGCNA, and the MEdarkred module was significantly related to PAH. The LASSO and random forest algorithms identified five signature genes: ARV1, KCNJ2, PEX11B, PITPNC1, and SCO1. The areas under the ROC curves of these signature genes were 0.917, 0.934, 0.947, 0.963, and 0.940, respectively. CIBERSORT suggested these signature genes may participate in immune cell infiltration. Conclusions ARV1, KCNJ2, PEX11B, PITPNC1, and SCO1 show remarkable diagnostic performance in PAH and are involved in immune cell infiltration.