ArticleThe Journal of international medical research2024
Identification of fatty acid metabolism signature genes in patients with pulmonary arterial hypertension using WGCNA and machine learning.
Article in The Journal of international medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
objectiveTo investigate the signature genes of fatty acid metabolism and their association with immune cells in pulmonary arterial hypertension (PAH).
methodsFatty 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.
resultsIn total, 817 DEGs were screened from the GSE33463 dataset. The data were clustered into six modules
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