A novel strategy for key gene identification in hypertrophic cardiomyopathy based on cuproptosis and multiple WGCNA analyses

Abstract Background Hypertrophic cardiomyopathy (HCM) is a potentially fatal disease, and early diagnosis is crucial for effective treatment. Microarray analysis and the discovery of cuproptosis, a new form of cell death, offer new possibilities for identifying potential biomarkers for early diagnosis. Purpose This study aimed to identify cuproptosis-related genes associated with HCM and potential biomarkers for early diagnosis using machine learning. Methods Expression data of cuproptosis-related genes were extracted from the GSE36961 dataset, and differential expression analysis was conducted. Immune infiltration analyses were also performed. Samples were categorized into two clusters, and cluster weighted gene coexpression network analysis (WGCNA) was performed based on HCM and cluster clinical data. The results were combined to develop the best machine learning model, which was verified through calibration and external validation (GSE1145 and GSE32453). Results Cuproptosis-related genes (NLRP3, ATP7B, ATP7A, SLC31A1, LIAS, LIPT1, DLD, DLAT, PDHB, and DBT) were differentially expressed between HCM and control samples. Five key genes (TSPAN12, ANP32C, C4ORF18, COL21A1, and HMGB2) were identified, which showed great efficiency in the diagnosis of HCM in external validation, with an AUC of 0.948. These genes may play critical roles in megakaryocyte differentiation, RAGE receptor binding, and protein digestion and absorption. Conclusions Cuproptosis-related genes may play significant roles in HCM, and TSPAN12, ANP32C, C4ORF18, COL21A1, and HMGB2 may be potential biomarkers for the early diagnosis of HCM. This study provides new insights for further research on HCM diagnosis and treatment.Flow chart of research designEvaluation and verification results