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In addition to utilizing the limma package to obtain differentially expressed genes, we also employed the WGCNA package to explore gene modules with correlation. Using a filtering criterion of 0.5 and the goodSamplesGenes function to eliminate unsatisfactory genes and samples, we established an unscaled co-expression network. Subsequently, we calculated adjacency using a default β=30 and an unscaled R2=0.9 as the soft threshold, and transformed adjacency into topological overlap matrix (TOM) to determine gene ratios and dissimilarities. Employing average linkage hierarchical clustering, we grouped genes with similar expression profiles into gene modules, with a preference towards larger modules, hence setting the minimum module size to 200. Lastly, we calculated module feature gene dissimilarity, selected the cut line of the module dendrogram to combine several modules for further investigation, and simultaneously completed visualization of the feature gene network. WGCNA analysis was utilized to identify important modules for ischemic heart failure.