The gene-expression profiles of total 56,753 genes were applied to explore the TMB-related modules using R package “WGCNA”. The correlations between sample traits and candidate modules are computed to determine the models highly correlated with traits, in which the genes are further analyzed to screen hub genes [26]. TMB value was employed as sample phenotype and a suited value of β was applied to build a scale‐free network. Then, a weighted adjacency matrix was converted to a topological overlap matrix (TOM) that measures the network connectivity of a gene. Genes with similar expression profiles were classified into different modules using hierarchical agglomerative clustering analysis, and the cutHeight value was set to 0.8. Module eigengenes (MEs) identifies expression patterns of all genes as a single characteristic expression profile within a given module. Besides, correlation analysis between module characteristic genes and sample traits was implemented by Pearson’s correlation test (*p < 0.05). Lastly, modules with the highest correlation were selected for further analyses.

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