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. 2021 Sep 22;12:738068. doi: 10.3389/fimmu.2021.738068

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Copyright © 2021 Liu, Lu and Li

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Establishment of genetic signatures related to glycolysis and immune response. (A) The variance of the expression value of each gene in the training set was calculated, and then the top 6,000 genes in the variance ranking were used to construct the WGCNA network. Seven non-gray modules were identified. (B) The green module depicting the highest correlation (r > 0.5, p < 0.0001) was considered the most correlated with glycolysis and immune response. (C) Using univariate Cox regression analysis, 238 prognostic-related candidate genes were extracted from the key genes from the green module. (D, E) The LASSO Cox regression model was used to identify the most robust biomarkers, and multivariate Cox stepwise regression was applied to construct a prognostic model. LASSO, minimum absolute shrinkage and selection operator; WGCNA, weighted gene co-expression network analysis.