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. 2022 May 27;13:843408. doi: 10.3389/fimmu.2022.843408

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Copyright © 2022 Wang, Dai, Shen, Yang, Yang, Yang, Qiu and Wang

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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The coexpression network established using weighted gene coexpression network analysis methods based on the (A) LIHC RNA-seq profiles from the TCGA-LIHC database, (B) GSE14520 dataset, and (C) the International Cancer Genome Consortium portal database. Heatmap demonstrating the correlation between module eigengenes and macrophages in the (D) TCGA-LIHC dataset, (E) GSE14520 dataset, and (F) International Cancer Genome Consortium portal dataset. (G) The yellow module had the strongest correlation with macrophage cell proportions in the TCGA-LIHC dataset (Cor = 0.69, P = 8.8e−91). (H) The yellow module had the strongest correlation with macrophage cell proportions in the GSE14520 cohort (Cor = 0.57, P = 2.2e−34). (I) The turquoise module had the strongest correlation with macrophage cell proportions in the ICGC (Cor = 0.77, P < 1e−200). (J) Venn diagram displaying the macrophage-related selected intersection genes from different datasets. (K, L) Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of macrophage-related intersecting genes.