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. 2022 Jun 26;10(18):5984–6000. doi: 10.12998/wjcc.v10.i18.5984

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©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.

This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/

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Identification of differentially expressed genes and Weighted Gene Co-expression Network Analysis. A: The differentially expressed genes analyzed in The Cancer Genome Atlas dataset. Top 30 upregulated and downregulated genes are shown; B and C: Soft-threshold power analysis revealed the scale-free fit index and the mean connectivity of network topology; D: Hierarchical cluster analysis of the coexpression module based on the dissimilarity measurement; E: Sample clustering based on expression data used to detect the outliers; F: Dendrogram of consensus module eigen genes. Groups of eigen genes below the red line merged owing to their similarity; G: The topological overlap matrix heatmap showing the overlap between the co-expression genes.