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. 2022 May 30;2022:6737241. doi: 10.1155/2022/6737241

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Copyright © 2022 Zhifeng Wang et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Identification of biomarkers and construction of prediction model. A heatmap depicts the log2(fold change) values of robust DEGs. A row represents a single gene, while a column represents a single dataset. Genes that are upregulated are highlighted in red, whereas those that are downregulated are highlighted in blue. The creation and assessment of a random forest classifier for the prediction of TME subtypes. (a) The 9 genes with the highest importance value were selected for classifier construction in the training dataset. (b) Validation of classifier in the testing dataset. (c) Validation of classifier in the independent validation dataset (TCGA-MIBC). (d) Correlation between TME subtype and therapeutic success rate IMvigor210. (e) Correlation between TME subtype and survival outcome in IMvigor210. DEGs: differentially expressed genes; RRA: robust rank aggregation.