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. 2024 Feb 19;14(2):e1586. doi: 10.1002/ctm2.1586

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© 2024 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.

This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Identifying dysregulated pathways in high‐artificial intelligence‐derived prognostic index (AIDPI) patients. (A) A heat map elucidates the expression of the AIDPI genes and their correlation with the immune score. Stars preceding brackets indicate adjusted p‐values from differential expression analysis (DEA), whereas numbers and stars within the brackets convey the correlation coefficient and associated p‐value, ns: p > .05; *p < .05; **p < .01; ***p < .001; ****p < .0001. (B) Gene set enrichment analysis (GSEA) results spotlight biological processes notably correlated with high (red) or low (blue) AIDPI. (C) A bubble plot shows findings from KEGG enrichment analysis based on differentially expressed genes (DEGs). (D) An enrichment map arranges enriched terms into a network, connecting the terms that have shared genes. (E) Boxplot contrasts average DNA methylation levels between two AIDPI groups. (F) A bar chart manifests empirical Bayes GSEA results based on epigenomic data. nREP, number of genes enriched in this pathway.