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. 2023 Mar 29;13:5153. doi: 10.1038/s41598-023-32276-2

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Construction and validation of cold and hot immune subtypes in TCGA-LUAD patients. (A–C) K = 2 was determined as the optimal value for Consensus clustering analysis. (D) UAMP plot of LUAD patients: cluster 1 (hot, n = 243) patients in red and cluster 2 (cold, n = 274) patients in blue. (E) Histopathological picture of the tumor tissue (H&E staining) and its TIL pattern (https://cancerimagingarchive.net/datascope/TCGA_TilMap). The TIL pattern is identified by a convolutional neural network, where red pixels denote TIL patches, blue pixels denote non-TIL tissue patches, and black pixels denote non-tissue patches. (F) Distribution of CYT score, Immune score, Stromal score, ESTIMATE score, and MHC score of patients in both immune subtypes. The abundance of immune cell infiltration in patients with both immune phenotypes was calculated using TIMER. (G) Sankey diagrams of TME subtypes corresponding to different immune phenotypes patients. (H) Prognostic differences between patients with different immune phenotypes. (***p < 0.001; **p < 0.01; *p < 0.05).