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. 2024 Dec 21;25(1):19. doi: 10.1007/s10238-024-01533-7

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

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Fig. 1 — Identification of the ICD-related clusters in pancreatic cancer. A The expression patterns of ICD genes in pancreatic cancer samples and normal pancreas tissues. B The heatmap of consensus clustering solution (k = 2) in pancreatic cancer samples. C The expression of ICD genes in two clusters. D Kaplan–Meier curve of overall survival in two clusters. E Kaplan–Meier curve of progression free survival in two clusters. F The heatmap of differently expressed genes between two clusters. G The volcano plot of differently expressed genes between two clusters. H GO analysis of highly expressed genes in the ICD-high clusters. I KEGG enrichment analysis of highly expressed genes in the ICD-high clusters. J GO analysis of highly expressed genes in the ICD-low clusters. K KEGG enrichment analysis of highly expressed genes in the ICD-low clusters. ***P < 0.001; **P < 0.01; *P < 0.05