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. 2024 Jan 31;29:92. doi: 10.1186/s40001-024-01672-3

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© The Author(s) 2024

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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — The tumor microenvironment and immunophenotype of patients with PCa in the TCGA cohort. A The ssGSEA algorithm and hierarchical cluster analysis were used to determine the immune characteristics and tumor microenvironment landscape of PCa patients. B Differences in the infiltration of 22 immune cells between the low-immunity and high-immunity groups were determined based on the CIBERSORT algorithm. The violin plot displays the ImmuneScore (C), StromalScore (D), EstimateScore (E), TumourPurity (F) between the low-immunity and high-immunity groups