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. 2024 Aug 19;43:232. doi: 10.1186/s13046-024-03111-x

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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 — Differences between treatment responders and non-responders are associated with RMRs. (A) UMAP plot displaying diverse cell types that have been identified. (B) Bar plot showing the percentage of different cell subpopulations in treatment responders and non-responders. (C) Volcano illustrating different expression genes between responders and non-responders. (D) Demonstrating pathways for differential gene enrichment with P < 0.05. (E) 46 RNA methylation regulators classified as writers, readers, and erasers from m6A, m5C, m1A, and m7G. (F) Heatmap displaying the correlation between RMRs expression and drug IC50 values of the GDSC database