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. 2024 Jun 3;9:142. doi: 10.1038/s41392-024-01853-w

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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/.

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Fig. 4 — Cell type identification and cancer cell characterization in MC38/NIH3T3-FAP tumor models. a UMAP plots of all cells. b Dot plots reveal characteristic marker genes across different cellular fractions. c Bar charts comparing the major cellular lineages across various treatments. d Bar charts depict the varied contribution of pathways to cellular communication. e Interaction networks emphasize specific cell-to-cell interactions via the CD137 (top) and CXCL (bottom) pathways in the combination treatment group. f Highlighted ligand-receptor interactions from Mono/Mac with cancer cells, T-cells, and NK cells, as indicated by CellChat. g UMAP plots of cancer cells. h Bar charts show the distribution of tumor cell subpopulations. i RNA trajectory analysis reflects the evolutionary progression of tumor cells. j Box line plots representing mean Mki67 and Ifit1 expression in cancer cells. k SCENIC analysis delineating differences in AUC values of transcription factors (TFs). l Regulatory network diagram centered on TFs Myc and Irf7