Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer
Abstract Background Tumor microenvironment (TME), particularly immune cell infiltration, programmed cell death (PCD) and stress, has increasingly become a focal point in colorectal cancer (CRC) treatment. Uncovering the intricate crosstalk between these factors can enhance our understanding of CRC,...
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BMC
2025-01-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-025-06143-9 |
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| author | Hao Liu Chuhan Zhang Sanfei Peng Yuhan Yin Yishi Xu Sihan Wu Liping Wang Yang Fu |
| author_facet | Hao Liu Chuhan Zhang Sanfei Peng Yuhan Yin Yishi Xu Sihan Wu Liping Wang Yang Fu |
| author_sort | Hao Liu |
| collection | DOAJ |
| description | Abstract Background Tumor microenvironment (TME), particularly immune cell infiltration, programmed cell death (PCD) and stress, has increasingly become a focal point in colorectal cancer (CRC) treatment. Uncovering the intricate crosstalk between these factors can enhance our understanding of CRC, guide therapeutic strategies, and improve patient prognosis. Methods We constructed an immune-related cell death and stress (ICDS) prognostic model utilizing machine learning methodologies. Furthermore, we performed enrichment analyses and deconvolution algorithms to elucidate the complex interactions between immune cell infiltration and the processes of PCD and stress within a substantial array of transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus data base (GEO) related to CRC. Single-cell sequencing and biochemical experiments were used to validate the interaction between the model genes and programmed cell death in tumor cells. Results The ICDS prognostic model exhibited robust predictive performance in seven independent cohorts, revealing an inverse correlation between model scores and patient prognosis. Meanwhile, the ICDS index was positively correlated with clinical stage. Model analysis indicated that patient subgroups with low ICDS index exhibited heightened immune activation features and elevated activity in PCD and stress pathways. Single-cell analysis further revealed that macrophages were the central drivers of immune characteristics underlying prognostic differences within the ICDS prognostic model. Pseudotime analysis and cellular experiments indicated that the model gene GAL3ST4 promotes the transition of macrophages toward an M2 pro-tumor phenotype. Furthermore, cell communication analysis and experimental validation revealed that the cuproptosis in tumor cells suppress GAL3ST4 expression, thereby inhibiting M2-like macrophage polarization. Conclusion In summary, we constructed the ICDS prognostic model and uncovered the mechanism by which tumor cells downregulate GAL3ST4 expression via cuproptosis to inhibit M2-like macrophage polarization, providing new targets and biomarkers for CRC treatment and prognosis evaluation. |
| format | Article |
| id | doaj-art-5b94feffde2946e2be14e85b0a88e34a |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-5b94feffde2946e2be14e85b0a88e34a2025-02-02T12:40:29ZengBMCJournal of Translational Medicine1479-58762025-01-0123111810.1186/s12967-025-06143-9Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancerHao Liu0Chuhan Zhang1Sanfei Peng2Yuhan Yin3Yishi Xu4Sihan Wu5Liping Wang6Yang Fu7Department of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Oncology, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Oncology, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Oncology, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou UniversityAbstract Background Tumor microenvironment (TME), particularly immune cell infiltration, programmed cell death (PCD) and stress, has increasingly become a focal point in colorectal cancer (CRC) treatment. Uncovering the intricate crosstalk between these factors can enhance our understanding of CRC, guide therapeutic strategies, and improve patient prognosis. Methods We constructed an immune-related cell death and stress (ICDS) prognostic model utilizing machine learning methodologies. Furthermore, we performed enrichment analyses and deconvolution algorithms to elucidate the complex interactions between immune cell infiltration and the processes of PCD and stress within a substantial array of transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus data base (GEO) related to CRC. Single-cell sequencing and biochemical experiments were used to validate the interaction between the model genes and programmed cell death in tumor cells. Results The ICDS prognostic model exhibited robust predictive performance in seven independent cohorts, revealing an inverse correlation between model scores and patient prognosis. Meanwhile, the ICDS index was positively correlated with clinical stage. Model analysis indicated that patient subgroups with low ICDS index exhibited heightened immune activation features and elevated activity in PCD and stress pathways. Single-cell analysis further revealed that macrophages were the central drivers of immune characteristics underlying prognostic differences within the ICDS prognostic model. Pseudotime analysis and cellular experiments indicated that the model gene GAL3ST4 promotes the transition of macrophages toward an M2 pro-tumor phenotype. Furthermore, cell communication analysis and experimental validation revealed that the cuproptosis in tumor cells suppress GAL3ST4 expression, thereby inhibiting M2-like macrophage polarization. Conclusion In summary, we constructed the ICDS prognostic model and uncovered the mechanism by which tumor cells downregulate GAL3ST4 expression via cuproptosis to inhibit M2-like macrophage polarization, providing new targets and biomarkers for CRC treatment and prognosis evaluation.https://doi.org/10.1186/s12967-025-06143-9Colorectal cancerMachine learningSingle cell RNA sequencingProgrammed cell deathMacrophage polarization |
| spellingShingle | Hao Liu Chuhan Zhang Sanfei Peng Yuhan Yin Yishi Xu Sihan Wu Liping Wang Yang Fu Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer Journal of Translational Medicine Colorectal cancer Machine learning Single cell RNA sequencing Programmed cell death Macrophage polarization |
| title | Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer |
| title_full | Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer |
| title_fullStr | Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer |
| title_full_unstemmed | Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer |
| title_short | Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer |
| title_sort | prognostic models of immune related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer |
| topic | Colorectal cancer Machine learning Single cell RNA sequencing Programmed cell death Macrophage polarization |
| url | https://doi.org/10.1186/s12967-025-06143-9 |
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