Prediction of immunotherapeutic responses by a classifier model based on inflammation-associated tumor microenvironment signatures in colorectal cancer.
[BACKGROUND] In recent years, the application of immunotherapy has greatly improved the prognosis of cancer patients.
APA
Gong Z, Feng Y, Tu J (2026). Prediction of immunotherapeutic responses by a classifier model based on inflammation-associated tumor microenvironment signatures in colorectal cancer.. Discover oncology, 17(1). https://doi.org/10.1007/s12672-026-04548-6
MLA
Gong Z, et al.. "Prediction of immunotherapeutic responses by a classifier model based on inflammation-associated tumor microenvironment signatures in colorectal cancer.." Discover oncology, vol. 17, no. 1, 2026.
PMID
41621034
Abstract
[BACKGROUND] In recent years, the application of immunotherapy has greatly improved the prognosis of cancer patients. However, a proportion of patients will acquire resistance to immunotherapy, leading to a lower response rate and poorer clinical outcome. The underlying mechanisms contributing to the therapeutic resistance and accurate biomarkers to predict immunotherapy responses remain unclear.
[METHODS] We comprehensively analyzed a single cell RNA-sequencing dataset of microsatellite instability-high colorectal cancer patients received anti-PD1 immunotherapy. We dissected the heterogeneity of the immunosuppressive tumor microenvironment contributing to the therapeutic resistance and highlighted on a correlation between pro-inflammatory factors and inhibited immune responses. We established a classifier model using Random Forest algorithm based on the common marker genes of inflammation-associated subpopulations. The validation of the model and further analysis between potential responders and non-responders was also performed in bulk RNA-seq cohorts.
[RESULTS] Three inflammation-related cell subgroups, including CEMIP+ Monocytes, CCL4 + Neutrophils and MMP3 + Fibroblasts were identified to be associated with immune-suppressed signatures and unfavorable responses to immunotherapy. The classifier model based on inflammatory signatures exhibited acceptable accuracy and robustness to predict immunotherapeutic responses across cancer types.
[CONCLUSION] Our study dissected the heterogeneity of the immunosuppressive tumor microenvironment and highlighted a correlation between pro-inflammation signatures and inhibited anti-tumor immunity. We also developed a novel classifier model based on inflammation-related signatures to predict patients’ responses to immunotherapy.
[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1007/s12672-026-04548-6.
[METHODS] We comprehensively analyzed a single cell RNA-sequencing dataset of microsatellite instability-high colorectal cancer patients received anti-PD1 immunotherapy. We dissected the heterogeneity of the immunosuppressive tumor microenvironment contributing to the therapeutic resistance and highlighted on a correlation between pro-inflammatory factors and inhibited immune responses. We established a classifier model using Random Forest algorithm based on the common marker genes of inflammation-associated subpopulations. The validation of the model and further analysis between potential responders and non-responders was also performed in bulk RNA-seq cohorts.
[RESULTS] Three inflammation-related cell subgroups, including CEMIP+ Monocytes, CCL4 + Neutrophils and MMP3 + Fibroblasts were identified to be associated with immune-suppressed signatures and unfavorable responses to immunotherapy. The classifier model based on inflammatory signatures exhibited acceptable accuracy and robustness to predict immunotherapeutic responses across cancer types.
[CONCLUSION] Our study dissected the heterogeneity of the immunosuppressive tumor microenvironment and highlighted a correlation between pro-inflammation signatures and inhibited anti-tumor immunity. We also developed a novel classifier model based on inflammation-related signatures to predict patients’ responses to immunotherapy.
[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1007/s12672-026-04548-6.
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