Work

Prediction of immunotherapeutic responses by a classifier model based on inflammation-associated tumor microenvironment signatures in colorectal cancer

Year: 2026

Type: article

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.

Source: Discover Oncology

DOI: 10.1007/s12672-026-04548-6

Authors: Ziqi Gong, Yuxian Feng, Jing Tu

Institutions: State Key Laboratory of Digital Medical Engineering, Southeast University

Language: English

Topic: Cancer Immunotherapy and Biomarkers

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,690