ArticleFrontiers in immunology2023
Integrating machine learning and single-cell trajectories to analyze T-cell exhaustion to predict prognosis and immunotherapy in colon cancer patients.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- RUNX2 drives adenoma-to-carcinoma transition in colon cancer.Cell death & disease · 2026Article
- Mapping Current Research Status and Emerging Frontiers of T-Cell Exhaustion: A Comprehensive Data-Mining-Based Study.Annals of surgical oncology · 2026Article
- Single cell atlas of canine natural killer cells identifies distinct circulating and tissue resident gene profiles.Frontiers in immunology · 2025Article
- Integrated Analysis of Single-Cell RNA Sequencing and Machine Learning Reveals a T Cell-Specific PANoptosis Signature Predicting Prognosis and Immunotherapy in Prostate Cancer.Human mutation · 2025Article
- Unraveling T cell exhaustion in the immune microenvironment of osteosarcoma via single-cell RNA transcriptome.Cancer immunology, immunotherapy : CII · 2024Article
- Application progress of artificial intelligence in tumor diagnosis and treatment.Frontiers in artificial intelligence · 2024Review
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
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Abstract
Introduction: The incidence of colon adenocarcinoma (COAD) has recently increased, and patients with advanced COAD have a poor prognosis due to treatment resistance. Combining conventional treatment with targeted therapy and immunotherapy has shown unexpectedly positive results in improving the prognosis of patients with COAD. More study is needed to determine the prognosis for patients with COAD and establish the appropriate course of treatment. Methods: This study aimed to explore the trajectory of T-cell exhaustion in COAD to predict the overall survival and treatment outcome of COAD patients. Clinical data were derived from the TCGA-COAD cohort through "UCSC", as well as the whole genome data. Prognostic genes driving T-cell trajectory differentiation were identified on the basis of single-cell trajectories and univariate Cox regression. Subsequently, T-cell exhaustion score (TES) was created by iterative LASSO regression. The potential biological logic associated with TES was explored through functional analysis, immune microenvironment assessment, immunotherapy response prediction, and in vitro experiments. Results: Data showed that patients with significant TES had fewer favorable outcomes. Expression, proliferation, and invasion of COAD cells treated with TXK siRNA were also examined by cellular experiments. Both univariate and multivariate Cox regression indicated that TES was an independent prognostic factor in patients with COAD; in addition, subgroup analysis supported this finding. Functional assay revealed that immune response and cytotoxicity pathways are associated with TES, as the subgroup with low TES has an active immune microenvironment. Furthermore, patients with low TES responded better to chemotherapy and immunotherapy. Conclusion: In this study, we systematically explored the T-cell exhaustion trajectory in COAD and developed a TES model to assess prognosis and provide guidelines for the treatment decision. This discovery gave rise to a fresh concept for novel therapeutic procedures for the clinical treatment of COAD.
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