AUTHOR=Shen Xiaogang , Zuo Xiaofei , Liang Liang , Wang Lin , Luo Bin 

TITLE=Integrating machine learning and single-cell trajectories to analyze T-cell exhaustion to predict prognosis and immunotherapy in colon cancer patients

JOURNAL=Frontiers in Immunology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2023.1162843

DOI=10.3389/fimmu.2023.1162843

ISSN=1664-3224

ABSTRACT=The incidence of colon cancer (CC) has recently increased, and patients with advanced CC 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 CC. More study is needed to determine the prognosis for patients with CC and establish the appropriate course of treatment. This study aimed to explore the trajectory of T-cell exhaustion in CC to predict the overall survival and treatment outcome of CC 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. Data showed that patients with significant TES had fewer favorable outcomes. Expression, proliferation, and invasion of CC 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 CC; 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. In this study, we systematically explored the T-cell exhaustion trajectory in CC 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 CC.