Zhang Chunlong, Sheng Qi, Zhang Xue, Xu Kang, Jin Xiaoyan, Zhou Weiwei, Zhang Mengying, Lv Dezhong, Yang Changbo, Li Yongsheng, Xu Juan, Li Xia
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China.
iScience. 2023 Mar 24;26(4):106484. doi: 10.1016/j.isci.2023.106484. eCollection 2023 Apr 21.
Exhausted T (TEX) cells are main immunotherapy targets in cancer, but it lacks a general identification method to characterize TEX cell in disease. To assess the characterization of TEX cell, we extract signature of TEX cell from large cancer and chronic infection cohorts. Based on single-cell transcriptomes, a systematic T cell exhaustion prediction (TEXP) model is designed to define TEX cell in cancer and chronic infection. We then prioritize 42 marker genes, including , , , and , which are associated with T cell exhaustion. TEXP could identify high TEX and low TEX subtypes in pan-cancer of TCGA. The high TEX subtypes are characterized by high immune score, immune cell infiltration, high expression of TEX marker genes and poor prognosis. In summary, TEXP and marker genes provide a resource for understanding the function of TEX cell, with implications for immune prediction and immunotherapy in chronic infection and cancer.
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