Prioritizing exhausted T cell marker genes highlights immune subtypes in pan-cancer

Chunlong Zhang; Qi Sheng; Xue Zhang; Kang Xu; Xiaoyan Jin; Weiwei Zhou; Mengying Zhang; Dezhong Lv; Changbo Yang; Yongsheng Li; Juan Xu; Xia Li

doi:10.1016/j.isci.2023.106484

Prioritizing exhausted T cell marker genes highlights immune subtypes in pan-cancer

iScience. 2023 Mar 24;26(4):106484. doi: 10.1016/j.isci.2023.106484. eCollection 2023 Apr 21.

Authors

Chunlong Zhang^{1

2}, Qi Sheng¹, Xue Zhang¹, Kang Xu¹, Xiaoyan Jin¹, Weiwei Zhou¹, Mengying Zhang¹, Dezhong Lv¹, Changbo Yang¹, Yongsheng Li³, Juan Xu¹, Xia Li^{1

3}

Affiliations

¹ College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
² College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China.
³ Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, Hainan 571199, China.

Abstract

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 HAVCR2, PDCD1, TOX, TIGIT and LAG3, 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.

Keywords: Bioinformatics; Cancer; Immunology; Transcriptomics.