A Pan-Cancer Analysis of CD161, a Potential New Immune Checkpoint

BACKGROUND: CD161, encoded by killer cell lectin-like receptor B1 gene, is a newly reported candidate inhibitor of tumour-infiltrating T cells. Antibody-mediated CD161 blockade enhances T cell-mediated killing of cancer cells in vitro and in vivo in several tumour types. We evaluated the role of CD161 using The Cancer Genome Atlas (TCGA) Pan-Cancer Data. METHODS: CD161 expression was analysed using RNAseq data from TCGA and the Genotype-Tissue Expression (GTEx) database. HPA, GeneCards, and String database were used to explore the protein information of CD161. The prognostic value of CD161 was analysed using clinical survival data from the TCGA. Enrichment analysis of CD161 was conducted using the R package “clusterProfiler”. We downloaded the immune cell infiltration score of TCGA samples from published articles and online databases and performed a correlation analysis between immune cell infiltration levels and CD161 expression. We further assessed the association between CD161 and immune checkpoints, immune activating genes, immunosuppressive genes, chemokines, and chemokine receptors. FINDINGS: CD161 was differentially expressed and predicted better survival status in most tumour types in TCGA. In addition, CD161 expression was significantly associated with immunoregulatory interactions between lymphoid and non-lymphoid cells. CD161 expression was closely correlated with T cell infiltration, immune checkpoints, immune activating genes, immunosuppressive genes, chemokines, and chemokine receptors. INTERPRETATION: Our results suggest that CD161 is a potential cancer biomarker. CD161 might synergize with other immune checkpoints to regulate the immune microenvironment, which could be applied in the development of new-targeted drugs for immunotherapy. FUNDING: This work was supported by the National Nature Science Foundation of China (grant numbers 81773008, 81672756, 81872399, 81972897), the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2015), the Natural Science Foundation of Guangdong Province (grant number 2017A030311023), the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program: 2017BT01S131 and the Guangzhou Technology Project (grant number 201804010044), National Key R&D Program of China (Grant Nos. 2020YFC2006400), Key-Area Research and Development Program of Guangdong Province (2019B020227004).