Article Text
PDF
Abstract
Background Liver cancer (LC) is a heterogeneous tumor with unfavorable prognosis. T cells play key roles in tumor surveillance and cancer immunotherapy. The aim of this study is to explore the role of T cell marker genes (TCMGs) in LC based on scRNA-Seq and bulk RNA-Seq data.
Methods The scRNA-Seq and bulk RNA-Seq data were from TCGA and GEO databases. The scRNA-Seq data were normalized and annotated with R packages ‘Seurat’ and ‘harmony’. The R packages ‘limma’ and ‘ConsensusClusterPlus’ were used to select the differentially expressed genes (DEGs) and identify molecular subgroups. The R package ‘immunedeconv’ and TIDE algorithm were used to predict the tumor microenvironment and immunotherapy response. The R packages ‘pRRophetic’ and ‘maftools’ were used to predict the IC50 level and visualize the somatic mutation. qRT-PCR and Western blot were applied to detect key gene expression.
Results A total of 8839 cells were classified into 8 kinds of cells, among which T cells were the most numerous (IDDF2024-ABS-0100 Figure 1. Identification of single-cell clusters and 3 subgroups based on TCMGs (A,B)). The DEGs in T cells were screened to intersect with the genes with differential expression and prognostic ability in LC (IDDF2024-ABS-0100 Figure 1. Identification of single-cell clusters and 3 subgroups based on TCMGs (C)). 12 TCMGs were selected and 3 subgroups were identified in LC cohort (IDDF2024-ABS-0100 Figure 1. Identification of single-cell clusters and 3 subgroups based on TCMGs (D,E)). IDDF2024-ABS-0100 Figure 1. Identification of single-cell clusters and 3 subgroups based on TCMGs (F) showed that the overall survival of subgroup G1 was poorest. Distinct immune cell infiltration, immunotherapy response, and immune checkpoint expression were observed among 3 subgroups (IDDF2024-ABS-0100 Figure 2. Immune cell infiltration and somatic mutation analysis among 3 subgroups (A-C)). G1 exhibited the best therapeutic efficacy of Sorafenib (IDDF2024-ABS-0100 Figure 2. Immune cell infiltration and somatic mutation analysis among 3 subgroups (D)). The somatic mutation was visualized in IDDF2024-ABS-0100 Figure 2. Immune cell infiltration and somatic mutation analysis among 3 subgroups (E). Univariate and multiple Cox analysis highlighted ATP6V0B as a key gene among TCMGs (IDDF2024-ABS-0100 Figure 3. Identification and validation of key TCMGs (A,B)). The higher expression of ATP6V0B in LC was further validated by qRT-PCR and Western blot (IDDF2024-ABS-0100 Figure 3. Identification and validation of key TCMGs (C-E)).
Identification of single-cell clusters and 3 subgroups based on TCMGs
Immune cell infiltration and somatic mutation analysis among 3 subgroups
Identification and validation of key TCMGs
Conclusions Our research explored the role of TCMGs in the molecular classification and prognosis of LC by scRNA-Seq and bulk RNA-Seq analysis and highlighted ATP6V0B as a valuable biomarker in LC.