A novel prognostic model based on single-cell RNA sequencing data for hepatocellular carcinoma

The tumour heterogeneous make-up of immune cell infiltrates is a key factor for the therapy response and prognosis of hepatocellular carcinoma (HCC). However, it is still a major challenge to comprehensively understand the tumour immune microenvironment (TIME) at the genetic and cellular levels.

Researchers from the Zhejiang University School of Medicine downloaded HCC single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, and gene expression data were retrieved from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was performed to evaluate the abundance of immune infiltrating cells. The researchers employed weighted gene coexpression network analysis (WGCNA) to construct a gene coexpression network. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were further used to construct a risk model. Moreover, the expression levels of model genes were assessed by qPCR.

The researchers defined 25 cell clusters based on the scRNA-seq dataset (GSE149614), and the clusters were labelled as various cell types by marker genes. Then, they constructed a weighted coexpression network and identified a total of 6 modules, among which the brown module was most highly correlated with tumours. Moreover, they found that the brown module was most closely related to monocytes (cluster 21). Through univariate Cox and LASSO analyses, the researchers constructed a 3-gene risk model (RiskScore = 0.257*Expression CSTB + 0.263* Expression TALDO1 + 0.313* Expression CLTA). This risk model showed excellent predictive efficacy for prognosis in the TCGA-LIHC and ICGC cohorts. Additionally, patients with high risk scores were found to be less likely to benefit from immunotherapy.

Construction of the risk model based on the key genes

Fig. 4

a The volcano map of difference analysis in the TCGA-LIHC cohort. b Venn diagram of tumorigenesis-related upregulated genes, monocyte (C21) marker genes and brown module genes. c Venn diagram of tumorigenesis-related downregulated genes, monocyte (C21) marker genes and brown module genes. d LASSO coefficient profile plots of each independent variable. e The partial likelihood deviance for the LASSO Cox regression analysis. f The expression of CLTA, TALDO1, and CSTB in the HCC cell line SK-hep-1 and the normal live cell line LO2, as determined by qRT-PCR. Abbreviations: TCGA-LIHC, The Cancer Genome Atlas Liver Hepatocellular Carcinoma; HCC, hepatocellular carcinoma; qRT-PCR, quantitative real time polymerase chain reaction

Lu J, Chen Y, Zhang X, Guo J, Xu K, Li L. (2022) A novel prognostic model based on single-cell RNA sequencing data for hepatocellular carcinoma. Cancer Cell Int 22(1):38. [article]

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