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- Title
Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq.
- Authors
Xiaorui Liu; Jingjing Li; Qingxiang Wang; Lu Bai; Jiyuan Xing; Xiaobo Hu; Shuang Li; Qinggang Li
- Abstract
Background: Studies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics. Methods: From the Gene Expression Omnibus (GEO) database, HCC scRNAseq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed. Results: Eleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO-Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability. Conclusion: To precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.
- Subjects
HEPATOCELLULAR carcinoma; TUMOR genetics; HETEROGENEITY; BIOMARKERS; RNA sequencing
- Publication
Frontiers in Immunology, 2022, Vol 13, p1
- ISSN
1664-3224
- Publication type
Article
- DOI
10.3389/fimmu.2022.1012303