Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq - Liu, Xiaorui; Li, Jingjing; Wang, Qingxiang; Bai, Lu; Xing, Jiyuan; Hu, Xiaobo; Li, Shuang; Li, Qinggang (Frontiers in Immunology, 2022)
Background: Evidences 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 scRNA-seq data were extracted. The FindCluster function was applied to analyze cell cluster. Autophagy-related genes were acquired from 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 prognostic risk model and multiple clinicopathological factors was constructed. Results: 11 cell clusters labelled as various cell types by immune cell markers were obtained from scRNA-seq GSE149614 dataset combined. The ssGSEA revealed that autophagy-related pathways were more enriched in malignant. 2 autophagy-related clusters (C1 and C2) were identified, in which, C1 predicted a better survival, enhanced immune infiltration, higher immunotherapy response. Lasso Cox regression established an 8-genes signature. Next, HCCDB18, GSA14520 and GSE76427 dataset confirmed a strong risk prediction ability of the signature. Moreover, low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram consisted of RiskScore and clinical features had better prediction ability. Conclusion: To precisely assess the prognostic risk, an 8-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.