Identification and validation of a novel innate lymphoid cell-based signature to predict prognosis and immune response in liver cancer by integrated single-cell RNA analysis and bulk RNA sequencing.

Background: Innate lymphoid cells (ILCs) exert tumor suppressive and tumor promoting effects. However, the prognostic significance of ILC-associated genes remains unclear in hepatocellular carcinoma (HCC). Hence, the aim of this research was to develop an innovative predictive risk classification system using bioinformatics examination.

Methods: We explored the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to gather data pertaining to HCC and its clinical details. Significantly different ILC-associated genes were investigated by Seurat analysis. The number of signaling interactions of ILCs with other cells was discovered by CellPhoneDB analysis. ClusterProfiler and Metascape were utilized to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on ILC genes. In order to identify potential ILC predictors, we utilized univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses, subsequently validating these predictors in TCGA and GEO groups. The multi-omics ILC signature model's clinical predictive capabilities, along with drug sensitivity and immune factor relations, were assessed using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and pRRophetic. We investigated the possible molecular pathways in our predictive ILC signature through the utilization of gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Five key genes were screened out by constructing a competing endogenous RNA (ceRNA) network using Cytoscape and their values in clinical indexes were demonstrated. Immunohistochemistry (IHC) in HCC cases confirmed the expression of these genes.

Results: ILC cell subsets were identified, and cell-cell communication analysis revealed that the signaling pathways involving ILC cell subsets were mostly abundant in the HCC microenvironment. Subsequently, 270 marker genes of the ILC clusters were subjected to GO and KEGG enrichment analysis. Furthermore, a total of 58 prognostically relevant genes were screened as features for prognostic prediction models. Next, the models were validated and clinically evaluated (P values of Kaplan-Meier survival curves below 0.05). Five key genes (C2, STK4, CALM1, IL7R, and RORA) were further screened by multi omics analysis of immune cell and factor and drug sensitivity and correlation analysis of tumor regulatory genes in liver cancer. Furthermore, the potential clinical value of the 5 key genes was confirmed in HCC patients. Finally, the IHC results confirmed the expression of C2, STK4, CALM1, IL7R, and RORA in HCC. Our experimental results provided preliminary evidence supporting the oncogenic roles of STK4 and CALM1, as well as the tumor-suppressive roles of C2, RORA, and IL7R in HCC.

Conclusions: A novel prognostic feature of ILC potentially involved in HCC was discovered. It showed high values in predicting patient overall survival (OS) as well as good differences in immunity and drug sensitivity. Therefore, targeting these ILC signatures may be a potential effective approach in HCC treatment.