Establishment and validation of a novel CD8+ T cell-associated prognostic signature for predicting clinical outcomes and immunotherapy response in hepatocellular carcinoma via integrating single-cell RNA-seq and bulk RNA-seq.

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We sourced single-cell RNA-sequencing (scRNA-seq) and bulk RNA-seq data for HCC from the GSE98638 dataset and The Cancer Genome Atlas (TCGA) repository. We utilized Weighted Gene Correlation Network Analysis (WGCNA) to identify CD8+ T cell-related genes. A clinical prognostic model for risk stratification was then constructed via Cox-Lasso regression analysis. The Immunophenotypic Score (IPS) was utilized to evaluate the potential of immunotherapeutic interventions in the categorized cohorts. Validation of the expression of CD8+ T cell-associated risk genes was performed using quantitative reverse transcription PCR (qRT-PCR). Integrating scRNA-seq with RNA-seq data, we identified five CD8+ T cell-related signature genes: IKBKE, ATP1B3, MSC, ADA, and BATF. Notably, HCC patients in the high-risk group had markedly decreased overall survival. Elevated infiltration levels of CD8+ T cells, B cells, and macrophages were observed in the high-risk group. Moreover, there was a positive correlation between the risk score and immune checkpoints (ICPs), including PDCD1, CD274, and CTLA4. Patients within the high-risk group subject to PD1 and CTLA4 blockade exhibited higher IPS levels. Additionally, the expression of the five risk genes was upregulated in HCC cell lines and tissues compared to normal cells and tissues. Our findings establish a prognostic signature based on CD8+ T cells, offering a potent predictive model for clinical outcomes and responsiveness to immunotherapy in HCC patients.", "claims": [{"id": "claim-method", "statement": "A clinical prognostic model for risk stratification was then constructed via Cox-Lasso regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38900330", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A clinical prognostic model for risk stratification was then constructed via Cox-Lasso regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data from this study were downloaded from online databases. The related data were downloaded from the GEO (https://www.ncbi.nlm.nih.gov/geo/), TCGA (https://dcc.icgc.org/), ICGC (https://dcc.icgc.org/projects-/LIRI-JP), HPA (https://www.proteinatlas.org/). Therefore, everyone can get the data online. 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