Identification of the CD8+ T-cell exhaustion signature of hepatocellular carcinoma for the prediction of prognosis and immune microenvironment by integrated analysis of bulk- and single-cell RNA sequencing data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39697729", "kind": "science-record", "revision": 2, "content_hash": "sha256:0bc73ac8bf068b81e8c75f2f890ad7dbdf653aa1326c5bd62238c67d94c459c5", "created_at": "2026-11-01T20:30:00Z", "updated_at": "2026-11-01T21:34:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of the CD8+ T-cell exhaustion signature of hepatocellular carcinoma for the prediction of prognosis and immune microenvironment by integrated analysis of bulk- and single-cell RNA sequencing data.", "doi": "10.21037/tcr-24-650", "pmid": "39697729", "authors": ["Jianhui Fan", "Qinghua Zhang", "Tiancong Huang", "Haitao Li", "Guoxu Fang"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Hepatocellular carcinoma (HCC) is a prevalent type of cancer with high incidence and mortality rates. It is the third most common cause of cancer-related deaths. CD8+ T cell exhaustion (TEX) is a progressive decline in T cell function due to sustained T cell receptor stimulation from continuous antigen exposure. Studies have shown that CD8+ TEX plays an important role in the anti-tumor immune process and is significantly correlated with patient prognosis. The aim of the research is to establish a reliable CD8+ TEX-based signature using single-cell RNA sequencing (scRNA-seq) and high-throughput RNA sequencing (RNA-seq), providing a new approach to evaluate HCC patient prognosis and immune microenvironment.\nMethods: The RNA-seq data of HCC patients were download from three different databases: The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC). HCC's 10× scRNA data were acquired from GSE149614. Based on single-cell sequencing data, CD8+ TEX-related genes were identified using uniform manifold approximation and projection (UMAP) algorithm, singleR, and marker gene methods. Afterwards, we proceeded to construct CD8+ TEX signature using differential gene analysis, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis. We also validated the CD8+ TEX signature in GEO and ICGC external cohorts and investigated clinical characteristics, chemotherapy sensitivity, mutation landscape, functional analysis, and immune cell infiltration in different risk groups.\nResults: The CD8+ TEX signature, consisting of 13 genes (HSPD1, UBB, DNAJB4, CALM1, LGALS3, BATF, COMMD3, IL7R, FDPS, DRAP1, RPS27L, PAPOLA, GPR171), was found to have a strong predictive effect on the prognosis of HCC. The Kaplan-Meier (KM) analysis showed that the overall survival (OS) rate of patients in the low-risk group was higher than that of patients in the high-risk group across different datasets and specific populations. The research findings suggested that the risk score was an independent predictor of HCC prognosis. The model based on clinical features and risk score has a strong predictive effect. We observed significant differences among various risk groups in terms of clinical characteristics, functional analysis, mutation landscape, chemotherapy sensitivity, and immune cell infiltration.\nConclusions: We constructed a CD8+ TEX signature to predict the survival probability of patients with HCC. We also found that the model could predict the sensitivity of targeted drugs and immune cell infiltration, and the risk score was negatively correlated with CD8+ T cell infiltration. In summary, the CD8+ TEX signature of HCC was constructed for the prediction of prognosis and immune microenvironment by integrated analysis of bulk and scRNA-seq data.", "claims": [{"id": "claim-method", "statement": "Afterwards, we proceeded to construct CD8+ TEX signature using differential gene analysis, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39697729", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Afterwards, we proceeded to construct CD8+ TEX signature using differential gene analysis, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Bulk RNA-seq data of 365 HCC samples of training dataset were obtained from The Cancer Genome Atlas (TCGA) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39697729", "locator": "sections[4] \"Acquisition and processing of scRNA-seq and bulk RNA-seq\" paragraph 2", "quote": "Bulk RNA-seq data of 365 HCC samples of training dataset were obtained from The Cancer Genome Atlas (TCGA) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Multivariate Cox regression analysis indicated that the risk score was an independent factor for OS in HCC [hazard ratio (HR) =1.163, 95% confidence interval (CI): 1.111–1.218, Figure 7B].", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Multivariate Cox regression analysis indicated that the risk score was an independent factor for OS in HCC [hazard ratio (HR) =1.163, 95% confidence interval (CI): 1.111–1.218, Figure 7B]."}, "evidence": [{"asset": "source:pmid:39697729", "locator": "sections[19] \"Construction of nomogram based on CD8+ T cell TEX signature combined with clinical characteristics\" paragraph 2", "quote": "Multivariate Cox regression analysis indicated that the risk score was an independent factor for OS in HCC [hazard ratio (HR) =1.163, 95% confidence interval (CI): 1.111–1.218, Figure 7B]."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Multivariate Cox regression analysis indicated that the risk score was an independent factor for OS in HCC [hazard ratio (HR) =1.163, 95% confidence interval (CI): 1.111–1.218, Figure 7B].", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39697729"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE14520"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE14520"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39697729", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-24-650", "sha256": 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