Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38533068", "kind": "science-record", "revision": 3, "content_hash": "sha256:7b3319298fde87ed2000cf9d59f52eb141e9603de22f5c43b5fe35c9f42fe014", "created_at": "2026-11-01T19:20:29Z", "updated_at": "2026-11-01T22:11:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic prediction using a gene signature developed based on exhausted T cells for liver cancer patients.", "doi": "10.1016/j.heliyon.2024.e28156", "pmid": "38533068", "authors": ["Yu Zhou", "Wanrui Wu", "Wei Cai", "Dong Zhang", "Weiwei Zhang", "Yunling Luo", "Fujing Cai", "Zhenjing Shi"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Liver hepatocellular carcinoma (LIHC) is a solid primary malignancy with poor prognosis. This study discovered key prognostic genes based on T cell exhaustion and used them to develop a prognostic prediction model for LIHC.\nMethods: SingleR's annotations combined with Seurat was used to automatically annotate the single-cell clustering results of the LIHC dataset GSE166635 downloaded from the Gene Expression Omnibus (GEO) database and to identify clusters related to exhausted T cells. Patients were classified using ConsensusClusterPlus package. Next, weighted gene co-expression network analysis (WGCNA) package was employed to distinguish key gene module, based on which least absolute shrinkage and selection operator (Lasso) and multi/univariate cox analysis were performed to construct a RiskScore system. Kaplan-Meier (KM) analysis and receiver operating characteristic curve (ROC) were employed to evaluate the efficacy of the model. To further optimize the risk model, a nomogram capable of predicting immune infiltration and immunotherapy sensitivity in different risk groups was developed. Expressions of genes were measured by quantitative real-time polymerase chain reaction (qRT-PCR), and immunofluorescence and Cell Counting Kit-8 (CCK-8) were performed for analyzing cell functions.\nResults: We obtained 18,413 cells and clustered them into 7 immune and non-immune cell subpopulations. Based on highly variable genes among T cell exhaustion clusters, 3 molecular subtypes (C1, C2 and C3) of LIHC were defined, with C3 subtype showing the highest score of exhausted T cells and a poor prognosis. The Lasso and multivariate cox analysis selected 7 risk genes from the green module, which were closely associated with the C3 subtype. All the patients were divided into low- and high-risk groups based on the medium value of RiskScore, and we found that high-risk patients had higher immune infiltration and immune escape and poorer prognosis. The nomogram exhibited a strong performance for predicting long-term LIHC prognosis. In vitro experiments revealed that the 7 risk genes all had a higher expression in HCC cells, and that both liver HCC cell numbers and cell viability were reduced by knocking down MMP-9.\nConclusion: We developed a RiskScore model for predicting LIHC prognosis based on the scRNA-seq and RNA-seq data. The RiskScore as an independent prognostic factor could improve the clinical treatment for LIHC patients.", "claims": [{"id": "claim-method", "statement": "Then Lasso and multivariate stepwise cox regression analysis was used to reduce the number of candidate signature genes and calculate the regression coefficient [42].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38533068", "locator": "sections[8] \"Development of a risk model related to T cell exhaustion and validation analysis\" paragraph 2", "quote": "Then Lasso and multivariate stepwise cox regression analysis was used to reduce the number of candidate signature genes and calculate the regression coefficient [42]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The dataset analyzed in this study is available in [GSE149614] at [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149614].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38533068", "locator": "sections[28] \"Data availability statement\" paragraph 2", "quote": "The dataset analyzed in this study is available in [GSE149614] at [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149614]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The RiskScore had an area under curve (AUC) of 0.82, 0.75 and 0.72 for of 1-, 3- and 5-year survival, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38533068", "locator": "sections[21] \"Construction and validation of a T cell exhaustion-related signature model for prognosis prediction\" paragraph 2", "quote": "The ROC analysis revealed that the RiskScore had excellent classification effectiveness, with an area under curve (AUC) [50] of 0.82, 0.75 and 0.72 for of 1-, 3- and 5-year survival, respectively (Fig. 6C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The RiskScore had an area under curve (AUC) of 0.82, 0.75 and 0.72 for of 1-, 3- and 5-year survival, respectively.", "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:38533068"], "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:38533068", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e28156", "sha256": 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The results of CCK8 further confirmed that inhibition of MMP9 co", "n HepaRG, and SK-HEP-1 cell lines. (L–M) The CCK8 results of siRNA-MMP9 was verified in HepaRG"], "order_basis": "paper_text_order:1328", "id": "assay:2"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["l for predicting LIHC prognosis based on the scRNA-seq and RNA-seq data. The RiskScore as an indepe"], "order_basis": "paper_text_order:2394", "id": "assay:3"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ng LIHC prognosis based on the scRNA-seq and RNA-seq data. The RiskScore as an independent progno"], "order_basis": "paper_text_order:2408", "id": "assay:4"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Material and methods Cell culture and transfection Human hepatocytes (THLE-2) purchased from Ce", "ructions of the manufacturer, MMP9 siRNA was transfected applying the Lipofectamine 2000 (Invitrogen,", "RT-PCR were used to verify the efficiency of transfection. The sequences of siRNA for MMP9 were as fol", "g the instructions of the manufacturer, MMP9 siRNA was transfected applying the Lipofectamine 2"], "order_basis": "paper_text_order:2573", "id": "assay:5"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["cook Biotech Company (Guangzhou, China) were cultured in BEGM (Gibco, USA) supplemented with fetal bo", "serve Center of China (Shanghai, China) were cultured in cultured in DMEM (Gibco, USA) containing 10%", "of China (Shanghai, China) were cultured in cultured in DMEM (Gibco, USA) containing 10% fetal bovin"], "order_basis": "paper_text_order:2670", "id": "assay:6"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Then Lasso and multivariate stepwise cox regression analysis was used to reduce the number of candidate signature genes and calculate the regression coefficient [42]."], "order_basis": "consumes_earlier_output", "id": "assay:7"}]}