A novel prognostic model based on single-cell RNA sequencing data for hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35078458", "kind": "science-record", "revision": 3, "content_hash": "sha256:6da7d63f83bd5f241dd734f16a4e0ca3ab41758346309c60ea9fafa8ff918d82", "created_at": "2026-11-01T17:36:48Z", "updated_at": "2026-11-01T21:27:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel prognostic model based on single-cell RNA sequencing data for hepatocellular carcinoma.", "doi": "10.1186/s12935-022-02469-2", "pmid": "35078458", "authors": ["Juan Lu", "Yanfei Chen", "Xiaoqian Zhang", "Jing Guo", "Kaijin Xu", "Lanjuan Li"], "journal": "Cancer cell international", "year": 2022, "abstract": "BACKGROUND: The tumour heterogeneous make-up of immune cell infiltrates is a key factor for the therapy response and prognosis of hepatocellular carcinoma (HCC). However, it is still a major challenge to comprehensively understand the tumour immune microenvironment (TIME) at the genetic and cellular levels.\nMETHODS: HCC single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) database, and gene expression data were retrieved from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was performed to evaluate the abundance of immune infiltrating cells. We employed weighted gene coexpression network analysis (WGCNA) to construct a gene coexpression network. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were further used to construct a risk model. Moreover, the expression levels of model genes were assessed by qPCR.\nRESULTS: We defined 25 cell clusters based on the scRNA-seq dataset (GSE149614), and the clusters were labelled as various cell types by marker genes. Then, we constructed a weighted coexpression network and identified a total of 6 modules, among which the brown module was most highly correlated with tumours. Moreover, we found that the brown module was most closely related to monocytes (cluster 21). Through univariate Cox and LASSO analyses, we constructed a 3-gene risk model (RiskScore = 0.257*Expression CSTB + 0.263* Expression TALDO1 + 0.313* Expression CLTA). This risk model showed excellent predictive efficacy for prognosis in the TCGA-LIHC and ICGC cohorts. Additionally, patients with high risk scores were found to be less likely to benefit from immunotherapy.\nCONCLUSIONS: We developed a 3-gene signature (including CLTA, TALDO1 and CSTB) based on the heterogeneity of the TIME to predict the survival outcome and immunotherapy response.", "claims": [{"id": "claim-method", "statement": "Then, a univariate Cox regression model identified 7 genes in the TCGA-LIHC cohort that were significantly correlated with overall survival. LASSO Cox regression analysis was used to further reduce the number of candidate genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35078458", "locator": "sections[15] \"Construction of the prognostic model based on the key genes\" paragraph 2", "quote": "Then, a univariate Cox regression model identified 7 genes in the TCGA-LIHC cohort that were significantly correlated with overall survival. LASSO Cox regression analysis was used to further reduce the number of candidate genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35078458", "locator": "sections[28] \"Availability of data and materials\" paragraph 2", "quote": "The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study constructed and validated a three-gene prognostic risk model for HCC using univariate Cox regression and LASSO analyses.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35078458", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Through univariate Cox and LASSO analyses, we constructed a 3-gene risk model (RiskScore = 0.257*Expression CSTB + 0.263* Expression TALDO1 + 0.313* Expression CLTA)."}, {"asset": "source:pmid:35078458", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "This risk model showed excellent predictive efficacy for prognosis in the TCGA-LIHC and ICGC cohorts."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study constructed and validated a three-gene prognostic risk model for HCC using univariate Cox regression and LASSO analyses.", "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:35078458"], "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. 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METHODS: HCC single-cell RNA sequencing (scRNA-seq) data were downloaded from the Ge", "ls. METHODS: HCC single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expressi", "TS: We defined 25 cell clusters based on the scRNA-seq dataset (GSE149614), and the clusters were l"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["novel prognostic model based on single-cell RNA sequencing data for hepatocellular carcinoma.", "nd cellular levels. METHODS: HCC single-cell RNA sequencing (scRNA-seq) data were downloaded from the Ge"], "order_basis": "paper_text_order:2", "id": "assay:2"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ssion levels of model genes were assessed by qPCR. RESULTS: We defined 25 cell clusters based", "Methods Cell culture and quantitative real-time PCR (qRT-PCR) The HCC cell line SK-Hep-1 and hea"], "order_basis": "paper_text_order:1083", "id": "assay:3"}, {"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, a univariate Cox regression model identified 7 genes in the TCGA-LIHC cohort that were significantly correlated with overall survival. LASSO Cox regression analysis was used to further reduce the number of candidate genes."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}