Development and evaluation of a risk score model based on a WNT score gene-associated signature for predicting the clinical outcome and the tumour microenvironment of hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38054921", "kind": "science-record", "revision": 3, "content_hash": "sha256:d0e6cbae5c97a56bca6e31daaaaac697dc0b540f9d5794af762f2434d0387508", "created_at": "2026-11-01T19:03:00Z", "updated_at": "2026-11-01T22:10:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and evaluation of a risk score model based on a WNT score gene-associated signature for predicting the clinical outcome and the tumour microenvironment of hepatocellular carcinoma.", "doi": "10.1177/03946320231218179", "pmid": "38054921", "authors": ["Penghui Li", "Xiao Ma", "Di Huang", "Xinyu Gu"], "journal": "International journal of immunopathology and pharmacology", "year": 2023, "abstract": "Background: Hepatocellular carcinoma (HCC) is currently one of the most life-threatening diseases worldwide. However, the factors, genes, and processes involved in the mechanisms of HCC initiation, development, and metastasis remain to be identified.Methods: WNT signalling pathways may play important roles in cancer initiation and progression. Thus, it would be informative to construct a WNT signature-based gene model for the prognosis of HCC and the prediction of therapeutic efficacy. We curated genomic profiles for HCC from The Cancer Genome Atlas (TCGA) and divided them into training and internal validation datasets. We also used samples from GSE14520 and HCCDB18 as validation datasets and clustered them by ConsensusClusterPlus analysis. We applied WebGestaltR to the WNT score-associated differentially expressed genes (DEGs) and conducted a signalling pathway enrichment analysis. We assessed the tumour immune microenvironment with ESTIMATE, Microenvironment Cell Populations (MCP)-counter, single-sample gene set enrichment analysis (ssGSEA), and tumour immune dysfunction and exclusion (TIDE).Results: We performed a least absolute shrinkage and selection operator (LASSO) regression analysis to identify the prognosis-related hub genes, identified the risk and protective factor genes associated with HCC, classified them into two clusters, and found that Cluster 2 had a significantly better prognosis than Cluster 1. Moreover, the latter had advanced clinical features compared with the former. Uridine-cytosine kinase 1 (UCK1), myristoylated alanine-rich C-kinase substrate-like protein 1 (MARCKSL1), P-antigen family member 1 (PAGE1), and killer cell lectin-like receptor B1 (KLRB1) were detected and used to construct a simplified prognostic model for HCC. The high risk score subgroup showed a poorer prognosis than the low risk score subgroup, and the model assessed HCC prognosis consistently and effectively.Conclusions: The WNT score-related gene-based model designed and evaluated herein had strong prognostic and predictive ability for HCC and could, therefore, facilitate decision-making in the prognosis and therapeutic efficacy assessment of HCC.", "claims": [{"id": "claim-method", "statement": "We then used the glmnet package in R to perform a LASSO regression analysis and reduce the number of genes in the risk model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38054921", "locator": "sections[21] \"Prognostic target gene identification\" paragraph 2", "quote": "We then used the glmnet package in R to perform a LASSO regression analysis and reduce the number of genes in the risk model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) and the liver hepatocellular carcinoma (LIHC) mRNA expression profiles with fragments per kilobase million (FPKM) values served as the training set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38054921", "locator": "sections[3] \"Data collection\" paragraph 2", "quote": "The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) and the liver hepatocellular carcinoma (LIHC) mRNA expression profiles with fragments per kilobase million (FPKM) values served as the training set."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The high risk score subgroup showed a poorer prognosis than the low risk score subgroup.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38054921", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The high risk score subgroup showed a poorer prognosis than the low risk score subgroup, and the model assessed HCC prognosis consistently and effectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The high risk score subgroup showed a poorer prognosis than the low risk score subgroup.", "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:38054921"], "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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