Basement membrane-related regulators for prediction of prognoses and responses to diverse therapies in hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37081465", "kind": "science-record", "revision": 2, "content_hash": "sha256:ff39c6d1d826afd44da8719f130ee3fd0a07a91830369141f6783ba1e1f55b4b", "created_at": "2026-11-01T19:01:44Z", "updated_at": "2026-11-01T21:30:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Basement membrane-related regulators for prediction of prognoses and responses to diverse therapies in hepatocellular carcinoma.", "doi": "10.1186/s12920-023-01504-z", "pmid": "37081465", "authors": ["Ruili Ding", "Chuanbing Zhao", "Yixin Jing", "Rong Chen", "Qingtao Meng"], "journal": "BMC medical genomics", "year": 2023, "abstract": "BACKGROUND: Hepatocellular carcinoma (HCC) remains a global health threat. Finding a novel biomarker for assessing the prognosis and new therapeutic targets is vital to treating this patient population. Our study aimed to explore the contribution of basement membrane-related regulators (BMR) to prognostic assessment and therapeutic response prediction in HCC.\nMATERIAL AND METHODS: The RNA sequencing and clinical information of HCC were downloaded from TCGA-LIHC, ICGC-JP, GSE14520, GSE104580, and CCLE datasets. The BMR signature was created by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and used to separate HCC patients into low- and high-risk groups. We conducted analyses using various R 4.1.3 software packages to compare prognoses and responses to immunotherapy, transcatheter arterial chemoembolization (TACE), and chemotherapeutic drugs between the groups. Additionally, stemness indices, molecular functions, and somatic mutation analyses were further explored in these subgroups.\nRESULTS: The BMR signature included 3 basement membrane-related genes (CTSA, P3H1, and ADAM9). We revealed that BMR signature was an independent risk contributor to poor prognosis in HCC, and high-risk group patients presented shorter overall survival. We discovered that patients in the high-risk group might be responsive to immunotherapy, while patients in the low-risk group may be susceptible to TACE therapy. Over 300 agents were screened to identify effective drugs for the two subgroups.\nCONCLUSION: Overall, basement membrane-related regulators represent novel biomarkers in HCC for assessing prognosis, response to immunotherapy, the effectiveness of TACE therapy, and drug susceptibility.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) was applied to select genes to construct the BMR risk score with the \"glmnet\" [25] package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37081465", "locator": "sections[6] \"Construction and validation of BMR\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) was applied to select genes to construct the BMR risk score with the \"glmnet\" [25] package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are available from public databases, including TCGA database (https://portal.gdc.cancer.gov/projects/TCGA-LIHC), ICGC database (https://dcc.icgc.org/projects/LIRI-JP), CCLE database (https://portals.broadinstitute.org/ccle), GEO (GSE104580 and GSE14520) database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37081465", "locator": "sections[39] \"notes\" paragraph 1", "quote": "The data used to support the findings of this study are available from public databases, including TCGA database (https://portal.gdc.cancer.gov/projects/TCGA-LIHC), ICGC database (https://dcc.icgc.org/projects/LIRI-JP), CCLE database (https://portals.broadinstitute.org/ccle), GEO (GSE104580 and GSE14520) database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:37081465"], "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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