Identification and validation of a cancer-associated fibroblasts-related scoring system to predict prognosis and immune landscape in hepatocellular carcinoma through integrated analysis of single-cell and bulk RNA-sequencing.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37857017", "kind": "science-record", "revision": 3, "content_hash": "sha256:ad4b57267dc7d420939ebfd3f1835728f5f5395cd43cc58a4e5efae998a808fb", "created_at": "2026-11-01T19:02:42Z", "updated_at": "2026-11-01T22:10:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of a cancer-associated fibroblasts-related scoring system to predict prognosis and immune landscape in hepatocellular carcinoma through integrated analysis of single-cell and bulk RNA-sequencing.", "doi": "10.18632/aging.205099", "pmid": "37857017", "authors": ["Lingling Bao", "Xuede Zhang", "Wenjuan Wang", "Bitao Jiang"], "journal": "Aging", "year": 2023, "abstract": "BACKGROUND: Cancer-associated fibroblasts (CAFs) regulate the malignant biological behaviour of hepatocellular carcinoma (HCC) as a significant component of the tumour immune microenvironment (TIME). This study aimed to develop a CAFs-based scoring system to predict the prognosis and TIME of patients with HCC.\nMETHODS: Data for the TCGA-LIHC and GSE14520 cohorts were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Single-cell RNA-sequencing data for HCC samples were retrieved from the GSE166635 cohort. The Least Absolute Shrinkage and Selection Operator algorithm was employed to develop a CAFs-related scoring system (CAFRss). The predictive value of the CAFRss was determined using Kaplan-Meier, Cox regression and Receiver Operating Characteristic curves. Additionally, the TIMER platform, single sample Gene Set Enrichment Analysis and the Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data algorithms were performed to determine the TIME landscape. Finally, the pRRophic algorithm was utilised for drug sensitivity analysis.\nRESULTS: The evaluation of the CAFRss system demonstrated its superior ability to predict the clinical outcome of patients with HCC. Additionally, CAFRss effectively distinguished HCC populations with distinct TIME landscapes. Furthermore, CAFRss-based risk stratification identified individuals with immune 'hot tumours' and predicted the survival of patients treated with ICBs.\nCONCLUSIONS: The developed CAFRss can serve as a predictive tool for determining the clinical outcome of HCC and differentiating populations with diverse TIME characteristics.", "claims": [{"id": "claim-method", "statement": "The Least Absolute Shrinkage and Selection Operator algorithm was employed to develop a CAFs-related scoring system (CAFRss).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37857017", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The Least Absolute Shrinkage and Selection Operator algorithm was employed to develop a CAFs-related scoring system (CAFRss)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The transcriptomic and clinical data for individuals with HCC were downloaded from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/repository) repository.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37857017", "locator": "sections[3] \"Data collection\" paragraph 2", "quote": "The transcriptomic and clinical data for individuals with HCC were downloaded from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/repository) repository."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The CAFRss was an independent prognostic indicator for patients with HCC, with hazard ratios of 3.654 and 3.045 in univariate and multivariate Cox regression analyses, respectively.", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "Uni- and multi-Cox regression analyses indicated that CAFRss was an independent prognostic indicator for patients with HCC, with hazard ratios of 3.654 (P < 0.001) and 3.045 (P < 0.001) (Figure 7A, 7B)."}, "evidence": [{"asset": "source:pmid:37857017", "locator": "sections[17] \"Evaluation of the CAFRss\" paragraph 2", "quote": "Uni- and multi-Cox regression analyses indicated that CAFRss was an independent prognostic indicator for patients with HCC, with hazard ratios of 3.654 (P < 0.001) and 3.045 (P < 0.001) (Figure 7A, 7B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The CAFRss was an independent prognostic indicator for patients with HCC, with hazard ratios of 3.654 and 3.045 in univariate and multivariate Cox regression analyses, 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:37857017"], "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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