Development and validation of a coagulation-related genes prognostic model for hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36894886", "kind": "science-record", "revision": 3, "content_hash": "sha256:517653265c2798f4fb1d10dd35ae7d09eb62fd1bc09028cc91ee1fe9c55cd8e4", "created_at": "2026-11-01T19:01:31Z", "updated_at": "2026-11-01T22:09:01Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and validation of a coagulation-related genes prognostic model for hepatocellular carcinoma.", "doi": "10.1186/s12859-023-05220-4", "pmid": "36894886", "authors": ["Wan-Xia Yang", "Hong-Wei Gao", "Jia-Bo Cui", "An-An Zhang", "Fang-Fang Wang", "Jian-Qin Xie", "Ming-Hua Lu", "Chong-Ge You"], "journal": "BMC bioinformatics", "year": 2023, "abstract": "BACKGROUND: Hepatocellular carcinoma (HCC) has a high incidence and mortality worldwide, which seriously threatens people's physical and mental health. Coagulation is closely related to the occurrence and development of HCC. Whether coagulation-related genes (CRGs) can be used as prognostic markers for HCC remains to be investigated.\nMETHODS: Firstly, we identified differentially expressed coagulation-related genes of HCC and control samples in the datasets GSE54236, GSE102079, TCGA-LIHC, and Genecards database. Then, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were used to determine the key CRGs and establish the coagulation-related risk score (CRRS) prognostic model in the TCGA-LIHC dataset. The predictive capability of the CRRS model was evaluated by Kaplan-Meier survival analysis and ROC analysis. External validation was performed in the ICGC-LIRI-JP dataset. Besides, combining risk score and age, gender, grade, and stage, a nomogram was constructed to quantify the survival probability. We further analyzed the correlation between risk score and functional enrichment, pathway, and tumor immune microenvironment.\nRESULTS: We identified 5 key CRGs (FLVCR1, CENPE, LCAT, CYP2C9, and NQO1) and constructed the CRRS prognostic model. The overall survival (OS) of the high-risk group was shorter than that of the low-risk group. The AUC values for 1 -, 3 -, and 5-year OS in the TCGA dataset were 0.769, 0.691, and 0.674, respectively. The Cox analysis showed that CRRS was an independent prognostic factor for HCC. A nomogram established with risk score, age, gender, grade, and stage, has a better prognostic value for HCC patients. In the high-risk group, CD4+T cells memory resting, NK cells activated, and B cells naive were significantly lower. The expression levels of immune checkpoint genes in the high-risk group were generally higher than that in the low-risk group.\nCONCLUSIONS: The CRRS model has reliable predictive value for the prognosis of HCC patients.", "claims": [{"id": "claim-method", "statement": "Then, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were used to determine the key CRGs and establish the coagulation-related risk score (CRRS) prognostic model in the TCGA-LIHC dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36894886", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Then, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were used to determine the key CRGs and establish the coagulation-related risk score (CRRS) prognostic model in the TCGA-LIHC dataset."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used in this study can be found in the GEO database (https://www.ncbi.nlm.nih.gov/geo/), TCGA database (https://portal.gdc.cancer.gov/) and ICGC database (https://dcc.icgc.org/). The CRGs were obtained from GeneCards database (https://www.genecards.org/). Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36894886", "locator": "sections[28] \"Availability of data and materials\" paragraph 2", "quote": "The datasets used in this study can be found in the GEO database (https://www.ncbi.nlm.nih.gov/geo/), TCGA database (https://portal.gdc.cancer.gov/) and ICGC database (https://dcc.icgc.org/). The CRGs were obtained from GeneCards database (https://www.genecards.org/). Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The CRRS model has reliable predictive value for the prognosis of HCC patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36894886", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The CRRS model has reliable predictive value for the prognosis of HCC patients."}, {"asset": "source:pmid:36894886", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The AUC values for 1 -, 3 -, and 5-year OS in the TCGA dataset were 0.769, 0.691, and 0.674, respectively."}, {"asset": "source:pmid:36894886", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The Cox analysis showed that CRRS was an independent prognostic factor for HCC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The CRRS model has reliable predictive value for the prognosis of HCC patients.", "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:36894886"], "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:GSE102079"], "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:GSE102079"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36894886", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12859-023-05220-4", "sha256": "sha256:b24da19b8c45df5b238be6926262307507a94cef7a4ecadc5f958f192cbb6f38", "bytes": 42226, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12859-023-05220-4", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9996845", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE102079", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102079", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE102079", "version": null, "note": "The datasets used in this study can be found in the GEO database (https://www.ncbi.nlm.nih.gov/geo/), TCGA database (https://portal.gdc.cancer.gov/) and ICGC database (https://dcc.icgc.org/). The CRGs were obtained from GeneCards database (https://www.genecards.org/). Further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets used in this study can be found in the GEO database (https://www.ncbi.nlm.nih.gov/geo/), TCGA database (https://portal.gdc.cancer.gov/) and ICGC database (https://dcc.icgc.org/). The CRGs were obtained from GeneCards database (https://www.genecards.org/). 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The CRGs were obtained from GeneCards database (https://www.genecards.org/). Further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets used in this study can be found in the GEO database (https://www.ncbi.nlm.nih.gov/geo/), TCGA database (https://portal.gdc.cancer.gov/) and ICGC database (https://dcc.icgc.org/). The CRGs were obtained from GeneCards database (https://www.genecards.org/). 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