Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30538557", "kind": "science-record", "revision": 9, "content_hash": "sha256:9badbf677a1efb2fcf6ebcc7407e2abe054871d845f2b1b967e344b8a5f56d5f", "created_at": "2026-11-01T15:40:39Z", "updated_at": "2026-11-01T22:43:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma.", "doi": "10.2147/CMAR.S181396", "pmid": "30538557", "authors": ["Yujia Zheng", "Yulin Liu", "Songfeng Zhao", "Zhetian Zheng", "Chunyi Shen", "Li An", "Yongliang Yuan"], "journal": "Cancer management and research", "year": 2018, "abstract": "BACKGROUND: Hepatocellular carcinoma (HCC) is a major cause of cancer mortality and an increasing incidence worldwide; however, there are very few effective diagnostic approaches and prognostic biomarkers.\nMATERIALS AND METHODS: One hundred forty-nine pairs of HCC samples from Gene Expression Omnibus (GEO) were obtained to screen differentially expressed genes (DEGs) between HCC and normal samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses, and protein-protein interaction network were used. Cox proportional hazards regression analysis was used to identify significant prognostic DEGs, with which a gene expression signature prognostic prediction model was identified in The Cancer Genome Atlas (TCGA) project discovery cohort. The robustness of this panel was assessed in the GSE14520 cohort. We verified details of the gene expression level of the key molecules through TCGA, GEO, and qPCR and used immunohistochemistry for substantiation in HCC tissues. The methylation states of these genes were also explored.\nRESULTS: Ninety-eight genes, consisting of 13 upregulated and 85 downregulated genes, were screened out in three datasets. KEGG and Gene ontology analysis for the DEGs revealed important biological features of each subtype. Protein-protein interaction network analysis was constructed, consisting of 64 nodes and 115 edges. A subset of four genes (SPINK1, TXNRD1, LCAT, and PZP) that formed a prognostic gene expression signature was established from TCGA and validated in GSE14520. Next, the expression details of the four genes were validated with TCGA, GEO, and clinical samples. The expression panels of the four genes were closely related to methylation states.\nCONCLUSION: This study identified a novel four-gene signature biomarker for predicting the prognosis of HCC. The biomarkers may also reveal molecular mechanisms underlying development of the disease and provide new insights into interventional strategies.", "claims": [{"id": "claim-method", "statement": "To identify DEGs between HCC and adjacent normal tissues, R (version 3.5.1, Auckland, New Zealand) was used to apply significance analysis of microarray with limma version 3.34.8 (http://www.bioconductor.org/packages/release/bioc/html/limma.html) package25 in these three GEO datasets consisting of GSE57957, GSE64041, and GSE76427.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30538557", "locator": "sections[5] \"Identification of differential expression genes\" paragraph 2", "quote": "To identify DEGs between HCC and adjacent normal tissues, R (version 3.5.1, Auckland, New Zealand) was used to apply significance analysis of microarray with limma version 3.34.8 (http://www.bioconductor.org/packages/release/bioc/html/limma.html) package25 in these three GEO datasets consisting of GSE57957, GSE64041, and GSE76427."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Three mRNA expression datasets were downloaded from the GEO (https://www.ncbi.nlm.nih.gov/geo/), with the accession number of GSE57957, GSE64041, and GSE76427, including 37, 60, and 52 paired HCC tissue samples, respectively, used for discovering DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30538557", "locator": "sections[4] \"Data study\" paragraph 2", "quote": "Three mRNA expression datasets were downloaded from the GEO (https://www.ncbi.nlm.nih.gov/geo/), with the accession number of GSE57957, GSE64041, and GSE76427, including 37, 60, and 52 paired HCC tissue samples, respectively, used for discovering DEGs."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:30538557"], "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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