Screening the genome for HCC-specific CpG methylation signatures as biomarkers for diagnosis and prognosis evaluation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34147096", "kind": "science-record", "revision": 7, "content_hash": "sha256:b9f62637cf3408dbe2d65decd36d32e2886940a40a8d55d7a0517f088d198a99", "created_at": "2026-11-01T15:41:39Z", "updated_at": "2026-11-01T22:04:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening the genome for HCC-specific CpG methylation signatures as biomarkers for diagnosis and prognosis evaluation.", "doi": "10.1186/s12920-021-01015-9", "pmid": "34147096", "authors": ["Rui-Kun Zhang", "Jia-Lin Liu"], "journal": "BMC medical genomics", "year": 2021, "abstract": "BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common and invasive malignant tumors in the world. The change in DNA methylation is a key event in HCC.\nMETHODS: Methylation datasets for HCC and 17 other types of cancer were downloaded from The Cancer Genome Atlas (TCGA). The CpG sites with large differences in methylation between tumor tissues and paracancerous tissues were identified. We used the HCC methylation dataset downloaded from the TCGA as the training set and removed the overlapping sites among all cancer datasets to ensure that only CpG sites specific to HCC remained. Logistic regression analysis was performed to select specific biomarkers that can be used to diagnose HCC, and two datasets-GSE157341 and GSE54503-downloaded from GEO as validation sets were used to validate our model. We also used a Cox regression model to select CpG sites related to patient prognosis.\nRESULTS: We identified 6 HCC-specific methylated CpG sites as biomarkers for HCC diagnosis. In the training set, the area under the receiver operating characteristic (ROC) curve (AUC) for the model containing all these sites was 0.971. The AUCs were 0.8802 and 0.9711 for the two validation sets from the GEO database. In addition, 3 other CpG sites were analyzed and used to create a risk scoring model for patient prognosis and survival prediction.\nCONCLUSIONS: Through the analysis of HCC methylation datasets from the TCGA and Gene Expression Omnibus (GEO) databases, potential biomarkers for HCC diagnosis and prognosis evaluation were ascertained.", "claims": [{"id": "claim-method", "statement": "Student’s t test was applied to compares the difference between the two groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34147096", "locator": "sections[5] \"DNA differential methylation analysis\" paragraph 2", "quote": "Student’s t test was applied to compares the difference between the two groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "TCGA: https://tcga-data.nci.nih.gov, now hosted at GDC: https:// portal.gdc.cancer.gov/. GSE157341: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157341. 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GSE54503: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54503"}], "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:34147096"], "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:GSE157341"], "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:GSE157341"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34147096", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-021-01015-9", "sha256": "sha256:08f42e5aac200b83d9bbeb3997f8e6275ad98c7f4c67ba918170a19973abed2f", "bytes": 39419, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12920-021-01015-9", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8214801", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE157341", "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=GSE157341", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE157341", "version": null, "note": "TCGA: https://tcga-data.nci.nih.gov, now hosted at GDC: https:// portal.gdc.cancer.gov/. 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