Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34692847", "kind": "science-record", "revision": 5, "content_hash": "sha256:e7e5344d7348fee0153da76993353e3dc1d03b01051600726c1b8fc8401322c5", "created_at": "2026-11-01T17:36:22Z", "updated_at": "2026-11-01T22:47:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network.", "doi": "10.1155/2021/9972011", "pmid": "34692847", "authors": ["Shunqiang Nong", "Xiaohao Chen", "Zechen Wang", "Guidan Xu", "Wujun Wei", "Bin Peng", "Lü Zhou", "Liuzhi Wei", "Jingjie Zhao", "Qiuju Wei", "Yibin Deng", "Lingzhang Meng"], "journal": "BioMed research international", "year": 2021, "abstract": "BACKGROUND: Increasing evidence demonstrated that long noncoding RNA (lncRNA) could affect inflammatory tumor immune microenvironment by modulating gene expression and could be used as a biomarker for HBC-related hepatocellular carcinoma (HCC) but still needs further research. The aim of the present study was to determine an lncRNA signature for the diagnosis of HBV-related HCC.\nMETHODS: HBV-related HCC expression profiles (GSE55092, GSE19665, and GSE84402) were abstracted from the GEO (Gene Expression Omnibus) data resource, and R package limma and RobustRankAggreg were employed to identify common differentially expressed genes (DEGs). Using machine learning, optimal diagnostic lncRNA molecular markers for HBV-related HCC were identified. The expression of candidate lncRNAs was cross-validated in GSE121248, and an ROC (receiver operating characteristic) curve of lncRNA biomarkers was carried out. Additionally, a coexpression network and functional annotation was built, after which a PPI (protein-protein interaction) network along with module analysis were conducted with the Cytoscape open source software.\nRESULT: A total of 38 DElncRNAs and 543 DEmRNAs were identified with a fold change larger than 2.0 and a P value < 0.05. By machine learning, AL356056.2, AL445524.1, TRIM52-AS1, AC093642.1, EHMT2-AS1, AC003991.1, AC008040.1, LINC00844, and LINC01018 were screened out as optional diagnostic lncRNA biosignatures for HBV-related HCC. The AUC (areas under the curve) of the SVM (support vector machine) model and random forest model were 0.957 and 0.904, respectively, and the specificity and sensitivity were 95.7 and 100% and 94.3 and 86.5%, respectively. The results of functional enrichment analysis showed that the integrated coexpressed DEmRNAs shared common cascades in the p53 signaling pathway, retinol metabolism, PI3K-Akt signaling cascade, and chemical carcinogenesis. The integrated DEmRNA PPI network complex was found to be comprised of 87 nodes, and two vital modules with a high degree were selected with the MCODE app.\nCONCLUSION: The present study identified nine potential diagnostic biomarkers for HBV-related HCC, all of which could potentially modulated gene expression related to inflammatory conditions in the tumor immune microenvironment. The functional annotation of the target DEmRNAs yielded novel evidence in evaluating the precise functions of lncRNA in HBV-related HCC.", "claims": [{"id": "claim-method", "statement": "The DElncRNAs along with the DEmRNAs between HBV-linked HCC and nontumor tissues were computed through the limma R package V3.5.2 in R software in each dataset. Afterwards, the RobustRankAggreg (RRA) R package was used to integrate and analyze the three gene lists that were subsequently sorted via the logFC value.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34692847", "locator": "sections[4] \"2.2. Screening for Differentially Expressed mRNAs and lncRNAs\" paragraph 2", "quote": "The DElncRNAs along with the DEmRNAs between HBV-linked HCC and nontumor tissues were computed through the limma R package V3.5.2 in R software in each dataset."}, {"asset": "source:pmid:34692847", "locator": "sections[4] \"2.2. Screening for Differentially Expressed mRNAs and lncRNAs\" paragraph 2", "quote": "Afterwards, the RobustRankAggreg (RRA) R package was used to integrate and analyze the three gene lists that were subsequently sorted via the logFC value."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets, as well as the code generated or analyzed in this work, can be accessed from the corresponding authors on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34692847", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "The datasets, as well as the code generated or analyzed in this work, can be accessed from the corresponding authors on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified nine potential diagnostic lncRNA biomarkers for HBV-related HCC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34692847", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The present study identified nine potential diagnostic biomarkers for HBV-related HCC, all of which could potentially modulated gene expression related to inflammatory conditions in the tumor immune microenvironment."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified nine potential diagnostic lncRNA biomarkers for HBV-related HCC.", "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:34692847"], "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:GSE121248"], "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:GSE121248"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34692847", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2021/9972011", "sha256": 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