Hepatocellular carcinoma: An analysis of the expression status of stress granules and their prognostic value.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38994142", "kind": "science-record", "revision": 2, "content_hash": "sha256:e916eea4e8dad1d1e7cde4a607379c268338cde6df30f4f4a66140be50e9da9d", "created_at": "2026-11-01T20:29:04Z", "updated_at": "2026-11-01T21:33:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Hepatocellular carcinoma: An analysis of the expression status of stress granules and their prognostic value.", "doi": "10.4251/wjgo.v16.i6.2571", "pmid": "38994142", "authors": ["Qing-Shuai Ren", "Qiu Sun", "Shu-Qin Cheng", "Li-Ming Du", "Ping-Xuan Guo"], "journal": "World journal of gastrointestinal oncology", "year": 2024, "abstract": "BACKGROUND: Hepatocellular carcinoma (HCC) is a global popular malignant tumor, which is difficult to cure, and the current treatment is limited.\nAIM: To analyze the impacts of stress granule (SG) genes on overall survival (OS), survival time, and prognosis in HCC.\nMETHODS: The combined The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC), GSE25097, and GSE36376 datasets were utilized to obtain genetic and clinical information. Optimal hub gene numbers and corresponding coefficients were determined using the Least absolute shrinkage and selection operator model approach, and genes for constructing risk scores and corresponding correlation coefficients were calculated according to multivariate Cox regression, respectively. The prognostic model's receiver operating characteristic (ROC) curve was produced and plotted utilizing the time ROC software package. Nomogram models were constructed to predict the outcomes at 1, 3, and 5-year OS prognostications with good prediction accuracy.\nRESULTS: We identified seven SG genes (DDX1, DKC1, BICC1, HNRNPUL1, CNOT6, DYRK3, CCDC124) having a prognostic significance and developed a risk score model. The findings of Kaplan-Meier analysis indicated that the group with a high risk exhibited significantly reduced OS in comparison with those of the low-risk group (P < 0.001). The nomogram model's findings indicate a significant enhancement in the accuracy of OS prediction for individuals with HCC in the TCGA-HCC cohort. Gene Ontology and Gene Set Enrichment Analysis suggested that these SGs might be involved in the cell cycle, RNA editing, and other biological processes.\nCONCLUSION: Based on the impact of SG genes on HCC prognosis, in the future, it will be used as a biomarker as well as a unique therapeutic target for the identification and treatment of HCC.", "claims": [{"id": "claim-method", "statement": "After that, The Least absolute shrinkage and selection operator (LASSO) algorithm was employed in the univariate Cox regression analysis in order to eliminate multicollinearity and determine which variables were significant.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38994142", "locator": "sections[5] \"Least absolute shrinkage and selection operator-Cox regression analysis to screen prognostic genes\" paragraph 2", "quote": "After that, The Least absolute shrinkage and selection operator (LASSO) algorithm was employed in the univariate Cox regression analysis in order to eliminate multicollinearity and determine which variables were significant."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary files. Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38994142", "locator": "sections[22] \"Data sharing statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary files. Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The high-risk group exhibited significantly reduced overall survival compared with the low-risk group (P < 0.001).", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "The findings of Kaplan-Meier analysis indicated that the group with a high risk exhibited significantly reduced OS in comparison with those of the low-risk group (P < 0.001)."}, "evidence": [{"asset": "source:pmid:38994142", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The findings of Kaplan-Meier analysis indicated that the group with a high risk exhibited significantly reduced OS in comparison with those of the low-risk group (P < 0.001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The high-risk group exhibited significantly reduced overall survival compared with the low-risk group (P < 0.001).", "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:38994142"], "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:GSE25097"], "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:GSE25097"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38994142", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.4251/wjgo.v16.i6.2571", "sha256": "sha256:27a0cea68d687e90243247b1808ed7f1522f9ed5514fb15bef3baf303941c55c", "bytes": 52425, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.4251/wjgo.v16.i6.2571", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11236250", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE25097", "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=GSE25097", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE25097", "version": null, "note": "The original contributions presented in the study are included in the article/supplementary files. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/supplementary files. 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