Liver failure diagnosis: key diagnostic biomarkers discovery and bioinformatic validation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40276677", "kind": "science-record", "revision": 4, "content_hash": "sha256:b12e32c323f1375d09ba067ff926e30deb898432af927776fba58ab2fc9539bc", "created_at": "2026-11-01T20:30:26Z", "updated_at": "2026-11-01T22:56:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Liver failure diagnosis: key diagnostic biomarkers discovery and bioinformatic validation.", "doi": "10.3389/fgene.2025.1554116", "pmid": "40276677", "authors": ["Quan Ye", "Kai Wang", "Hong Ye"], "journal": "Frontiers in genetics", "year": 2025, "abstract": "Background: Glutathione peroxidase 3 (GPX3) is a strong antioxidant. While elevated GPX3 levels are linked to diverse pathologies, its role in liver failure (LF) remains underexplored. This study investigates GPX3's diagnostic potential and mechanistic contributions to LF pathogenesis.\nMethods: We integrated two high-quality liver tissue datasets (GSE38941 and GSE14668) from the Gene Expression Omnibus (GEO) database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted to identify potential biomarkers associated with liver failure. The Comparative Toxicogenomics Database was used to predict the function of GPX3. In addition, in our study, we verified the target gene mRNA expression level in 40 patients with acute or chronic acute liver failure (ACHBLF) by RT-QCPR experiment and detect the methylation status of GPX3 promoter of ACHBLF patients with methylation specific PCR (MSP).\nResults: The results demonstrate that GPX3 drives pathogenic mechanisms in liver failure through oxidative stress-related pathways (e.g., collagen cross-linking, extracellular matrix remodeling) and immune dysregulation (e.g., macrophage activation, PD-1/CTLA-4 signaling). CPX8, PRDX6, GPX4, GSS, GSR, TXN, GPX7, PPARGC1A, ALOX15, and ALOX5 have been identified as key immune-related genes. Furthermore, there were significant differences in immune cell infiltration between the high and low expression groups of GPX3 groups. Immune infiltration analysis demonstrated strong correlations between GPX3 expression and key immune markers (p < 0.05), suggesting its role in modulating inflammatory responses. Additionally, GPX3 increased susceptibility to aerosols, cyclosporin and dexamethasone was observed in patients with elevated levels of GPX3. The mRNA expression of GPX3 was much higher in ACHBLF patients than in other groups. In ACHBLF patients, the group with GPX3 methylated promoter had higher mortality than those without.\nConclusion: In conclusion, GPX3 is a promising diagnostic biomarker for liver failure. Its promoter methylation status may serve as a prognostic indicator, highlighting its therapeutic potential.", "claims": [{"id": "claim-method", "statement": "The DEGs between the two sets of data were obtained by using the Limma package (version 3.54.2) in R: Using the Limma package (|logFC| > 1, FDR-adjusted p < 0.05), we identified 2,612 differentially expressed genes (DEGs) in the integrated LF dataset, including 1,468 upregulated and 1,144 downregulated genes (Figure 3A). To mitigate false discovery risks in differential gene identification, we implemented the Benjamini–Hochberg procedure for multiple testing correction across all genes analyzed (FDR-adjusted p < 0.05).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40276677", "locator": "sections[22] \"3.2 DEGs associated with LF\" paragraph 2", "quote": "The DEGs between the two sets of data were obtained by using the Limma package (version 3.54.2) in R: Using the Limma package (|logFC| > 1, FDR-adjusted p < 0.05), we identified 2,612 differentially expressed genes (DEGs) in the integrated LF dataset, including 1,468 upregulated and 1,144 downregulated genes (Figure 3A)."}, {"asset": "source:pmid:40276677", "locator": "sections[5] \"2.2 A differential expression analysis method\" paragraph 2", "quote": "To mitigate false discovery risks in differential gene identification, we implemented the Benjamini–Hochberg procedure for multiple testing correction across all genes analyzed (FDR-adjusted p < 0.05)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: GSE38941.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40276677", "locator": "sections[32] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: GSE38941."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Immune infiltration analysis demonstrated strong correlations between GPX3 expression and key immune markers (p < 0.05), suggesting its role in modulating inflammatory responses.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "Immune infiltration analysis demonstrated strong correlations between GPX3 expression and key immune markers (p < 0.05), suggesting its role in modulating inflammatory responses."}, "evidence": [{"asset": "source:pmid:40276677", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Immune infiltration analysis demonstrated strong correlations between GPX3 expression and key immune markers (p < 0.05), suggesting its role in modulating inflammatory responses."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Immune infiltration analysis demonstrated strong correlations between GPX3 expression and key immune markers (p < 0.05), suggesting its role in modulating inflammatory responses.", "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:40276677"], "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:GSE14668"], "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:GSE14668"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40276677", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2025.1554116", "sha256": "sha256:eb7b1369db5a8df588909940c66b9c03336e4562b60e5b88bf243f098e43b406", "bytes": 48208, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2025.1554116", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12020437", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE14668", "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=GSE14668", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE14668", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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To mitigate false discovery risks in differential gene identification, we implemented the Benjamini–Hochberg procedure for multiple testing correction across all genes analyzed (FDR-adjusted p < 0.05)."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}