Identification and validation for biomarkers associated with mitochondrial metabolism in chronic obstructive pulmonary disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40927188", "kind": "science-record", "revision": 3, "content_hash": "sha256:e15c3854ad09e701785af6c37945708651504360da9d3aa891ed85550ab0d018", "created_at": "2026-11-01T20:31:01Z", "updated_at": "2026-11-01T22:14:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation for biomarkers associated with mitochondrial metabolism in chronic obstructive pulmonary disease.", "doi": "10.3389/fmed.2025.1612390", "pmid": "40927188", "authors": ["Wenjun Wang", "Lanxiang Wu", "Chao Ouyang", "Tao Huang"], "journal": "Frontiers in medicine", "year": 2025, "abstract": "Background: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease. However, the biological role of mitochondrial metabolism (MM) in COPD remains poorly understood. This study aimed to explore the underlying mechanisms of MM in COPD using bioinformatics methods.\nMethods: The datasets GSE57148 and GSE8581 were downloaded from Gene Expression Omnibus (GEO), and 1,234 mitochondrial metabolism-related genes (MM-RGs) were downloaded from the literature. In GSE57148 dataset, differentially expressed genes (DEGs) were determined. The intersection of DEGs and MM-RGs was taken to obtain candidate genes. Protein-protein interaction (PPI) network was used to obtained candidate key genes. Machine learning was employed to detect key genes. The biomarkers were identified through expression validation and receiver operating characteristic (ROC) curves. Subsequently, a nomogram was developed to forecast the likelihood of developing COPD. In addition, functional enrichment analysis, immune infiltration, molecular regulatory network, and drug prediction were carried out. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry analysis were used to verify DEGs of lung tissues of COPD patients and controls.\nResults: Adenine phosphoribosyltransferase (APRT) and lecithin-cholesterol acyltransferase (LCAT) were identified as potential biomarkers. Subsequently, a nomogram was formulated based on these two biomarkers, revealing their significant diagnostic potential. Pathways co-enriched by two biomarkers included ribosome, among others. Immune infiltration analysis showed that 15 types of immune cells were differential immune cells. APRT predicted a total of 30 miRNAs and LCAT predicted a total of 17 miRNAs. APRT was predicted to be targeted by 30 microRNAs (miRNAs), while LCAT was associated with 17 miRNAs. Additionally, 178 transcription factors (TFs) were predicted to regulate APRT, and 86 TFs were predicted for LCAT. TFs shared by both biomarkers include SPI1, CTCF and BCL3, etc. Finally, drug prediction analysis found a total of 114 target drugs for APRT and 156 target drugs for LCAT. The mRNA and protein expression of APRT and LCAT were significantly decreased in COPD patients' lung tissues.\nConclusion: APRT and LCAT were identified as biomarkers for COPD, and this provides deeper understanding into the mechanisms behind COPD and identifies potential markers for early diagnosis and therapeutic intervention.", "claims": [{"id": "claim-method", "statement": "Statistical analyses were conducted using R software (v 4.2.2), with the Wilcoxon employed to assess differences among groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40927188", "locator": "sections[12] \"Statistical analysis\" paragraph 2", "quote": "Statistical analyses were conducted using R software (v 4.2.2), with the Wilcoxon employed to assess differences among groups."}], "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 in the article/Supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40927188", "locator": "sections[25] \"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 in the article/Supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "APRT and LCAT were identified as COPD biomarkers, and a nomogram based on them achieved an AUC of 0.841.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40927188", "locator": "sections[16] \"Constructing a model with exceptional diagnostic capability for COPD\" paragraph 2", "quote": "In ROC curve, the AUC value was 0.841; thus, the ROC curve indicated that the nomogram performed well clinical utility (Figure 4C)."}, {"asset": "source:pmid:40927188", "locator": "sections[15] \"APRT and LCAT as biomarkers\" paragraph 2", "quote": "Therefore, the APRT and LCAT were selected as biomarkers for this study (Figure 3E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "APRT and LCAT were identified as COPD biomarkers, and a nomogram based on them achieved an AUC of 0.841.", "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:40927188"], "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:GSE57148"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE57148"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40927188", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmed.2025.1612390", "sha256": "sha256:4de37ea95d2cf2ba31f3a32d9546765db4dbbaba3ade5c19fd5169407558592b", "bytes": 64976, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fmed.2025.1612390", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12414765", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE57148", "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=GSE57148", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE57148", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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