Mitochondrial insights: key biomarkers and potential treatments for diabetic nephropathy and sarcopenia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40703651", "kind": "science-record", "revision": 4, "content_hash": "sha256:573f7c1088b46fe6475645b12e0132c7ab81c19e990994aea1228b9d54de3215", "created_at": "2026-11-01T20:30:48Z", "updated_at": "2026-11-01T22:56:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Mitochondrial insights: key biomarkers and potential treatments for diabetic nephropathy and sarcopenia.", "doi": "10.3389/fcell.2025.1596204", "pmid": "40703651", "authors": ["Yi Wei Chen", "Shan He", "Yu Wang", "Lian Ying Hu", "Qin Kai Chen", "Si Yi Liu"], "journal": "Frontiers in cell and developmental biology", "year": 2025, "abstract": "Introduction: Clinical studies reveal bidirectional links between sarcopenia (SP) and diabetic nephropathy (DN). Damage to mitochondria in DN may result in diminished energy production, which consequently triggers SP. Consequently, mitochondria seem to function as critical nodes connecting DN with SP. The objective of this research was to pinpoint biomarkers associated with mitochondrial dysfunction in DN and SP.\nMethods: By analyzing the Gene Expression Omnibus (GEO) repository, we identified shared differentially expressed genes (DEGs) in the DN (GSE96804, GSE30528) and SP (GSE1428, GSE136344) datasets that displayed similar expression trends in mitochondrial genes. Using Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), and Random Forest (RF) algorithms, we identified three key mitochondrial hub genes. Diagnostic nomograms were created to predict DN and SP risk. We assessed immune infiltration using CIBERSORT and built a drug-gene network with Cytoscape. Molecular docking determined binding affinities between potential drugs and hub genes, which were validated in the datasets.\nResults: Analysis of GEO datasets identified 80 shared DEGs between DN and SP, including 10 mitochondria-related genes. Utilizing four machine learning algorithms (LASSO, SVM, XGBoost, RF), we pinpointed three mitochondrial hub genes. Subsequent validation confirmed two key mitochondria-related genes - HTT and TTC19 - as shared diagnostic biomarkers for both DN and SP. These biomarkers demonstrated strong diagnostic power (AUC >0.8), leading to the construction of diagnostic nomograms. Immune infiltration analysis revealed elevated M1 macrophages in DN and increased M2 macrophages in SP, with both biomarkers showing significant correlations with various immune cells. Gene set enrichment analysis linked HTT and TTC19 to mitochondrial metabolic processes. Crucially, in silico drug prediction identified 156 potential drugs, and molecular docking confirmed a high binding affinity between acetaminophen and the HTT protein.\nConclusion: Our study identifies HTT and TTC19 as novel mitochondria-immune related biomarkers common to both DN and SP, providing insights into their shared pathogenesis involving mitochondrial dysfunction and immune dysregulation. The computational prediction of acetaminophen interaction highlights a potential avenue for therapeutic exploration. Further clinical and mechanistic studies are warranted to validate these findings and elucidate the underlying pathways.", "claims": [{"id": "claim-method", "statement": "We used the “limma” package in R software to perform differential analysis on the expression profile data of the DN - Datasets and SP - Datasets respectively, and screened the genes with the criteria of |logFC| > 0.25 and p. value <0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40703651", "locator": "sections[4] \"2.2 Differential expression gene analysis\" paragraph 2", "quote": "We used the “limma” package in R software to perform differential analysis on the expression profile data of the DN - Datasets and SP - Datasets respectively, and screened the genes with the criteria of |logFC| > 0.25 and p. value <0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40703651", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "HTT and TTC19 were validated as shared diagnostic biomarkers for both diabetic nephropathy and sarcopenia.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40703651", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Subsequent validation confirmed two key mitochondria-related genes - HTT and TTC19 - as shared diagnostic biomarkers for both DN and SP."}, {"asset": "source:pmid:40703651", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "These biomarkers demonstrated strong diagnostic power (AUC >0.8), leading to the construction of diagnostic nomograms."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "HTT and TTC19 were validated as shared diagnostic biomarkers for both diabetic nephropathy and sarcopenia.", "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:40703651"], "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:GSE136344"], "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:GSE136344"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40703651", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcell.2025.1596204", "sha256": 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