The regulatory network of potential transcription factors and MiRNAs of mitochondria-related genes for sarcopenia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36171891", "kind": "science-record", "revision": 4, "content_hash": "sha256:04d523ceb751841d19679d5a8077c98fe82c73a2179e4191148140549db7f44d", "created_at": "2026-11-01T18:15:27Z", "updated_at": "2026-11-01T22:07:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The regulatory network of potential transcription factors and MiRNAs of mitochondria-related genes for sarcopenia.", "doi": "10.3389/fgene.2022.975886", "pmid": "36171891", "authors": ["Wanrui Fu", "Guzailinuer Kadeer", "Yaqi He", "Ying Feng"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Mitochondrial dysfunction is a significant contributor to sarcopenia, but the mechanism remains unclear. Methods: In the present study, we downloaded GSE117525 and GSE8479 datasets from Gene Expression Omnibus (GEO), then the weighted correlation network analysis (WGCNA) was used to construct scale-free co-expression networks respectively. The key genes of aging muscle were obtained by overlapping key modules of two networks. Receiver operating characteristic (ROC) curve was drawn to explore the diagnostic efficacy of key genes. Finally, a transcription factor-key gene network was constructed based on ChEA3 platform and hTFtarget database, and a miRNA-key gene network was constructed using starBase and the multimiR R package. Results: The most positively or negatively correlated modules of the two datasets were identified, and genes related to oxidative phosphorylation and mitochondrial ribosomal proteins were identified as key genes. The diagnostic values were confirmed with ROC curves by self-verification (GSE117525 and GSE8479) and external verification (GSE47881). Then, Yin Yang 1 (YY1) was identified as the most important transcription factor of the transcription factor-key gene network. In addition, miRNAs related to key genes were also predicted. Conclusion: The findings of the present study provide a novel insight into the pathological mechanism of sarcopenia.", "claims": [{"id": "claim-method", "statement": "In the present study, we downloaded GSE117525 and GSE8479 datasets from Gene Expression Omnibus (GEO), then the weighted correlation network analysis (WGCNA) was used to construct scale-free co-expression networks respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36171891", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "In the present study, we downloaded GSE117525 and GSE8479 datasets from Gene Expression Omnibus (GEO), then the weighted correlation network analysis (WGCNA) was used to construct scale-free co-expression networks respectively."}], "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 author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36171891", "locator": "sections[20] \"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 author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Weighted gene co-expression network analysis identified 29 positively regulating and 160 negatively regulating genes, with the negatively regulating genes associated with mitochondrial respiration, oxidative phosphorylation, and related pathways.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36171891", "locator": "sections[13] \"Identification of the shared pathways\" paragraph 2", "quote": "Using the GO and KEGG enrichment analyses, we further explored the common regulatory pathways that 29 positively-regulating and 160 negatively-regulating genes screened by WGCNA respectively."}, {"asset": "source:pmid:36171891", "locator": "sections[13] \"Identification of the shared pathways\" paragraph 2", "quote": "As for the negatively-regulating genes, the GO analysis showed that biological processes (BP) were enriched in cellular respiration, ATP metabolic process, mitochondrial electron transport, NADH to ubiquinone as well as fatty acid metabolic process, and cellular composition (CC) terms were enriched in mitochondrial matrix and NADH dehydrogenase complex, and molecular functions (MF) were enriched in electron transfer activity, and NADH dehydrogenase activity (Figure 2A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Weighted gene co-expression network analysis identified 29 positively regulating and 160 negatively regulating genes, with the negatively regulating genes associated with mitochondrial respiration, oxidative phosphorylation, and related pathways.", "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:36171891"], "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:GSE117525"], "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:GSE117525"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36171891", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.975886", "sha256": 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