Autophagy-Related Genes in Atherosclerosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34306596", "kind": "science-record", "revision": 8, "content_hash": "sha256:e6bef272df8204b8a2296ba16ab5bedc6c15970ca5cda72f3c4c25de5509d7fa", "created_at": "2026-11-01T15:41:45Z", "updated_at": "2026-11-01T22:47:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Autophagy-Related Genes in Atherosclerosis.", "doi": "10.1155/2021/6402206", "pmid": "34306596", "authors": ["Yuankun Chen", "Ao Zeng", "Shumiao He", "Siqing He", "Chunmei Li", "Wenjie Mei", "Qun Lu"], "journal": "Journal of healthcare engineering", "year": 2021, "abstract": "Background: Atherosclerosis (AS) is a common chronic vascular inflammatory disease and one of the main causes of cardiovascular/cerebrovascular diseases (CVDs). Autophagy-related genes (ARGs) play a crucial part in pathophysiological processes of AS. However, the expression profile of ARGs has rarely been adopted to explore the relationship between autophagy and AS. Therefore, using the expression profile of ARGs to explore the relationship between autophagy and AS may provide new insights for the treatment of CVDs.\nMethods: The differentially expressed ARGs of the GSE57691 dataset were obtained from the Human Autophagy Database (HADb) and the Gene Expression Omnibus (GEO) database, and the GSE57691 dataset contains 9 aortic atheroma tissues and 10 normal aortic tissues. The differentially expressed ARGs of the GSE57691 dataset were analyzed by protein-protein interaction (PPI), gene ontology analysis (GO), and Kyoto Encyclopedia of Genes and Genomes analysis (KEGG) and were chosen to explore related miRNAs/transcriptional factors.\nResults: The GSE57691 dataset had a total of 41 differentially expressed ARGs. The GO analysis results revealed that ARGs were mainly enriched in autophagy, autophagosome, and protein serine/threonine kinase activity. KEGG analysis results showed that ARGs were mainly enriched in autophagy-animal and longevity regulating signaling pathways. Expressions of ATG5, MAP1LC3B, MAPK3, MAPK8, and RB1CC1 were regarded as focus in the PPI regulatory networks. Furthermore, 11 related miRNAs and 6 related transcription factors were obtained by miRNAs/transcription factor target network analysis.\nConclusions: Autophagy and ARGs may play a vital role in regulating the pathophysiology of AS.", "claims": [{"id": "claim-method", "statement": "The limma method in R was used to analyze the differential expression of ARGs between the AS group and control group [13].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34306596", "locator": "sections[4] \"2.2. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE57691"], "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:GSE57691"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34306596", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2021/6402206", "sha256": "sha256:4829cae3eb492a5afc77d9635895d29e20b5975df8aa94be8e04832d546c1d90", "bytes": 18624, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2021/6402206", "version": null, "note": "Full article fetched from PMC; 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