Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34714891", "kind": "science-record", "revision": 4, "content_hash": "sha256:99f4d1208aa249707d5e6f6b09147976228080d99806e87400256683c9107c35", "created_at": "2026-11-01T17:36:23Z", "updated_at": "2026-11-01T22:05:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms.", "doi": "10.1371/journal.pone.0259475", "pmid": "34714891", "authors": ["Ya Xi Zhu", "Jia Qiang Huang", "Yu Yang Ming", "Zhao Zhuang", "Hong Xia"], "journal": "PloS one", "year": 2021, "abstract": "Tendinopathy is a complex multifaceted tendinopathy often associated with overuse and with its high prevalence resulting in significant health care costs. At present, the pathogenesis and effective treatment of tendinopathy are still not sufficiently elucidated. The purpose of this research is to intensely explore the genes, functional pathways, and immune infiltration characteristics of the occurrence and development of tendinopathy. The gene expression profile of GSE106292, GSE26051 and GSE167226 are downloaded from GEO (NCBI comprehensive gene expression database) and analyzed by WGCNA software bag using R software, GSE26051, GSE167226 data set is combined to screen the differential gene analysis. We subsequently performed gene enrichment analysis of Gene Ontology (GO) and \"Kyoto Encyclopedia of Genes and Genomes\" (KEGG), and immune cell infiltration analysis. By constructing the LASSO regression model, Support vector machine (SVM-REF) and Gaussian mixture model (GMMs) algorithms are used to screen, to identify early diagnostic genes. We have obtained a total of 171 DEGs through WGCNA analysis and differentially expressed genes (DEGs) screening. By GO and KEGG enrichment analysis, it is found that these dysregulated genes were related to mTOR, HIF-1, MAPK, NF-κB and VEGF signaling pathways. Immune infiltration analysis showed that M1 macrophages, activated mast cells and activated NK cells had infiltration significance. After analysis of THE LASSO SVM-REF and GMMs algorithms, we found that the gene MACROD1 may be a gene for early diagnosis. We identified the potential of tendon disease early diagnosis way and immune gene regulation MACROD1 key infiltration characteristics based on comprehensive bioinformatics analysis. These hub genes and functional pathways may as early biomarkers of tendon injuries and molecular therapy level target is used to guide drug and basic research.", "claims": [{"id": "claim-method", "statement": "Lasso logistic regression is a machine learning method, which determines the variable by finding the λ value with the smallest classification error [30].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34714891", "locator": "sections[8] \"Screening and identification of gene prediction model for early diagnosis\" paragraph 2", "quote": "Lasso logistic regression is a machine learning method, which determines the variable by finding the λ value with the smallest classification error [30]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GSE106292 data sets included the gene expression profiles of 35 cases of tendon, bone, muscle, cartilage and ligament.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34714891", "locator": "sections[3] \"Data collection and download\" paragraph 2", "quote": "The GSE106292 data sets included the gene expression profiles of 35 cases of tendon, bone, muscle, cartilage and ligament."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The analysis identified six potential hub genes—MACROD1, CES2, SKIV2L, LYNX1, MFSD10, and PIEZO1—for early diagnosis of tendinopathy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34714891", "locator": "sections[14] \"Screening and identification of hub genes in tendinopathy\" paragraph 6", "quote": "Finally, we screened 6 potential hub genes, including MACROD1, CES2, SKIV2L, LYNX1, MFSD10 and PIEZO1 (Fig 9A)."}, {"asset": "source:pmid:34714891", "locator": "sections[14] \"Screening and identification of hub genes in tendinopathy\" paragraph 2", "quote": "In this study, the AUC of the training set is 0.981>0.7, indicating that the built model has good real performance, and the AUC of the test set is 0.807>0.7, indicating that the built model has good generalization performance and excellent verification performance (Fig 7C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The analysis identified six potential hub genes—MACROD1, CES2, SKIV2L, LYNX1, MFSD10, and PIEZO1—for early diagnosis of tendinopathy.", "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:34714891"], "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. 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