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

PLoS One. 2021 Oct 29;16(10):e0259475. doi: 10.1371/journal.pone.0259475. eCollection 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Carboxylic Ester Hydrolases / genetics
  • Computational Biology / methods
  • Genetic Predisposition to Disease*
  • Humans
  • Hypoxia-Inducible Factor 1, alpha Subunit / genetics
  • MAP Kinase Signaling System / genetics
  • Machine Learning*
  • NF-kappa B / genetics
  • TOR Serine-Threonine Kinases / genetics
  • Tendinopathy / genetics*
  • Vascular Endothelial Growth Factor A / genetics

Substances

  • HIF1A protein, human
  • Hypoxia-Inducible Factor 1, alpha Subunit
  • NF-kappa B
  • Vascular Endothelial Growth Factor A
  • TOR Serine-Threonine Kinases
  • Carboxylic Ester Hydrolases
  • MACROD1 protein, human

Grants and funding

This work was supported by Xiangtan Science and Technology Planning Project (Project No.: SF-YB20181006) and Xiangtan Arthroscopy Minimally Invasive Diagnosis and Treatment Clinical Medical Technology Demonstration Base Funding Project (Project No. SF-LCYL20191003).