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

Ya xi Zhu, Jia qiang Huang, Yu yang Ming, Zhao Zhuang, Hong Xia

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.

Background
Tendinopathy is usually described as pathological changes in injured and diseased tendons, which in turn lead to limb pain and functional decline. It is characterized by abnormalities in the molecular structure, composition and cell matrix of the tendon [1]. In recent years, the prevalence of tendinopathy has gradually increased, and some tendinopathy patients have long-term or permanent limb dysfunction and loss [2]. Tendinopathy is more common in limbs and accounts for 30%-50% of muscle skeletal system and locomotor system diseases [3,4]. It is well known that the causes of tendinopathy include internal and external factors.

Materials and methods
GEO (Gene Expression Omnibus) database (http://www.ncbi.nlm.nih.gov/geo/) is an international public database, used to store and provide free microarray, second-generation sequencing and high-throughput functional genome data sets [16]. We searched and downloaded the GSE106292, the GSE26051, the GSE167226 data set using the R software GEO database [17–19]. The GSE106292 data sets included the gene expression profiles of 35 cases of tendon, bone, muscle, cartilage and ligament. The GSE26051 data sets included gene expression profiles from 23 patients with chronic tendonopathy and 23 normal tendons; the GSE167226 data sets included gene expression profiles from 19 patients with tendonopathy.

Discussion
Tendinopathy is not only a very common chronic disease, but also a disease that lacks real effective treatment [33]. So far, tendinopathy is still a major challenge in musculoskeletal diseases due to the widespread disease population, low cure rate, and huge medical expenditure. However, the mechanism of the occurrence and development of tendinopathy is not yet fully understood. At present, there are many hypotheses about the etiology of tendinopathy, including Biomechanical theory [34], inflammation theory [33], apoptosis theory [35], vascular or neurogenic theory [36], etc. Although these theoretical models closely link the basic science of tendinopathy with clinical applications, none of the theoretical theories can fully clarify the pathological mechanism of tendinopathy and the complex relationship between tendon pain and function.

Conclusion
In conclusion, based on the comprehensive bioinformatics analysis method, we identified the potential early hub genes, key regulatory pathways and immune infiltration characteristics of tenopathy. This will help to provide new insights into the future drug and molecular mechanism of tendon disease.

Citation: Zhu Yx, Huang Jq, Ming Yy, Zhuang Z, Xia H (2021) Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms. PLoS ONE 16(10): e0259475. https://doi.org/10.1371/journal.pone.0259475

Editor: Qi Zhao, University of Science and Technology Liaoning, CHINA

Received: August 14, 2021; Accepted: October 19, 2021; Published: October 29, 2021

Copyright: © 2021 Zhu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106292 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26051 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE167226.

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).

Competing interests: The authors have declared that no competing interests exist.

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