Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods

Frontiers in Genetics
Jing LiTao Huang

Abstract

Osteoarthritis (OA) is a complex disease that affects articular joints and may cause disability. The incidence of OA is extremely high. Most elderly people have the symptoms of osteoarthritis. The physiotherapy of OA is time consuming, and the chances of full recovery from OA are very minimal. The most effective way of fighting OA is early diagnosis and early intervention. Liquid biopsy has become a popular noninvasive test. To find the blood gene expression signature for OA, we reanalyzed the publicly available blood gene expression profiles of 106 patients with OA and 33 control samples using an automatic computational pipeline based on advanced feature selection methods. Finally, a compact 23-gene set was identified. On the basis of these 23 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. Its sensitivity (Sn), specificity (Sp), accuracy (ACC), and Mathew's correlation coefficient (MCC) were 0.991, 0.909, 0.971, and 0.920, respectively. Obviously, the performance needed to be validated in an independent large dataset, but the in-depth biological analysis of the 23 biomarkers showed great promise and suggested that mRNA surveillance pathway and multicellular...Continue Reading

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Citations

Mar 3, 2020·Frontiers in Bioengineering and Biotechnology·Hao ZhangBin Zhang
Sep 5, 2019·International Journal of Molecular Sciences·Lei ChenYu-Dong Cai
Mar 3, 2020·Frontiers in Genetics·Jisong ZhangEnguo Chen
Feb 9, 2021·Rheumatology International·Wenpeng ZhangWeimin Xu
Nov 27, 2020·Frontiers in Cell and Developmental Biology·Zhen WuXinwei Xu

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Datasets Mentioned

BETA
GSE48556

Methods Mentioned

BETA
biopsy

Software Mentioned

Metascape

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