Gene expression signature in endemic osteoarthritis by microarray analysis.

Xi Wang,Zheng Li,Wuhong Tan,Xiong Guo,Feng Zhang,Jingjing Zheng,Mikko Lammi,Fangfang Yu,Sen Wang,Cuiyan Wu,Yanxia Lei,Yujie Ning,Hanjie Yu

doi:10.3390/ijms160511465

Abstract

Kashin-Beck Disease (KBD) is an endemic osteochondropathy with an unknown pathogenesis. Diagnosis of KBD is effective only in advanced cases, which eliminates the possibility of early treatment and leads to an inevitable exacerbation of symptoms. Therefore, we aim to identify an accurate blood-based gene signature for the detection of KBD. Previously published gene expression profile data on cartilage and peripheral blood mononuclear cells (PBMCs) from adults with KBD were compared to select potential target genes. Microarray analysis was conducted to evaluate the expression of the target genes in a cohort of 100 KBD patients and 100 healthy controls. A gene expression signature was identified using a training set, which was subsequently validated using an independent test set with a minimum redundancy maximum relevance (mRMR) algorithm and support vector machine (SVM) algorithm. Fifty unique genes were differentially expressed between KBD patients and healthy controls. A 20-gene signature was identified that distinguished between KBD patients and controls with 90% accuracy, 85% sensitivity, and 95% specificity. This study identified a 20-gene signature that accurately distinguishes between patients with KBD and controls using peripheral blood samples. These results promote the further development of blood-based genetic biomarkers for detection of KBD.

Highlights

Kashin-Beck disease (KBD) is an endemic osteochondropathy that manifests with significant alterations in chondrocyte phenotype, necrosis, apoptosis, and abnormal terminal chondrocyte differentiation [1]
The minimum redundancy maximum relevance (mRMR) algorithm was used to select a subset of n genes which exhibited a maximum relevance to the clinical status of the Kashin-Beck Disease (KBD) and a minimum redundancy within the 50 differentially expressed genes identified by microarray analyses
The support vector machine (SVM) model was built in the inner training set by using the linear kernel based on n genes selected by mRMR

Summary

Introduction

Kashin-Beck disease (KBD) is an endemic osteochondropathy that manifests with significant alterations in chondrocyte phenotype, necrosis, apoptosis, and abnormal terminal chondrocyte differentiation [1]. The diagnosis and determination of the severity of KBD is primarily based on the symptoms, such as joint pain, enlargement of phalanges or other joints, limitation of motion, arthralgia, deformity of limbs and metaphysis changes observed radiologically. This is effective only for diagnosis of advanced cases, which eliminates the possibility of early treatment and lead to inevitable exacerbation and irreversibility of symptoms like deformity of limbs and dwarfism. The treatments of KBD, which include symptomatic treatment, articular injection of sodium hyaluronate to relieve the pain and symptoms, and arthroplasty [5] to replace the damaged cartilage with artificial joints, are the only effective measures to treat the patients. Early diagnosis plays a critical role in the therapy of KBD, several problems exist: (1) clinical signs and symptoms become obvious only after joint degeneration has occurred; and (2) the progression of cartilage injury and degradation is slow, making it difficult to detect the disease in the early stage

Objectives

Results

Conclusion