Wang Xi, Ning Yujie, Zhang Feng, Yu Fangfang, Tan Wuhong, Lei Yanxia, Wu Cuiyan, Zheng Jingjing, Wang Sen, Yu Hanjie, Li Zheng, Lammi Mikko J, Guo Xiong
School of Public Health, Xi'an Jiaotong University Health Science Center, Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, No. 76 Yanta West Road, Xi'an 710061, China.
National Engineering Research Center for Miniaturized Detection Systems, Northwest University, Xi'an 710069, China.
Int J Mol Sci. 2015 May 19;16(5):11465-81. doi: 10.3390/ijms160511465.
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.
大骨节病（KBD）是一种发病机制不明的地方性骨软骨病。大骨节病的诊断仅在晚期病例中有效，这排除了早期治疗的可能性，并导致症状不可避免地加重。因此，我们旨在确定一种准确的基于血液的基因特征用于大骨节病的检测。将先前发表的关于成年大骨节病患者软骨和外周血单个核细胞（PBMC）的基因表达谱数据进行比较，以选择潜在的靶基因。对100例大骨节病患者和100例健康对照组成的队列进行微阵列分析，以评估靶基因的表达。使用训练集确定基因表达特征，随后使用具有最小冗余最大相关性（mRMR）算法和支持向量机（SVM）算法的独立测试集对其进行验证。大骨节病患者和健康对照之间有50个独特基因差异表达。确定了一个由20个基因组成的特征，其区分大骨节病患者和对照的准确率为90%，灵敏度为85%，特异性为95%。本研究确定了一个由20个基因组成的特征，该特征可使用外周血样本准确区分大骨节病患者和对照。这些结果促进了用于大骨节病检测的基于血液的遗传生物标志物的进一步发展。
