Identification of osteoporosis based on gene biomarkers using support vector machine

Nanning Lv; Zhangzhe Zhou; Shuangjun He; Xiaofeng Shao; Xinfeng Zhou; Xiaoxiao Feng; Zhonglai Qian; Yijian Zhang; Mingming Liu

doi:10.1515/med-2022-0507

Identification of osteoporosis based on gene biomarkers using support vector machine

Open Med (Wars). 2022 Jul 7;17(1):1216-1227. doi: 10.1515/med-2022-0507. eCollection 2022.

Authors

Nanning Lv¹, Zhangzhe Zhou², Shuangjun He³, Xiaofeng Shao², Xinfeng Zhou², Xiaoxiao Feng¹, Zhonglai Qian², Yijian Zhang², Mingming Liu¹

Affiliations

¹ Department of Orthopedic Surgery, The Second People's Hospital of Lianyungang, Lianyungang, Jiangsu 222003, China.
² Department of Orthopedic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215000, China.
³ Department of Orthopedic Surgery, Affiliated Danyang Hospital of Nantong University, The People's Hospital of Danyang, Danyang, Jiangsu 212300, China.

Abstract

Osteoporosis is a major health concern worldwide. The present study aimed to identify effective biomarkers for osteoporosis detection. In osteoporosis, 559 differentially expressed genes (DEGs) were enriched in PI3K-Akt signaling pathway and Foxo signaling pathway. Weighted gene co-expression network analysis showed that green, pink, and tan modules were clinically significant modules, and that six genes (VEGFA, DDX5, SOD2, HNRNPD, EIF5B, and HSP90B1) were identified as "real" hub genes in the protein-protein interaction network, co-expression network, and 559 DEGs. The sensitivity and specificity of the support vector machine (SVM) for identifying patients with osteoporosis was 100%, with an area under curve of 1 in both training and validation datasets. Our results indicated that the current system using the SVM method could identify patients with osteoporosis.

Keywords: differentially expressed genes; osteoporosis; protein–protein interaction; support vector machine; weighted gene co-expression network analysis.