Construction of a 5-feature gene model by support vector machine for classifying osteoporosis samples

Bioengineered

Zeyu Yang¹  Minwei Hu¹  Changhui Sun¹  Jiong Lu¹  Yinan Chen¹  Ling Zou¹  Yaozeng Xu² 

[1] Department of Orthopedics, Ruijin Hospital LuWan Branch, School of Medicine, Shanghai Jiaotong University School of Medicine, Shanghai, Chin;Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, Chin;

关键词: Osteoporosis;    differentially expressed genes;    protein–protein interaction;    support vector machine;    gene signature;    bioinformatics;   

DOI  :  10.1080/21655979.2021.1971026

来源: Taylor & Francis

【 摘 要 】

Osteoporosis is a progressive bone disease in the elderly and lacks an effective classification method of patients. This study constructed a gene signature for an accurate prediction and classification of osteoporosis patients. Three gene expression datasets of osteoporosis samples were acquired from the Gene Expression Omnibus database with pre-set criteria. Differentially expressed genes (DEGs) between normal and diseased osteoporosis samples were screened using Limma package in R language. Protein–protein interaction (PPI) network was established based on interaction data of the DEGs from the Human Protein Reference Database. Classification accuracy of the classifier was assessed with sensitivity, specificity and area under curve (AUC) using the pROC package in the R. Pathway enrichment analysis was performed on feature genes with clusterProfiler. A total of 310 differentially expressed genes between two samples were associated with positive regulation of protein secretion and cytokine secretion, neutrophil-mediated immunity, and neutrophil activation. PPI network of DEGs consisted of 12 genes. A SVM classifier based on five feature genes was developed to classify osteoporosis samples, showing a higher prediction accuracy and AUC for GSE35959, GSE62402, GSE13850, GSE56814, GSE56815 and GSE7429 datasets. A SVM classifier with a high accuracy was developed for predicting osteoporosis. The genes included may be the potential feature genes in osteoporosis development.AbbreviationsDEGs: Differentially expressed genes; PPI: protein–protein interaction; WHO: World Health Organization; SVM: Support vector machine; GEO: Gene Expression Omnibus; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology; BP: Biological Process; CC: Cellular Component; MF: Molecular Function; SVM: Support vector machines

【 授权许可】

CC BY