A novel prognostic 6-gene signature for osteoporosis

• Published in: Frontiers in endocrinology (Lausanne) Vol. 13; p. 968397
• Main Authors: Zhao, Yu, Yan, Jieping, Zhu, Yimiao, Han, Zhenping, Li, Tingting, Wang, Lijuan
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 21.09.2022
• Subjects: diagnosis; Endocrinology; GEO datasets; machine learning; osteoporosis; WGCNA
• ISSN: 1664-2392; 1664-2392
• DOI: 10.3389/fendo.2022.968397

Introduction The incidence of osteoporosis (OP) keeps increasing due to global aging of the population. Therefore, identifying the diagnostic and prognostic biomarkers of OP is of great significance. Methods mRNA data from OP and non-OP samples were obtained from GEO database, which were divided into training set (GSE35959) and testing sets (GSE7158, GSE62402, GSE7429 and GSE56815). Gene modules most significantly related to OP were revealed using weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) between OP and normal samples in training set were identified using limma R package. Thereafter, above two gene sets were intersected to obtain the genes potentially related to OP. Protein-protein interaction (PPI) pairs were screened by STRING database and visualized using Cytoscape, while the plug-in cytoHubba was used to screen hub genes by determining their topological parameters. Afterwards, a diagnostic model was constructed using those hub genes, whose creditability was further evaluated by testing sets. Results The results of WGCNA analysis found the Black module was most significantly related to OP, which included altogether 1286 genes. Meanwhile, 2771 DEGs were discovered between OP patients and the normal controls. After taking the intersection, 479 genes were identified potentially correlated with the development of OP. Subsequently, six hub genes were discovered through PPI network construction and node topological analysis. Finally, we constructed a support vector machine model based on these six genes, which can accurately classified training and testing set samples into OP and normal groups. Conclusion Our current study constructed a six hub genes-based diagnostic model for OP. Our findings may shed some light on the research of the early diagnosis for OP and had certain practical significance.