Prediction of pivotal pathways and hub genes associated with osteoporosis by Gibbs sampling

Osteoporosis (OP) is a common metabolic bone disease with high incidence, and is recognized as a major public health problem worldwide. It is essential to clarify the pathogenesis of the disease for improving the diagnosis, prevention and treatment of OP. The aim of this study was to clarify the pivotal pathways and hub genes in OP using Gibbs sampling. The gene expression profile datasets were obtained from Gene Expression Omnibus (GEO) database. The pathways were enriched by Kyoto Encyclopedia of Genes and Genomes (KEGG) with genes intersection ≥5 based on gene expression profile data. Then, the acquired pathways were converted into Markov chains (MC). Gibbs sampling was conducted to obtain a new MC. In addition, the average probabilities of each pathway in two states containing human mesenchymal stem cells (hMSC) _middle-aged and hMSC_elderly were calculated through Markov chain Monte Carlo (MCMC) algorithm. Moreover, gene expression variation was taken into account to adjust the probability. Pivotal pathways were identified under adjusted posterior value >0.8. Then, Gibbs sampling was implemented to find hub genes from pathways. There were 280 pathways determined by the gene intersection ≥5. Gibbs sampling identified two disturbed pathways (pathways in cancer and influenza A) and two hub genes (cyclin A1 and WNT2) under the adjusted probability >0.8. Gene expression analysis showed that all the disturbed pathways and hub genes had increased expression levels in hMSC_middle-aged samples compared with hMSC_elderly samples. We identified two pivotal pathways and two hub genes in OP using Gibbs sampling. The results contribute to the understanding of underlying pathogenesis and could be considered as potential biomarkers for the therapy of OP.