Detection of significant pathways in osteoporosis based on graph clustering.

Osteoporosis is the most common and serious skeletal disorder among the elderly, characterized by a low bone mineral density (BMD). Low bone mass in the elderly is highly dependent on their peak bone mass (PBM) as young adults. Circulating monocytes serve as early progenitors of osteoclasts and produce significant molecules for bone metabolism. An improved understanding of the biology and genetics of osteoclast differentiation at the pathway level is likely to be beneficial for the development of novel targeted approaches for osteoporosis. The objective of this study was to explore gene expression profiles comprehensively by grouping individual differentially expressed genes (DEGs) into gene sets and pathways using the graph clustering approach and Gene Ontology (GO) term enrichment analysis. The results indicated that the DEGs between high and low PBM samples were grouped into nine gene sets. The genes in clusters 1 and 8 (including GBP1, STAT1, CXCL10 and EIF2AK2) may be associated with osteoclast differentiation by the immune system response. The genes in clusters 2, 7 and 9 (including SOCS3, SOD2, ATF3, ADM EGR2 and BCL2A1) may be associated with osteoclast differentiation by responses to various stimuli. This study provides a number of candidate genes that warrant further investigation, including DDX60, HERC5, RSAD2, SIGLEC1, CMPK2, MX1, SEPING1, EPSTI1, C9orf72, PHLDA2, PFKFB3, PLEKHG2, ANKRD28, IL1RN and RNF19B.