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Post-menopausal osteoporosis is one of the most common bone diseases in women. The aim of the present study was to predict the diagnostic function modules from a differential co-expression gene network in order to enhance the current understanding of the biological processes and to promote the early prevention and intervention of post-menopausal osteoporosis. The diagnostic function modules were extracted from a differential co-expression network by the established protein-protein interaction (PPI) network analysis. First, significant genes were identified from the differential co-expression network, which were regarded as seed genes. Starting from the seed genes, the sub-networks in this disease, referred to as diagnostic function modules, were exhaustively searched and prioritized through a snowball sampling strategy to identify genes to accurately predict clinical outcomes. In addition, crucial function inference was performed for each diagnostic function module. Based on the microarray and PPI data, the differential co-expression network was constructed, which contained 1,607 genes and 4,197 interactions. A total of 110 seed genes were identified, and nine diagnostic modules that accurately distinguished post-menopausal osteoporosis from healthy controls were screened out from these seed genes. The diagnostic modules may be associated with five functional pathways with emphasis on metabolism. A total of nine diagnostic functional modules screened in the present study may be considered as potential targets for predicting the clinical outcomes of post-menopausal osteoporosis, and may contribute to the early diagnosis and therapy of osteoporosis.

Systemic tracking of diagnostic function modules for post-menopausal osteoporosis in a differential co-expression network view