The present study aimed to determine candidate genes, chemicals and mechanisms underlying postmenopausal osteoporosis (PMOP). A gene expression profile (accession no. GSE68303), which included 12 tissue samples from ovariectomized mice (OVX group) and 11 normal tissue samples from sham surgery mice (control group), was downloaded from the Gene Expression Omnibus database. The identification of differentially expressed genes (DEGs), and Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses, was performed, followed by an investigation of protein-protein interactions (PPI), PPI modules, transcription factors (TFs) and chemicals. A total of 784 upregulated and 729 downregulated DEGs between the two groups were identified. Furthermore, 2 upregulated modules and 6 downregulated modules were determined. The upregulated DEGs in modules were enriched in ‘sensory perception of smell’ function and ‘olfactory transduction’ pathway, and a number of genes belonging to the olfactory receptor (OLFR) family were identified in upregulated modules. The downregulated DEGs in modules were enriched in ‘DNA replication initiation’ function and ‘cell cycle’ pathway. A total of 8 TFs, including SP1 TF (SP1) and protein C-ets-1 (ETS1), were associated with PMOP. Furthermore, estradiol and resveratrol were identified as key chemicals in the chemical-gene interaction network. Therefore, TFs, including SP1 and ETS1, in addition to members of the OLFR gene family, may be employed as novel targets for treatment of PMOP. Furthermore, functions including ‘sensory perception of smell’ and ‘replication initiation’, and ‘olfactory transduction’ and ‘cell cycle’ pathways, may serve roles in PMOP. In addition, based on the chemical-gene interaction network, estradiol and resveratrol may also be considered for the treatment PMOP.
The heart function-related pathway, cell cycle, immune system and angiogenesis may be dysregulated in patients with poorer prognosis. The significant nodes (e.g., TGFBR1, TGFBR2, ACVR1 and SHC1) in the PPI network may be potential biomarkers for predicting outcomes for patients with pancreatic cancer. The significant miRNAs and gene targets may be potential biomarkers or therapeutic targets for PDAC.
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