Bioinformatics methods for identifying differentially expressed genes and signaling pathways in nano-silica stimulated macrophages - Lin Zhang; Changfu Hao; Juan Li; Yaqian Qu; Lei Bao; Yiping Li; Zhongzheng Yue; Miao Zhang; Xinghao Yu; Huiting Chen; Jianhui Zhang; Di Wang; et al. (Tumor Biology)
The incidence of disease relating to nanoparticle exposure has been rising rapidly in recent years, for which there is no effective treatment. Macrophage is suggested to play a crucial role in the development of pulmonary disease. To investigate the changes in macrophage after being stimulated by nanometer silica dust and to explore potential biomarkers and signaling pathways, the gene chip GSE13005 was downloaded from Gene Expression Omnibus database, which contained 21 samples: 3 samples per group and 7 groups in total. Macrophages in the control group were cultured in serum-free medium, while the experimental groups were treated with nanometer silica dust in different sizes and concentrations, respectively. To identify the differentially expressed genes and explore their potential functions, we adopted the gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and also constructed protein–protein interaction network. As a result, 1972 differentially expressed genes were identified from 22,690 microarray data in the gene chip, 1069 genes were upregulated and 903 genes were downregulated. Results of the gene ontology analysis indicated that the differentially expressed genes were widely distributed in intracellular and extracellular regions, regulating macrophage apoptosis, inflammatory response, and cell differentiation. The Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that the majority of differentially expressed genes were enriched in cytokine–cytokine receptor interaction, cancer or phagosome transcriptional misregulation. The top 10 hub genes, S100a9, Nos3, Psmd14, Psmd4, Lck, Atp6v1h, Jun, Foxh1, Pex14, and Fadd were identified from protein–protein interaction network. In addition, Nos3, Psmd14, Atp6v1h, and Jun were clustered into module M2 (r c  = 0.74, p < 0.01), which mainly regulates cell carcinogenesis and antivirus process. In conclusion, differentially expressed genes screened from this study may provide new insights into the exploration of mechanisms, biomarkers, and therapeutic targets for diseases relating to nanoparticle exposure.