Bioinformatics analysis of gene expression profile data to screen key genes involved in pulmonary sarcoidosis.

Sarcoidosis is a multisystemic inflammatory and granulomatous disease that occurs in almost all populations and affects multiple organs. Meanwhile, its most common manifestation is pulmonary sarcoidosis. This study aimed to identify effective biomarkers for the diagnosis and therapy of pulmonary sarcoidosis. GSE16538 was downloaded from Gene Expression Omnibus, including 6 pulmonary sarcoidosis samples and 6 normal lung samples. Then, differentially expressed genes (DEGs) were identified by limma package in R. After the expression values of the DEGs were extracted, hierarchical clustering analysis was performed for the DEGs using the pheatmap package in R. Subsequently, protein-protein interaction (PPI) pairs among the DEGs were searched by STRING or REACTOME databases, and then PPI networks were visualized by Cytoscape software. Using DAVID and KOBAS, functional and pathway enrichment analyses separately were performed for the DEGs involved in the PPI network. Total 208 DEGs were identified in pulmonary sarcoidosis samples, including 179 up-regulated genes and 29 down-regulated genes. Hierarchical clustering showed that the DEGs could clearly distinguish the pulmonary sarcoidosis samples from the normal lung samples. In the PPI network constructed by STRING database, CXCL9, STAT1, CCL5, CXCL11 and GBP1 had higher degrees and betweenness values, and could interact with each other. Functional enrichment showed that CXCL9, CXCL11 and CCL5 were enriched in immune response. Moreover, STAT1 was enriched in pathways of chemokine signaling pathway and JAK-STAT signaling pathway. CXCL9, CXCL11, STAT1, CCL5 and GBP1 might be implicated in pulmonary sarcoidosis through interacting with each other.

Li H ，Zhao X ，Wang J ，Zong M ，Yang H ... -  《-》

Bioinformatics analysis of gene expression profile data to screen key genes involved in intracranial aneurysms.

Guo T ，Hou D ，Yu D 《-》

The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data.

Zhang C ，Peng L ，Zhang Y ，Liu Z ，Li W ，Chen S ，Li G ... -  《-》

Analysis of the protein-protein interaction networks of differentially expressed genes in pulmonary embolism.

Wang H ，Wang C ，Zhang L ，Lu Y ，Duan Q ，Gong Z ，Liang A ，Song H ，Wang L ... -  《-》

Predicting of disease genes for gestational diabetes mellitus based on network and functional consistency.

Gestational diabetes mellitus (GDM) is a world-widely prevalent disease with adverse outcomes. This study aims to identify its disease genes through bioinformatics analysis. The raw gene expression profiling (ID: GSE19649) was downloaded from Gene Expression Omnibus database, including 3 GDM and 2 healthy control specimens. Then limma package in R was utilized to identify differentially expressed genes (DEGs, criteria: p value <0.05 and |log2 FC|>1). Simultaneously, known disease genes of GDM were downloaded from Online Mendelian Inheritance in Man database. Then, DEGs and known disease genes were uploaded to STRING to investigate their protein-protein interactions (PPIs). Gene pairs with confidence score >0.8 were utilized to construct PPI network. Furthermore, pathway and functional enrichment analyses were performed through KOBAS (criterion: p value <0.05) and DAVID (The Database for Annotation, Visualization and Integrated Discovery) software (criterion: false discovery rate <0.05), respectively. A total of 404 DEGs were identified, including 273 up-regulated and 131 down-regulated DEGs. Moreover, 68 known disease genes of GDM were obtained. Then, 190 gene pairs were identified to significantly interact with each other. After deleting PPIs between DEGs, PPI network was constructed, consisting of 115 gene pairs. Furthermore, genes in PPI network were significantly enriched in 10 functions and 8 pathways. Based on PPI network and functional consistency, 6 candidate genes of GDM were considered to be candidate disease genes of GDM, including CYP1A1, LEPR, ESR1, GYS2, AGRP, and CACNA1G. However, further studies are required to validate these results.

Zhang Q ，He M ，Wang J ，Liu S ，Cheng H ，Cheng Y ... -  《-》