BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: CXCL9, CXCL11, STAT1, CCL5 and GBP1 might be implicated in pulmonary sarcoidosis through interacting with each other.
BACKGROUND: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. METHODS: 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. RESULTS: 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. CONCLUSION:CXCL9, CXCL11, STAT1, CCL5 and GBP1 might be implicated in pulmonary sarcoidosis through interacting with each other.
Authors: William Damsky; Durga Thakral; Meaghan K McGeary; Jonathan Leventhal; Anjela Galan; Brett King Journal: J Am Acad Dermatol Date: 2019-06-08 Impact factor: 11.527
Authors: Nicholas K Arger; Melissa E Ho; Isabel E Allen; Bryan S Benn; Prescott G Woodruff; Laura L Koth Journal: Respir Med Date: 2019-11-20 Impact factor: 3.415