Serum concentrations of A disintegrin and metalloproteinase 9 (ADAM9) mRNA as a promising novel marker for the detection of pulmonary sarcoidosis

Identification of Potential Biomarkers and Related Transcription Factors in Peripheral Blood of Tuberculosis Patients

Tuberculosis (TB), one major threat to humans, can infect one third of the worldwide population, and cause more than one million deaths each year. This study aimed to identify the effective diagnosis and therapy biomarkers of TB. Hence, we analyzed two microarray datasets (GSE54992 and GSE62525) derived from the Gene Expression Omnibus (GEO) database to find the differentially expressed genes (DEGs) of peripheral blood mononuclear cell (PBMC) between TB patients and healthy specimens. Functional and pathway enrichment of the DEGs were analyzed by Metascape database. Protein-protein interaction (PPI) network among the DEGs were constructed by STRING databases and visualized in Cytoscape software. The related transcription factors regulatory network of the DEGs was also constructed. A total of 190 DEGs including 36 up-regulated genes and 154 down-regulated genes were obtained in TB samples. Gene functional enrichment analysis showed that these DEGs were enriched in T cell activation, chemotaxis, leukocyte activation involved in immune response, cytokine secretion, head development, etc. The top six hub genes (namely, LRRK2, FYN, GART, CCR7, CXCR5, and FASLG) and two significant modules were got from PPI network of DEGs. Vital transcriptional factors, such as FoxC1 and GATA2, were discovered with close interaction with these six hub DEGs. By systemic bioinformatic analysis, many DEGs associated with TB were screened, and these identified hub DEGs may be potential biomarkers for diagnosis and treatment of TB in the future.

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

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.