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【Objective】 To make bioinformatics analysis of inflammatory cardiomyopathy so as to screen out hub genes related to etiology and therapeutic targets. 【Methods】 Differential expression analysis of inflammatory cardiomyopathy gene chip data from Gene Expression Omnibus （GEO） Database was carried out via GEO2R tool. Protein-protein interaction（PPI）network and hub genes identification were realized by String database and CytoHubba. GO and KEGG enrichment analysis for functional annotation and pathway analysis of hub genes were conducted by R language. Web-based enrichment analysis platform Enrichr and Drug Signatures database were applied to screen out candidate drugs targeting hub genes for inflammatory cardiomyopathy. 【Results】 The 149 DEGs were statistically significant， among which 44 were upregulated and 105 were downregulated. To identify hub genes， PPI network consisting of 37 nodes and 116 edges was constructed， and 16 hub genes were NDUFB7， POLR2L， NDUFS7， UQCR11， NDUFA13， NDUFA2， PHPT1， NDUFB10， UBA52， ATP5D， NDUFA3， COX6B1， POLR2J， COX4I2， AURKAIP1 and MRPL41. Hub genes were enriched to 113 different GO terms， and the most significant terms were mitochondrial ATP synthesis coupled electron transport， respiratory electron transport chain， oxidative phosphorylation， respiratory chain， mitochondrial inner membrane， NADH dehydrogenase activity and oxidoreductase activity. DEGs were enriched to 13 different signal pathways， including oxidative phosphorylation， non-alcoholic fatty liver disease， diabetic cardiomyopathy， and cardiac muscle contraction. We screened out candidate drugs targeting hub genes， namely， metformin hydrochloride， clindamycin， and hydralazine. 【Conclusion】 Hub genes screened out by decoding the expression profiles are convolved in the etiology and mechanism of inflammatory cardiomyopathy， which might serve as latent therapeutic targets and benefit patients with inflammatory cardiomyopathy.
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Objective To perform bioinformatics analysis of the genetic chip data of rheumatoid arthritis (RA) in order to search for the characteristic gene expression profiles. Methods Differential expression analysis of RA Gene chip data in GEO database was performed using GEO2R, and GO and KEGG enrichment analysis of functional annotation and pathway analysis of differentially expressed genes (DEGs) were conducted by DAVID6.8 and R language. Protein-protein interaction (PPI) and target genes acquisition were realized by String-database and software Cytoscape3.7.1. Results The 1 184 DEGs in synovial tissues isolated from the knee joints of RA patients were statistically significant. Among them 664 were up-regulated and 520 were down-regulated. DEGs were enriched to 70 different GOterms, and the most significant terms were signal transduction, plasma membrane and protein binding. DEGs were enriched to 62 different signal pathways, including cytokine-cytokine receptor interaction, osteoclast differentiation, rheumatoid arthritis, Th17 cell differentiation, and IL17 signal pathway. PPI analysis screened out 19 pivotal target genes, namely, NKG7, BCL6, SEMA4D, NFIL3, RAC2, MLIP, SEL1L3, GUSBP11, IGLV1-44, IGLJ3, IGLC1, IGKV1OR2-118, IGKV1OR2-108, IGKC, IGHV4-31, IGHV3-23, IGHM, IGHD and CYAT1. Conclusion Partial DEGs screened out by analyzing the expression profiles are involved in the key links affecting the development of synovial inflammation in RA, which may provide an important theoretical basis for early diagnosis and treatment of this disease and development of targeted drugs.
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@#[Abstract] Objective: Bioinformatics combined with Gene Expression Omnibus (GEO) was used to screen key genes involved in the development of gastric cancer in order to obtain molecular markers for diagnosis, target selection and prognosis prediction of gastric cancer. Methods: The chip data sets related to gastric cancer (GC) from the GEO database were downloaded, and differentially expressed genes (DEG) were screened. Functional enrichment analysis on DEG was performed, and protein-protein interaction network (PPI) was constructed to screen key genes. Then, co-expression networks were further constructed, and survival curves were drawn and hierarchical clustering analysis was performed. Results: A total of 261 GC-related DEGs were selected, and 14 key genes were obtained through analysis, which were PLOD1, PLOD3, COL1A1, COL1A2, COL2A1, COL3A1, COL4A1, COL4A2, COL8A1, COL12A1, COL15A1, ITGA2, LUM and SERPINH1. Key genes are mainly involved in biological processes such as generation of collagen fiber tissues, extracellular matrix tissues, extracellular structure tissues, skin morphogenesis, collagen biosynthesis and vascular development. Survival curve analysis showed that the change in the expression of COL3A1 (P=0.0241) significantly reduced the overall survival rate of patients with gastric cancer; the change in the expression of ITGA2 (P=0.0679) also showed a correlation with the reduction of disease-free survival in gastric cancer patients. Compared with normal gastric tissues, hierarchical cluster analysis showed that the expressions of genes PLOD1, PLOD3, COL3A1, ITGA2, COL1A2, COL1A1, COL4A1, LUM, COL12A1, SERPINH1 and COL8A1 in GC tissues were up-regulated. Conclusion: The key genes obtained after screening can be used as potential molecular markers for early diagnosis, treatment target selection and prognosis judgment of gastric cancer, which provide reference for subsequent research.