Expression and regulatory network of long noncoding RNA in rats after spinal cord hemisection injury.

Long noncoding RNAs (lncRNAs) participate in a variety of biological processes and diseases. However, the expression and function of lncRNAs after spinal cord injury has not been extensively analyzed. In this study of right side hemisection of the spinal cord at T10, we detected the expression of lncRNAs in the proximal tissue of T10 lamina at different time points and found 445 lncRNAs and 6522 mRNA were differentially expressed. We divided the differentially expressed lncRNAs into 26 expression trends and analyzed Profile 25 and Profile 2, the two expression trends with the most significant difference. Our results showed that the expression of 68 lncRNAs in Profile 25 rose first and remained high 3 days post-injury. There were 387 mRNAs co-expressed with the 68 lncRNAs in Profile 25. The co-expression network showed that the co-expressed genes were mainly enriched in cell division, inflammatory response, FcγR-mediated cell phagocytosis signaling pathway, cell cycle and apoptosis. The expression of 56 lncRNAs in Profile2 first declined and remained low after 3 days post-injury. There were 387 mRNAs co-expressed with the 56 lncRNAs in Profile 2. The co-expression network showed that the co-expressed genes were mainly enriched in the chemical synaptic transmission process and in the signaling pathway of neuroactive ligand-receptor interaction. The results provided the expression and regulatory network of the main lncRNAs after spinal cord injury and clarified their co-expressed gene enriched biological processes and signaling pathways. These findings provide a new direction for the clinical treatment of spinal cord injury.

Bioinformatics analysis identifies potential diagnostic signatures for coronary artery disease.

BACKGROUND: Coronary artery disease (CAD) is the leading cause of mortality worldwide. We aimed to screen out potential gene signatures and construct a diagnostic model for CAD. METHOD: We downloaded two mRNA profiles, GSE66360 and GSE60993, and performed analyses of differential expression, gene ontology terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The STRING database was used to identify protein-protein interactions (PPI). PPI network visualization and screening out of key genes were performed using Cytoscape software. Finally, a diagnostic model was constructed. RESULTS: A total of 2127 differentially expressed genes (DEGs) were identified in GSE66360, and 527 DEGs in GSE60993. Of the 153 DEGs from both datasets that showed differential expression between CAD patients and controls, 471 biological process terms, 35 cellular component terms, 17 molecular function terms, and 49 KEGG pathways were significantly enriched. The top 20 key genes in the PPI network were identified, and a diagnostic model constructed from five optimal genes that could efficiently separate CAD patients from controls. CONCLUSION: We identified several potential biomarkers for CAD and built a logistic regression model that will provide a valuable reference for future clinical diagnoses and guide therapeutic strategies.

Transcriptome analysis of tongue cancer based on high­throughput sequencing.

Tongue cancer is one of the most common types of cancer, but its molecular etiology and pathogenesis remain unclear. The aim of the present study was to elucidate the pathogenesis of tongue cancer and investigate novel potential diagnostic and therapeutic targets. Four matched pairs of tongue cancer and paracancerous tissues were collected for RNA sequencing (RNA­Seq), and the differentially expressed genes were analyzed. The RNA­Seq data of tongue cancer tissues were further analyzed using bioinformatics and reverse transcription­quantitative PCR analysis. The sequenced reads were quantified and qualified in accordance with the analysis demands. The transcriptomes of the tongue cancer tissues and paired paracancerous tissues were analyzed, and 1,700 upregulated and 2,249 downregulated genes were identified. Gene Ontology analysis uncovered a significant enrichment in the terms associated with extracellular matrix (ECM) organization, cell adhesion and collagen catabolic processes. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these differentially expressed genes were mainly enriched in the focal adhesion pathway, ECM­receptor interaction pathway, phosphoinositide 3­kinase (PI3K)­Akt pathway, and cell adhesion molecules. Comprehensive analyses of the gene tree and pathway network revealed that the majority of cell cycle genes were upregulated, while the majority of the genes associated with intracellular response, cell adhesion and cell differentiation were downregulated. The ECM­receptor interaction, focal adhesion kinase (FAK) and PI3K­Akt pathways were closely associated with one another and held key positions in differential signaling pathways. The ECM­receptor, FAK and PI3K­Akt signaling pathways were found to synergistically promote tongue cancer occurrence and progression, and may serve as potential diagnostic and therapeutic targets for this type of cancer.

Screening and identification of key biomarkers in lung squamous cell carcinoma by bioinformatics analysis.

The high mortality rate of lung squamous cell carcinoma (LUSC) is in part due to the lack of early detection of its biomarkers. The identification of key molecules involved in LUSC is therefore required to improve clinical diagnosis and treatment outcomes. The present study used the microarray datasets GSE31552, GSE6044 and GSE12428 from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted to construct the protein-protein interaction network of DEGs and hub genes module using STRING and Cytoscape. The 67 DEGs identified consisted of 42 upregulated genes and 25 downregulated genes. The pathways predicted by KEGG and GO enrichment analyses of DEGs mainly included cell cycle, cell proliferation, glycolysis or gluconeogenesis, and tetrahydrofolate metabolic process. Further analysis of the University of California Santa Cruz and ONCOMINE databases identified 17 hub genes. Overall, the present study demonstrated hub genes that were closely associated with clinical tissue samples of LUSC, and identified TYMS, CCNB2 and RFC4 as potential novel biomarkers of LUSC. The findings of the present study contribute to an improved understanding of the molecular mechanisms of carcinogenesis and progression of LUSC, and assist with the identification of potential diagnostic and therapeutic targets of LUSC.

Identification of prognosis-related genes in hepatocellular carcinoma based on bioinformatical analysis / 中国肿瘤生物治疗杂志

@# Objective: To identify the differentially expressed genes (DEGs) between hepatocellular carcinoma (HCC) tissues and normal liver tissues by bioinformatic methods, and to explore the intrinsic mechanism of these candidate genes involving in the occurrence and development of HCC from transcriptome level as well as the clinical significance of their associations with the prognosis of HCC patients. Methods: Gene expression profiles of GSE45267, GSE64041, GSE84402 and TCGA were downloaded from GEO (Gene Expression Omnibus) and TCGA(The Cancer GenomeAtlas), respectively. R software and Bioconductor packages were used to identify the DEGs between HCC tissues and para-cancer tissues, and then Gene Ontology (GO) Enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Protein-Protein Interaction (PPI) network analysis and survival analysis were performed. Results: Forty-six up-regulated genes and 154 down-regulated genes were screened out,and GO enrichment analysis showed that these DEGs were mainly related to cell division, proliferation, cycle regulation, oxidation-reduction process and certain metabolic pathways. KEGG pathway analysis revealed that DEGs were mainly involved in tryptophan metabolism, retinol metabolism and other metabolic pathways as well as p53 pathway. Over-expression of a panel of up-regulated genes (CCNA2, CDK1, DLGAP5, KIF20A, KPNA2 and MELK) was shown to be significantly negatively correlated with the prognosis of HCC patients in the TCGA dataset (all P<0.01). Conclusion: A set of up-regulated hub genes that are negatively correlated with prognosis will provide potential guiding value for the clinical research on the diagnosis and treatment of HCC.

Identification of key pathways and biomarkers in sorafenib-resistant hepatocellular carcinoma using bioinformatics analysis.

Hepatocellular carcinoma (HCC) is one of the most common malignant types of cancer, with a high mortality rate. Sorafenib is the sole approved oral clinical therapy against advanced HCC. However, individual patients exhibit varying responses to sorafenib and the development of sorafenib resistance has been a new challenge for its clinical efficacy. The current study identified gene biomarkers and key pathways in sorafenib-resistant HCC using bioinformatics analysis. Gene dataset GSE73571 was obtained from the Gene Expression Omnibus (GEO) database, including four sorafenib-acquired resistant and three sorafenib-sensitive HCC phenotypes. Differentially expressed genes (DEGs) were identified using the web tool GEO2R. Functional and pathway enrichment of DEGs were analyzed using the Database for Annotation, Visualization and Integrated Discovery and the protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape. A total of 1,319 DEGs were selected, which included 593 upregulated and 726 downregulated genes. Functional and pathway enrichment analysis revealed DEGs enriched in negative regulation of endopeptidase activity, cholesterol homeostasis, DNA replication and repair, coagulation cascades, insulin resistance, RNA transport, cell cycle and others. Eight hub genes, including kininogen 1, vascular cell adhesion molecule 1, apolipoprotein C3, alpha 2-HS glycoprotein, erb-b2 receptor tyrosine kinase 2, secreted protein acidic and cysteine rich, vitronectin and vimentin were identified from the PPI network. In conclusion, the present study identified DEGs and key genes in sorafenib-resistant HCC, which further the knowledge of potential mechanisms in the development of sorafenib resistance and may provide potential targets for early diagnosis and new treatments for sorafenib-resistant HCC.

Differential expression of circular RNA in patients with chronic HBV infection of different stages and related bioinformatic analysis / 中华临床感染病杂志

Objective To investigate the differential expression of circular RNA ( circRNA ) in patients with chronic HBV infection of different stages.Methods Seven patients with chronic HBV infection admitted in Taizhou People's Hospital from October 2014 to October 2015 were enrolled, including 4 with chronic hepatitis B ( CHB ) and 3 chronic HBV carriers;3 healthy subjects served as controls. Peripheral blood mononuclear cells (PBMCs) were separated,and the expression of circRNA molecules in PBMCs were detected by new generation of circRNA microarray and validated by fluorescent quantitative PCR.The interaction sites between circRNA and miRNA were predicted with Arraystar miRNA target prediction software.Target genes regulated by the circRNA related to miRNA were analyzed by Gene oncology (Go) and Kyoto encyclopedia of genes and genomes (KEGG) analysis.SPSS 17.0 software was used for statistical analysis.Results Compared with the healthy controls , 137 circRNA molecules of differential expression were found in patients with chronic hepatitis B , of which 89 were up-regulated and 48 were down-regulated; while 444 circRNA molecules of differential expression , of which 130 were up-regulated (>5 fold in 34 ) and 314 down-regulated , were found in chronic HBV carriers.Compared with chronic HBV carriers , 1041 circRNA molecules of differential expression were found in CHB patients , including 663 up-regulated and 378 down-regulated (>5 fold in 54).There were many miRNA responsive elements which complementary with seed regions on miRNA in different circRNA molecules.Target gene analysis demonstrated that 533 target genes regulated by hsa_circ_0038646 were related to miRNAs , 249 target genes found in hsa_circ_0087354 were related to microRNAs.GO analysis showed that function of target genes regulated by hsa_circ_0038646 related to miRNA mainly enriched in activin binding.Function of target genes regulated by hsa_circ_0087354 related to miRNA mainly enriched in armadillo repeat domain binding.KEGG analysis showed that hsa_circ_0038646 molecules related to miRNA mainly involved in T cell receptor , estrogen receptor signaling pathway and so on.Hsa_circ_0087354 molecules related to miRNA mainly involved in adherens junction , MAPK signaling pathway and so on. Conclusion There are differential expressions of circRNA in patients at different clinical stages of chronic HBV infection , which might be involved in immune regulation of chronic HBV infection through the regulation of multiple target genes and signaling pathways.