Bioinformatics analyses of differentially expressed genes associated with spinal cord injury:a microarray-based analysis in a mouse model

Gene spectrum analysis has shown that gene expression and signaling pathways change dramatically after spinal cord injury,which may affect the microenvironment of the damaged site.Microarray analysis provides a new opportunity for investigating diagnosis,treatment,and prognosis of spinal cord injury.However,differentially expressed genes are not consistent among studies,and many key genes and signaling pathways have not yet been accurately studied.GSE5296 was retrieved from the Gene Expression Omnibus DataSet.Differentially expressed genes were obtained using R/Bioconductor software(expression changed at least two-fold;P < 0.05).Database for Annotation,Visualization and Integrated Discovery was used for functional annotation of differentially expressed genes and Animal Transcription Factor Database for predicting potential transcription factors.The resulting transcription regulatory protein interaction network was mapped to screen representative genes and investigate their diagnostic and therapeutic value for disease.In total,this study identified 109 genes that were upregulated and 30 that were downregulated at 0.5,4,and 24 hours,and 3,7,and 28 days after spinal cord injury.The number of downregulated genes was smaller than the number of upregulated genes at each time point.Database for Annotation,Visualization and Integrated Discovery analysis found that many inflammation-related pathways were upregulated in injured spinal cord.Additionally,expression levels of these inflammation-related genes were maintained for at least 28 days.Moreover,399 regulation modes and 77 nodes were shown in the protein-protein interaction network of upregulated differentially expressed genes.Among the 10 upregulated differentially expressed genes with the highest degrees of distribution,six genes were transcription factors.Among these transcription factors,ATF3 showed the greatest change.ATF3 was upregulated within 30 minutes,and its expression levels remained high at28 days after spinal cord injury.These key genes screened by bioinformatics tools can be used as biological markers to diagnose diseases and provide a reference for identifying therapeutic targets.

Identification of metastasis-associated genes in colorectal cancer through an integrated genomic and transcriptomic analysis

Objective： Identification of colorectal cancer （CRC） metastasis genes is one of the most important issues in CRC research. For the purpose of mining CRC metastasis-associated genes, an integrated analysis of mJcroarray data was presented, by combined with evidence acquired from comparative genornic hybridization （CGH） data. Methods： Gene expression profile data of CRC samples were obtained at Gene Expression Omnibus （GEO） website. The 15 important chromosomal aberration sites detected by using CGH technology were used for integrated genomic and transcriptomic analysis. Significant Analysis of Microarray （SAM） was used to detect significantly differentially expressed genes across the whole genome. The overlapping genes were selected in their corresponding chromosomal aberration regions, and analyzed by using the Database for Annotation, Visualization and Integrated Discovery （DAVID）. Finally, SVM-T-RFE gene selection algorithm was applied to identify ted genes in CRC. Results： A minimum gene set was obtained with the minimum number [14] of genes, and the highest classification accuracy （100%） in both PRI and META datasets. A fraction of selected genes are associated with CRC or its metastasis. Conclusions- Our results demonstrated that integration analysis is an effective strategy for mining cancer- associated genes.