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Gene Sequence Analysis and Screening of Feature Genes in Spinal Cord Injury

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Gene Sequence Analysis and Screening of Feature Genes in Spinal Cord Injury MOLECULAR MEDICINE REPORTS 11: 3615-3620, 2015 Gene sequence analysis and screening of feature genes in spinal cord injury ZONGDE YANG1*, XIN CHEN2*, REN LIU3*, CHUANFENG WANG1, YINCHUAN ZHAO1, ZHICAI SHI1 and MING LI1 Departments of 1Spine Surgery and 2Neurosurgery, Changhai Hospital, Second Military Medical University, Shanghai 200433; 3Department of Anesthesia, Fuzhou General Hospital of Nanjing Military Command Affiliated to Fujian Medical University, Fuzhou, Fujian 350025, P.R. China Received April 3, 2014; Accepted December 19, 2014 DOI: 10.3892/mmr.2015.3220 Abstract. The aim of the present study was to screen for Introduction feature genes associated with spinal cord injury (SCI), in order to identify the underlying pathogenic mechanisms. The spinal cord is a part of the central nervous system in humans Differentially expressed genes were screened for using and other vertebrates (1). Spinal cord injury (SCI) is damage to pre-processing data. Kyoto Encyclopedia of Genes and the spinal cord, which is categorized according to the extent of Genomes pathway enrichment analysis was performed to loss of function, loss of sensation and the inability of the indi- analyze and identify the genes involved in pathways asso- vidual to stand or walk (2). It often results in confinement to a ciated with SCI. Subsequently, Gene Ontology enrichment wheelchair and a lifetime of medical comorbidity (3). SCI may analysis and Uniprot tissue analysis were used to screen out result from serious accidents, including road traffic accidents genes specifically expressed in spinal cord tissue.In addition, or sports injuries, but may also occur accompanying serious a protein-protein interaction network was used to demon- diseases, including developmental disorders, neurodegen- strate possible associations among SCI-associated feature erative diseases or demyelinating diseases. Multiple sclerosis, genes. Finally, a link was identified between feature genes transverse myelitis resulting from stroke or inflammation and and SCI by analyzing protein domains in coding areas of the vascular malformations can all result in severe consequences three feature genes. The cytochrome c oxidase subunit Va, and high-disability due to SCI (4). adenosine triphosphate (ATP) synthase, H+ transporting, Several genes and signaling pathways are involved in spinal mitochondrial F1 complex, α subunit 1 and cardiac muscle cord injury (5). Expression of nerve growth factor, brain-derived and mitochondrial β-F1-ATPase may be downregulated in neurotrophic factor (BDNF), neurotrophin-3 (NT-3), p75 SCI, resulting in destruction of the mitochondrial electron low‑affinity nerve growth factor receptor, transforming tyrosine transport chain and membrane-bound enzyme complexes/ kinase B and interleukin (IL)-6 have been reported to increase ion transporters, thus, affecting the normal function of in non-neuronal cells and neuronal cells, suggesting that these nerves. The three screened feature genes have the potential molecules may be involved in promoting axonal sprouting in the to become candidate target molecules to monitor, diagnose injured spinal cord (6). Furthermore, it has been demonstrated and treat SCI and may be beneficial for the early diagnosis that upregulation of IL-1β, BDNF and NT-3 in the injured spinal and therapeutic control of the condition. cord is attenuated by treatment with high-dose glucocorticoids, with the suggestion that the downregulation of BDNF and NT-3 may be disadvantageous to the survival and axonal sprouting of spinal neurons (7). As for the pathways involved, a previous study revealed that the Rho signaling pathway may be a potential target for therapeutic interventions following SCI (8). In addi- Correspondence to: Dr Zhicai Shi or Dr Ming Li, Department tion, apoptosis signal-regulating kinase l and stress-activated of Spine Surgery, Changhai Hospital, Second Military Medical mitogen-activated protein kinase pathways, have also been University, 168 Changhai Road, Shanghai 200433, P.R. China reported to be involved in the transmission of apoptotic signals E-mail: [email protected] E-mail: [email protected] following SCI (9). However, identification and evaluation of specific and associated genes of SCI, which assist in the clinical *Contributed equally diagnosis and treatment of SCI, remain to be elucidated. In the present study, bioinformatics methods were used Key words: spinal cord injury, pathway analysis, feature genes, to assess the abnormal gene expression in SCI to determine protein-protein interaction network the associated feature genes. Critical genes were screened using expression profiling microarray data. Pathway analysis and protein-protein interaction (PPI) network analysis were performed on the proteins involved in SCI to investigate their 3616 YANG et al: FEATURE GENES IN SPINAL CORD INJURY function. The aim of the present study was to explore the Uniprot (UP) tissue analysis. GO analysis has become a molecular mechanisms of SCI and identify potential therapeutic commonly used approach for functional investigations of target genes for the treatment of SCI. large-scale genomic or transcriptomic data (19). DAVID, a high-throughput and integrated data-mining environment, Materials and methods analyzes gene lists derived from high-throughput genomic experiments (20). Data preprocessing and differential expression analysis. The In the present study, UP tissue analysis was performed on transcription profile of GSE2599 was downloaded from the DEGs in disease-associated metabolic pathways to identify Gene Expression Omnibus database (http://www.ncbi.nlm.nih. the genes associated with spinal cord tissue. Therefore, the gov/geo/), which was based on the Affymetrix Rat Genome abnormally expressed genes in the injured spinal cord 35 days U34 array (Affymetrix, Santa Clara, CA, USA) and deposited after injury were selected to distinguish these genes from by Aimone et al (10). A total of six tissue specimens were those, which were expressed not solely in injured spinal cord. available for further analysis, including three SCI samples, obtained from female Fischer 344 rats (165-200 g) 35 days Construction of the PPI network. PPI analysis was performed after SCI, and three normal tissues, as described in the original on the DEGs using Search Tool for the Retrieval of Interacting experiment (10). The annotation information of all probe sets Genes/Proteins (STRING; http://www.string-db.org/) online was provided by Affymetrix, where the raw data (CEL) file database (21). Combined_score was used to measure the strength was downloaded. of the interaction of protein pairs and only the interaction with Initially, the probe-level data in the CEL files were combined_score > 0.4 was selected as significant. Subsequently, converted into expression measures. For each sample, the critical genes, which exhibited >45 interactions with other expression values of all the probes for a particular gene were genes, were selected. The feature genes associated with SCI reduced to a single value by calculating the average expres- were identified by comparing the critical genes with the DEGs sion value. Probes corresponding to more than one gene 35 days after SCI. Finally, the PPI network was constructed were discarded. Subsequently, the data with the low signal using Cytoscape software (http://cytoscape.org/) (22,23), based strength was missing data and the missing data was imputed on the STRING database, to determine the association between using the K-nearest neighbor averaging (KNN) method (11) feature genes and the interacting genes, which may trigger SCI. and the complete data were standardized (12). The Samr package in R language (13) was used to identify differen- Protein domain analysis of specific genes. Coding area predic- tially expressed genes (DEGs) between three samples in the tion of the critical genes associated with SCI was performed control group (normal specimen) and three samples in the using the GENSCAN (http://genes.mit.edu/GENSCAN.html) experimental group (samples with SCI). In order to circum- online software programme (24). Subsequently, the Pfam (25) vent the multi-test problem, which may induce an excess of database was used to examine the protein domain for further false positive results, the Benjamini-Hochberg procedure (14) protein domain analysis. was used to adjust the raw P-values into false discovery rate (FDR). FDR<0.05 and |logFC|>1.5 were used as the cut-off Results criteria for DEG identification. Data pre‑processing and screening for DEGs. The results Kyoto Encyclopedia of Genes and Genomes (KEGG) of data pre-processing are shown in Fig. 1. Following data pathway and Gene Ontology (GO) enrichment analysis. pre-processing, the median was almost identical between Based on the deficiency of individual gene analysis, gene the samples, indicating good normalization and that the data set enrichment analysis evaluates differential expression was suitable for further analysis. A total of 929 DEGs were patterns of gene groups to distinguish whether their biolog- screened for, including 339 upregulated genes and 590 down- ical functions and characteristics differ (15). In the present regulated genes (Fig. 2). study, the P-value indicated the probability that a gene was randomly endowed a GO function and it was usually used KEGG pathway enrichment analysis. As shown in Table I, the as the criterion for assigning a certain function to a module. pathways associated with SCI included Huntington's disease A lower P-value increased the
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