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BackgroundTumor necrosis factor (TNF)-α variant is closely linked to sepsis syndrome and mortality after severe trauma. We aimed to identify feature genes associated with the TNF rs1800629 A allele in trauma patients and help to direct them toward alternative successful treatment. MethodsIn this study, we used 58 sets of gene expression data from Gene Expression Omnibus to predict the feature genes associated with the TNF rs1800629 A allele in trauma patients. We applied support vector machine (SVM) classifier model for classification prediction combining with leave-one-out cross validation method. Functional annotation of feature genes was carried out to study the biological function using database for annotation, visualization, and integrated discovery (DAVID). ResultsA total of 133 feature genes were screened out and was well differentiated in the training set (14 patients with variant, 15 with wild type). Moreover, SVM classifier peaked in predictive accuracy with 100% correct rate in training set and 86.2% in testing set. Interestingly, functional annotation showed that feature genes, such as HMOX1 (heme oxygenase (decycling) 1) and RPS7 (ribosomal protein S7) were mainly enriched in terms of cell proliferation and ribosome. ConclusionHMOX1 and RPS7 may be key feature genes associated with the TNF rs1800629 A allele and may play a crucial role in the inflammatory response in trauma patients. Moreover, the cell proliferation and ribosome pathway may contribute to the progression of severe trauma. A total of 133 feature genes were screened out in training set.SVM classifier peaked at 100% and 86.2% correct rate in training and validation sets.Feature genes were mainly enriched in cell proliferation and ribosome pathway.

Colorectal cancer (CRC) is among the most lethal human cancers, but the mechanism of the cancer is still unclear enough. We aimed to explore the key genes in CRC progression. The gene expression profile (GSE4183) of CRC was obtained from Gene Expression Omnibus database which included 8 normal samples, 15 adenoma samples, 15 CRC samples and 15 inflammatory bowel disease (IBD) samples. Thereinto, 8 normal, 15 adenoma, and 15 CRC samples were chosen for our research. The differentially expressed genes (DEGs) in normal vs. adenoma, normal vs. CRC, and adenoma vs. CRC, were identified using the Wilcoxon test method in R respectively. The interactive network of DEGs was constructed to select the significant modules using the Pearson's correlation. Meanwhile, transcriptional network of DEGs was also constructed using the g: Profiler. Totally, 2,741 DEGs in normal vs. adenoma, 1,484 DEGs in normal vs. CRC, and 396 DEGs in adenoma vs. CRC were identified. Moreover, function analysis of DEGs in each group showed FcR-mediated phagocytosis pathway in module 1, cardiac muscle contraction pathway in module 6, and Jak-STAT signaling pathway in module 19 were also enriched. Furthermore, MZF1 and AP2 were the transcription factor in module 6, with the target SP1, while SP1 was also a transcription in module 20. DEGs like NCF1, AKT, SP1, AP2, MZF1, and TPM might be used as specific biomarkers in CRC development. Therapy targeting on the functions of these key genes might provide novel perspective for CRC treatment.

Evidence suggested that the -174GC and -634CG polymorphisms in interleukin-6 (IL6) promoter region may modulate risk of lung cancer; however, the conclusion was still inconclusive. Therefore, we performed this meta-analysis to determine the association between IL6 -174GC and -634CG polymorphisms and lung cancer risk. The association strength was measured by odds ratios (ORs) and 95% confidence intervals (CI). Egger's test and Begg's test were performed to detect potential publication bias. By searching PubMed, EMBASE and China National Knowledge Infrastructure, we included 16 eligible studies in this meta-analysis, involving 6,202 lung cancer cases and 7,067 controls. Five studies about -174GC polymorphism and 11 studies about -634CG polymorphism were analyzed. By pooling eligible studies, we found no significant association of -174GC with lung cancer risk (C vs. G: OR = 1.029; 95% CI, 0.957-1.106; heterogeneity, P = 0.478) and no statistic association of -634CG with lung cancer susceptibility (G vs. C: OR = 1.050; 95% CI, 0.893-1.235; Heterogeneity, P0.001). No significant publication bias was observed. In conclusion, we found that -634CG and -174GC polymorphisms in IL6 promoter region were not associated with lung cancer risk.

Background The study aimed to investigate the expression changes of genes in colorectal cancer (CRC) and screen the potential molecular targets. Methods The GSE37178 of mRNA expression profile including the CRC samples extracted by surgical resection and the paired normal samples was downloaded from Gene Expression Omnibus database. The genes whose expressions were changed at four different time points were screened and clustered using Mfuzz package. Then DAVID was used to perform the functional and pathway enrichment analysis for genes in different clusters. The protein–protein interaction (PPI) networks were constructed for genes in the clusters according to the STRING database. Furthermore, the related-transcription factors (TFs) and microRNAs (miRNAs) were obtained based on the resources in databases and then were combined with the PPI networks in each cluster to construct the integrated network containing genes, TFs and miRNAs. Results As a result, 314 genes were clustered into four groups. Genes in cluster 1 and cluster 2 showed a decreasing trend, while genes in cluster 3 and cluster 4 presented an increasing trend. Then 18 TFs (e.g., TCF4, MEF2C and FOS) and 18 miRNAs (e.g., miR-382, miR-217, miR-1184, miR-326 and miR-330-5p) were identified and three integrated networks for cluster 1, 3, and 4 were constructed. Conclusions The results implied that expression of PITX2, VSNL1, TCF4, MEF2C and FOS are time-related and associated with CRC development, accompanied by several miRNAs including miR-382, miR-217, miR-21, miR-1184, miR-326 and miR-330-5p. All of them might be used as potential diagnostic or therapeutic target molecules for CRC.

This is a bioinformatic study designed to investigate the time-course expression changes of microRNAs (miRNAs) after spinal cord injury (SCI).To investigate the mechanism of SCI self-repair at miRNAs level and target genes level.SCI results in loss of sensory and locomotor function, and SCI self-repair might provide clinical therapies; however, the mechanism of SCI self-repair remains unclear.The miRNA expression profile (GSE19890) of adult female Wistar brown rats (Rattus norvegicus) in SCI (laminectony and contusion), sham (laminectony but no contusion), and control (untreated) groups was downloaded from Gene Expression Omnibus. Totally, 35 chips were available, including five controls, five SCI-1-day, five SCI-3-day, five SCI-7-day, five sham-1-day, five sham-3-day, and five sham-7-day. Betr and limma package were used to screen time-course differentially expressed miRNAs (DEmiRNAs), followed by Bayesian hierarchical clustering (BHC), synergetic and functional enrichment analysis through BHC and cluster Profiler packages, respectively. Furthermore, STRING database and Cytoscape software were used to construct interaction networks between time-course DEmiRNAs, and GenCLip2.0 software was applied to pathway enrichment for key genes associated with nervous system.Totally, 68 time-course DEmiRNAs were identified and divided into 15 BHC clusters. Then, 100 time-course DEmiRNA pairs with synergetic function were identified, and time-course DEmiRNAs and target genes interaction networks were constructed, in which 10 genes (AKT1, VEGFA, CTNNB1, IGF1, APP, PTEN, CDC42, BDNF, SOD2, and IFNG) with highest degrees were found. Furthermore, key genes were significantly enriched in neurotrophin signaling pathway.After SCI, miRNAs might collectively regulate target genes, facilitating or inhibiting self-repair. Modulation of these miRNAs might provide novel therapies for SCI treatment.N/A.

Objectives: To investigate the underlying molecular mechanisms of coronary artery disease (CAD) using microarray expression profiles. Methods: The microRNA (miRNA) expression-profiling dataset GSE28858 was obtained from the Gene Expression Omnibus database, including 24 samples from 12 patients with CAD and 12 age- and sex-matched healthy controls. Differentially expressed miRNAs were identified with false discovery rate (FDR) = 1% by the SAM (Significant Analysis of Microarray) algorithm. The target genes of selected differentials expressed miRNAs that were not only related to CAD, but were also in two databases (TargetScan, miRanda). Then, the interactive objects of selected target genes were predicted using the STRING database to construct an interaction network (confidence score = 0.4). These target genes and interactive objects were put into the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, and the significant signaling pathway was obtained by hypergeometric function enrichment analysis (p < 0.05). Results: MiRNA-526b was the only differentially expressed miRNA that was upregulated in patients with CAD (FDR = 1%). Toll-like receptor 4 (TLR4) was the target gene of miRNA-526b that occurred with the highest frequency. The objects that interacted with TLR4 were predicted using the STRING database and the interaction network was obtained. The vascular endothelial growth factor (VEGF) signaling pathway was the only selected significant pathway related with CAD in the interaction network (p < 0.05). Conclusion: The miRNA-526b is significantly upregulated in patients with CAD and the target gene of miRNA-526b participates in the VEGF signaling pathway. Whether or not the miRNA-526b can be used as a biomarker remains to be elucidated in a larger prospective study.