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Identification of genes and functional coexpression modules closely related to ulcerative colitis by gene datasets analysis.

ABSTRACT: Background:Ulcerative colitis is a type of inflammatory bowel disease posing a great threat to the public health worldwide. Previously, gene expression studies of mucosal colonic biopsies have provided some insight into the pathophysiological mechanisms in ulcerative colitis; however, the exact pathogenesis is unclear. The purpose of this study is to identify the most related genes and pathways of UC by bioinformatics, so as to reveal the core of the pathogenesis. Methods:Genome-wide gene expression datasets involving ulcerative colitis patients were collected from gene expression omnibus database. To identify most close genes, an integrated analysis of gene expression signature was performed by employing robust rank aggregation method. We used weighted gene co-expression network analysis to explore the functional modules involved in ulcerative colitis pathogenesis. Besides, biological process and pathways analysis of co-expression modules were figured out by gene ontology enrichment analysis using Metascape. Results:A total of 328 ulcerative colitis patients and 138 healthy controls were from 14 datasets. The 150 most significant differentially expressed genes are likely to include causative genes of disease, and further studies are needed to demonstrate this. Seven main functional modules were identified, which pathway enrichment analysis indicated were associated with many biological processes. Pathways such as 'extracellular matrix, immune inflammatory response, cell cycle, material metabolism' are consistent with the core mechanism of ulcerative colitis. However, 'defense response to virus' and 'herpes simplex infection' suggest that viral infection is one of the aetiological agents. Besides, 'Signaling by Receptor Tyrosine Kinases' and 'pathway in cancer' provide new clues for the study of the risk and process of ulcerative colitis cancerization.

SUBMITTER: Zhu J

PROVIDER: S-EPMC6858811 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of genes and functional coexpression modules closely related to ulcerative colitis by gene datasets analysis.

Zhu Jie J Wang Zheng Z Chen Fengzhe F Liu Changhong C

PeerJ 20191113

<h4>Background</h4>Ulcerative colitis is a type of inflammatory bowel disease posing a great threat to the public health worldwide. Previously, gene expression studies of mucosal colonic biopsies have provided some insight into the pathophysiological mechanisms in ulcerative colitis; however, the exact pathogenesis is unclear. The purpose of this study is to identify the most related genes and pathways of UC by bioinformatics, so as to reveal the core of the pathogenesis.<h4>Methods</h4>Genome-w ...[more]

PMID: 31741804

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Project description:BackgroundUlcerative colitis (UC) is an idiopathic, chronic disorder characterized by inflammation, injury, and disruption of the colonic mucosa. However, there are still many difficulties in the diagnosis and differential diagnosis of UC. An increasing amount of research has shown a connection between ferroptosis and the etiology of UC. Therefore, our study aimed to identify the key genes related to ferroptosis in UC to provide new ideas for diagnosis UC.MethodsGene expression profiles of normal and UC samples were extracted from the Gene Expression Omnibus (GEO) database. By combining differentially expressed genes (DEGs), Weighted correlation network analysis (WGCNA) genes, and ferroptosis-related genes, hub genes were identified and then screened using Lasso regression. Based on the key genes, gene ontology (GO) and gene set enrichment analysis (GSEA) analyses were performed. We used NaiveBeyas, Logistic, IBk, and RandomForest algorithms to build a disease diagnosis model using the hub genes. The model was validated using GSE87473 as the validation set.ResultsGene expression matrices of GSE87466 and GSE75214 were downloaded from the GEO database, including 184 UC patients and 43 control samples. A total of 699 DEGs were obtained. From FerrDb, 565 genes related to ferroptosis were identified. The 1,513 genes with the highest absolute correlation coefficient value in the MEblue module were obtained from WGCNA analysis. Five hub genes (LCN2, MUC1, PARP8, PLIN2, and TIMP1) were identified using the Lasso regression algorithm based on the overlapped DEGs, WGCNA-identified genes, and ferroptosis-related genes. GO and GSEA analyses revealed that 5 hub genes were identified as being involved in the negative regulation of transcription by competitive promoter binding, cellular response to citrate cycle_tca_cycle, cytosolic_dna_sensing pathway, UV-A, and beta-alanine metabolism. The logistic algorithm's values of the area under the curve (AUC)were 1.000 and 0.995 for training and validation cohorts, and sensitivity is 0.962, specificity is 1.000, respectively, as determined by comparing various methods.ConclusionsThe previously described hub genes were identified as being intimately related to ferroptosis in UC and capable of distinguishing UC patients from controls. By detecting the expression of several genes, this model may aid in diagnosing UC and understanding the etiology and treatment of the disease.

Project description:BackgroundPatients with ulcerative colitis (UC) are predisposed to colitis-associated colorectal cancer (CAC). However, the transcriptional mechanism of the transformation from UC to CAC is not fully understood.MethodologyFirstly, we showed that CAC and non-UC-associated CRC were very similar in gene expression. Secondly, based on multiple datasets for UC and CRC, we extracted differentially expressed (DE) genes in UC and CRC versus normal controls, respectively. Thirdly, we compared the dysregulation directions (upregulation or downregulation) between DE genes of UC and CRC in CRC-related functions overrepresented with the DE genes of CRC, and proposed a regulatory model to explain the CRC-like dysregulation of genes in UC. A case study for "positive regulation of immune system process" was done to reveal the functional implication of DE genes with reversal dysregulations in these two diseases.Principal findingsIn all the 44 detected CRC-related functions except for "viral transcription", the dysregulation directions of DE genes in UC were significantly similar with their counterparts in CRC, and such CRC-like dysregulation in UC could be regulated by transcription factors affected by pro-inflammatory stimuli for colitis. A small portion of genes in each CRC-related function were dysregulated in opposite directions in the two diseases. The case study showed that genes related to humoral immunity specifically expressed in B cells tended to be upregulated in UC but downregulated in CRC.ConclusionsThe CRC-like dysregulation of genes in CRC-related functions in UC patients provides hints for understanding the transcriptional basis for UC to CRC transition. A small portion of genes with distinct dysregulation directions in each of the CRC-related functions in the two diseases implicate that their reversal dysregulations might be critical for UC to CRC transition. The cases study indicates that the humoral immune response might be inhibited during the transformation from UC to CRC.

Project description:BackgroundUlcerative colitis-associated colorectal cancer (UC-CRC) is a life-threatening complication of ulcerative colitis (UC). The mechanisms underlying UC-CRC remain to be elucidated. The purpose of this study was to explore the key genes and biological processes contributing to colitis-associated dysplasia (CAD) or carcinogenesis in UC via database mining, thus offering opportunities for early prediction and intervention of UC-CRC.MethodsMicroarray datasets (GSE47908 and GSE87466) were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between groups of GSE47908 were identified using the "limma" R package. Weighted gene co-expression network analysis (WGCNA) based on DEGs between the CAD and control groups was conducted subsequently. Functional enrichment analysis was performed, and hub genes of selected modules were identified using the "clusterProfiler" R package. Single-gene gene set enrichment analysis (GSEA) was conducted to predict significant biological processes and pathways associated with the specified gene.ResultsSix functional modules were identified based on 4929 DEGs. Green and blue modules were selected because of their consistent correlation with UC and CAD, and the highest correlation coefficient with the progress of UC-associated carcinogenesis. Functional enrichment analysis revealed that genes of these two modules were significantly enriched in biological processes, including mitochondrial dysfunction, cell-cell junction, and immune responses. However, GSEA based on differential expression analysis between sporadic colorectal cancer (CRC) and normal controls from The Cancer Genome Atlas (TCGA) indicated that mitochondrial dysfunction may not be the major carcinogenic mechanism underlying sporadic CRC. Thirteen hub genes (SLC25A3, ACO2, AIFM1, ATP5A1, DLD, TFE3, UQCRC1, ADIPOR2, SLC35D1, TOR1AIP1, PRR5L, ATOX1, and DTX3) were identified. Their expression trends were validated in UC patients of GSE87466, and their potential carcinogenic effects in UC were supported by their known functions and other relevant studies reported in the literature. Single-gene GSEA indicated that biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to angiogenesis and immune response were positively correlated with the upregulation of TFE3, whereas those related to mitochondrial function and energy metabolism were negatively correlated with the upregulation of TFE3.ConclusionsUsing WGCNA, this study found two gene modules that were significantly correlated with CAD, of which 13 hub genes were identified as the potential key genes. The critical biological processes in which the genes of these two modules were significantly enriched include mitochondrial dysfunction, cell-cell junction, and immune responses. TFE3, a transcription factor related to mitochondrial function and cancers, may play a central role in UC-associated carcinogenesis.

Project description:Purpose:Despite advances in characterizing the neurobiology of emotional disorders, there is still a significant lack of scientific understanding of the pathophysiological mechanisms governing major depressive disorder (MDD). This study attempted to elucidate the molecular circuitry of MDD and to identify more potential genes associated with the pathogenesis of the disease. Patients and Methods:Microarray data from the GSE98793 dataset were downloaded from the NCBI Gene Expression Omnibus (GEO) database, including 128 patients with MDD and 64 healthy controls. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to obtain further biological insight into the top three key modules. The protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed, as well. Results:We filtered 3276 genes that were considered significant DEGs for further WGCNA analysis. By performing WGCNA, we found that the turquoise, blue and brown functional modules were all strongly correlated with MDD development, including immune response, neutrophil degranulation, ribosome biogenesis, T cell activation, glycosaminoglycan biosynthetic process, and protein serine/threonine kinase activator activity. Hub genes were identified in the key functional modules that might have a role in the progression of MDD. Functional annotation showed that these modules primarily enriched such KEGG pathways as the TNF signaling pathway, T cell receptor signaling pathway, primary immunodeficiency, Th1, Th2 and Th17 cell differentiation, autophagy and RNA degradation and oxidative phosphorylation. These results suggest that these genes are closely related to autophagy and cellular immune function. Conclusion:The results of this study may help to elucidate the pathophysiology of MDD development at the molecular level and explore the potential molecular mechanisms for new interventional strategies.

Project description:BackgroundWhen plants are subjected to cold stress, they undergo a series of molecular and physiological changes to protect themselves from injury. Indica cultivars can usually withstand only mild cold stress in a relatively short period. Hormone-mediated defence response plays an important role in cold stress. Weighted gene co-expression network analysis (WGCNA) is a very useful tool for studying the correlation between genes, identifying modules with high phenotype correlation, and identifying Hub genes in different modules. Many studies have elucidated the molecular mechanisms of cold tolerance in different plants, but little information about the recovery process after cold stress is available.ResultsTo understand the molecular mechanism of cold tolerance in rice, we performed comprehensive transcriptome analyses during cold treatment and recovery stage in two cultivars of near-isogenic lines (9311 and DC907). Twelve transcriptomes in two rice cultivars were determined. A total of 2509 new genes were predicted by fragment splicing and assembly, and 7506 differentially expressed genes were identified by pairwise comparison. A total of 26 modules were obtained by expression-network analysis, 12 of which were highly correlated with cold stress or recovery treatment. We further identified candidate Hub genes associated with specific modules and analysed their regulatory relationships based on coexpression data. Results showed that various plant-hormone regulatory genes acted together to protect plants from physiological damage under short-term low-temperature stress. We speculated that this may be common in rice. Under long-term cold stress, rice improved the tolerance to low-temperature stress by promoting autophagy, sugar synthesis, and metabolism.ConclusionThrough WGCNA analysis at the transcriptome level, we provided a potential regulatory mechanism for the cold stress and recovery of rice cultivars and identified candidate central genes. Our findings provided an important reference for the future cultivation of rice strains with good tolerance.

Dataset Information

Identification of genes and functional coexpression modules closely related to ulcerative colitis by gene datasets analysis.

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Identification of genes and functional coexpression modules closely related to ulcerative colitis by gene datasets analysis.

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