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Identification of key genes and pathways downstream of the ?-catenin-TCF7L1 complex in pancreatic cancer cells using bioinformatics analysis.

ABSTRACT: As a key component of the Wnt signaling pathway, the ?-catenin-transcription factor 7 like 1 (TCF7L1) complex activates transcription and regulates downstream target genes that serve important roles in the pathology of pancreatic cancer. To identify associated key genes and pathways downstream of the ?-catenin-TCF7L1 complex in pancreatic cancer cells, the current study used the gene expression profiles GSE57728 and GSE90926 downloaded from the Gene Expression Omnibus. GSE57728 is an array containing information regarding ?-catenin knockdown and GSE90926 was developed by high throughput sequencing to provide information regarding TCF7L1 knockdown. Subsequently, differentially expressed genes (DEGs) were sorted separately and the shared 88 DEGs, including 37 upregulated and 51 downregulated genes, were screened. Clustering analysis of these DEGs was performed by heatmap analysis. Functional and pathway enrichment analyses were then performed using FunRich software and Database for Annotation, Visualization and Integrated Discovery, which revealed that the DEGs were predominantly enriched in terms associated with transport, transcription factor activity, and cytokine and chemokine mediated signaling pathway process. A DEG-associated protein-protein interaction (PPI) network, consisting of 58 nodes and 171 edges, was then constructed using Cytoscape software and the 15 genes with top node degrees were selected as the hub genes. Overall survival (OS) analysis of the 88 DEGs was performed and the relevant gene expression datasets were downloaded from The Cancer Genome Atlas. Consequently, three upregulated and seven downregulated genes were identified to be associated with prognosis. Furthermore, high expression levels of five downregulated genes, including CXCL5, CYP27C1, FUBP1, CDK14 and TRIM24, were associated with worse OS. In addition, CDK14 and TRIM24 were revealed as hub genes in the PPI network and both were confirmed to be involved in the Wnt/?-catenin pathway and phosphoinositide 3-kinase/Akt signaling pathway. Promoter analysis was also applied to the five downregulated DEGs associated with prognosis, which revealed that TCF7L1 may serve as a transcription factor of the DEGs. In conclusion, the genes and pathways identified in the current study may provide potential targets for the diagnosis and treatment of pancreatic cancer.

SUBMITTER: Yuan YH

PROVIDER: S-EPMC6607041 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Identification of key genes and pathways downstream of the β-catenin-TCF7L1 complex in pancreatic cancer cells using bioinformatics analysis.

Yuan Yi-Hang YH Zhou Jian J Zhang Yan Y Xu Meng-Dan MD Wu Jing J Li Wei W Wu Meng-Yao MY Li Dao-Ming DM

Oncology letters 20190606 2

As a key component of the Wnt signaling pathway, the β-catenin-transcription factor 7 like 1 (TCF7L1) complex activates transcription and regulates downstream target genes that serve important roles in the pathology of pancreatic cancer. To identify associated key genes and pathways downstream of the β-catenin-TCF7L1 complex in pancreatic cancer cells, the current study used the gene expression profiles GSE57728 and GSE90926 downloaded from the Gene Expression Omnibus. GSE57728 is an array conta ...[more]

PMID: 31423172

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Project description:Nasopharyngeal carcinoma (NPC) is one of the most common malignancies in the head and neck. The aim of the current study was to identify the key pathways and genes involved in NPC through bioinformatics analysis and to identify potential molecular mechanisms underlying NPC proliferation and progression. Three gene expression profiles (GSE12452, GSE34573 and GSE64634) were downloaded from the Gene Expression Omnibus database. A total of 76 samples were analyzed, of which 59 were NPC samples and 17 were normal samples. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were subsequently conducted. The protein-protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed using Cytoscape software. Analysis of GSE12452, GSE34573 and GSE64634 datasets identified 1,301 (553 upregulated and 748 downregulated), 1,232 (348 upregulated and 884 downregulated) and 1,218 (555 upregulated and 663 downregulated) DEGs, respectively. Using Venn diagram analysis, 268 DEGs (59 upregulated and 209 downregulated) that intersected all three datasets, were selected for further analysis. The results of GO analysis revealed that upregulated DEGs were significantly enriched in biological processes, including 'cell adhesion', 'cell division', 'mitosis' and 'mitotic cell cycle'. The downregulated DEGs were mainly enriched in 'microtubule-based movement', 'cilium movement', 'cilium axoneme assembly' and 'epithelial cell differentiation'. The KEGG pathway analysis results revealed that the upregulated DEGs were highly associated with several pathways, including 'extracellular matrix-receptor interaction', 'human papillomavirus infection', 'arrhythmogenic right ventricular cardiomyopathy' and 'focal adhesion', whereas the downregulated DEGs were enriched in 'metabolic pathways', 'Huntington's disease', 'fluid shear stress and atherosclerosis' and 'chemical carcinogenesis'. On the basis of the PPI network of the DEGs, the following top 10 hub genes were identified: Dynein axonemal light intermediate chain 1, dynein axonemal intermediate chain 2, calmodulin 1, coiled-coil domain containing 114, dynein axonemal heavy chain 5, radial spoke head 9 homolog, radial spoke head component 4A, NDC80 kinetochore complex component, thymidylate synthetase and coiled-coil domain containing 39. In conclusion, by performing a comprehensive bioinformatics analysis of DEGs, putative targets that could be used to elucidate the molecular mechanisms underlying NPC were identified.

Project description:BackgroundAtherosclerosis (AS) is a chronic inflammatory disease that might induce severe cardiovascular events, such as myocardial infarction and cerebral infarction. These risk factors in the pathogenesis of AS remain uncertain and further research is needed. This study aims to explore the potential molecular mechanisms of AS by bioinformatics analyses.MethodsGSE100927 gene expression profiles, including 69 AS samples and 35 healthy controls, were downloaded from Gene Expression Omnibus database and indenfied for key genes and pathways in AS.ResultsA total of 443 differentially expressed genes (DEGs) between control and AS were identified, including 323 down-regulated genes and 120 up-regulated genes. The Gene ontology terms enriched by the up-regulated DEGs were associated with the regulation of leukocyte activation, endocytic vesicle, and cytokine binding, while the down-regulated DEGs were associated with negative regulation of cell growth, extracellular matrix, and G protein-coupled receptor binding. KEGG pathway analysis showed that the up-regulated DEGs were enriched in Osteoclast differentiation and Phagosome, while the down-regulated DEGs were enriched in vascular smooth muscle contraction and cGMP-PKG signaling pathway. Using the modular analysis of Cytoscape, we identified 3 modules mainly involved in Leishmaniasis and Osteoclast differentiation. The GSEA analysis showed the up-regulated gene sets were enriched in the ribosome, ascorbated metabolism, and propanoate metabolism. The LASSO Cox regression analysis showed the top 3 genes were TNF, CX3CR1, and COL1R1. Finally, we found these immune cells were conferred significantly higher infiltrating density in the AS group.ConclusionsOur data showed the pathway of Osteoclast differentiation and Leishmaniasis was involved in the AS process and we developed a three-gene model base on the prognosis of AS. These findings clarified the gene regulatory network of AS and may provide a novel target for AS therapy.

Dataset Information

Identification of key genes and pathways downstream of the ?-catenin-TCF7L1 complex in pancreatic cancer cells using bioinformatics analysis.

Publications

Identification of key genes and pathways downstream of the β-catenin-TCF7L1 complex in pancreatic cancer cells using bioinformatics analysis.

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