Dataset Information

Evaluation of the target genes of arsenic trioxide in pancreatic cancer by bioinformatics analysis.

ABSTRACT: The aim of the present study was to evaluate the potential network of arsenic trioxide (ATO) target genes in pancreatic cancer. The DrugBank, STITCH, cBioPortal, Kaplan-Meier plotter and Oncomine websites were used to analyze the association of ATO and its target genes with pancreatic cancer. Initially, 19 ATO target genes were identified, along with their associated protein-protein interaction networks and Kyoto Encyclopedia of Genes and Genomes pathways. ATO was found to be associated with multiple types of cancer, and the most common solid cancer was pancreatic cancer. A total of 6 ATO target genes (namely AKT1, CCND1, CDKN2A, IKBKB, MAPK1 and MAPK3) were found to be associated with pancreatic cancer. Next, the mutation information of the 6 ATO target genes in pancreatic cancer was collected. A total of 20 ATO interacting genes were identified, which were mainly involved in hepatitis B, prostate cancer, pathways in cancer, glioma and chronic myeloid leukemia. Finally, the genes CCND1 and MAPK1 were detected to be prognostic factors in patients with pancreatic cancer. In conclusion, bioinformatics analysis may help elucidate the molecular mechanisms underlying the involvement of ATO in pancreatic cancer, enabling more effective treatment of this disease.

SUBMITTER: Zhou CY

PROVIDER: S-EPMC6781497 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Evaluation of the target genes of arsenic trioxide in pancreatic cancer by bioinformatics analysis.

Zhou Cong-Ya CY Gong Liu-Yun LY Liao Rong R Weng Ning-Na NN Feng Yao-Yue YY Dong Yi-Ping YP Zhu Hong H Zhao Ya-Qin YQ Zhang Yuan-Yuan YY Zhu Qing Q Han Su-Xia SX

Oncology letters 20190919 5

The aim of the present study was to evaluate the potential network of arsenic trioxide (ATO) target genes in pancreatic cancer. The DrugBank, STITCH, cBioPortal, Kaplan-Meier plotter and Oncomine websites were used to analyze the association of ATO and its target genes with pancreatic cancer. Initially, 19 ATO target genes were identified, along with their associated protein-protein interaction networks and Kyoto Encyclopedia of Genes and Genomes pathways. ATO was found to be associated with mul ...[more]

PMID: 31612027

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Project description:Pancreatic ductal adenocarcinoma (PDAC) is one of the most complicated and fatally pathogenic human malignancies. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of PDAC. The aim of the present study was to identify the key genes and signaling pathways associated with PDAC using bioinformatics analysis. Four transcriptome microarray datasets (GSE15471, GSE55643, GSE62165 and GSE91035) were acquired from Gene Expression Omnibus datasets, which included 226 PDAC samples and 65 normal pancreatic tissue samples. We screened differentially expressed genes (DEGs) with GEO2R and investigated their biological function by Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. The overall survival data was obtained from UALCAN, which calculated the data shared with The Cancer Genome Atlas. In addition, a protein-protein interaction (PPI) network of the DEGs was constructed by STRING and Cytoscape software. The four sets of DEGs exhibited an intersection consisting of 205 genes (142 up-regulated and 63 down-regulated), which may be associated with PDAC. GO analysis showed that the 205 DEGs were significantly enriched in the plasma membrane, cell adhesion molecule activity and the Energy pathways, and glycine, serine, threonine metabolism were the most enriched pathways according to KEGG pathway analysis. Kaplan-Meier survival analysis revealed that 22 of 205 common genes were significantly associated with the overall survival of pancreatic cancer patients. In the PPI network and sub-network, DKK1 and HMGA2 were considered as hub genes with high connectivity degrees. DKK1 and HMGA2 are strongly associated with WNT3A and TP53 separately, which indicates that they may play an important role in the Wnt and P53 signaling pathways. Using integrated bioinformatics analysis, we identified DKK1 and HMGA2 as candidate genes in PDAC, which may improve our understanding of the mechanisms of the pathogenesis and integration; the two genes may be therapeutic targets and prognostic markers for PDAC.

Project description:BackgroundIn this study, miRNAs and their critical target genes related to the prognosis of pancreatic cancer were screened based on bioinformatics analysis to provide targets for the prognosis and treatment of pancreatic cancer.MethodsR software was used to screen differentially expressed miRNAs (DEMs) and genes (DEGs) downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. A miRNA Cox proportional hazards regression model was constructed based on the miRNAs, and a miRNA prognostic model was generated. The target genes of the prognostic miRNAs were predicted using TargetScan and miRDB and then intersected with the DEGs to obtain common genes. The functions of the common genes were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. A protein-protein interaction (PPI) network of the common genes was constructed with the STRING database and visualized with Cytoscape software. Key genes were also screened with the MCODE and cytoHubba plug-ins of Cytoscape. Finally, a prognostic model formed by the key gene was also established to help evaluate the reliability of this screening process.ResultsA prognostic model containing four downregulated miRNAs (hsa-mir-424, hsa-mir-3613, hsa-mir-4772 and hsa-mir-126) related to the prognosis of pancreatic cancer was constructed. A total of 118 common genes were enriched in two KEGG pathways and 33 GO functional annotations, including extracellular matrix (ECM)-receptor interaction and cell adhesion. Nine key genes related to pancreatic cancer were also obtained: MMP14, ITGA2, THBS2, COL1A1, COL3A1, COL11A1, COL6A3, COL12A1 and COL5A2. The prognostic model formed by nine key genes also possessed good prognostic ability.ConclusionsThe prognostic model consisting of four miRNAs can reliably predict the prognosis of patients with pancreatic cancer. In addition, the screened nine key genes, which can also form a reliable prognostic model, are significantly related to the occurrence and development of pancreatic cancer. Among them, one novel miRNA (hsa-mir-4772) and two novel genes (COL12A1 and COL5A2) associated with pancreatic cancer have great potential to be used as prognostic factors and therapeutic targets for this tumor.

Dataset Information

Evaluation of the target genes of arsenic trioxide in pancreatic cancer by bioinformatics analysis.

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Evaluation of the target genes of arsenic trioxide in pancreatic cancer by bioinformatics analysis.

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