Dataset Information

Identification of Prognostic Biomarkers by Combined mRNA and miRNA Expression Microarray Analysis in Pancreatic Cancer.

ABSTRACT: Pancreatic cancer is the fourth leading cause for cancer-related death, and early diagnosis is one key to improve the survival rate of this disease. Molecular biomarkers are an important method for diagnostic use in pancreatic cancer. We used data from three mRNA microarray datasets and a microRNA dataset (GSE16515, GSE15471, GSE28735, and GSE41372) to identify potential key genes. Differentially expressed genes (DEGs) and microRNAs (DEMs) were identified. Functional, pathway enrichment, and protein-protein interaction analyses were performed on common DEGs across all datasets. The target genes of the DEMs were identified. DEMs targets that were also DEGs were further scrutinized using overall survival analysis. A total of 236 DEGs and 21 DEMs were identified. There were a total of four DEGs (ECT2, NR5A2, NRP2, and TGFBI), which were also predicted target genes of DEMs. Overall survival analysis showed that high expression levels of three of these genes (ECT2, NRP2, and TGFBI) were associated with poor overall survival for pancreatic cancer patients. The basic expression of DEGs in pancreas stood lower level in various organ tissues. The expression of ECT2 and NRP2 was higher in different pancreatic cancer cell lines than normal pancreas cell line. Knockout of ECT2 by Crispr Cas9 gene editing system decreased proliferation and migration ability in pancreatic cancer cell line MiaPaCa2. In conclusion, we think that data mining method can do well in biomarker screening, and ECT2 and NRP2 can play as potential biomarker or therapy target by Crispr Cas9 in pancreatic cancer.

SUBMITTER: Liu B

PROVIDER: S-EPMC6154866 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Identification of Prognostic Biomarkers by Combined mRNA and miRNA Expression Microarray Analysis in Pancreatic Cancer.

Liu Bin B Yang Hai H Taher Leila L Denz Axel A Grützmann Robert R Pilarsky Christian C Weber Georg F GF

Translational oncology 20180406 3

Pancreatic cancer is the fourth leading cause for cancer-related death, and early diagnosis is one key to improve the survival rate of this disease. Molecular biomarkers are an important method for diagnostic use in pancreatic cancer. We used data from three mRNA microarray datasets and a microRNA dataset (GSE16515, GSE15471, GSE28735, and GSE41372) to identify potential key genes. Differentially expressed genes (DEGs) and microRNAs (DEMs) were identified. Functional, pathway enrichment, and pro ...[more]

PMID: 29631214

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Project description:Nowadays, the current bioinformatic methods have been increasingly applied in the field of oncological research. In this study, we expect a better understanding of the molecular mechanism of gastric cancer from the bioinformatic methods. By systematically addressing the differential expression of microRNAs (miRNAs) and mRNAs between gastric cancer specimens and normal gastric specimens with the application of bioinformatics tools, A total of 206 DEGs and 38 DEMs were identified. The Gene Ontology (GO) analysis of Annotation, Visualization and Integrated Discovery (DAVID) database revealed that the differentially expressed genes (DEGs) were significantly enriched in biological process, molecular function and cellular component, while Kyoto Encyclopedia of Genes and Genomes (KEGG) database showed DEGs were significantly enriched in 8 signal pathways. The miRNA-gene regulatory network was constructed based on 385 miRNA-gene (DEM-DEG) pairs, consisting of 35 miRNAs and 107 target genes. In the regulatory network, the top 5 up-regulated genes were Transmembrane Protease, Serine 11B (TMPRSS11B), regulator of G protein signaling 1 (RGS1), cysteine rich angiogenic inducer 61 (CYR61), inhibin subunit beta A (INHBA), syntrophin gamma 1 (SNTG1), and the top 5 down-regulated genes were tumor necrosis factor receptor superfamily, member 19 (TNFRSF19), pleckstrin homology domain containing B2 (PLEKHB2), Tax1 binding protein 3 (TAX1BP3), presenilin enhancer, gamma-secretase subunit (PSENEN), NME/NM23 nucleoside diphosphate kinase 3 (NME3). Based on the gastric cancer patient database from Kaplan-Meier Plotter tools, we found that 8 of 10 genes with most significant changes in the miRNA-gene regulatory network possessed a prognostic value for survival time of gastric cancer patients. Patients with higher level of RGS1, PLEKHB2, TAX1BP3 and PSENEN in gastric cancer had a longer survival time compared with the patients with lower level of these genes. On the contrary, patients with higher level of INHBA, SNTG1, TNFRSF19 and NME3 were found associated with a shorter survival time. In conclusion, our findings provided several potential targets regarding gastric cancer, which may result in a new strategy to treat gastric cancer from a system rather than a single-gene perspective.

Project description:AbstractMicroRNAs (miRNAs) in tumor and tumor-adjacent tissues can be effective diagnostic and prognostic markers to monitor tumor occurrence and progression. Despite improvements in the diagnosis and treatment of esophageal cancer (EC), the survival rate is <25%; consequently, more effective EC-specific prognostic biomarkers are urgently needed to design effective treatment regimens. In this study, we focused on identifying independent prognostic miRNA signatures in tumor and tumor-adjacent tissues in EC.We screened candidate miRNAs using a genome-wide miRNA transcriptome dataset from The Cancer Genome Atlas (TCGA) database that included 82 patients with esophageal adenocarcinoma (EADC) and 83 patients with esophageal squamous cell carcinoma (ESCC). We validated potential prognostic miRNA markers using a microarray profiling dataset that included information of 32 patients with EADC and 44 patients with ESCC from the Gene Expression Omnibus database. TCGA dataset was additionally used to identify differentially expressed mRNAs (DEMs) between the tumor and tumor-adjacent tissues. Univariate and multivariate Cox analyses were performed to detect the relationship between miRNAs and the overall survival of patients with EC. Kaplan-Meier method was applied to assess the survival differences between groups with differential miRNA expression. Lastly, functional enrichment analysis was conducted using miRWalk 2.0 online database for annotation.Although there was a considerable difference between the DEMs of EADC and ESCC, 73 DEMs were differentially expressed in both EADC and ESCC samples in TCGA dataset. Cox regression and Kaplan-Meier survival analyses showed that a higher expression of hsa-miR-186-5p and hsa-let-7d-5p was independently associated with a poor prognosis of EADC and ESCC, respectively. Furthermore, gene functional enrichment analysis revealed that the target genes of hsa-miR-186-5p and hsa-let-7d-5p participated in various cancer-related pathways, including the MAPK signaling pathway, proteoglycans in cancer, and AGE-RAGE signaling pathway.Our results revealed that hsa-miR-186-5p and hsa-let-7d-5p could be used as independent prognostic biomarkers for EADC and ESCC, respectively.

Dataset Information

Identification of Prognostic Biomarkers by Combined mRNA and miRNA Expression Microarray Analysis in Pancreatic Cancer.

Publications

Identification of Prognostic Biomarkers by Combined mRNA and miRNA Expression Microarray Analysis in Pancreatic Cancer.

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