Integrating MicroRNA Expression Profiling Studies to Systematically Evaluate the Diagnostic Value of MicroRNAs in Pancreatic Cancer and Validate Their Prognostic Significance with the Cancer Genome Atlas Data

• Published in: Cellular physiology and biochemistry Vol. 49; no. 2; pp. 678 - 695
• Main Authors: Zhang, Zhigang, Pan, Bing, Lv, Shaocheng, Ji, Zhiwei, Wu, Qian, Lang, Ren, He, Qiang, Zhao, Xin
• Format: Journal Article
• Language: English
• Published: Basel, Switzerland S. Karger AG 01.01.2018; Cell Physiol Biochem Press GmbH & Co KG
• Subjects: Area Under Curve; Biomarkers; Biomarkers, Tumor - genetics; Breast cancer; Databases, Genetic; Gene expression; Gene Expression Profiling; Genomes; Humans; Medical diagnosis; Medical prognosis; Meta-analysis; MicroRNA; MicroRNAs; MicroRNAs - metabolism; Odds Ratio; Original Paper; Pancreatic cancer; Pancreatic Neoplasms - diagnosis; Pancreatic Neoplasms - genetics; Pancreatic Neoplasms - mortality; Prognosis; Proportional Hazards Models; Prostate cancer; ROC Curve; Survival Rate; TCGA; MicroRNA; Pancreatic cancer; TCGA; Meta-analysis
• ISSN: 1015-8987; 1421-9778
• DOI: 10.1159/000493033

Background/Aims: MicroRNAs (miRNAs) are promising biomarkers for pancreatic cancer (PaCa). However, systemic and unified evaluations of the diagnostic value of miRNAs are lacking. Therefore, we performed a systematic evaluation based on miRNA expression profiling studies. Methods: We obtained miRNA expression profiling studies from Gene Expression Omnibus (GEO) and ArrayExpress (AE) databases and calculated the pooled sensitivity, specificity, and summary area under a receiver operating characteristic (ROC) curve for every miRNA. According to the area under the curve (AUC), we identified the miRNAs with diagnostic potentiality and validated their prognostic role in The Cancer Genome Atlas (TCGA) data. Gene Ontology (GO) annotations and pathway enrichments of the target genes of the miRNAs were evaluated using bioinformatics tools. Results: Ten miRNA expression profiling studies including 958 patients were used in this diagnostic meta-analysis. A total of 693 miRNAs were measured in more than 9 studies. The top 50 miRNAs with high predictive values for PaCa were identified. Among them, miR-130b had the best predictive value for PaCa (pooled sensitivity: 0.73 [95% confidence intervals (CI) 0.44-0.91], specificity: 0.81 [95% CI 0.59–0.93], and AUC: 0.84 [95% CI 0.73–0.95]). We identified nine miRNAs (miR-23a, miR-30a, miR-125a, miR-129-1, miR-181b-1, miR-203, miR-221, miR-222, and miR-1301) associated with overall survival in PaCa patients by combining our results with TCGA data. The results of a Cox model revealed that two miRNAs (miR-30a [hazard ratio (HR)=2.43, 95% CI 1.05-5.59; p=0.037] and miR-203 [HR=3.14, 95% CI 1.28-7.71; p=0.012]) were independent risk factors for prognosis in PaCa patients. In total, 405 target genes of the nine miRNAs were enriched with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and cancer-associated pathways such as Ras signaling pathways, phospholipase D signaling pathway, and AMP-activated protein kinase (AMPK) signaling pathway were revealed among the top 20 enriched pathways. There were significant negative correlations between miR-181b-1 and miR-125a expression levels and the methylation status of their promoter region. Conclusion: Our study performed a systematic evaluation of the diagnostic value of miRNAs based on miRNA expression profiling studies. We identified that miR-23a, miR-30a, miR-125a, miR-129-1, miR-181b-1, miR-203, miR-221, miR-222, and miR-1301 had moderate diagnostic value for PaCa and predicted overall survival in PaCa patients.