Prognostic microRNAs and their potential molecular mechanism in pancreatic cancer: A study based on The Cancer Genome Atlas and bioinformatics investigation.
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| Abstract |
Although certain biomarkers that are directly associated with the overall survival (OS) of patients with pancreatic adenocarcinoma (PAAD) have been identified, the efficacy of a single factor is limited to predicting the prognosis. The aim of the present study was to identify a combination micro (mi)RNA signature that enhanced the prognostic prediction for PAAD. Following analysis of the data available from The Cancer Genome Atlas (TCGA), 175 PAAD samples were selected for the present study, and the associations between 494 miRNAs and OS were investigated. The prognostic value of all miRNAs was analyzed by multivariate Cox regression, and the miRNAs were ranked according to the hazard ratio (HR) and P‑values. The top 5 miRNAs (miR‑1301, miR‑125a, miR‑376c, miR‑328 and miR‑376b) were significantly associated with OS (HR=0.139; 95% confidence interval, 0.043‑0.443; P<0.001), thus demonstrating that this panel was able to serve as an independent prognostic factor for PAAD. In addition, the present study also predicted the target genes of the top 10 miRNAs with the highest prognostic values using 12 different prediction software, and enrichment signaling pathway analyses elucidated that several pathways may be markedly associated with these miRNAs, including 'Pathways in cancer', 'Chronic myeloid leukemia', 'Glioma' and 'MicroRNAs in cancer'. Lastly, ubiquitin C, epidermal growth factor receptor, estrogen receptor 1, mitogen‑activated protein kinase 1, mothers against decapentaplegic homolog 4 and androgen receptor may be the hub genes revealed by STRING analysis. The present study identified several miRNAs, particularly a five‑miRNA‑pool, that may be reliable, independent factors for predicting survival in patients with PAAD. However, the underlying molecular mechanisms require further investigation in the future.
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| Authors | Liang Liang, Dan-Ming Wei, Jian-Jun Li, Dian-Zhong Luo, Gang Chen, Yi-Wu Dang, Xiao-Yong Cai |
| Journal | Molecular medicine reports (Mol Med Rep) Vol. 17 Issue 1 Pg. 939-951 (Jan 2018) ISSN: 1791-3004 [Electronic] Greece |
| PMID | 29115476 (Publication Type: Journal Article) |
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