ABSTRACT. expression is aberrant in various types of human cancer. However, the prognostic value of miR-494 in pancreatic cancer remains unclear. The level of miR-494 expression was determined in 99 pairs of primary pancreatic cancer and their corresponding, adjacent non-tumor tissues by using quantitative reverse transcriptase polymerase chain reaction. We also analyzed the associations between miR-494 expression and clinicopathological features. The survival correlations were analyzed by using the KaplanMeier method and Cox proportional hazards model. The level of miR-494 expression was significantly downregulated in pancreatic cancer tissues (mean relative expression level ± SD, 0.48 ± 0.11) as compared to matched adjacent normal tissues (1.80 ± 0.28, P < 0.05). We found 18153-18159 (2015) significant correlations between the miR-494 expression levels and TNM stage (P = 0.009), lymphatic invasion (P = 0.036), vascular invasion (P = 0.011), distant metastasis (P = 0.007), and tumor grade (P = 0.031). Pancreatic cancer patients with a low miR-494 expression level had a shorter overall survival than those with a high miR-494 expression level (P < 0.05). Reduced miR-494 expression in pancreatic cancer tissues is correlated with tumor progression and might be an independent, poor prognostic factor for patients with pancreatic cancer.
Heterogeneity across cancer makes it difficult to find driver genes with intermediate (2-20%) and low frequency (<2%) mutations 1 , and we are potentially missing entire classes of networks (or pathways) of biological and therapeutic value. Here, we quantify the extent to which cancer genes across 21 tumor types have an increased burden of mutations in their immediate gene network derived from functional genomics data. We formalize a classifier that accurately calculates the significance level of a gene's network mutation burden (NMB) and show it can accurately predict known cancer genes and recently proposed driver genes in the majority of tested tumours. Our approach predicts 62 putative cancer genes, including 35 with clear connection to cancer and 27 genes, which point to new cancer biology. NMB identifies proportionally more (4x) low-frequency mutated genes as putative cancer genes than gene-based tests, and provides molecular clues in patients without established driver mutations. Our quantitative and comparative analysis of pan-cancer networks across 21 tumour types gives new insights into the biological and genetic architecture of cancers and enables additional discovery from existing cancer genomes. The framework we present here should become increasingly useful with more sequencing data in the future.
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