Integrating high-throughput microRNA and mRNA expression data to identify risk mRNA signature for pancreatic cancer prognosis
Integrating high-throughput microRNA and mRNA expression data to identify risk mRNA signature for pancreatic cancer prognosis
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整合高通量 microRNA 和 mRNA 表达数据来识别胰腺癌预后的风险 mRNA 特征
DOI：
10.1002/jcb.29576
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发表时间：
2020
影响因子：
4
通讯作者：
Sun Xueying
中科院分区：
文献类型：
--
作者：
Wang Ping;Li Weidong;Zhai Bo;Jiang Xian;Jiang Hongchi;Zhang Chunlong;Sun Xueying
胰腺癌是一种消化系统恶性肿瘤，其特点是预后不良。通过使用高通量表达谱已鉴定出许多预后信使 RNA (mRNA) 特征。 MicroRNA (miRNA) 在调节多种细胞功能中发挥着关键作用。然而，目前尚未报道用于研究胰腺癌预后机制的 miRNA 和 mRNA 综合分析。在本研究中，我们首先根据癌症基因组图谱数据集鉴定了预后 mRNA 和 miRNA，然后进行富集分析，以探讨 mRNA 水平上参与胰腺癌预后的潜在生物学机制。此外，我们对 mRNA 和 miRNA 进行了综合分析，以确定预后子通路，这些子通路与胰腺癌基因和肿瘤标志密切相关，并参与缺氧、氧化磷酸化和外源代谢。同时，我们执行了基于全局网络、预后 mRNA 和 miRNA 的随机游走算法，并确定了最高风险 mRNA 作为预后特征。最后，使用独立的测试集来确认顶级 mRNA 特征的预测能力，其中涉及的大多数基因都是已知的癌基因。总之，我们通过全面探索胰腺癌预后进行了一系列综合分析，并系统地优化了临床使用的预后特征。
Pancreatic cancer is a malignancy of the digestive system characterized by poor prognosis. A number of prognostic messenger RNA (mRNA) signatures have been identified by using the high‐throughput expression profiles. MicroRNAs (miRNA) play a critical role in regulating multiple cellular functions. However, no such integrated analysis of miRNAs and mRNAs for studying the prognostic mechanisms of pancreatic cancer has been reported. In this study, we first identified prognostic mRNAs and miRNAs based on The Cancer Genome Atlas datasets, and then performed an enrichment analysis to explore the underlying biological mechanisms involved in pancreatic cancer prognosis at the mRNA level. Furthermore, we performed an integrated analysis of mRNAs and miRNAs to identify prognostic subpathways, which were closely associated with pancreatic cancer genes and tumor hallmarks and involved in hypoxia, oxidative phosphyorylation and xenobiotic metabolisms. Meanwhile, we performed a random walk algorithm based on global network, prognostic mRNAs and miRNAs, and identified top risk mRNAs as the prognostic signature. Finally, an independent testing set was used to confirm the predictive power of the top mRNA signature, and most of these genes involved were known oncogenes. In conclusion, we performed a series of integrated analyses by comprehensively exploring pancreatic cancer prognosis and systematically optimized the prognostic signature for clinical use.