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Transcriptome-Wide Analysis of UTRs in Non-Small Cell Lung Cancer Reveals Cancer-Related Genes with SNV-Induced Changes on RNA Secondary Structure and miRNA Target Sites

机译:非小细胞肺癌中UTR的转录组全分析揭示了SNV诱导的RNA二级结构和miRNA靶位点变化的癌症相关基因

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Traditional mutation assessment methods generally focus on predicting disruptive changes in protein-coding regions rather than non-coding regulatory regions like untranslated regions (UTRs) of mRNAs. The UTRs, however, are known to have many sequence and structural motifs that can regulate translational and transcriptional efficiency and stability of mRNAs through interaction with RNA-binding proteins and other non-coding RNAs like microRNAs (miRNAs). In a recent study, transcriptomes of tumor cells harboring mutant and wild-type KRAS (V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) genes in patients with non-small cell lung cancer (NSCLC) have been sequenced to identify single nucleotide variations (SNVs). About 40% of the total SNVs (73,717) identified were mapped to UTRs, but omitted in the previous analysis. To meet this obvious demand for analysis of the UTRs, we designed a comprehensive pipeline to predict the effect of SNVs on two major regulatory elements, secondary structure and miRNA target sites. Out of 29,290 SNVs in 6462 genes, we predict 472 SNVs (in 408 genes) affecting local RNA secondary structure, 490 SNVs (in 447 genes) affecting miRNA target sites and 48 that do both. Together these disruptive SNVs were present in 803 different genes, out of which 188 (23.4%) were previously known to be cancer-associated. Notably, this ratio is significantly higher (one-sided Fisher's exact test p-value = 0.032) than the ratio (20.8%) of known cancer-associated genes (n = 1347) in our initial data set (n = 6462). Network analysis shows that the genes harboring disruptive SNVs were involved in molecular mechanisms of cancer, and the signaling pathways of LPS-stimulated MAPK, IL-6, iNOS, EIF2 and mTOR. In conclusion, we have found hundreds of SNVs which are highly disruptive with respect to changes in the secondary structure and miRNA target sites within UTRs. These changes hold the potential to alter the expression of known cancer genes or genes linked to cancer-associated pathways.
机译:传统的突变评估方法通常着重于预测蛋白质编码区的破坏性变化,而不是预测非编码调控区,如mRNA的非翻译区(UTR)。但是,UTR具有许多序列和结构基序,可以通过与RNA结合蛋白和其他非编码RNA(如microRNA(miRNA))相互作用来调节mRNA的翻译和转录效率以及稳定性。在最近的一项研究中,对具有非小细胞肺癌(NSCLC)患者突变和野生型KRAS(V-Ki-ras2 Kirsten大鼠肉瘤病毒癌基因同源物)基因的肿瘤细胞的转录组进行了测序,以鉴定单核苷酸变异(SNV)。确定的全部SNV(73,717)中约有40%被映射到UTR,但在先前的分析中被省略了。为了满足对UTR分析的明显需求,我们设计了一条全面的管道来预测SNV对两个主要调控元件(二级结构和miRNA目标位点)的影响。在6462个基因中的29,290个SNV中,我们预测有472个SNV(在408个基因中)会影响局部RNA二级结构,有490个SNV(在447个基因中)会影响miRNA靶位点,有48个同时出现。这些破坏性SNV共同存在于803个不同的基因中,其中188个(23.4%)先前已知与癌症相关。值得注意的是,该比率(我们的单侧Fisher精确检验p值= 0.032)比我们的初始数据集中(n = 6462)的已知癌症相关基因(n = 1347)的比率(20.8%)要高得多。网络分析表明,携带破坏性SNV的基因与癌症的分子机制有关,并且LPS刺激的MAPK,IL-6,iNOS,EIF2和mTOR的信号传导途径也参与其中。总之,我们发现了数百个SNV,它们对UTR内二级结构和miRNA靶位点的变化具有高度破坏性。这些变化具有改变已知癌症基因或与癌症相关途径相关的基因表达的潜力。

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