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Abstract：
Mutations and gene expression are the two most studied genomic features in cancer research. In the last decade, the combined advances in genomic technology and computational algorithms have broadened mutation research with the concept of mutation density and expanded the traditional scope of protein-coding RNA to noncoding RNAs. However, mutation density analysis had yet to be integrated with non-coding RNAs. In this study, we examined long non-coding RNA (lncRNA) mutation density patterns of 57 unique cancer types using 80 cancer cohorts. Our analysis revealed that lncRNAs exhibit mutation density patterns reminiscent to those of protein-coding mRNAs. These patterns include mutation peak and dip around transcription start sites of lncRNA. In many cohorts, these patterns justified statistically significant transcription strand bias, and the transcription strand bias was shared between lncRNAs and mRNAs. We further quantified transcription strand biases with a Log Odds Ratio metric and showed that some of these biases are associated with patient prognosis. The prognostic effect may be exerted due to strong Transcription-coupled repair mechanisms associated with the individual patient. For the first time, our study combined mutational density patterns with lncRNA mutations, and the results demonstrated remarkably comparable patterns between protein-coding mRNA and lncRNA, further illustrating lncRNA\'s potential roles in cancer research.
摘要：
突变和基因表达是癌症研究中研究最多的两个基因组特征。在过去的十年里，基因组技术和计算算法的联合进步通过突变密度的概念拓宽了突变研究,并将传统的蛋白质编码RNA的范围扩展到非编码RNA.然而,突变密度分析尚未与非编码RNA整合。在这项研究中,我们使用80个癌症队列检测了57种独特癌症类型的长链非编码RNA(lncRNA)突变密度模式.我们的分析显示，lncRNAs表现出突变密度模式，让人联想到编码蛋白质的mRNAs。这些模式包括lncRNA转录起始位点周围的突变峰和下降。在许多队列中，这些模式证明了统计上显著的转录链偏差，转录链偏倚在lncRNAs和mRNAs之间共享。我们用对数赔率比度量进一步定量转录链偏差,并显示这些偏差中的一些与患者预后相关。由于与个体患者相关的强转录偶联修复机制,可以发挥预后效果。第一次,我们的研究将突变密度模式与lncRNA突变相结合，结果证明了编码蛋白质的mRNA和lncRNA之间具有明显的可比性，进一步说明lncRNA在癌症研究中的潜在作用。
