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。在过去的十年中，基因组技术和计算算法的综合进步利用突变密度的概念拓宽了突变研究，并将蛋白质编码RNA的传统范围扩展到非编码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 个癌症队列检查了 57 种独特癌症类型的长非编码 RNA (lncRNA) 突变密度模式。我们的分析表明，lncRNA 表现出的突变密度模式与蛋白质编码 mRNA 的突变密度模式相似。这些模式包括 lncRNA 转录起始位点周围的突变峰和谷。在许多队列中，这些模式证明了统计上显着的转录链偏差是合理的，并且转录链偏差在 lncRNA 和 mRNA 之间共享。我们进一步使用对数优势比指标量化转录链偏差，并表明其中一些偏差与患者预后相关。由于与个体患者相关的强转录耦合修复机制，可能会产生预后效果。我们的研究首次将突变密度模式与 lncRNA 突变结合起来，结果表明蛋白质编码 mRNA 和 lncRNA 之间的模式非常相似，进一步说明了 lncRNA 在癌症研究中的潜在作用。© 2023 作者。

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.© 2023 The Authors.