Long Chengmei, Jian Jinbo, Li Xinchang, Wang Gongxian, Wang Jingen
Department of Organ Transplantation, Jiangxi Provincial People's Hospital, School of Medicine, Nanchang University, Nanchang, Jiangxi 330006, P.R. China.
Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong 256603, P.R. China.
Oncol Lett. 2017 Apr;13(4):2151-2160. doi: 10.3892/ol.2017.5689. Epub 2017 Feb 7.
An accumulation of driver mutations is important for cancer formation and progression, and leads to the disruption of genes and signaling pathways. The identification of driver mutations and genes has been the subject of numerous previous studies. The present study was performed to identify cancer-driving mutations and genes in renal cell carcinoma (RCC), prioritizing noncoding variants with a high functional impact, in order to analyze the most informative features. Sorting Intolerant From Tolerant (SIFT), Polymorphism Phenotyping version 2 (Polyphen2) and MutationAssessor were applied to predict deleterious mutations in the coding genome. OncodriveFM and OncodriveCLUST were used to detect potential driver genes and signaling pathways. The functional impact of noncoding variants was evaluated using Combined Annotation Dependent Depletion, FunSeq2 and Genome-Wide Annotation of Variants. Noncoding features were analyzed with respect to their enrichment of high-scoring variants. A total of 1,327 coding mutations in clear cell RCC, 258 in chromophobe RCC and 1,186 in papillary RCC were predicted to be deleterious by all three of MutationAssessor, Polyphen2 and SIFT. In total, 77 genes were positively selected by OncodriveFM and 1 by OncodriveCLUST, 45 of which were recurrently mutated genes. In addition, 10 signaling pathways were recurrently mutated and had a high functional impact bias (FM bias), and 31 novel signaling pathways with high FM bias were identified. Furthermore, noncoding regulatory features and conserved regions contained numerous high-scoring variants, and expression, replication time, GC content and recombination rate were positively correlated with the densities of high-scoring variants. In conclusion, the present study identified a list of cancer-driving genes and signaling pathways, features like regulatory elements, conserved regions, replication time, expression, GC content and recombination rate are major factors that affect the distribution of high-scoring non-coding mutations in kidney cancer.
驱动突变的积累对癌症的形成和进展至关重要，并导致基因和信号通路的破坏。驱动突变和基因的鉴定一直是以往众多研究的主题。本研究旨在鉴定肾细胞癌（RCC）中的癌症驱动突变和基因，优先考虑具有高功能影响的非编码变异，以分析最具信息性的特征。应用从耐受中筛选不耐受（SIFT）、多态性表型分析版本2（Polyphen2）和突变评估器来预测编码基因组中的有害突变。使用OncodriveFM和OncodriveCLUST检测潜在的驱动基因和信号通路。使用联合注释依赖缺失、FunSeq2和全基因组变异注释评估非编码变异的功能影响。分析了非编码特征中高分变异的富集情况。突变评估器、Polyphen2和SIFT这三者共同预测，透明细胞RCC中共有1327个编码突变、嫌色细胞RCC中有258个编码突变、乳头状RCC中有1186个编码突变具有有害性。OncodriveFM共阳性选择了77个基因，OncodriveCLUST阳性选择了1个基因，其中45个是反复突变的基因。此外，10条信号通路反复发生突变并具有高功能影响偏差（FM偏差），还鉴定出31条具有高FM偏差的新信号通路。此外，非编码调控特征和保守区域包含大量高分变异，并且表达、复制时间、GC含量和重组率与高分变异的密度呈正相关。总之，本研究鉴定出了一系列癌症驱动基因和信号通路，调控元件、保守区域、复制时间、表达、GC含量和重组率等特征是影响肾癌中高分非编码突变分布的主要因素。
