LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features
LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features
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LUADpp：基于体细胞突变特征的肺腺癌预后的有效预测模型
DOI：
10.1186/s12885-019-5433-7
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发表时间：
2019-03
期刊：
影响因子：
3.8
通讯作者：
Wang Yejun
中科院分区：
文献类型：
--
作者：
Yu Jiaxian;Hu Yueming;Xu Yafei;Wang Jue;Kuang Jiajie;Zhang Wei;Shao Jianlin;Guo Dianjing;Wang Yejun
背景肺腺癌是最常见的肺癌类型。全基因组测序研究揭示了肺腺癌的基因组图谱。然而，目前尚不清楚基因改变是否可以指导预后预测。有效的遗传标记及其基础的预测模型也缺乏用于预后评估。方法我们从癌症基因组图谱中获取了371例肺腺癌的体细胞突变数据和临床数据。将病例分为两个预后组（3年生存期），比较各组间基因的体细胞突变频率，然后建立计算模型来离散不同的预后。结果发现肺腺癌患者预后良好（≥3年生存期）组的基因突变率高于预后不良（< 3年生存期）组。参与细胞间粘附和运动的基因显着富集在顶级基因列表中，且预后良好组和不良预后组之间的突变率存在差异。具有基因体细胞突变特征的支持向量机模型可以很好地预测预后，并且随着特征尺寸的增加，性能得到提高。 85 个基因模型的平均交叉验证准确度达到 81%，接收者操作特征 (ROC) 曲线的曲线下面积 (AUC) 达到 0.896。该模型还表现出良好的分期预后预测性能，ROC曲线平均AUC为0.846。结论肺腺癌的预后与体细胞基因突变有关。遗传标记可用于预后预测，并为个人医疗提供指导。
BackgroundLung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation.MethodsWe obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis.ResultsGenes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and anAreaUnder theCurve (AUC) of 0.896 for theReceiverOperatingCharacteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves.ConclusionThe prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine.
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影响因子：
3.7
作者：
Wan YW;Sabbagh E;Raese R;Qian Y;Luo D;Denvir J;Vallyathan V;Castranova V;Guo NL
通讯作者：
Guo NL
影响因子：
120.1
作者：
Daniel Jones
通讯作者：
Daniel Jones
DOI：
10.1891/9780826121646.0002
发表时间：
2018-09
期刊：
Cancer Rehabilitation
影响因子：
--
作者：
K. Miller;R. Siegel;R. Khan;A. Jemal
通讯作者：
K. Miller;R. Siegel;R. Khan;A. Jemal
影响因子：
82.9
作者：
Kohno T;Ichikawa H;Totoki Y;Yasuda K;Hiramoto M;Nammo T;Sakamoto H;Tsuta K;Furuta K;Shimada Y;Iwakawa R;Ogiwara H;Oike T;Enari M;Schetter AJ;Okayama H;Haugen A;Skaug V;Chiku S;Yamanaka I;Arai Y;Watanabe S;Sekine I;Ogawa S;Harris CC;Tsuda H;Yoshida T;Yokota J;Shibata T
通讯作者：
Shibata T
影响因子：
28.2
作者：
Drilon A;Wang L;Hasanovic A;Suehara Y;Lipson D;Stephens P;Ross J;Miller V;Ginsberg M;Zakowski MF;Kris MG;Ladanyi M;Rizvi N
通讯作者：
Rizvi N