Lin Sheng-Yan, Miao Ya-Ru, Hu Fei-Fei, Hu Hui, Zhang Qiong, Li Qiubai, Chen Zhichao, Guo An-Yuan
Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
J Cancer. 2020 Jan 1;11(1):251-259. doi: 10.7150/jca.35382. eCollection 2020.
Cytogenetically normal acute myeloid leukemia (CN-AML) is a large proportion of AMLs with diverse prognostic outcomes. Identifying membrane protein genes as prognostic factors to stratify CN-AML patients will be critical to improve their outcomes. This study aims to identify prognostic factors to stratify CN-AML patients to choose better treatments and improve their outcomes. CN-AML data were from TCGA cohort (n = 79) and four GEO datasets. We identified independent prognostic genes by Cox regression and Kaplan-Meier methods, and constructed linear regression model using LASSO algorithm. The prediction error curve was calculated using R package "pec". Based on independent prognostic membrane genes, we constructed a regression model for CN-AML prognosis prediction: score = (0.0492 * ) - (0.0018 * ) + (0.0131 * ) + (0.2058 * ) + (0.0234 * ) - (0.3658 * ), which was named as MPG6 (6-Membrane Protein Gene) score. Tested in multiple CN-AML datasets, consistent results showed that CN-AML patients with high MPG6 score had poor survival, higher WBC count and shorter EFS. Comparing with other reported scoring models, the benchmark result of MPG6 achieved better association with survival in multiple cohorts. Moreover, by combining with other clinical indicators in CN-AML, MPG6 could improve the performance of survival prediction and serve as a robust prognostic factor. We identified the MPG6 score as a stable indicator with great potential for clinical application in risk stratification and outcome prediction in CN-AML.
细胞遗传学正常的急性髓系白血病（CN-AML）占急性髓系白血病的很大比例，其预后结果多样。将膜蛋白基因鉴定为分层CN-AML患者的预后因素对于改善其预后至关重要。本研究旨在确定分层CN-AML患者的预后因素，以便选择更好的治疗方法并改善其预后。CN-AML数据来自TCGA队列（n = 79）和四个GEO数据集。我们通过Cox回归和Kaplan-Meier方法鉴定了独立的预后基因，并使用LASSO算法构建了线性回归模型。使用R包“pec”计算预测误差曲线。基于独立的预后膜基因，我们构建了一个用于CN-AML预后预测的回归模型：评分 =（0.0492× ） - （0.0018× ） + （0.0131× ） + （0.2058× ） + （0.0234× ） - （0.3658× ），其被命名为MPG6（6-膜蛋白基因）评分。在多个CN-AML数据集中进行测试，一致的结果表明，MPG6评分高的CN-AML患者生存较差，白细胞计数较高，无事件生存期较短。与其他报道的评分模型相比，MPG6在多个队列中的基准结果与生存的关联更好。此外，通过与CN-AML中的其他临床指标相结合，MPG6可以提高生存预测的性能，并作为一个可靠的预后因素。我们将MPG6评分鉴定为一个稳定的指标，在CN-AML的风险分层和预后预测中具有很大的临床应用潜力。
