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A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine

机译:基于支持向量机的MSI和生存预测胃癌LNCRNA模型

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BACKGROUND:Recent studies have shown that long non-coding RNAs (lncRNAs) play a crucial role in the induction of cancer through epigenetic regulation, transcriptional regulation, post-transcriptional regulation and other aspects, thus participating in various biological processes such as cell proliferation, differentiation and apoptosis. As a new nova of anti-tumor therapy, immunotherapy has been shown to be effective in many tumors of which PD-1/PD-L1 monoclonal antibodies has been proofed to increase overall survival rate in advanced gastric cancer (GC). Microsatellite instability (MSI) was known as a biomarker of response to PD-1/PD-L1 monoclonal antibodies therapy. The aim of this study was to identify lncRNAs signatures able to classify MSI status and create a predictive model associated with MSI for GC patients.METHODS:Using the data of Stomach adenocarcinoma from The Cancer Genome Atlas (TCGA), we developed and validated a lncRNAs model for automatic MSI classification using a machine learning technology - support vector machine (SVM). The C-index was adopted to evaluate its accuracy. The prognostic values of overall survival (OS) and disease-free survival (DFS) were also assessed in this model.RESULTS:Using the SVM, a lncRNAs model was established consisting of 16 lncRNA features. In the training cohort with 94 GC patients, accuracy was confirmed with AUC 0.976 (95% CI, 0.952 to 0.999). Veracity was also confirmed in the validation cohort (40 GC patients) with AUC 0.950 (0.889 to 0.999). High predicted score was correlated with better DFS in the patients with stage I-III and lower OS with stage I-IV.CONCLUSION:This study identify 16 LncRNAs signatures able to classify MSI status. The correlation between lncRNAs and MSI status indicates the potential roles of lncRNAs interacting in immunotherapy for GC patients. The pathway of these lncRNAs which might be a target in PD-1/PD-L1 immunotherapy are needed to be further study.
机译:背景:最近的研究表明,长期的非编码RNA(LNCRNA)通过表观遗传调控,转录调控,转录后调节和其他方面在癌症诱导中起着至关重要的作用,从而参与细胞增殖如细胞增殖等各种生物学过程,分化和细胞凋亡。作为抗肿瘤治疗的新新星,在许多肿瘤中已被证明在许多肿瘤中被证明是有效的,其中PD-1 / PD-L1单克隆抗体已经证明,以提高晚期胃癌(GC)的整体存活率。微卫星不稳定性(MSI)被称为对PD-1 / PD-L1单克隆抗体治疗的反应的生物标志物。本研究的目的是识别能够分类MSI状态的LNCRNA签名,并创建与GC患者的MSI相关的预测模型。方法:使用来自癌症基因组Atlas(TCGA)的胃腺癌数据,我们开发并验证了一个LNCRNA使用机器学习技术的自动MSI分类模型 - 支持向量机(SVM)。采用C索引来评估其准确性。在该模型中还评估了总存活(OS)和无病生存期(DFS)的预后值。结果:使用SVM,由16个LNCRNA特征组成LNCRNA模型。在培训队列中,患有94例GC患者,用AUC 0.976(95%CI,0.952至0.999)确认精度。在验证队列(40 GC患者)中也确认了验证的核心,0.950(0.889至0.999)。高预测得分与阶段I-III患者和阶段IV的患者中的更好的DFS相关联。结论:本研究确定了能够分类MSI状态的16个LNCRNA签名。 LNCRNA和MSI状态之间的相关性表明了LNCRNA与GC患者免疫疗法中的潜在作用。这些LNCRNA的途径可能是PD-1 / PD-L1免疫疗法中的靶标进行进一步研究。

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