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Differentially Expressed lncRNAs in Gastric Cancer Patients: A Potential Biomarker for Gastric Cancer Prognosis

机译:胃癌患者中差异表达的lncRNAs:胃癌预后的潜在生物标志物

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Current studies indicate that long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers and implicated with prognosis in gastric cancer (GC). We intended to generate a multi-lncRNA signature to improve prognostic prediction of GC. By analyzing ten paired GC and adjacent normal mucosa tissues, 339 differentially expressed lncRNAs were identified as the candidate prognostic biomarkers in GC. Then we used LASSO Cox regression method to build a 12-lncRNA signature and validated it in another independent GEO dataset. An innovative 12-lncRNA signature was established, and it was significantly associated with the disease free survival (DFS) in the training dataset. By applying the 12-lncRNA signature, the training cohort patients could be categorized into high-risk or low-risk subgroup with significantly different DFS (HR = 4.52, 95%CI= 2.49-8.20, P < 0.0001). Similar results were obtained in another independent GEO dataset (HR=1.58, 95%CI=1.05 - 2.38, P=0.0270). Further analysis showed that the prognostic value of this 12-lncRNA signature was independent of AJCC stage and postoperative chemotherapy. Receiver operating characteristic (ROC) analysis showed that the area under receiver operating characteristic curve (AUC) of combined model reached 0.869. Additionally, a well-performed nomogram was constructed for clinicians. Moreover, single-sample gene-set enrichment analysis (ssGSEA) showed that a group of pathways related to drug resistance and cancer metastasis significantly enriched in the high risk patients. A useful innovative 12-lncRNA signature was established for prognostic evaluation of GC. It might complement clinicopathological features and facilitate personalized management of GC.
机译:当前的研究表明,长的非编码RNA(lncRNA)通常在癌症中异常表达,并与胃癌(GC)的预后有关。我们打算生成一个多lncRNA签名,以改善GC的预后预测。通过分析十对配对的GC和相邻的正常黏膜组织,鉴定出339个差异表达的lncRNAs作为GC中预后的候选生物标志物。然后,我们使用LASSO Cox回归方法建立了12-lncRNA签名,并在另一个独立的GEO数据集中对其进行了验证。建立了创新的12-lncRNA签名,并且该签名与训练数据集中的无病生存时间(DFS)显着相关。通过应用12-lncRNA签名,可以将训练队列患者分为DFS显着不同的高风险或低风险亚组(HR = 4.52,95%CI = 2.49-8.20,P <0.0001)。在另一个独立的GEO数据集中也获得了类似的结果(HR = 1.58,95%CI = 1.05-2.38,P = 0.0270)。进一步的分析表明,这种12-lncRNA标志的预后价值与AJCC分期和术后化疗无关。接收机工作特性(ROC)分析表明,组合模型下接收机工作特性曲线(AUC)的面积达到0.869。此外,还为临床医生构建了性能良好的列线图。此外,单样本基因集富集分析(ssGSEA)表明,与耐药性和癌症转移相关的一组途径在高风险患者中显着丰富。建立了有用的创新12-lncRNA签名,用于GC的预后评估。它可以补充临床病理学特征并促进GC的个性化管理。

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