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李梦涵, 肖琼, 高鹏, 付昱, 孙晨蕊, 宋永喜. 基于TCGA数据库的消化道肿瘤LncRNA预后风险评分模型[J]. 肿瘤防治研究, 2022, 49(6): 606-611. DOI: 10.3971/j.issn.1000-8578.2022.21.1159
引用本文: 李梦涵, 肖琼, 高鹏, 付昱, 孙晨蕊, 宋永喜. 基于TCGA数据库的消化道肿瘤LncRNA预后风险评分模型[J]. 肿瘤防治研究, 2022, 49(6): 606-611. DOI: 10.3971/j.issn.1000-8578.2022.21.1159
LI Menghan, XIAO Qiong, GAO Peng, FU Yu, SUN Chenrui, SONG Yongxi. LncRNA Prognostic Risk Scoring Model for Gastrointestinal Tumors Based on TCGA Database[J]. Cancer Research on Prevention and Treatment, 2022, 49(6): 606-611. DOI: 10.3971/j.issn.1000-8578.2022.21.1159
Citation: LI Menghan, XIAO Qiong, GAO Peng, FU Yu, SUN Chenrui, SONG Yongxi. LncRNA Prognostic Risk Scoring Model for Gastrointestinal Tumors Based on TCGA Database[J]. Cancer Research on Prevention and Treatment, 2022, 49(6): 606-611. DOI: 10.3971/j.issn.1000-8578.2022.21.1159

基于TCGA数据库的消化道肿瘤LncRNA预后风险评分模型

LncRNA Prognostic Risk Scoring Model for Gastrointestinal Tumors Based on TCGA Database

  • 摘要:
    目的  建立基于TCGA数据库的消化道肿瘤lncRNA预后风险模型并评价患者预后。
    方法  收集TCGA数据库中食管癌、胃癌、结肠癌、直肠癌患者的资料,进行Cox单因素分析和Lasso及Cox多因素分析构建预后风险评分模型。对模型进行验证与独立性检验,通过时间依赖的ROC曲线分析评价模型的临床应用价值。
    结果  得到基于13个lncRNA的预后风险模型,训练集与验证集三年AUC分别为0.746与0.704。将混合癌种数据集划分为高低风险组进行生存分析,低风险组5年生存率显著高于高风险组,且在各个癌种中,低风险组五年生存率均高于高风险组。对该模型与年龄、性别、TNM分期等临床特征进行多因素Cox分析显示,风险评分可以独立于其他临床指标进行预后预测。
    结论  本研究构建了13基因预后风险评分模型,该模型所得风险评分可作为消化道肿瘤预后的独立预测因子。

     

    Abstract:
    Objective  To establish a lncRNA prognostic risk model for gastrointestinal tumors based on the TCGA database and evaluate the prognosis of patients.
    Methods  We collected the data of patients with esophageal cancer, gastric cancer, colon cancer and rectal cancer in the TCGA database. Univariate Cox analysis, Lasso and multivariate Cox analysis were performed to construct the prognostic risk scoring model. The model was validated and tested for independence. Time-dependent ROC curve analysis was performed to evaluate the clinical application value of the model.
    Results  We established a prognostic risk model based on 13 lncRNAs. The three-year AUC of the training set and the validation set were 0.746 and 0.704, respectively. The pan-cancer data set was divided into high- and low-risk groups for survival analysis. The 5-year survival rate of the low-risk group was significantly higher than that of the high-risk group; among all cancer types, the five-year survival rates of the low-risk group were higher than those of the high-risk group. Multivariate Cox analysis showed that the risk score could be an independent indicator of prognosis.
    Conclusion  The 13-gene prognostic risk score model is constructed successfully. The risk score obtained by this model can be used as an independent prognostic predictor of the patients with gastrointestinal cancer.

     

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