[1]丁 丹,赵荣昌,丁 燕,等.利用糖酵解相关LncRNA构建肺腺癌患者的预后模型[J].医学信息,2024,37(05):1-11,19.[doi:10.3969/j.issn.1006-1959.2024.05.001]
　DING Dan,ZHAO Rong-chang,DING Yan,et al.Construct a Prognostic Model for Patients with Lung Adenocarcinoma by Using Glycolysis-related LncRNA[J].Journal of Medical Information,2024,37(05):1-11,19.[doi:10.3969/j.issn.1006-1959.2024.05.001]
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利用糖酵解相关LncRNA构建肺腺癌患者的预后模型()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
37卷
期数:
2024年05期
页码:
1-11,19
栏目:
生物信息学
出版日期:
2024-03-01

文章信息/Info

Title:
Construct a Prognostic Model for Patients with Lung Adenocarcinoma by Using Glycolysis-related LncRNA
文章编号:
1006-1959（2024）05-0001-07
作者:
丁 丹; 赵荣昌; 丁 燕; 等
（泰兴市人民医院肿瘤内科，江苏 泰兴 225400）
Author(s):
DING Dan; ZHAO Rong-chang; DING Yan; et al.
(Department of Oncology,Taixing People’s Hospital,Taixing 225400,Jiangsu,China)
关键词:
肺腺癌; 糖酵解; lncRNA; 预后; 列线图; 药物敏感性
Keywords:
Lung adenocarcinoma; Glycolysis; lncRNA; Prognostic; Nomogram; Drug sensitivity
分类号:
R734.2
DOI:
10.3969/j.issn.1006-1959.2024.05.001
文献标志码:
A
摘要:
目的 利用糖酵解相关LncRNA构建肺腺癌患者的预后模型，帮助临床预测个体化药物疗效和疾病复发情况。方法 综合TCGA和GSEA数据库，筛选与肺腺癌中糖酵解相关lncRNA表达数据，利用LASSO和Cox回归分析构建预后模型，绘制受试者工作特征曲线（ROC）并加以校准，将临床病理特征和风险评分进行整合构建列线图，分析免疫细胞分布、免疫相关分子和药物敏感性的差异与风险评分的关系。结果 在GSEA数据库中共选取出4个有效糖酵解基因集（BioCarta、Hallmark、KEGG、REACTOME和WP），与TCGA数据中的lncRNA表达数据结合获得1025个与糖酵解相关的lncRNA。差异分析获得186个在肿瘤组织和正常组织间差异表达的糖酵解相关lncRNA；单因素Cox、LASSO回归分析获得19个与预后相关的lncRNA。多因素Cox比例风险回归分析获得了由12个lncRNA 组成的预测模型。模型ACU提示预测性能较好，1、3、5年生存时间的AUC分别为0.711、0.713和0.699，并且可将肺腺癌区分为高、低风险组，高、低风险组的总生存期（OS）比较，差异有统计学意义（P＜0.05）。单因素和多因素Cox分析显示，风险评分可作为预测肺腺癌生存状态的独立预后指标，并且风险评分的预测性能优于其它临床病理特征。此外，不同的性别、T、N、M和Stage分期的风险评分比较，差异有统计学意义（P＜0.05）。风险评分与临床病理特征构建的列线图对1、3、5年预后的预测能力均有提升（1、3、5年生存时间的AUC分别为0.741、0.750和0.715）。高、低风险组间免疫微环境比较，差异有统计学意义（P＜0.05），表现为多数免疫细胞与低风险评分呈正相关。药物敏感性分析提示丝裂霉素C、紫杉醇、雷帕霉素、多西他赛和厄洛替尼的药物敏感性在高、低风险组间也存在区别。结论 糖酵解相关lncRNA构建的肺腺癌预后模型可以高效准确的预测肺腺癌患者的预后，具有一定的临床意义。
Abstract:
Objective To construct a prognostic model of lung adenocarcinoma patients by using glycolysis-related LncRNA, and to help predict the efficacy of individualized drugs and disease recurrence.Methods The TCGA and GSEA databases were used to screen the expression data of lncRNA related to glycolysis in lung adenocarcinoma. The prognostic model was constructed by LASSO and Cox regression analysis. The receiver operating characteristic curve (ROC) was drawn and calibrated. The clinicopathological features and risk scores were integrated to construct a nomogram. The relationship between immune cell distribution, immune-related molecules and drug sensitivity and risk score was analyzed.Results Four effective glycolysis gene sets (BioCarta, Hallmark, KEGG, REACTOME and WP) were selected from the GSEA database, and 1025 glycolystic-related lncRNAs were obtained by combining with the expression data of lncRNAs in the TCGA data. A total of 186 glycolytic-related lncRNAs were differentially expressed between tumor and normal tissues by differential analysis, and 19 prognostic related lncRNAs were obtained by univariate COX and LASSO regression analysis. A prediction model consisting of 12 lncRNAs was obtained by Cox proportional hazard regression analysis. The ACU value of the model suggested that the prediction performance was good, and the AUC of 1, 3 and 5 years survival time were 0.711, 0.713 and 0.699, respectively. The patients with lung adenocarcinoma could be divided into high and low risk groups, and the difference of overall survival (OS) between the two groups was statistically significant (P<0.05). Univariate and multivariate Cox analysis showed that risk score could be used as an independent prognostic indicator for the survival of lung adenocarcinoma, and the risk score predicted better than other clinicopathologic features. In addition, there were statistically significant differences in risk scores between genders, T, N, M, and Stage (P<0.05). Risk scores and histograms constructed with clinicopathological features improved prognostic ability at 1,3, and 5 years (AUC at 1, 3, and 5 years survival time was 0.741, 0.750, and 0.715, respectively). There were statistically significant differences in immune microenvironment between the high and low risk groups, showing that most immune cells were positively correlated with the low risk score. Drug sensitivity analysis suggested that there were significant differences in drug sensitivity of mitomycin C, paclitaxel, rapamycin, docetaxel and erotinib between the two groups.Conclusion The prognosis model of lung adenocarcinoma constructed by glycolysis-related lncRNA can effectively and accurately predict the prognosis of patients with lung adenocarcinoma, which has certain clinical significance.

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更新日期/Last Update: 1900-01-01