[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]
点击复制

利用糖酵解相关LncRNA构建肺腺癌患者的预后模型()
分享到：

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.

参考文献/References:

[1]Barta JA,Powell CA,Wisnivesky JP.Global Epidemiology of Lung Cancer[J].Ann Glob Health,2019,85(1):8.[2]Bade BC,Dela Cruz CS.Lung Cancer 2020: Epidemiology, Etiology, and Prevention[J].Clin Chest Med,2020,41(1):1-24.[3]Travis WD,Brambilla E,Burke AP,et al.Introduction to The 2015 World Health Organization Classification of Tumors of the Lung, Pleura, Thymus, and Heart[J].J Thorac Oncol,2015,10(9):1240-1242.[4]Schwartz L,Supuran CT,Alfarouk KO.The Warburg Effect and the Hallmarks of Cancer[J].Anticancer Agents Med Chem,2017,17(2):164-170.[5]Vaupel P,Schmidberger H,Mayer A.The Warburg effect: essential part of metabolic reprogramming and central contributor to cancer progression[J].Int J Radiat Biol,2019,95(7):912-919.[6]Deng F,Zhou R,Lin C,et al.Tumor-secreted dickkopf2 accelerates aerobic glycolysis and promotes angiogenesis in colorectal cancer[J].Theranostics,2019,9(4):1001-1014.[7]Reinfeld BI,Rathmell WK,Kim TK,et al.The therapeutic implications of immunosuppressive tumor aerobic glycolysis[J].Cell Mol Immunol,2022,19(1):46-58.[8]Yang J,Ren B,Yang G,et al.The enhancement of glycolysis regulates pancreatic cancer metastasis[J].Cell Mol Life Sci,2020,77(2):305-321.[9]Zhang L,Zhang Z,Yu Z.Identification of a novel glycolysis-related gene signature for predicting metastasis and survival in patients with lung adenocarcinoma[J].J Transl Med,2019,17(1):423.[10]Liu J,Li S,Feng G,et al.Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma[J].Cancer Cell Int,2020,20:183.[11]Wu C,Cai X,Yan J,et al.Identification of Novel Glycolysis-Related Gene Signatures Associated With Prognosis of Patients With Clear Cell Renal Cell Carcinoma Based on TCGA[J].Front Genet,2020,11:589663.[12]Zhou W,Zhang S,Cai Z,et al.A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma[J].Peer J,2020,8:e9944.[13]Tang J,Luo Y,Wu G.A glycolysis-related gene expression signature in predicting recurrence of breast cancer[J].Aging (Albany NY),2020,12(24):24983-24994.[14]Zhao W,Geng D,Li S,et al.LncRNA HOTAIR influences cell growth, migration, invasion, and apoptosis via the miR-20a-5p/HMGA2 axis in breast cancer[J].Cancer Med,2018,7(3):842-855.[15]Zhang X,Yao J,Shi H,et al.LncRNA TINCR/microRNA-107/CD36 regulates cell proliferation and apoptosis in colorectal cancer via PPAR signaling pathway based on bioinformatics analysis[J].Biol Chem,2019,400(5):663-675.[16]Cheng P,Lu P,Guan J,et al.LncRNA KCNQ1OT1 controls cell proliferation, differentiation and apoptosis by sponging miR-326 to regulate c-Myc expression in acute myeloid leukemia[J].Neoplasma,2020,67(2):238-248. [17]Zhou M,Zhang Z,Zhao H,et al.An Immune-Related Six-lncRNA Signature to Improve Prognosis Prediction of Glioblastoma Multiforme[J].Mol Neurobiol,2018,55(5):3684-3697.[18]Li S,Chen S,Wang B,et al.A Robust 6-lncRNA Prognostic Signature for Predicting the Prognosis of Patients With Colorectal Cancer Metastasis[J].Front Med (Lausanne),2020,7:56.[19]Iaccarino I,Klapper W.LncRNA as Cancer Biomarkers[J].Methods Mol Biol,2021,2348:27-41. [20]Rocco D,Della Gravara L,Battiloro C,et al.The treatment of advanced lung adenocarcinoma with activating EGFR mutations[J].Expert Opin Pharmacother,2021,22(18):2475-2482.[21]Kang J,Zhang XC,Chen HJ,et al.Complex ALK Fusions Are Associated With Better Prognosis in Advanced Non-Small Cell Lung Cancer[J].Front Oncol,2020,10:596937.[22]Herbst RS,Giaccone G,de Marinis F,et al.Atezolizumab for First-Line Treatment of PD-L1-Selected Patients with NSCLC[J].N Engl J Med,2020,383(14):1328-1339.[23]Sparano JA,Gray RJ,Makower DF,et al.Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer[J].N Engl J Med,2018,379(2):111-121.[24]Luo C,Lei M,Zhang Y,et al.Systematic construction and validation of an immune prognostic model for lung adenocarcinoma[J].J Cell Mol Med,2020,24(2):1233-1244.[25]Wang X,Yao S,Xiao Z,et al.Development and validation of a survival model for lung adenocarcinoma based on autophagy-associated genes[J].J Transl Med,2020,18(1):149.[26]Gao X,Tang M,Tian S,et al.A ferroptosis-related gene signature predicts overall survival in patients with lung adenocarcinoma[J].Future Oncol,2021,17(12):1533-1544.[27]Li JP,Li R,Liu X,et al.A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma[J].Front Oncol,2020,10:560779.[28]Wang Y,He R,Ma L.Characterization of lncRNA-Associated ceRNA Network to Reveal Potential Prognostic Biomarkers in Lung Adenocarcinoma[J].Front Bioeng Biotechnol,2020,8:266.[29]Geng W,Lv Z,Fan J,et al.Identification of the Prognostic Significance of Somatic Mutation-Derived LncRNA Signatures of Genomic Instability in Lung Adenocarcinoma[J].Front Cell Dev Biol,2021,9:657667.[30]Jiang A,Liu N,Bai S,et al.Identification and validation of an autophagy-related long non-coding RNA signature as a prognostic biomarker for patients with lung adenocarcinoma[J].J Thorac Dis,2021,13(2):720-734.[31]Wang L,Zhao H,Xu Y,et al.Systematic identification of lincRNA-based prognostic biomarkers by integrating lincRNA expression and copy number variation in lung adenocarcinoma[J].Int J Cancer,2019,144(7):1723-1734.[32]Xiao G,Wang P,Zheng X,et al.FAM83A-AS1 promotes lung adenocarcinoma cell migration and invasion by targeting miR-150-5p and modifying MMP14[J].Cell Cycle,2019,18(21):2972-2985.[33]Kim YC,Wu Q,Chen J,et al.The transcriptome of human CD34+ hematopoietic stem-progenitor cells[J].Proc Natl Acad Sci U S A,2009,106(20):8278-8283.

相似文献/References:

[1]刘治利,葛明建.肺部磨玻璃结节的CT特征与治疗策略进展[J].医学信息,2018,31(11):49.[doi:10.3969/j.issn.1006-1959.2018.11.016]
　LIU Zhi-li,GE Ming-jian.Advances in CT Features and Treatment Strategies of Pulmonary Ground-glass Nodule[J].Journal of Medical Information,2018,31(05):49.[doi:10.3969/j.issn.1006-1959.2018.11.016]
[2]彭玉龙,张 露,陈荣辉,等.吉西他滨与培美曲塞联合卡铂治疗晚期肺腺癌的临床观察[J].医学信息,2018,31(11):124.[doi:10.3969/j.issn.1006-1959.2018.11.039]
　PENG Yu-long,ZHANG Lu,CHEN Rong-hui,et al.Clinical Observation of Gemcitabine and Pemetrexed Combined with Carboplatin in Treatment of Advanced Lung Adenocarcinoma[J].Journal of Medical Information,2018,31(05):124.[doi:10.3969/j.issn.1006-1959.2018.11.039]
[3]葛丽艳,王 琦,朱 宏,等.基于数据挖掘分析GPR35表达对肺腺癌预后的影响[J].医学信息,2018,31(23):86.[doi:10.3969/j.issn.1006-1959.2018.23.024]
　GE Li-yan,WANG Qi,ZHU Hong,et al.The Effect of GPR35 Expression on Prognosis of Lung Adenocarcinoma was Analyzed Based on Data Mining[J].Journal of Medical Information,2018,31(05):86.[doi:10.3969/j.issn.1006-1959.2018.23.024]
[4]刘贤丰,李子俊.恩度联合培美曲塞与顺铂治疗晚期肺腺癌的临床效果观察[J].医学信息,2019,32(03):148.[doi:10.3969/j.issn.1006-1959.2019.03.048]
　LIU Xian-feng,LI Zi-jun.Clinical Observation of Endo Combined with Pemetrexed and Cisplatin in the Treatment of Advanced Lung Adenocarcinoma[J].Journal of Medical Information,2019,32(05):148.[doi:10.3969/j.issn.1006-1959.2019.03.048]
[5]黄琪峰,郑琳琳,张 菁.生物信息学分析筛选肺腺癌靶基因及评估预后的价值[J].医学信息,2019,32(22):62.[doi:10.3969/j.issn.1006-1959.2019.22.020]
　HUANG Qi-feng,ZHENG Lin-lin,ZHANG Jing.Bioinformatics Analysis of Screening Lung Adenocarcinoma Target Genes and Evaluating Prognostic Value[J].Journal of Medical Information,2019,32(05):62.[doi:10.3969/j.issn.1006-1959.2019.22.020]
[6]唐怀慧,王忠帅,邵茜茜,等.基于TCGA数据库肺腺癌RNAs构建ceRNA网络的综合分析[J].医学信息,2020,33(07):90.[doi:10.3969/j.issn.1006-1959.2020.07.025]
　TANG Huai-hui,WANG Zhong-shuai,SHAO Qian-qian,et al.Comprehensive Analysis of Constructing ceRNA Network Based on TCGA Database of Lung Adenocarcinoma RNAs[J].Journal of Medical Information,2020,33(05):90.[doi:10.3969/j.issn.1006-1959.2020.07.025]
[7]莫俊贤,冼 磊.miRNA-21及PTEN在肺腺癌组织中的表达及意义[J].医学信息,2020,33(09):77.[doi:10.3969/j.issn.1006-1959.2020.09.022]
　MO Jun-xian,XIAN Lei.Expression and Significance of miRNA-21 and PTEN in Lung Adenocarcinoma[J].Journal of Medical Information,2020,33(05):77.[doi:10.3969/j.issn.1006-1959.2020.09.022]
[8]张祥鑫,董星星,耿 涛,等.基于与铁死亡相关基因的肺腺癌的新型预后模型构建[J].医学信息,2021,34(15):96.[doi:10.3969/j.issn.1006-1959.2021.15.025]
　ZHANG Xiang-xin,DONG Xing-xing,GENG Tao,et al.Construction of A Novel Prognostic Model of Lung Adenocarcinoma Based on Genes Related to Iron Death[J].Journal of Medical Information,2021,34(05):96.[doi:10.3969/j.issn.1006-1959.2021.15.025]
[9]吴 迪,柴仲秋,周 冰.靶向癌蛋白调控Warburg效应的中医药研究进展[J].医学信息,2022,35(13):42.[doi:10.3969/j.issn.1006-1959.2022.13.008]
　WU Di,CHAI Zhong-qiu,ZHOU Bing.Research Progress of Traditional Chinese Medicine Targeting Oncoproteins to Regulate Warburg Effect[J].Journal of Medical Information,2022,35(05):42.[doi:10.3969/j.issn.1006-1959.2022.13.008]
[10]李 敖,王玉然,王文浩.基于公共数据库分析自噬相关基因在肺腺癌患者中的预后意义[J].医学信息,2022,35(22):17.[doi:10.3969/j.issn.1006-1959.2022.22.003]
　LI Ao,WANG Yu-ran,WANG Wen-hao.Prognostic Significance of Autophagy-related Genes in Patients with Lung Adenocarcinoma Based on Public Database Analysis[J].Journal of Medical Information,2022,35(05):17.[doi:10.3969/j.issn.1006-1959.2022.22.003]

更新日期/Last Update: 1900-01-01