[1]思 璐,秦秋雯,沈 印,等.基于生物信息学分析筛选肺腺癌免疫预后相关lncRNA[J].医学信息,2023,36(01):1-6.[doi:10.3969/j.issn.1006-1959.2023.01.001]
　SI Lu,QIN Qiu-wen,SHEN Yin,et al.Screening of lncRNA Related to Immune Prognosis of Lung Adenocarcinoma Based on Bioinformatics Analysis[J].Journal of Medical Information,2023,36(01):1-6.[doi:10.3969/j.issn.1006-1959.2023.01.001]
点击复制

基于生物信息学分析筛选肺腺癌免疫预后相关lncRNA()
分享到：

医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
36卷
期数:
2023年01期
页码:
1-6
栏目:
生物信息学
出版日期:
2023-01-01

文章信息/Info

Title:
Screening of lncRNA Related to Immune Prognosis of Lung Adenocarcinoma Based on Bioinformatics Analysis
文章编号:
1006-1959（2023）01-0001-06
作者:
思 璐; 秦秋雯; 沈 印; 等
（广西壮族自治区人民医院国际医疗部，广西 南宁 530021）
Author(s):
SI Lu; QIN Qiu-wen; SHEN Yin; et al.
(Department of International Medical,People’s Hospital of Guangxi Zhuang Autonomous Region,Nanning 530021,Guangxi,China)
关键词:
肺腺癌; lncRNA; 风险模型; 生物标志物
Keywords:
Lung adenocarcinoma; lncRNA; Risk model; Biomarkers
分类号:
R734.2
DOI:
10.3969/j.issn.1006-1959.2023.01.001
文献标志码:
A
摘要:
目的 基于TCGA和Molecular Signatures Database数据，利用生物信息学方法鉴定潜在的免疫预后相关的lncRNA（IRL）生物标志物，用于肺腺癌患者的预后预测。方法 从TCGA获取肺腺癌IRL数据集和相应的临床参数；从Molecular Signatures Database v7.0中提取免疫基因。通过构建免疫基因共表达网络鉴定免疫相关的IRL。随后对IRL进行Cox回归分析、Lasso回归分析筛选肺腺癌预后相关的IRL生物标志。通过生存分析、ROC曲线分析评估IRL预测患者生存预后的价值。结果 共鉴定了1124个IRL，通过单因素Cox回归分析，初步筛选出18个和肺腺癌预后相关的IRL，且通过Lasso回归分析对预后相关的IRL进行二次选择后得到14个IRL与肺腺癌的生存预后相关。Cox分析筛选出其中8个IRL作为预后的独立危险因子用于构建风险评分模型。K-M生存分析显示，低风险组生存时间长于高风险组（P＜0.001）。ROC曲线分析显示，风险评分、年龄、性别、分期、T、M、N对肺腺癌预后的曲线下的面积分别为0.827、0.498、0.579、0.733、0.673、0.508、0.685。对风险模型绘制风险曲线，结果显示随着风险值的升高，患者生存时间逐渐下降，死亡人数逐渐增多。结论 IRL构建的8个风险模型可能是肺腺癌患者潜在的预测预后生物标志物。
Abstract:
Objective To identify potential immunoprognostic lncRNA (IRL) biomarkers by bioinformatics methods based on TCGA and Molecular Signatures Database data for prognosis prediction of lung adenocarcinoma patients.Methods The IRL dataset of lung adenocarcinoma and corresponding clinical parameters were obtained from TCGA. Immune genes were extracted from Molecular Signatures Database v7.0. Immune-related IRL was identified by constructing an immune gene co-expression network. Cox regression analysis and Lasso regression analysis were used to screen IRL biomarkers related to the prognosis of lung adenocarcinoma. The value of IRL in predicting the survival prognosis of patients was evaluated by survival analysis and ROC curve analysis.Results A total of 1124 IRLs were identified. Through univariate Cox regression analysis, 18 IRLs related to the prognosis of lung adenocarcinoma were initially screened, and 14 IRLs related to the survival and prognosis of lung adenocarcinoma were obtained after secondary selection of prognosis-related IRLs by Lasso regression analysis. Cox analysis screened 8 IRLs as independent risk factors for prognosis to construct a risk scoring model. K-M survival analysis showed that the survival time of the low-risk group was longer than that of the high-risk group (P<0.001). ROC curve analysis showed that the area under the curve of risk score, age, gender, stage, T, M and N for the prognosis of lung adenocarcinoma was 0.827, 0.498, 0.579, 0.733, 0.673, 0.508 and 0.685, respectively. The risk curve was drawn for the risk model. The results showed that with the increase of the risk value, the survival time of the patients gradually decreased, and the number of deaths gradually increased.Conclusion Eight risk models constructed by IRL may be potential prognostic biomarkers for patients with lung adenocarcinoma.

参考文献/References:

[1]Freddie B,Jacques F,Isabelle S,et al.Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J].CA Cancer J Clin,2018,68(6):394-424.[2]Meng W,Ye Z,Cui R,et al.MicroRNA-31 Predicts the Presence of Lymph Node Metastases and Survival in Patients with Lung Adenocarcinoma[J].Clinical Cancer Research,2013,19(19):5423-5433.[3]Denisenko TV,Budkevich IN,Zhivotovsky B.Cell death-based treatment of lung adenocarcinoma[J].Cell Death Dis,2018,9(2):117.[4]Molina JR,Yang P,Cassivi SD,et al.Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship[J].Mayo Clin Proc,2008,83(5):584-594.[5]Fry WA,Phillips JL,Menck HR.Ten-year survey of lung cancer treatment and survival in hospitals in the United States: a national cancer data base report[J].Cancer,1999,86(9):1867-1876.[6]Quinn JJ,Chang HY.Unique features of long non-coding RNA biogenesis and function[J].Nature Reviews Genetics,2015,17(1):47-62.[7]Cesana M,Cacchiarelli D,Legnini I,et al.A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA[J].Cell,2011,147(2):358-369.[8]Ferrè F,Colantoni A,Helmer-Citterich M.Revealing protein-lncRNA interaction[J].Brief Bioinform,2016,17(1):106-116.[9]van Dijk N,Funt SA,Blank CU,et al.The Cancer Immunogram as a Framework for Personalized Immunotherapy in Urothelial Cancer[J].Eur Urol,2019,75(3):435-444.[10]Desrichard A,Snyder A,Chan TA.Cancer Neoantigens and Applications for Immunotherapy[J].Clin Cancer Res,2016,22(4):807-812.[11]Angell H,Galon J.From the immune contexture to the Immunoscore: the role of prognostic and predictive immune markers in cancer[J].Curr Opin Immunol,2013,25(2):261-267.[12]Shen Y,Peng X,Shen C.Identification and validation of immune-related lncRNA prognostic signature for breast cancer[J].Genomics,2020,112(3):2640-2646.[13]Liao X,Zhu G,Huang R,et al.Identification of potential prognostic microRNA biomarkers for predicting survival in patients with hepatocellular carcinoma[J].Cancer Manag Res,2018,10:787-803.[14]彭慧,秦凯,戴宇翃,等.基于TCGA数据库的胶质母细胞瘤LncRNA风险预测模型的建立[J].肿瘤防治研究,2019,46(5):35-38.[15]杨飞龙,洪锴,赵国江,等.基于长链非编码RNA的生物信息学分析构建膀胱癌预后模型并确定预后生物标志物[J].北京大学学报(医学版),2019,51(4):615-622.[16]Wang Z,Jensen MA,Zenklusen JC.A Practical Guide to The Cancer Genome Atlas (TCGA)[J].Methods in Molecular Biology,2016,1418:111.[17]Deng M,Br?覿gelmann J,Schultze JL,et al.Web-TCGA: an online platform for integrated analysis of molecular cancer data sets[J].BMC Bioinformatics,2016,17:72. [18]Skuratovskaia D,Vulf M,Komar A,et al.Promising Directions in Atherosclerosis Treatment Based on Epigenetic Regulation Using MicroRNAs and Long Noncoding RNAs[J].Biomolecules,2019,9(6):226.[19]Hu G,Niu F,Humburg BA,et al.Molecular mechanisms of long noncoding RNAs and their role in disease pathogenesis[J].Oncotarget,2018,9(26):18648-18663.[20]Kopp F,Mendell JT.Functional Classification and Experimental Dissection of Long Noncoding RNAs[J].Cell,2018,172(3):393-407.[21]Tan YT,Lin JF,Li T,et al.LncRNA-mediated posttranslational modifications and reprogramming of energy metabolism in cancer[J].Cancer Commun (Lond),2021,41(2):109-120.[22]Li Y,Egranov SD,Yang L,et al.Molecular mechanisms of long noncoding RNAs-mediated cancer metastasis[J].Genes,Chromosomes and Cancer,2019,58(4):200-207.[23]He RZ,Luo DX,Mo YY.Emerging roles of lncRNAs in the post-transcriptional regulation in cancer[J].Genes & Diseases,2019,6(1):6-15.[24]Huang D,Chen J,Yang L,et al.NKILA lncRNA promotes tumor immune evasion by sensitizing T cells to activation-induced cell death[J].Nat Immunol,2018,19(10):1112-1125.[25]Sun X,Li S,Lv X,et al.Immune-Related Long Non-coding RNA Constructs a Prognostic Signature of Ovarian Cancer[J].Biol Proced Online,2021,23(1):24.[26]Schmitt A,Chang H.Long Noncoding RNAs in Cancer Pathways[J].Cancer Cell,2016,29(4):452-463.[27]Bhan A,Soleimani M,Mandal SS.Long Noncoding RNA and Cancer: A New Paradigm[J].Cancer Res,2017,77(15):3965-3981.[28]Zhang A,Xu M,Mo YY.Role of the lncRNA-p53 regulatory network in cancer[J].Journal of Molecular Cell Biology,2014,6(3):181-191.

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(01):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(01):124.[doi:10.3969/j.issn.1006-1959.2018.11.039]
[3]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(01):148.[doi:10.3969/j.issn.1006-1959.2019.03.048]
[4]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(01):86.[doi:10.3969/j.issn.1006-1959.2018.23.024]
[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(01):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(01):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(01):77.[doi:10.3969/j.issn.1006-1959.2020.09.022]
[8]彭莉蓉,黎村艳,史 杨.肿瘤微环境免疫细胞中差异lncRNA对肝细胞癌预后的预测价值[J].医学信息,2022,35(20):18.[doi:10.3969/j.issn.1006-1959.2022.20.005]
　PENG Li-rong,LI Cun-yan,SHI Yang.The Predictive Value of Differential lncRNA in Tumor Microenvironment Immune Cells for the Prognosis of Hepatocellular Carcinoma[J].Journal of Medical Information,2022,35(01):18.[doi:10.3969/j.issn.1006-1959.2022.20.005]
[9]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(01):17.[doi:10.3969/j.issn.1006-1959.2022.22.003]
[10]孔德伟,吴 铭,张 岩.肺腺癌骨转移肿瘤标记物的研究进展[J].医学信息,2022,35(23):171.[doi:10.3969/j.issn.1006-1959.2022.23.038]
　KONG De-wei,WU Ming,ZHANG Yan.Research Progress on Tumor Markers of Bone Metastasis in Lung Adenocarcinoma[J].Journal of Medical Information,2022,35(01):171.[doi:10.3969/j.issn.1006-1959.2022.23.038]
[11]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.[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(01):1.[doi:10.3969/j.issn.1006-1959.2024.05.001]

更新日期/Last Update: 1900-01-01