基于免疫相关lncRNAs的胃癌早期诊断和预后风险模型的构建和验证

doi:10.11904/j.issn.1002-3070.2022.02.004

实用肿瘤学杂志 ›› 2022, Vol. 36 ›› Issue (2): 119-126.doi: 10.11904/j.issn.1002-3070.2022.02.004

基于免疫相关lncRNAs的胃癌早期诊断和预后风险模型的构建和验证

王洪英¹, 李岩¹, 韩鑫浩¹, 魏孝礼², 贾慧珣³, 袁文娟⁴, 张秋菊¹

1.哈尔滨医科大学公共卫生学院卫生统计教研室(哈尔滨 150086);
2.哈尔滨医科大学附属肿瘤医院消化内科第一病房;
3.上海交通大学附属第一人民医院;
4.桦川县悦来镇卫生院

收稿日期:2021-12-21 修回日期:2022-03-11 出版日期:2022-04-28 发布日期:2022-04-28
通讯作者: 张秋菊,E-mail:zhangqj@hrbmu.edu.cn
作者简介:王洪英,女,(1995-),硕士研究生,从事肿瘤多组学的研究。
基金资助:
国家自然基金面上项目(编号:82073666);国家自然基金青年基金(编号:82003554);北京医学奖励基金会项目(编号:YXJL-2020-0785-0976)

Construction and validation of a risk model for early diagnosis and prognosis of gastric cancer based on immune-related lncRNAs

WANG Hongying¹, LI Yan¹, HAN Xinhao¹, WEI Xiaoli², JIA Huixun³, YUAN Wenjuan⁴, ZHANG Qiuju¹

1. Department of Health Statistics,School of Public Health,Harbin Medical University,Harbin 150086,China;
2. Ward 1,Department of Gastroenterology,Harbin Medical University Cancer Hospital;
3. The First People′s Hospital Affiliated to Shanghai Jiao Tong University;
4. Yuelai Town Health Center,Huachuan County

Received:2021-12-21 Revised:2022-03-11 Online:2022-04-28 Published:2022-04-28

摘要/Abstract

摘要： 目的基于胃癌免疫相关lncRNAs,建立早期诊断模型和预后风险模型,为胃癌早期诊断和预后预测提供数据支持。方法利用TCGA-STAD数据集与immport数据库筛选胃癌相关免疫lncRNAs。将TCGA-STAD作为训练集,利用logistic回归分析构建胃癌早期诊断模型;单因素Cox和LASSO回归分析用于筛选影响患者总生存期的免疫lncRNAs并构建预后基因标签,最后结合基因标签和患者临床指标,构建胃癌预后风险模型。胃癌基因芯片数据集GSE54129和GSE62254分别作为诊断和预后模型的验证集进行外部验证。结果筛选出9个免疫相关lncRNAs用于构建胃癌早期诊断模型,模型构建和验证的Hosmer-Lemeshow检验P值分别为0.9982和1.0000,ROC曲线下面积分别为0.991和0.958。获得6个影响患者总生存期的免疫lncRNAs用于构建基因标签。根据标签风险得分中位数将患者分为高、低风险组,生存分析表明高风险组患者总生存率低于低风险组患者(训练集及验证集,P＜0.05)。生存状态预测基因标签构建构建及验证的C-index分别为0.61和0.59,1、3、5年总生存率ROC曲线下面积分别为0.623、0.623、0.677和0.581、0.613、0.622。多因素Cox回归分析表明基因标签、年龄、肿瘤分期是影响胃癌患者总生存率的独立预后因素,以此构建预后风险模型。C-index、ROC曲线和校准曲线分析表明,预后风险模型的预测效能优于基因标签。结论早期诊断模型可以有效协助胃癌早期筛查;基因标签是影响胃癌患者总生存率的独立因素,可以中等程度地预测患者的预后,相比而言,预后风险模型可以进一步提高模型的预测能力。

关键词: 胃癌, 诊断, 预后, 免疫相关长链非编码RNAs

Abstract: Objective The aim of this study was to establish early diagnosis model and a prognostic risk model on immune-related lncRNAs of gastric cancer based,and to provide data support for early diagnosis and prediction prognosis of gastric cancer.Methods The TCGA-STAD dataset and the immport database were used to screen gastric cancer-related immune lncRNAs.The TCGA-STAD was used as the training set to construct a model for early diagnosis model of gastric cancer by logistic regression analysis.Univariate Cox and LASSO regression analyses were used to screen immune lncRNAs that affected the overall survival of patients and construct prognostic gene signature.Finally,the gene signature and patient clinical indicators were combined to construct a risk model of gastric cancer prognosis.Gastric cancer gene chip datasets GSE54129 and GSE62254 were used as validation sets for external validation of the diagnostic and prognostic models,respectively.Results Nine immune-related lncRNAs were screened out to construct an early diagnostic model of gastric cancer.The Hosmer-Lemeshow test P-values of model construction and validation were 0.9982 and 1.0000,respectively,and the areas under the ROC curve were 0.991 and 0.958,respectively.Six immune lncRNAs affecting the overall survival of patients were obtained to construct the gene signature.Patients were divided into the high and low risk groups according to the median signature risk score.Survival analysis showed that the overall survival rate of patients in the high risk group was lower than that of the patients in the low risk group(training set and validation set,P＜0.05).The C-index of the survival status prediction genetic signature construction and validation were 0.61 and 0.59,respectively,and the areas under ROC curves for 1-,3- and 5-year overall survival rates were 0.623,0.623,0.677 and 0.581,0.613,0.622,respectively.Multivariate Cox regression analysis showed that gene signature,age and tumor stage were independent factors affecting the overall survival rate of gastric cancer patients,and a prognostic risk model was constructed.C-index,ROC curve and calibration curve analysis showed that the predictive power of the prognostic risk model was better than that of the gene signature.Conclusion The early diagnostic model can effectively assist the early screening of gastric cancer.The gene signature,an independent factor affecting the overall survival rate of gastric cancer patients, which can predict the prognosis of patients to a moderate degree.In comparison,the prognostic risk model can further improve the predictive ability of the model.

Key words: Gastric cancer, Diagnosis, Prognosis, Immune-related long non-coding RNAs

中图分类号:

R735.2

王洪英, 李岩, 韩鑫浩, 魏孝礼, 贾慧珣, 袁文娟, 张秋菊. 基于免疫相关lncRNAs的胃癌早期诊断和预后风险模型的构建和验证[J]. 实用肿瘤学杂志, 2022, 36(2): 119-126.

WANG Hongying, LI Yan, HAN Xinhao, WEI Xiaoli, JIA Huixun, YUAN Wenjuan, ZHANG Qiuju. Construction and validation of a risk model for early diagnosis and prognosis of gastric cancer based on immune-related lncRNAs[J]. Journal of Practical Oncology, 2022, 36(2): 119-126.

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: http://journal09.magtechjournal.com/Jwk3_syzlxzz/CN/10.11904/j.issn.1002-3070.2022.02.004

http://journal09.magtechjournal.com/Jwk3_syzlxzz/CN/Y2022/V36/I2/119

参考文献

1 Sung H,Ferlay J,Siegel RL,et al.Global Cancer Statistics 2020:GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J].CA Cancer J Clin,2021,71(3):209-249.
2 刘科.胃癌分期不同,5年生存率差别大[J].家庭医药.快乐养生,2020,15(7):48.
3 Feng X,Huang S.Effect and mechanism of long noncoding RNAs HOTAIR on occurrence and development of gastric cancer[J].J Cell Biochem,2017,120(5):6899-6907.
4 Li T,Mo X,Fu L,et al.Molecular mechanisms of long noncoding RNAs on gastric cancer[J].Oncotarget,2016,7(8):8601-8612.
5 Jiang MC,Ni JJ,Cui WY,et al.Emerging roles of lncRNA in cancer and therapeutic opportunities[J].Am J Cancer Res,2019,9(7):1354-1366.
6 Ooi CH,Ivanova T,Wu J,et al.Oncogenic pathway combinations predict clinical prognosis in gastric cancer[J].PLoS Genet,2009,5(10):e1000676.
7 Cristescu R,Lee J,Nebozhyn M,et al.Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes[J].Nat Med,2015,21(5):449-456.
8 Love MI,Huber W,Anders S.Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[J].Genome Biol,2014,15(12):550.
9 Shen Q,Polom K,Williams C,et al.A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer[J].EBio Medicine,2019,44:322-333.
10 Bhan A,Soleimani M,Mandal SS.Long noncoding RNA and cancer:a new paradigm[J].Cancer Res,2017,77(15):3965-3981.
11 Zheng Q,Wu F,Dai WY,et al.Aberrant expression of UCA1 in gastric cancer and its clinical significance[J].Clin Transl Oncol,2015,17(8):640-646.
12 Li C,Wang H,Meng S,et al.lncRNA GAS8-AS1 regulates cancer cell proliferation and predicts poor survival of patients with gastric cancer[J].Oncology Letters,2022,23(2):48.
13 Liang X,Yu G,Zha L,et al.Identification and comprehensive prognostic analysis of a novel chemokine-related lncRNA signature and immune landscape in gastric cancer[J].Front Cell Dev Biol,2022,9:797341.
14 Han T,Xu D,Zhu J,et al.Identification of a robust signature for clinical outcomes and immunotherapy response in gastric cancer:based on N6-methyladenosine related long noncoding RNAs[J].Cancer Cell Int,2021,21(1):432.
15 Han L,Hao YP,Wang JH,et al.Knockdown of LINC02465 suppresses gastric cancer cell growth and metastasis Via PI3K/AKT pathway[J].Hum Gene Ther Clin Dev,2019,30(1):19-28.
16 Huang W,Liu Z,Li Y,et al.Identification of long noncoding RNAs biomarkers for diagnosis and prognosis in patients with colon adenocarcinoma[J].J Cell Biochem,2019,120(3):4121-4131.
17 Chen Q,Huang X,Dong X,et al.Long non-coding RNA ERICH3-AS1 is an unfavorable prognostic factor for gastric cancer[J].Peer J,2020,8:e8050.
18 Ma Y,Xu XL,Huang HG,et al.LncRNA TDRG1 promotes the aggressiveness of gastric carcinoma through regulating miR-873-5p/HDGF axis[J].Biomed Pharmacother,2020,121:109425.
19 阮班来.通过共表达网络分析(WGCNA)构建与胃癌发展进程相关的关键LncRNAs网络[D].沈阳:中国医科大学,2019.

[1]	尹翼, 欧朝阳, 何爱琴. GAB2在卵巢癌组织中的表达及其与预后的相关性[J]. 实用肿瘤学杂志, 2022, 36(2): 150-155.
[2]	陈赫, 王秋程, 程文. 自动乳腺超声诊断系统在乳腺癌方面的应用进展[J]. 实用肿瘤学杂志, 2022, 36(2): 193-196.
[3]	赵宝山, 梁冠盈, 许圣洁, 乔慧, 陈洪霞, 马莹, 鞠学萍, 王麟. PELP1表达与胃癌演进及预后相关性分析[J]. 实用肿瘤学杂志, 2022, 36(1): 31-36.
[4]	李伟华, 张广凤, 马骊骊, 何品, 王雯, 李顶夫. 基于TCGA数据库代谢相关基因构建乳腺癌预后模型[J]. 实用肿瘤学杂志, 2022, 36(1): 37-43.
[5]	王榆平, 思美丽, 孙小霞, 郭玉琳. HPIP在子宫内膜癌中高表达及其在生存预后分析中的意义[J]. 实用肿瘤学杂志, 2022, 36(1): 44-48.
[6]	曹世玉, 姜永冬. 基因表达与多基因检测以及生物标志物对Luminal A型乳腺癌预后的影响[J]. 实用肿瘤学杂志, 2022, 36(1): 89-93.
[7]	邬丽婷, 李大兵, 邓小霞, 李柏松. 2013—2020年重庆市北碚区胃癌疾病负担变化趋势及预测[J]. 实用肿瘤学杂志, 2021, 35(6): 495-499.
[8]	王丽娜, 王乐, 靳红, 李奇, 成海燕. UBQLN1在宫颈癌中的表达及其对预后的影响[J]. 实用肿瘤学杂志, 2021, 35(6): 506-510.
[9]	陈奕心, 孙轶华, 程洋洋, 何凯桐, 郭婷. 术前炎症相关指标与可切除胃癌患者预后的相关性[J]. 实用肿瘤学杂志, 2021, 35(6): 517-522.
[10]	于园园, 孟千皓, 王广雨. 胃癌免疫治疗困境与未来方向[J]. 实用肿瘤学杂志, 2021, 35(6): 552-556.
[11]	卢俊蓉, 王惠文, 何东风. SALL4高表达在食管鳞癌临床病理及预后判定中的意义[J]. 实用肿瘤学杂志, 2021, 35(5): 420-425.
[12]	穆慧娟, 李海娜, 吕艺, 孙丽华, 王丽娟, 礼彦侠, 于飞, 潘国伟, 刘莉. 辽宁省三城市居民胃癌10年生存率及其影响因素分析[J]. 实用肿瘤学杂志, 2021, 35(4): 339-343.
[13]	余幼林, 余军林, 胡超华, 沈雄山, 李卫民, 卢婷, 杨峰, 杨青青, 刘程浩. 基于生物信息学方法分析LAYN在甲状腺癌中临床预后价值及对免疫细胞浸润的影响[J]. 实用肿瘤学杂志, 2021, 35(4): 357-364.
[14]	刘文, 乔文波. 程序性死亡蛋白1及其配体在结直肠癌中的临床研究现状[J]. 实用肿瘤学杂志, 2021, 35(4): 370-375.
[15]	董晓平, 张雪, 郭冬利, 贺宇彤. 肺鳞状细胞癌预后相关影响因素研究进展[J]. 实用肿瘤学杂志, 2021, 35(4): 380-385.

基于免疫相关lncRNAs的胃癌早期诊断和预后风险模型的构建和验证

Construction and validation of a risk model for early diagnosis and prognosis of gastric cancer based on immune-related lncRNAs

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价