[1]钟双泽,陈尚金,林汉胜,等.基于TCGA 数据库生物信息学分析构建肾癌N6- 甲基腺苷相关LncRNA 配对模型及其预后预测价值研究[J].现代检验医学杂志,2024,39(02):68-74.[doi:10.3969/j.issn.1671-7414.2024.02.013]
　ZHONG Shuangze,CHEN Shangjin,LIN Hansheng,et al.Construction of N6-methyladenosine Related LncRNA Pairing Model for Renal Cell Carcinoma Based on Bioinformatics Analysis of TCGA Database and Its Prognostic Value Research[J].Journal of Modern Laboratory Medicine,2024,39(02):68-74.[doi:10.3969/j.issn.1671-7414.2024.02.013]
点击复制

基于TCGA 数据库生物信息学分析构建肾癌N6- 甲基腺苷相关LncRNA 配对模型及其预后预测价值研究()
分享到：

《现代检验医学杂志》[ISSN:/CN:]

卷:
第39卷
期数:
2024年02期
页码:
68-74
栏目:
论著
出版日期:
2024-03-31

文章信息/Info

Title:
Construction of N6-methyladenosine Related LncRNA Pairing Model for Renal Cell Carcinoma Based on Bioinformatics Analysis of TCGA Database and Its Prognostic Value Research
文章编号:
1671-7414（2024）02-068-07
作者:
钟双泽1; 陈尚金2; 林汉胜1; 罗远成1; 胡国帆1; 何京伟1
（1. 广东医科大学，广东湛江 524023；2. 广东医科大学附属阳江医院，广东阳江 529599）
Author(s):
ZHONG Shuangze1;  CHEN Shangjin2;  LIN Hansheng1;  LUO Yuancheng1;  HU Guofan1;  HE Jingwei1
（1. Guangdong Medical University, Guangdong Zhanjiang 524023，China; 2. Yangjiang Hospital Affiliated to Guangdong Medical University, Guangdong Yangjiang 529599, China）
关键词:
肾癌; 生物信息学; N6- 甲基腺苷; 长链非编码RNA
分类号:
R737.11；R730.43
DOI:
10.3969/j.issn.1671-7414.2024.02.013
文献标志码:
A
摘要:
目的　探究基于癌症基因组图谱（the cancer genome atlas，TCGA）数据库生物信息学分析构建肾癌N6- 甲基腺苷相关长链非编码RNA（long non-coding RNA，LncRNA）配对模型及其预后预测价值。方法　从TCGA 数据库中下载肾癌的RNA-sep 转录组数据及相关临床信息，后通过Perl 软件对转录组数据进行数据整理、分离LncRNA 和信使RNA（messenger RNA, mRNA）。总共得到564 例肾癌患者的肾癌组织和72 例正常组织，最终纳入540 例肾癌患者。使用caret 将540 例肾癌患者采用随机数据表法分为训练集组（n=275）和验证集组（n=265）。根据单因素和多因素COX 回归分析建立N6- 甲基腺苷相关LncRNA 配对模型。以LASSO 回归算法获取风险评估方程。根据该方程分别计算出风险评分，并以中位风险值最佳临界点将所有患者分为高风险组及低风险组。采用Kaplan-Meier 生存分析对总体样本中高、低风险组患者的生存差异作出生存曲线图。利用Cluster Profiler 软件包中对基因本体论（gene ontology，GO）和京都基因与基因组百库全书（Kyoto encyclopedia of genes and genomes，KEGG）进行通路富集分析。运用R 软件分析N6- 甲基腺苷相关LncRNA 配对模型与免疫细胞浸润的关系。结果　根据Kaplan-Meier 生存分析显示，在训练组中，低风险组患者总生存期明显高于高风险组患者（P ＜ 0.05）。与高风险组相比，低风险组G1 ～ 2，G3 ～ 4，Ⅰ～Ⅱ期、Ⅲ～Ⅳ期、年龄≤ 65 岁、＞ 65 岁患者总生存期较高（P ＜ 0.05）。对高、低风险组获取差异基因富集分析：主要富集含有肌收缩、横纹肌细胞分化、肌原纤维、受体激活活性、血管平滑肌收缩等。高风险组和低风险组最高的驱动基因进行展示变异频率及变异信息，其风险评分与T 细胞、浆细胞的浸润程度呈正相关（r=0.638，P=0.001）。结论　基于生物信息学分析N6- 甲基腺苷相关LncRNA 配对模型有助于预测肾癌患者的预后。为肾癌预后评估和最佳治疗策略提供了新思路，有助于未来进一步分析胃癌发生及发展的分子机制。
Abstract:
Objective　To construct N6-methyladenosine related long non-coding RNA (LncRNA) pairing model for renal cell carcinoma based on bioinformatics analysis of the cancer ganome atlas(TCGA) database and to explore its prognosis value. Methods　Transcriptome data of RNA-sep for renal cell carcinoma and its related clinical information were downloaded from the TCGA database. Perl software was used to organize and separate LncRNA and messenger RNA(mRNA) from the transcriptome data. A total of 564 tissues from renal cell carcinoma cases and 72 normal tissues were obtained, and thus 540 renal cancer patients were eventually included. Random data table method was used to divide 540 patients with renal cancer into a training group (n=275) and a validation group (n=265) by caret. M6A related LncRNA pairing models were established based on the single factor and multivariate COX regression analysis. The risk assessment equation was obtained using the LASSO regression algorithm. The risk scores were calculated based on this equation, and the optimal critical point of the median risk value was applied to divide all patients into high-risk and low-risk groups. Kaplan-Meier survival analysis was used to make a survival curve for the differences between high and low risk groups in the overall sample. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were conducted using the Cluster Profiler software package. The relationship between N6-methyladenosine related LncRNA pairing model and immune cell infiltration was analyzed by R software. Results　Kaplan-Meier survival analysis showed the total survival time of patients in the low-risk group was significantly higher than that of patients in the high-risk group of the training group (P<0.05). Compared with high risk group, the overall survival time of patients (G1 ～ 2, G3 ～ 4, Ⅰ～Ⅱ , or Ⅲ～Ⅳ , age ≤ 65 years, or patients >65 years old) in low risk group was higher (P<0.05). Differential gene enrichment analysis was obtained for high and low risk groups, which mainly enriched with many differential genes such as muscle contraction, rhabdomytic cell differentiation, myofibril, receptor activation activity, and vascular smooth muscle contraction. The highest driver genes in high risk group and low risk group exhibited mutation frequency and mutation information, and their risk score was positively correlated with the degree of T cell and plasma cell infiltration (r=0.638, P=0.001). Conclusion　Bioinformatics-based analysis of the N6-methyladenosine related LncRNA pairing models can be helpful to predict the prognosis of patients with renal cancer. It provides new ideas for the prognosis evaluation and optimal treatment strategy of renal cancer, and contributes to further analyzing the molecular mechanism of the occurrence and development of gastric cancer in the future.

参考文献/References:

[1] LI Fengzhi, ALJAHDALI I A M, ZHANG Renyuan,et al. Kidney cancer biomarkers and targets for therapeutics: survivin (BIRC5), XIAP, MCL-1, HIF1α,HIF2α, NRF2, MDM2, MDM4, p53, KRAS and AKT in renal cell carcinoma[J]. Journal of Experimental & Clinical Cancer Research, 2021, 40(1): 254.
[2] TIAN Xi, XU Wenhao, XU Fujiang, et al. Identification of prognostic biomarkers in papillary renal cell carcinoma and PTTG1 may serve as a biomarker for predicting immunotherapy response[J]. Annals of Medicine, 2022, 54(1): 211-226.
[3] GUI Chengpeng, WEI Jinhuan, CHEN Yuhang, et al. A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma[J]. Briefings in Bioinformatics, 2021, 22(6): bbab173.
[4] DUDANI S, DE VELASCO G, WELLS J C, et al. Evaluation of clear cell, papillary, and chromophobe renal cell carcinoma metastasis sites and association with survival[J]. JAMA Network Open, 2021, 4(1):e2021869.
[5] HASANOV E, YEBOA D N, TUCKER M D, et al. An interdisciplinary consensus on the management of brain metastases in patients with renal cell carcinoma[J]. CA:A Cancer Journal for Clinicians, 2022, 72(5): 454-489.
[6] WANG Qi, TANG Hanmin, LUO Xuehui, et al. Immune-associated gene signatures serve as a promising biomarker of immunotherapeutic prognosis for renal clear cell carcinoma[J]. Frontiers in Immunology, 2022, 13: 890150.
[7] REUSTLE A, MENIG L S, LEUTHOLD P, et al. Nicotinamide-N-methyltransferase is a promising metabolic drug target for primary and metastatic clear cell renal cell carcinoma[J]. Clinical and Translational Medicine, 2022, 12(6): e883.
[8] WANG Bingran, XUE Yizheng, ZHAI Wei. Integration of tumor microenvironment in patient-derived organoid models help define precision medicine of renal cell carcinoma[J]. Frontiers in Immunology, 2022, 13:902060.
[9] COOLEY L S, RUDEWICZ J, SOULEYREAU W,et al. Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression[J]. Molecular Cancer, 2021,20(1): 136.
[10] HUA Shan, XIE Zhiwen, ZHANG Yongqing, et al. Identification and validation of an immune-related gene prognostic signature for clear cell renal carcinoma[J].Frontiers in Immunology, 2022, 13: 869297.
[11] 岳蔷薇, 徐乐, 张东升．基于YTHDC2，IGF2BP2和HNRNPC 的头颈部鳞状细胞癌N6- 甲基腺苷风险模型构建及临床应用评估[J]．华西口腔医学杂志,2022, 40(6): 704-709. YUE Qiangwei, XU Le, ZHANG Dongsheng. Construction and clinical evaluation of N6-methyladenosine risk signature of YTHDC2,IGF2BP2,and HNRNPC in head and neck squamous cell carcinoma[J]. West China Journal of Stomatology, 2022, 40(6): 704-709.
[12] 高明珠, 王进有, 张涛, 等．肾癌组织YAP1/TAZ 蛋白表达与临床病理特征及患者远期生存的相关性研究[J]. 现代检验医学杂志, 2022, 37(6)：1-6, 51． GAO Mingzhu, WANG Jinyou, ZHANG Tao, et al. Study on the expressions of YAP1/TAZ proteins in renal cell carcinoma and its relationships with clinicopathology and long-term survival of patients[J].Journal of Modern Laboratory Medicine, 2022, 37(6):1-6, 51.
[13] 肖胜英, 闫志广, 曾福仁, 等．N6- 甲基腺苷相关LncRNAs 是预测肾癌患者预后和免疫浸润的潜在生物标志物[J]. 中国免疫学杂志, 2022, 38(19)：2358-2365． XIAO Shengying, YAN Zhiguang, ZENG Furen, et al. N6-methylandenosine-related LncRNAs are potential biomarkers for predicting prognoses and immune infiltrates in patients with renal cell carcinoma[J].Chinese Journal of Immunology, 2022, 38(19): 2358-2365.
[14] 韦韡, 张雯, 曹飞.N6- 甲基腺苷甲基化对结直肠癌细胞中程序性死亡配体1 表达的影响[J]. 中国临床药理学杂志, 2023, 39(3): 391-394． WEI Wei, ZHANG Wen, CAO Fei. Effect of N6-methyladenosine methylation on the expression of programmed death ligand 1 in colorectal cancer cells[J].The Chinese Journal of Clinical Pharmacology, 2023,39(3): 391-394.
[15] 王小艳, 崔洪银, 谢青文, 等.N6- 甲基腺苷依赖性pri-miR-17-92 成熟激活AKT/mTOR 途径促进子宫内膜癌进展[J]. 中华内分泌外科杂志, 2022, 16(6):698-702. WANG Xiaoyan, CUI Hongyin, XIE Qingwen, et al. N 6-methyladenosine-dependent pri-miR-17-92 mature activates AKT/mTOR pathway to promote endometrial cancer progression[J]. Chinese Journal of Endocrine Surgery, 2022, 16(6): 698-702.
[16] 李念燊, 韩杏倩, 何金阳, 等. 基于N6- 甲基腺苷相关长链非编码核糖核酸表达的喉鳞状细胞癌预后分析[J]. 中国耳鼻咽喉颅底外科杂志, 2023, 29(1):81-91． LI Nianshen, HAN Xingqian, HE Jinyang, et al. Prognostic analysis of laryngeal squamous cell carcinoma based on the expression of N6-methyladenosine-related LncRNAs[J]. Chinese Journal of Otorhinolaryngology-skull Base Surgery, 2023,29(1): 81-91.
[17] 阎柄睿, 王鹏, 李雨珊, 等．m6A 修饰调控RP11-426A6.5 在喉鳞状细胞癌中的作用及机制研究[J].中华耳鼻咽喉头颈外科杂志, 2022, 57(12): 1470-1478． YAN Bingrui, WANG Peng, LI Yushan, et al. Roles and mechanisms of m6A modification regulating RP11-426A6.5 in laryngeal squamous cell carcinoma[J].Chinese Journal of Otolaryngology Head and Neck Surgery, 2022, 57(12): 1470-1478.
[18] 倪书勤, 何元．N6- 甲基腺苷诱导LncRNA PVT1 靶向作用MYC 对氯胺酮治疗的乳腺癌细胞干性的影响[J]. 中华内分泌外科杂志, 2022, 16(2): 174-179． NI Shuqin, HE Yuan. Effects of N6-methyladenosineinduced LncRNA PVT1 targeting MYC on the stemness of ketamine-treated breast cancer cells[J].Chinese Journal of Endocrine Surgery, 2022, 16(2):174-179.
[19] 刘宁, 江帆, 陈之巨, 等．M6A 甲基化调控因子对结直肠癌预后及细胞生物学行为的影响[J]. 陆军军医大学学报, 2022,44(11):1126-1135． LIU Ning, JIANG Fan, CHEN Zhiju, et al. Effects of N6-methyladenosine methylation regulators on prognosis and cell biological behaviors of colorectal cancer[J]. Journal of Third Military Medical University,2022,44(11):1126-1135.
[20] 刘郴郴, 宋正帅, 章小平．肾透明细胞癌舒尼替尼耐药的分子标志物筛选及相关生物学功能分析[J].华中科技大学学报( 医学版), 2021, 50(2):142-151. LIU Chenchen, SONG Zhengshuai, ZHANG Xiaoping. Screening of new biomarkers for sunitinib resistance in clear cell renal cell carcinoma and analysis of related biological functions[J]. Acta Medicinae Universitatis Scientiae Et Technologiae Huazhong(Medical Edition),2021, 50(2): 142-151.
[21] 邹元章, 卢俅, 陈兵海. 基于生物信息学分析miR-130b-3p 在肾透明细胞癌中的表达、靶基因及预后价值[J]. 中国免疫学杂志, 2021, 37(13): 1614-1618. ZOU Yuanzhang, LU Qiu, CHEN Binghai. Expression,target genes and prognostic value of miR-130b-3p in renal clear cell carcinoma based on bioinformatics method[J]. Chinese Journal of Immunology, 2021,37(13): 1614-1618.
[22] 张文珺, 牛小伟, 刘永铭.N6- 甲基腺苷甲基化在射血分数保留性心力衰竭中的作用的研究进展[J]. 心血管病学进展, 2022, 43(1): 44-47. ZHANG Wenjun, NIU Xiaowei, LIU Yongming. Role of N6-methyladenosine methylation in heart failure with preserved ejection fraction[J]. Advances in Cardiovascular Diseases, 2022, 43(1): 44-47.

相似文献/References:

[1]陈龙梅,杨振华.基于GEO数据库对类风湿性关节炎相关基因筛选及生物信息学分析[J].现代检验医学杂志,2021,36(02):49.[doi:doi:10.3969/j.issn.1671-7414.2021.02.012]
　CHEN Long-mei,YANG Zhen-hua.Gene Screening and Bioinformatics Analysis of Rheumatoid Arthritis Based on GEO Database[J].Journal of Modern Laboratory Medicine,2021,36(02):49.[doi:doi:10.3969/j.issn.1671-7414.2021.02.012]
[2]吴良银a,李文丽b,刘　俊b.基于GEO数据的病毒相关性肝癌潜在生物基因标志物的筛选及生物信息学分析[J].现代检验医学杂志,2021,36(06):106.[doi:10.3969/j.issn.1671-7414.2021.06.022]
　WU Liang-yin,LI Wen-li,LIU Jun.Screening and Bioinformatics Analysis of Potential Biomarkers for Virus-associated Hepatocellular Carcinoma Based on GEO Data[J].Journal of Modern Laboratory Medicine,2021,36(02):106.[doi:10.3969/j.issn.1671-7414.2021.06.022]
[3]张涛元,丁雪梅,李 俏,等.人非小细胞肺癌组织中转录因子E2F 家族表达与临床病理特征及预后的相关性分析[J].现代检验医学杂志,2022,37(04):87.[doi:10.3969/j.issn.1671-7414.2022.04.017]
　ZHANG Tao-yuan,DING Xue-mei,LI Qiao,et al.Correlation Analysis of Transcription Factor E2F Family Expression with Clinicopathological Features and Prognosis in Human Non-small Cell Lung Cancer[J].Journal of Modern Laboratory Medicine,2022,37(02):87.[doi:10.3969/j.issn.1671-7414.2022.04.017]
[4]高明珠,王进有,张 涛,等.肾癌组织YAP1/TAZ 蛋白表达与临床病理特征及患者远期生存的相关性研究[J].现代检验医学杂志,2022,37(06):1.[doi:10.3969/j.issn.1671-7414.2022.06.001]
　GAO Ming-zhu,WANG Jin-you,ZHANG Tao,et al.Study on the Expressions of YAP1/TAZ Proteins in Renal Cell Carcinoma and Its Relationships with Clinicopathology and Long-term Survival of Patients[J].Journal of Modern Laboratory Medicine,2022,37(02):1.[doi:10.3969/j.issn.1671-7414.2022.06.001]
[5]侯芳霞,刘 琳,张 维,等.基于GEO 数据库筛选稳定性心绞痛患者外周血关键差异基因及诊断模型构建[J].现代检验医学杂志,2022,37(06):19.[doi:10.3969/j.issn.1671-7414.2022.06.004]
　HOU Fang-xia,LIU Lin,ZHANG Wei,et al.Identification of Hub Genes and Differential Expression Genes for Peripheral Blood Samples of Stable Angina Pectoris Based on GEO Databases[J].Journal of Modern Laboratory Medicine,2022,37(02):19.[doi:10.3969/j.issn.1671-7414.2022.06.004]
[6]毛 俊,沈秀芬,马 润,等.基于TCGA 和GEO 数据库建立了肝内胆管癌的预后风险模型及验证分析[J].现代检验医学杂志,2023,38(03):40.[doi:10.3969/j.issn.1671-7414.2023.03.008]
　MAO Jun,SHEN Xiu-fen,MA Run,et al.Establishment and Verification of Prognostic Risk Model of Intrahepatic Cholangiocarcinoma Based on TCGA and GEO Database[J].Journal of Modern Laboratory Medicine,2023,38(02):40.[doi:10.3969/j.issn.1671-7414.2023.03.008]
[7]侯 丽,张 丽,唐 婧,等.基于GEO 对多发性骨髓瘤关键基因生物信息学分析及免疫浸润模式与验证[J].现代检验医学杂志,2023,38(05):23.[doi:10.3969/j.issn.1671-7414.2023.05.005]
　HOU Li,ZHANG Li,TANG Jing,et al.Bioinformatics Analysis and Verify Core Genes and Immune Infiltration Patterns in Multiple Myeloma Based on GEO[J].Journal of Modern Laboratory Medicine,2023,38(02):23.[doi:10.3969/j.issn.1671-7414.2023.05.005]
[8]曹 君,金婕妤,张 胜,等.生物信息学方法筛选IL-3和IL-3+SCF诱导的小鼠骨髓来源肥大细胞的差异表达基因及相关信号通路分析[J].现代检验医学杂志,2024,39(01):16.[doi:10.3969/j.issn.1671-7414.2024.01.004]
　CAO Jun,JIN Jieyu,ZHANG Sheng,et al.Screening of IL-3 and IL-3+SCF Induce Differentially Expressed Genes and Signaling Pathways in Bone Marrow-derived Mast Cells Based on Bioinformatics[J].Journal of Modern Laboratory Medicine,2024,39(02):16.[doi:10.3969/j.issn.1671-7414.2024.01.004]
[9]刁 迅,范绮雨,耿良栋,等.基于生物信息学分析双硫死亡相关基因PDLIM1 mRNA在多种肿瘤中的表达及临床应用价值[J].现代检验医学杂志,2024,39(01):36.[doi:10.3969/j.issn.1671-7414.2024.01.007]
　DIAO Xun,FAN Qiyu,GENG Liangdong,et al.Analysis of Expression in Disulfidptosis-Related Gene PDLIM1 mRNA in Various Tumors and Its Clinical Application Value Based on Bioinformatics[J].Journal of Modern Laboratory Medicine,2024,39(02):36.[doi:10.3969/j.issn.1671-7414.2024.01.007]

备注/Memo

备注/Memo:
基金项目：阳江市科技立项：单基因在肾癌中的生物信息学及免疫预后相关性分析。
作者简介：钟双泽（1997-），男，主治医生，主要从事肾癌医学相关的临床研究，E-mail: 2894452518@qq.com。
通讯作者：何京伟（1974-），男，主任医师，主要从事肾癌医学相关的临床研究，E-mail: 18900808800@163.com。
更新日期/Last Update: 2024-03-15