[1]黄 成,易尚辉,查文婷,等.基于生物信息学分析筛选舌鳞状细胞癌核心基因及其预后价值[J].医学信息,2020,33(03):6-12.[doi:10.3969/j.issn.1006-1959.2020.03.002]
　HUANG Cheng,YI Shang-hui,ZHA Wen-ting,et al.Screening Core Genes of Tongue Squamous Cell Carcinoma Based on Bioinformatics Analysis and Its Prognostic Value[J].Medical Information,2020,33(03):6-12.[doi:10.3969/j.issn.1006-1959.2020.03.002]
点击复制

基于生物信息学分析筛选舌鳞状细胞癌核心基因及其预后价值()
分享到：

医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
33卷
期数:
2020年03期
页码:
6-12
栏目:
出版日期:
2020-02-01

文章信息/Info

Title:
Screening Core Genes of Tongue Squamous Cell Carcinoma Based on Bioinformatics Analysis and Its Prognostic Value
文章编号:
1006-1959（2020）03-0006-07
作者:
黄 成; 易尚辉; 查文婷; 等
（湖南师范大学医学院，湖南 长沙 410001）
Author(s):
HUANG Cheng; YI Shang-hui; ZHA Wen-ting; LYU Yuan
(Hunan Normal University Medical College,Changsha 410001,Hunan,China)
关键词:
舌鳞状细胞癌; 生物信息学; 核心基因; 共表达网络
Keywords:
Tongue squamous cell carcinoma; Bioinformatics; Core genes; Co-expression network
分类号:
R739.91
DOI:
10.3969/j.issn.1006-1959.2020.03.002
文献标志码:
A
摘要:
目的 通过对GEO数据库提供的基因芯片数据进行挖掘，结合生物信息学分析基因表达谱，获取舌鳞状细胞癌（TSCC）核心基因，利用生存分析初步验证核心基因对舌鳞状细胞癌的预测效果。方法 从GEO数据库下载舌鳞状细胞癌相关芯片数据（GSE9844），获得了26例TSCC组织样本和12例癌旁组织样本的全基因组转录组谱，采用SAM算法筛选出TSCC与癌旁组织间的差异表达基因，并借助GEO的gene信息库对基因功能进行描述，筛选出TSCC与癌旁组织间的差异细胞信号通路，构建决定TSCC的基因共表达网络，通过GEPIA数据库来初步验证共表达网络中的核心基因是否与TSCC患者的生存预后存在相关性。结果 筛选出2074个差异表达基因，包括1119个上调基因和955个下调基因。以2074个差异表达基因作为共表达网络的构建基础，共纳入230个差异表达基因，筛选出5个TSCC核心的基因（ADCY4、PLA2G12A、MAOB、PDE2A、CYP2C9），通过GEPIA数据库对核心基因进行生存分析，初步验证共表达网络中高表达的ADCY4基因与TSCC总体生存率呈正相关（P=0.014），高表达PLA2G12A基因与TSCC总体生存率呈负相关（P=0.0029），MAOB、PDE2A及CYP2C9基因患者生存率比较，差异无统计学意义（P＞0.05）。结论 通过生物信息学方法分析影响TSCC的核心基因，最终筛选出2个差异表达非常显著且对患者预后影响明显的基因，对TSCC的诊断和预后治疗提供了新思路，提高TSCC机制的研究效率。
Abstract:
Objective To obtain core genes of tongue squamous cell carcinoma (TSCC) by mining gene chip data provided by the GEO database and analyzing gene expression profiles in combination with bioinformatics, and use survival analysis to initially verify the role of core genes in tongue squamous cell carcinoma forecast effect. Methods Tongue squamous cell carcinoma-related chip data (GSE9844) was downloaded from the GEO database, and the genome-wide transcriptome profiles of 26 TSCC tissue samples and 12 adjacent cancer tissue samples were obtained. The differences between TSCC and adjacent cancer tissues were screened using the SAM algorithm express genes, and use GEO’s gene information database to describe gene functions, screen differential signaling pathways between TSCC and adjacent tissues, construct a gene co-expression network that determines TSCC, and use the GEPIA database to initially verify the co-expression network. Whether the core genes are correlated with the survival prognosis of patients with TSCC.Results 2074 differentially expressed genes were screened, including 1119 up-regulated genes and 955 down-regulated genes. Using 2,074 differentially expressed genes as the basis for the construction of a co-expression network, a total of 230 differentially expressed genes were included, and 5 TSCC core genes (ADCY4, PLA2G12A, MAOB, PDE2A, CYP2C9) were selected, and the core genes were survived through the GEPIA database. The analysis showed that the highly expressed ADCY4 gene in the co-expression network was positively correlated with the overall survival rate of TSCC (P = 0.014), the highly expressed PLA2G12A gene was negatively correlated with the overall survival rate of TSCC (P = 0.0029), and patients with MAOB, PDE2A, and CYP2C9 genes,there was no significant difference in survival rate (P> 0.05).Conclusion The core genes affecting TSCC were analyzed by bioinformatics methods. 2 genes with very significant differential expression and significant effects on patients’ prognosis were finally screened, which provided new ideas for the diagnosis and prognosis of TSCC and improved the research efficiency of TSCC mechanism.

参考文献/References:

[1]Chan LL,Jiang P.Bioinformatics analysis of circulating cell-free DNA sequencing data[J].Clin Biochem,2015,48(15):962-975. [2]Ranganathan S,Tan T,Schonbach C.InCoB2014:bioinformatics to tackle the datato knowledge challenge.Introduction[J].BMC Bioinformatics,2014,15(16):1471-2105. [3]Ow TJ,Upadhyay K,Belbin TJ,et al.Bioinformatics in otolaryngology research.Part one: concepts in DNA sequencing and gene expression analysis[J].J Laryngol Otol,2014,128(10):848-858. [4]Meldolesi E,van Soest J,Damiani A,et al.Standardized data collection to build prediction models in oncology:a prototype for rectal cancer[J].Future Oncol,2016,12(1):119-136. [5]Perry PM.Harnessing the power of big data and data analysis to improve healthcare entities[J]. Healthc Financ Manage,2016,70(1):74-75. [6]D?觟brossy L.Epidemiology of head and neck cancer:Magnitude of the problem[J].Cancer Metastasis Rev,2005,24(1):9-17. [7]Timar J,Csuka O,Remenar E,et al.Progression ofhead and neck squamous cell cancer[J].Cancer Metastasis Rev,2005,24(1):107-127. [8]Mackenzie J,Ah-See K,Thakker N,et al.Increasing incidence of oral cancer amongst youngpersons:what is the aetiology[J].Oral Oncol,2000,36(4):387-389. [9]Annertz K,Anderson H,Biorklund A,et al.Incidence and survival of squamouscell carcinoma of the tongue in Scandinavia,with special reference to young adults[J].Int J Cancer,2002,101(1):95-99. [10]Ye H,Yu T,Temam S,et al.Transcriptomic dissection of tongue squamous cell carcinoma[J]. BMC Genomics,2008,9(1):69-70. [11]Jez S,Martin M,South S,et al.Variants of unknown significance on chromosomal microarray analysis:parental perspectives[J].J Community Genet,2015,6(4):343-349. [12]Kanehisa M,Sato Y,Morishima K.BlastKOALA and GhostKOALA:KEGG Tools for Functional Characterization of Genome and Metagenome Sequences[J].J Mol Biol,2016,428(4):726-731. [13]Cheng L,Lin H,Hu Y,et al.Gene function prediction based on the Gene Ontology hierarchical structure[J].PLoS One,2014,9(9):e84685. [14]Kanehisa M,Sato Y,Kawashima M,et al.KEGG as a reference resource for gene and protein annotation[J].Nucleic Acids Res,2016,44(D1):17. [15]Blake JA,Chan J,Kishore R,et al.Gene Ontology Consortium:going forward[J].Nucleic Acids Research,2015,43(Database issue):1049-1056. [16]Nigrovic PA,Muscal E,Riebschleger M,et al.AMIGO:a novel approach to the mentorship gap in pediatric rheumatology[J].J Pediatr,2013,164(2):226-227.e1-e3. [17]Peltola MA,Kuja-Panula J,Liuhanen J,et al.AMIGO-Kv2.1 Potassium Channel Complex Is Associated With Schizophrenia-Related Phenotypes[J].Schizophr Bull,2016,42(1):191-201. [18]Zhou T,Zhang Y,Wu P,et al.Potential biomarkers and latent pathways for vasculitis based on latent pathway identification analysis[J].Int J Rheum Dis,2014,17(6):671-678. [19]魏选东.基于芯片分析的乳腺癌预后核心基因筛选及其预测效果分析[D].湖南师范大学,2018. [20]Iancu OD,Colville A,Darakjian P,et al.Coexpression and cosplicing network app roaches for the study of mammalian brain transcriptomes[J].Int Rev Neurobiol,2014,116(1):73-93. [21]汪涛,蒋庆华,彭佳杰,等.基因共表达网络的构建及分析方法研究综述[J].智能计算机与应用,2014(6):51-54,57. [22]洪胜君.基于转录组测序数据的基因共表达网络研究[D].复旦大学,2013. [23]王安训.舌鳞状细胞癌侵袭和转移的研究进展[J].口腔疾病防治,2016,24(5):261-266. [24]Nsman A,Bersani C,Lindquist D,et al.Human papillomavirus and po-tentiallyrelevant biomarkers in tonsillar and base of tongue squamouscell carcinoma[J].Anticancer Res,2017,37(10):5319-5328． [25]高桂林,朱斌,颜孟雄.舌鳞状细胞癌相关差异基因的生物信息学及预后分析[J].临床口腔医学杂志,2018(3):145-149. [26]Langfelder P,Horvath S.WGCNA:an R package for weighted correlation network analysis[J]. BMC Bioinformatics,2008,9(1):559.

相似文献/References:

[1]杨宵月,李建伟.LncRNA调控人类疾病关系数据库的研究[J].医学信息,2019,32(12):28.[doi:10.3969/j.issn.1006-1959.2019.12.010]
　YANG Xiao-yue,LI Jian-wei.LncRNA Regulation of Human Disease Relationship Database[J].Medical Information,2019,32(03):28.[doi:10.3969/j.issn.1006-1959.2019.12.010]
[2]操利超,张核子,余晨笛,等.CLDN11在结肠癌中的预后价值及其作为潜在免疫治疗靶点的研究[J].医学信息,2022,35(14):10.[doi:10.3969/j.issn.1006-1959.2022.14.002]
　CAO Li-chao,ZHANG He-zi,YU Chen-di,et al.The Prognostic Value of CLDN11 in Colon Cancer and its Potential Immunotherapy Targets[J].Medical Information,2022,35(03):10.[doi:10.3969/j.issn.1006-1959.2022.14.002]
[3]王婷婷,温凌杜,王子弘,等.基于DNA甲基化鉴定口腔鳞状细胞癌预后生物标志物[J].医学信息,2022,35(13):28.[doi:10.3969/j.issn.1006-1959.2022.13.005]
　WANG Ting-ting,WEN Ling-du,WANG Zi-hong,et al.Identification of Prognostic Biomarkers for Oral Squamous Cell Carcinoma Based on DNA Methylation[J].Medical Information,2022,35(03):28.[doi:10.3969/j.issn.1006-1959.2022.13.005]
[4]黄琪峰,郑琳琳,张 菁.甲状腺癌中miR-222关键靶基因预测及其信号通路分析[J].医学信息,2020,33(01):85.[doi:10.3969/j.issn.1006-1959.2020.01.026]
　HUANG Qi-feng,ZHENG Lin-lin,ZHANG Jing.Prediction of Key Target Genes of miR-222 in Thyroid Cancer and Analysis of Its Signal Pathway[J].Medical Information,2020,33(03):85.[doi:10.3969/j.issn.1006-1959.2020.01.026]
[5]李熹阳,谷明宇,华 琳.影响前列腺癌风险的关键基因识别[J].医学信息,2020,33(02):80.[doi:10.3969/j.issn.1006-1959.2020.02.022]
　LI Xi-yang,GU Ming-yu,HUA Lin.Identification of Key Genes Affecting Prostate Cancer Risk[J].Medical Information,2020,33(03):80.[doi:10.3969/j.issn.1006-1959.2020.02.022]
[6]吴超颖,陈 冬,吴超群,等.头颈部鳞状细胞癌预后相关的miRNAs的生物信息学分析[J].医学信息,2020,33(02):61.[doi:10.3969/j.issn.1006-1959.2020.02.017]
　WU Chao-ying,CHEN Dong,WU Chao-qun,et al.Bioinformatics Analysis of Prognosis-related miRNAs in Head and Neck Squamous Cell Carcinoma[J].Medical Information,2020,33(03):61.[doi:10.3969/j.issn.1006-1959.2020.02.017]
[7]胡昕倩,余雅婕,方 明.垂体瘤的基因芯片数据生物信息学分析[J].医学信息,2020,33(06):90.[doi:10.3969/j.issn.1006-1959.2020.06.026]
　HU Xin-qian,YU Ya-jie,FANG Ming.Bioinformatics Analysis of Gene Chip Data for Pituitary Tumors[J].Medical Information,2020,33(03):90.[doi:10.3969/j.issn.1006-1959.2020.06.026]
[8]马晓玉,岳欣蕾,韩佳玲,等.蛋白质-适配体相互作用预测的方法[J].医学信息,2020,33(10):27.[doi:10.3969/j.issn.1006-1959.2020.10.008]
　MA Xiao-yu,YUE Xin-lei,HAN Jia-ling,et al.Method for Predicting Protein-aptamer Interaction[J].Medical Information,2020,33(03):27.[doi:10.3969/j.issn.1006-1959.2020.10.008]
[9]王 淮,杨健康.基于生物信息学分析F13A1基因及蛋白质[J].医学信息,2020,33(11):52.[doi:10.3969/j.issn.1006-1959.2020.11.017]
　WANG Huai,YANG Jian-kang.Analysis of F13A1 Gene and Protein Based on Bioinformatics[J].Medical Information,2020,33(03):52.[doi:10.3969/j.issn.1006-1959.2020.11.017]
[10]操利超,巴 颖,卢晓萍,等.基于TCGA和GEO数据库构建结肠癌预后模型[J].医学信息,2021,34(24):27.[doi:10.3969/j.issn.1006-1959.2021.24.006]
　CAO Li-chao,BA Ying,LU Xiao-ping,et al.Prognostic Model of Colon Cancer Based on TCGA and GEO Database[J].Medical Information,2021,34(03):27.[doi:10.3969/j.issn.1006-1959.2021.24.006]

更新日期/Last Update: 2020-02-01