[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]
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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()
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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.

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更新日期/Last Update: 2020-02-01