基于生物信息学分析挖掘非小细胞肺癌中的预后基因
Mining Prognostic Genes in Non-Small Cell Lung Cancer Based on Bioinformatics Analysis
摘要: 非小细胞肺癌(non-small cell lung cancer, NSCLC)仍是当今世界死亡率最高的恶性肿瘤，但其发生和发展的分子机制的改变仍不清楚。本研究中，我们通过基因表达综合(Gene Expression Omnibus, GEO)数据库下载基因表达微阵列数据GSE11830，例用edgeR软件筛选肿瘤组织及周围正常组织之间上调和下调最明显的20个差异基因。为进一步了解差异基因的功能及机制，我们通过基因本体论(Gene Ontology, GO)数据库及京都基因与基因组百科全书(Kyoto Encyclopedia of Gene and Genomes, KEGG)数据库对这40个差异基因的进行功能及通路富集进行分析。此外，我们通过癌症基因组数据库(The Cancer Genome Atlas, TCGA)下载NSCLC患者的临床信息，并对40个差异基因进行生存分析，发现5个上调基因及3个下调基因与NSCLC生存期影响较为显著。利用cBioPortal可视化工具对这8个关键基因进行基因突变及DNA扩增频率的分析，发现PKHD1L1、MME和IGSF10可能是NSCLC发生及影响预后的关键基因。
Abstract: Non-small cell lung cancer (NSCLC) is still the malignant tumor with the highest mortality rate in the world, but the molecular mechanism of occurrence and development of NSCLC is still unclear. In this study, we downloaded gene expression microarray data GSE11830 through the Gene Expression Omnibus (GEO) database, and used the edgeR software to screen the 20 most significantly up-regulated and down-regulated genes between tumor tissues and surrounding normal tissues. In order to further understand the functions and mechanisms of differential genes, we used the Gene Ontology (GO) database and the Kyoto Encyclopedia of Gene and Genomes (KEGG) database to perform enrichment analysis of these 40 differential genes. In addition, we downloaded the clinical information of NSCLC patients through The Cancer Genome Atlas (TCGA), and analyzed the survival of 40 differential genes, and found that 5 up-regulated genes and 3 down-regulated genes have a significant impact on NSCLC survival. In addition, we use cBioPortal which was a visualization tool to analyze the gene mutation and DNA amplification frequency of these 8 key genes, and it is found that PKHD1L1, MME and IGSF10 may be the key genes for the occurrence and prognosis of NSCLC.
文章引用：韩骐蔓, 朱静娟, 张传涛, 王力, 张晓春. 基于生物信息学分析挖掘非小细胞肺癌中的预后基因[J]. 临床医学进展, 2021, 11(4): 1655-1664. https://doi.org/10.12677/ACM.2021.114238

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