Gene clustering, enrichment and survival analysis of differentially expressed genes in Low Grade Glioma between different genders by big data analysis

Proceedings of the 2019 International Conference on Data Mining and Machine Learning Pub Date : 2019-04-28 DOI:10.1145/3335656.3335699

Jianzhi Deng, Xiaohui Cheng, Yuehan Zhou

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引用次数: 9

Abstract

In this study, we aim to reveal the relationship between differentially expressed mRNAs and genders in low grade glioma (LGG) patients. Based on the downloaded RNA-seq files of LGG patients from The Cancer Genome Atlas database (TCGA), 89 differentially expressed mRNAs between male and female were screened out by clustering analysis. The differentially expressed mRNAs were analyzed by DAVID and KOBAS online tools. The differentially expressed mRNAs were enriched in 67 gene ontology terms, including cellular components, biological processes and molecular functions group and 7 signaling pathways. Then, the differentially expressed mRNAs were divided into two parts according to the expression level. The high-expressed mRNAs and low-expressed mRNAs were all analyzed with the clinical survival data by Kaplan-meier method and kmplot survival curves. In comparison with the LGG female patients, male patients with a differential expression mRNAs were closely related to the higher risk of LGG.

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大数据分析低分级胶质瘤不同性别差异表达基因聚类、富集及生存分析

在这项研究中，我们旨在揭示低级别胶质瘤(LGG)患者差异表达mrna与性别之间的关系。基于从the Cancer Genome Atlas database (TCGA)中下载的LGG患者RNA-seq文件，通过聚类分析筛选出89个男女差异表达mrna。通过DAVID和KOBAS在线工具分析差异表达mrna。差异表达mrna富集于67个基因本体术语，包括细胞组分、生物过程和分子功能群以及7个信号通路。然后，根据表达水平将差异mrna分成两部分。采用Kaplan-meier法和kmplot生存曲线分析高表达mrna和低表达mrna的临床生存数据。与LGG女性患者相比，mrna表达差异的男性患者与LGG的高风险密切相关。

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Proceedings of the 2019 International Conference on Data Mining and Machine Learning

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