基于基因共表达网络分析的头颈部鳞状细胞癌基因模块挖掘及关键基因鉴定
Mining of gene modules and identification of key genes in head and neck squamous cell carcinoma based on gene co-expression network analysis.
作者信息
Zhao Qian, Zhang Yan, Zhang Xue, Sun Yeqing, Lin Zhengkui
机构信息
College of Information Science and Technology.
Institute of Environmental System Biology, College of Environmental Science and Engineering, Dalian Maritime University, Dalian, China.
出版信息
Medicine (Baltimore). 2020 Dec 4;99(49):e22655. doi: 10.1097/MD.0000000000022655.
To explore the gene modules and key genes of head and neck squamous cell carcinoma (HNSCC), a bioinformatics algorithm based on the gene co-expression network analysis was proposed in this study.Firstly, differentially expressed genes (DEGs) were identified and a gene co-expression network (i-GCN) was constructed with Pearson correlation analysis. Then, the gene modules were identified with 5 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Gene Ontology (GO) analysis was used to annotate the biological pathways of the gene modules. Then, the key genes were identified with 2 methods, gene significance (GS) and PageRank algorithm. Moreover, we used the Disgenet database to search the related diseases of the key genes. Lastly, the online software onclnc was used to perform the survival analysis on the key genes and draw survival curves.There were 2600 up-regulated and 1547 down-regulated genes identified in HNSCC. An i-GCN was constructed with Pearson correlation analysis. Then, the i-GCN was divided into 9 gene modules. The result of association analysis showed that, sex was mainly related to mitosis and meiosis processes, event was mainly related to responding to interferons, viruses and T cell differentiation processes, T stage was mainly related to muscle development and contraction, regulation of protein transport activity processes, N stage was mainly related to mitosis and meiosis processes, while M stage was mainly related to responding to interferons and immune response processes. Lastly, 34 key genes were identified, such as CDKN2A, HOXA1, CDC7, PPL, EVPL, PXN, PDGFRB, CALD1, and NUSAP1. Among them, HOXA1, PXN, and NUSAP1 were negatively correlated with the survival prognosis.HOXA1, PXN, and NUSAP1 might play important roles in the progression of HNSCC and severed as potential biomarkers for future diagnosis.
为了探究头颈部鳞状细胞癌（HNSCC）的基因模块和关键基因，本研究提出了一种基于基因共表达网络分析的生物信息学算法。首先，鉴定差异表达基因（DEG），并通过Pearson相关分析构建基因共表达网络（i-GCN）。然后，使用5种不同的群落检测算法鉴定基因模块，并进行基因模块与临床指标之间的相关性分析。利用基因本体（GO）分析对基因模块的生物学途径进行注释。接着，用基因显著性（GS）和PageRank算法这两种方法鉴定关键基因。此外，我们使用Disgenet数据库搜索关键基因的相关疾病。最后，使用在线软件onclnc对关键基因进行生存分析并绘制生存曲线。在HNSCC中鉴定出2600个上调基因和1547个下调基因。通过Pearson相关分析构建了一个i-GCN。然后，将i-GCN分为9个基因模块。关联分析结果表明，性别主要与有丝分裂和减数分裂过程相关，事件主要与对干扰素、病毒的反应及T细胞分化过程相关，T分期主要与肌肉发育和收缩、蛋白质转运活性调节过程相关，N分期主要与有丝分裂和减数分裂过程相关，而M分期主要与对干扰素的反应和免疫反应过程相关。最后，鉴定出34个关键基因，如CDKN2A、HOXA1、CDC7、PPL、EVPL、PXN、PDGFRB、CALD1和NUSAP1。其中，HOXA1、PXN和NUSAP1与生存预后呈负相关。HOXA1、PXN和NUSAP1可能在HNSCC的进展中起重要作用，并可作为未来诊断的潜在生物标志物。
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