Identification of Survival-Associated Hub Genes in Pancreatic Adenocarcinoma Based on WGCNA.
Identification of Survival-Associated Hub Genes in Pancreatic Adenocarcinoma Based on WGCNA.
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DOI：
10.3389/fgene.2021.814798
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发表时间：
2021
影响因子：
3.7
通讯作者：
Ma B
中科院分区：
文献类型：
--
作者：
Huang L;Ye T;Wang J;Gu X;Ma R;Sheng L;Ma B
胰腺癌是全球癌症相关死亡的主要原因之一。由于胰腺癌早期临床症状不明显，大多数患者在诊断时已发现有转移。由于缺乏有效的诊断标志物和治疗靶点，胰腺癌的筛查和治疗困难。最根本的问题是我们对致癌过程中的调控机制知之甚少。本研究采用加权基因共表达网络分析（WGCNA）方法，利用美国癌症基因组图谱（TCGA）中的胰腺癌基因表达谱构建基因相互作用网络。STRING用于生物网络的构建和可视化。在网络中总共检测到22个模块，其中黄色和粉红色模块与胰腺癌的相关性最显著。发现了包括PKMYT1、WDHD1、ASF1B和RAD18在内的数十个新基因。进一步的生存分析显示了它们对胰腺癌的诊断和治疗有价值。我们的研究开创了基于网络的算法在肿瘤病因学中的应用，并发现了几个有希望的胰腺癌检测和治疗的调节因子。
Pancreatic adenocarcinoma is one of the leading causes of cancer-related death worldwide. Since little clinical symptoms were shown in the early period of pancreatic adenocarcinoma, most patients were found to carry metastases when diagnosis. The lack of effective diagnosis biomarkers and therapeutic targets makes pancreatic adenocarcinoma difficult to screen and cure. The fundamental problem is we know very little about the regulatory mechanisms during carcinogenesis. Here, we employed weighted gene co-expression network analysis (WGCNA) to build gene interaction network using expression profile of pancreatic adenocarcinoma from The Cancer Genome Atlas (TCGA). STRING was used for the construction and visualization of biological networks. A total of 22 modules were detected in the network, among which yellow and pink modules showed the most significant associations with pancreatic adenocarcinoma. Dozens of new genes including PKMYT1, WDHD1, ASF1B, and RAD18 were identified. Further survival analysis yielded their valuable effects on the diagnosis and treatment of pancreatic adenocarcinoma. Our study pioneered network-based algorithm in the application of tumor etiology and discovered several promising regulators for pancreatic adenocarcinoma detection and therapy.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
WEE 家族激酶的系统表达分析揭示了 PKMYT1 在乳腺癌发生中的重要性。
影响因子：
8.5
作者：
Liu, Yu;Qi, Jian;Yang, Wulin
通讯作者：
Yang, Wulin
影响因子：
8
作者：
Kanu N;Zhang T;Burrell RA;Chakraborty A;Cronshaw J;DaCosta C;Grönroos E;Pemberton HN;Anderton E;Gonzalez L;Sabbioneda S;Ulrich HD;Swanton C;Behrens A
通讯作者：
Behrens A
影响因子：
4.5
作者：
Liu, Bo;Hu, Yu;Huang, Ya-Xun
通讯作者：
Huang, Ya-Xun