Table1_Identification of Survival-Associated Hub Genes in Pancreatic Adenocarcinoma Based on WGCNA.XLS

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.

胰腺导管腺癌（Pancreatic adenocarcinoma）是全球范围内癌症相关死亡的主要诱因之一。由于该疾病早期临床症状隐匿，多数患者确诊时已出现远处转移。由于缺乏有效的诊断生物标志物与治疗靶点，胰腺导管腺癌难以实现早期筛查与根治。当前研究的核心瓶颈在于，我们对其癌变进程中的分子调控机制尚所知甚少。本研究依托癌症基因组图谱（The Cancer Genome Atlas, TCGA）提供的胰腺导管腺癌基因表达谱数据，采用加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）构建基因互作网络，并借助STRING数据库完成生物网络的构建与可视化。最终共检测得到22个基因共表达模块，其中黄色模块与粉色模块与胰腺导管腺癌的相关性最为显著。本研究鉴定出包括PKMYT1、WDHD1、ASF1B及RAD18在内的数十个新的候选基因。后续生存分析证实，上述基因在胰腺导管腺癌的诊断与治疗中具有重要应用价值。本研究首次将基于网络的算法应用于肿瘤病因学研究，并发现了数个可用于胰腺导管腺癌检测与治疗的潜在调控因子。