College of Public Health, Zhengzhou University, Zhengzhou, China.
Laboratory of Molecular Biology, Henan Luoyang Orthopedic Hospital (Henan Provincial Orthopedic Hospital), Zhengzhou, China.
Front Immunol. 2021 Mar 18;12:649551. doi: 10.3389/fimmu.2021.649551. eCollection 2021.
Pancreatic cancer is a lethal malignancy with a poor prognosis. This study aims to identify pancreatic cancer-related genes and develop a robust diagnostic model to detect this disease. Weighted gene co-expression network analysis (WGCNA) was used to determine potential hub genes for pancreatic cancer. Their mRNA and protein expression levels were validated through reverse transcription PCR (RT-PCR) and immunohistochemical (IHC). Diagnostic models were developed by eight machine learning algorithms and ten-fold cross-validation. Four hub genes (, and ) were identified based on bioinformatics. RT-PCR showed that the four hub genes were expressed at medium to high levels, IHC revealed that their protein expression levels were higher in pancreatic cancer tissues. For the panel of these four genes, eight models performed with 0.87-0.92 area under the curve value (AUC), 0.91-0.94 sensitivity, and 0.84-0.86 specificity in the validation cohort. In the external validation set, these models also showed good performance (0.86-0.98 AUC, 0.84-1.00 sensitivity, and 0.86-1.00 specificity). In conclusion, this study has identified four hub genes that might be closely related to pancreatic cancer: , and . Four-gene panels might provide a theoretical basis for the diagnosis of pancreatic cancer.
胰腺癌是一种预后不良的致命恶性肿瘤。本研究旨在鉴定胰腺癌相关基因，并开发一种稳健的诊断模型来检测这种疾病。加权基因共表达网络分析（WGCNA）用于确定胰腺癌的潜在关键基因。通过逆转录 PCR（RT-PCR）和免疫组织化学（IHC）验证其 mRNA 和蛋白质表达水平。通过八种机器学习算法和十折交叉验证开发了诊断模型。基于生物信息学确定了四个枢纽基因（、和）。RT-PCR 显示这四个枢纽基因的表达水平处于中高水平，免疫组织化学显示其蛋白表达水平在胰腺癌组织中更高。对于这四个基因的组合，八个模型在验证队列中具有 0.87-0.92 的曲线下面积值（AUC）、0.91-0.94 的灵敏度和 0.84-0.86 的特异性。在外部验证集中，这些模型也表现出良好的性能（0.86-0.98 AUC、0.84-1.00 灵敏度和 0.86-1.00 特异性）。总之，本研究鉴定了四个可能与胰腺癌密切相关的枢纽基因：、和。四基因组合可能为胰腺癌的诊断提供理论基础。
