Department of Surgery, Kangnam Sacred Heart Hospital, Hallym University College of Medicine.
Department of Otolaryngology-Head and Neck Surgery, Division of Precision Medicine, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea.
Medicine (Baltimore). 2021 Apr 9;100(14):e24969. doi: 10.1097/MD.0000000000024969.
Pancreatic cancer has a very high mortality with a 5-year survival of <5%. The purpose of this study was to classify specific molecular subtypes associated with prognosis of pancreatic cancer using The Cancer Genome Atlas (TCGA) multiplatform genomic data.Multiplatform genomic data (N = 178), including gene expression, copy number alteration, and somatic mutation data, were obtained from cancer browser (https://genome-cancer.ucsc.edu, cohort: TCGA Pancreatic Cancer). Clinical data including survival results were analyzed. We also used validation cohort (GSE50827) to confirm the robustness of these molecular subtypes in pancreatic cancer.When we performed unsupervised clustering using TCGA gene expression data, we found three distinct molecular subtypes associated with different survival results. Copy number alteration and somatic mutation data showed different genomic patterns for these three subtypes. Ingenuity pathway analysis revealed that each subtype showed differentially altered pathways. Using each subtype-specific genes (200 were selected), we could predict molecular subtype in another cohort, confirming the robustness of these molecular subtypes of pancreatic cancer. Cox regression analysis revealed that molecular subtype is the only significant prognostic factor for pancreatic cancer (P = .042, 95% confidence interval 0.523-0.98).Genomic analysis of pancreatic cancer revealed 3 distinct molecular subtypes associated with different survival results. Using these subtype-specific genes and prediction model, we could predict molecular subtype associated with prognosis of pancreatic cancer.
胰腺癌的死亡率非常高，5 年生存率<5%。本研究旨在使用癌症基因组图谱（TCGA）多平台基因组数据对与胰腺癌预后相关的特定分子亚型进行分类。从癌症浏览器（https://genome-cancer.ucsc.edu，队列：TCGA 胰腺癌）获得了多平台基因组数据（N=178），包括基因表达、拷贝数改变和体细胞突变数据。分析了包括生存结果在内的临床数据。我们还使用验证队列（GSE50827）来确认这些分子亚型在胰腺癌中的稳健性。当我们使用 TCGA 基因表达数据进行无监督聚类时，我们发现了与不同生存结果相关的三个不同的分子亚型。拷贝数改变和体细胞突变数据显示了这三种亚型的不同基因组模式。通路分析揭示了每个亚型都显示出不同的改变途径。使用每个亚型特异性基因（选择了 200 个），我们可以在另一个队列中预测分子亚型，证实了这些胰腺癌分子亚型的稳健性。Cox 回归分析显示，分子亚型是胰腺癌唯一显著的预后因素（P=0.042，95%置信区间 0.523-0.98）。胰腺癌的基因组分析显示了与不同生存结果相关的 3 个不同的分子亚型。使用这些亚型特异性基因和预测模型，我们可以预测与胰腺癌预后相关的分子亚型。
