Zhang Heying, Zeng Juan, Tan Yongqiang, Lu Lin, Sun Cheng, Liang Yusi, Zou Huawei, Yang Xianghong, Tan Yonggang
Department of Oncology, Shengjing Hospital, China Medical University, Shenyang, People's Republic of China,
Google Inc., Google Ads, Los Angeles, CA, USA.
Onco Targets Ther. 2018 Sep 12;11:5811-5819. doi: 10.2147/OTT.S163139. eCollection 2018.
The relationship between molecular heterogeneity and clinical features of pancreatic cancer remains unclear. In this study, pancreatic cancer was divided into different subgroups to explore its specific molecular characteristics and potential therapeutic targets.
Expression profiling data were downloaded from The Cancer Genome Atlas database and standardized. Bioinformatics techniques such as unsupervised hierarchical clustering was used to explore the optimal molecular subgroups in pancreatic cancer. Clinical pathological features and pathways in each subgroup were also analyzed to find out the potential clinical applications and initial promotive mechanisms of pancreatic cancer.
Pancreatic cancer was divided into three subgroups based on different gene expression features. Patients included in each subgroup had specific biological features and responded significantly different to chemotherapy.
Three distinct subgroups of pancreatic cancer were identified, which means that patients in each subgroup might benefit from targeted individual management.
胰腺癌的分子异质性与临床特征之间的关系仍不清楚。在本研究中，将胰腺癌分为不同亚组以探索其特定分子特征和潜在治疗靶点。
从癌症基因组图谱数据库下载表达谱数据并进行标准化处理。使用无监督层次聚类等生物信息学技术探索胰腺癌的最佳分子亚组。还分析了每个亚组的临床病理特征和信号通路，以找出胰腺癌的潜在临床应用和初步促进机制。
根据不同基因表达特征将胰腺癌分为三个亚组。每个亚组中的患者具有特定生物学特征，对化疗的反应差异显著。
确定了胰腺癌的三个不同亚组，这意味着每个亚组中的患者可能从针对性的个体化管理中获益。
