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Subgroup analysis reveals molecular heterogeneity and provides potential precise treatment for pancreatic cancers

Authors Zhang H, Zeng J, Tan Y, Lu L, Sun C, Liang Y, Zou H, Yang X, Tan Y

Received 19 January 2018

Accepted for publication 22 May 2018

Published 12 September 2018 Volume 2018:11 Pages 5811—5819


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 3

Editor who approved publication: Dr XuYu Yang

Heying Zhang,1 Juan Zeng,1 Yongqiang Tan,2 Lin Lu,3 Cheng Sun,1 Yusi Liang,1 Huawei Zou,1 Xianghong Yang,4 Yonggang Tan1

1Department of Oncology, Shengjing Hospital, China Medical University, Shenyang, People’s Republic of China; 2Google Inc., Google Ads, Los Angeles, CA, USA; 3Department of Radiology, Columbia University Medical Center, New York, NY, USA; 4Department of Pathology, Shengjing Hospital, China Medical University, Shenyang, People’s Republic of China

Background: 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.
Patients and methods: 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.
Results: 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.
Conclusion: Three distinct subgroups of pancreatic cancer were identified, which means that patients in each subgroup might benefit from targeted individual management.

Keywords: pancreatic cancer, TCGA, bioinformatics, therapeutic target

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