Work

The use of Multidimensional Data to Identify the Molecular Biomarker for Pancreatic Ductal Adenocarcinoma

Year: 2013

Type: article

Abstract: Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease, and the patient has an extremely poor overall survival with a less than 5% 5-year survival rate. Development of potential biomarkers provides a critical foundation for the diagnosis of PDAC. In this project, we have adopted an integrative approach to simultaneously identify biomarker and generate testable hypothesis from multidimensional omics data. We first examine genes for which expression levels are correlated with survival data. The gene list was screened with TF regulation, predicted miRNA targets information, and KEGG pathways. We identified that 273 candidate genes are correlated with patient survival data. 12 TF regulation gene sets, 11 miRNAs targets gene sets, and 15 KEGG pathways are enriched with these survival genes. Notably, CEBPA/miRNA32/PER2 signaling to the clock rhythm qualifies this pathway as a suitable target for therapeutic intervention in PDAC. PER2 expression was highly associated with survival data, thus representing a novel biomarker for earlier detection of PDAC.

Source: BioMed Research International

DOI: 10.1155/2013/798054

Authors: Liwei Zhuang, Yue Qi, Yun Wu, Nannan Liu, Yili Fu

Institutions: Harbin Medical University, Harbin Institute of Technology, Fourth Affiliated Hospital of Harbin Medical University, State Key Laboratory of Robotics and Systems

Language: English

Topic: Pancreatic and Hepatic Oncology Research

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,680