Bioinformatics analysis of transcription profiling of solid pseudopapillary neoplasm of the pancreas

Zhang,Yongping; Han,Xu; Wu,Hao; Zhou,Yifeng

doi:10.3892/mmr.2017.6800

Bioinformatics analysis of transcription profiling of solid pseudopapillary neoplasm of the pancreas

Authors:
- Yongping Zhang
- Xu Han
- Hao Wu
- Yifeng Zhou
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Affiliations: Department of Digestion, Xin Chang People's Hospital, Pancreatic Disease Research Center of Shanghai, Xinchang, Zhejiang 312500, P.R. China, Department of Gastroenterology, Shanghai Changhai Hospital, Second Military Medical University of China, Pancreatic Disease Research Center of Shanghai, Shanghai 214000, P.R. China, Digestive Department, Hangzhou First People's Hospital, Pancreatic Disease Research Center of Shanghai, Hangzhou, Zhejiang 310006, P.R. China
Published online on: June 19, 2017 https://doi.org/10.3892/mmr.2017.6800
Pages: 1635-1642
Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Solid pseudopapillary neoplasm (SPN) of the pancreas is a low-grade malignant neoplasm that accounts for ~5% of cystic pancreatic tumors and ~0.9‑2.7% of exocrine pancreatic tumors. The transcription profiling data (GSE43795) of 14 SPN and 6 control samples were downloaded from the Gene Expression Omnibus (GEO) database. Using the Limma package, Student's t‑tests were performed to identify differentially expressed genes (DEGs) between SPN and control samples [with the following criterion: False discovery rate (FDR)<0.01 and log2 fold‑change (FC)≥3]. Pathway and functional enrichment analyses were performed to investigate the biological processes that the DEGs were involved in. Protein‑protein interaction (PPI) network and sub‑network analyses were conducted to comprehensively understand the interactions between DEGs. The screened DEGs were further annotated according to information relating to transcription factors and tumor associated genes (TAGs). A total of 710 upregulated and 710 downregulated DEGs were observed, including 74 transcriptional factors and 124 TAGs. Membrane metallo‑endopeptidase (MME), matrix metalloproteinase (MMP)-2 and MMP‑9 were also identified as key TAGs. Following PPI network analysis, hub nodes of epidermal growth factor receptor (EGFR), proto‑oncogene tyrosine protein kinase Fyn (FYN), c‑JUN (JUN), glucagon (GCG), c‑Myc (MYC) and CD44 were identified, the majority of which participate in the epidermal growth factor receptor (ErbB) and gonadotropin-releasing hormone (GnRH) signaling pathways. A sub‑network involving 70 gene nodes was also identified, with EGFR as the central gene. MME, MMP‑2 and MMP‑9 contribute to proliferative diabetic retinopathy and also involved in SPN. The genes EGFR, FYN, JUN, GCG, MYC and CD44 may therefore be key genes in SPN, and the ErbB and GnRH signaling pathways may be an important contributor to SPN progression.

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