ArticleMolecular medicine reports2017
Bioinformatics analysis of transcription profiling of solid pseudopapillary neoplasm of the pancreas.
Article in Molecular medicine reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Sex hormone receptor signaling networks drive pancreatic solid-pseudopapillary neoplasm pathogenesis and therapy.Discover oncology · 2025Review
- Single-cell RNA sequencing of solid pseudopapillary neoplasms of the pancreas in children.Cancer science · 2023Article
- An Integrated Data Analysis of mRNA, miRNA and Signaling Pathways in Pancreatic Cancer.Biochemical genetics · 2021Article
- Solid-pseudopapillary Neoplasms of the Pancreas is still an Enigma: a Clinicopathological Review.Pathology oncology research : POR · 2020Review
- Surgical Resection of 78 Pancreatic Solid Pseudopapillary Tumors: a 30-Year Single Institutional Experience.Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2020Article
- Ping-Pong-Tumor and Host in Pancreatic Cancer Progression.Frontiers in oncology · 2019Review
- Identification of key candidate genes and miRNA‑mRNA target pairs in chronic lymphocytic leukemia by integrated bioinformatics analysis.Molecular medicine reports · 2019Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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