Background <p>Only a limited number of biomarkers guide personalized management of pancreatic neuroendocrine tumors (PanNETs). Transcriptome profiling of microRNA (miRs) and mRNA has shown value in segregating PanNETs and identifying patients more likely to respond to treatment. Because miRs are key regulators of mRNA expression, we sought to integrate expression data from both RNA species into miR-mRNA interaction networks to advance our understanding of PanNET biology.</p> Methods <p>We used deep miR/mRNA sequencing on six low-grade/high-risk, well-differentiated PanNETs compared with seven non-diseased tissues to identify differentially expressed miRs/mRNAs. Then we crossed a list of differentially expressed mRNAs with a list of in silico predicted mRNA targets of the most and least abundant miRs to generate high probability miR-mRNA interaction networks.</p> Results <p>Gene ontology and pathway analyses revealed several miR-mRNA pairs implicated in cellular processes and pathways suggesting perturbed neuroendocrine function (miR-7 and <i>Reg</i> family genes), cell adhesion (miR-216 family and <i>NLGN1</i>, <i>NCAM1</i>, and <i>CNTN1</i>; miR-670 and the claudins, <i>CLDN1</i> and <i>CLDN2</i>), and metabolic processes (miR-670 and <i>BCAT1</i>/<i>MPST</i>; miR-129 and <i>CTH</i>).</p> Conclusion <p>These novel miR-mRNA interaction networks identified dysregulated pathways not observed when assessing mRNA alone and provide a foundation for further investigation of their utility as diagnostic and predictive biomarkers.</p>

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Deep sequencing reveals distinct microRNA-mRNA signatures that differentiate pancreatic neuroendocrine tumor from non-diseased pancreas tissue

  • N Matyasovska,
  • N Valkova,
  • M Gala,
  • S Bendikova,
  • A Abdulhamed,
  • V. Palicka,
  • Neil Renwick,
  • Pavol Čekan,
  • Evan Paul

摘要

Background

Only a limited number of biomarkers guide personalized management of pancreatic neuroendocrine tumors (PanNETs). Transcriptome profiling of microRNA (miRs) and mRNA has shown value in segregating PanNETs and identifying patients more likely to respond to treatment. Because miRs are key regulators of mRNA expression, we sought to integrate expression data from both RNA species into miR-mRNA interaction networks to advance our understanding of PanNET biology.

Methods

We used deep miR/mRNA sequencing on six low-grade/high-risk, well-differentiated PanNETs compared with seven non-diseased tissues to identify differentially expressed miRs/mRNAs. Then we crossed a list of differentially expressed mRNAs with a list of in silico predicted mRNA targets of the most and least abundant miRs to generate high probability miR-mRNA interaction networks.

Results

Gene ontology and pathway analyses revealed several miR-mRNA pairs implicated in cellular processes and pathways suggesting perturbed neuroendocrine function (miR-7 and Reg family genes), cell adhesion (miR-216 family and NLGN1, NCAM1, and CNTN1; miR-670 and the claudins, CLDN1 and CLDN2), and metabolic processes (miR-670 and BCAT1/MPST; miR-129 and CTH).

Conclusion

These novel miR-mRNA interaction networks identified dysregulated pathways not observed when assessing mRNA alone and provide a foundation for further investigation of their utility as diagnostic and predictive biomarkers.