Transcriptomic profiling of pancreatic neuroendocrine tumors: dysregulation of WNT, MAPK, PI3K, neddylation pathways and potential non-invasive biomarkers

TL;DR

This study identifies key signaling pathways and potential blood-based biomarkers for pancreatic neuroendocrine tumors using transcriptomic data.

Contribution

The study reveals novel dysregulated pathways and candidate non-invasive biomarkers specific to pancreatic neuroendocrine tumors.

Findings

1,210 differentially expressed genes were identified in pancreatic neuroendocrine tumors compared to adjacent tissues.

Key dysregulated pathways include WNT, MAPK, and PI3K/AKT/mTOR signaling related to tumor growth and survival.

28 cell surface and 24 secretome-related genes were upregulated, suggesting potential for liquid biopsy biomarkers.

Abstract

The study aimed to identify altered signaling pathways and potential non-invasive biomarkers for pancreatic neuroendocrine tumors (PanNETs) through transcriptomic profiling of tumor tissues. The analysis encompassed samples from non-functional PanNETs (NF-PanNETs), insulinomas, and tumor-adjacent pancreatic tissues (TAPT). In the differential expression analysis comparing PanNETs and TAPTs, we identified 1,210 differentially expressed genes at a false discovery rate significance threshold of < 0.05 and with Log2FoldChange values of > 0.5 and <−0.5. Further pathway enrichment analysis revealed a multitude of overrepresented signaling pathways related to cell proliferation, survival, and tumorigenesis. Significant findings included the Beta-catenin-independent and TCF-dependent WNT signaling pathways, MAPK1/MAPK3 signaling, and terms associated with PI3K/AKT/mTOR signaling. Among the list of DEGs, we also identified 28 upregulated genes encoding cell surface proteins and 24 upregulated genes encoding cancer-associated secretome proteins. Since the proteins of these genes are found in the bloodstream, there is potential for further testing of these markers as biomarkers for liquid biopsy assays. Overall, these findings underscore the promise of transcriptomic landscape analysis in identifying PanNET-specific non-invasive biomarkers and uncovering potential therapeutic targets.

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