Nonfunctional pancreatic neuroendocrine tumors (NF-PanNETs) exhibit varied clinical behaviors ranging from indolent to highly malignant. Current understanding of the molecular pathology of NF-PanNETs is insufficient for their clinical management, with a recognized challenge being the need to identify patients with aggressive tumors who should receive intensive therapeutic interventions. This study aims to characterize the gene expression and chromatin regulatory landscape of NF-PanNET tumors, to explore pro-cancer regulatory mechanisms, and to identify both potential therapeutic targets and prognostic biomarker(s) for NF-PanNETs.
In this study, through scRNA-seq (n=10) and scATAC-seq (n=4) profiling, we characterized the gene expression and chromatin regulatory landscape of clinically resected NF-PanNET tumors, ultimately discovering the pro-cancer impacts of AGR2high cells. Moreover, and beyond showing that the transcription factor FOXM1 directly drives this cell type’s proliferative transcriptional programmed demonstrating that chemical inhibition of this master regulator confers therapeutic benefits, our study demonstrates the excellent performance of simple IHC staining against AGR2 as a clinically informative marker of NF-PanNET prognosis.
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The three raw data (result data) used in this topic are provided by Zenodo website with 
Analysis from scRNA-seq data：
- Figure 1 drawn in the main text can be obtained from the following tutorial Figure1.
- Figure 2 drawn in the main text can be obtained from the following tutorial Figure2.
- Figure S1 drawn in the Supplementary text can be obtained from the following tutorial FigureS1.
- Figure S2 drawn in the Supplementary text can be obtained from the following tutorial FigureS2.
Analysis from scATAC-seq data：
- Figure 3 drawn in the main text can be obtained from the following tutorial Figure3.
- Figure S3 drawn in the Supplementary text can be obtained from the following tutorial FigureS3.
Analysis from BULK data：
- Figure S3 drawn in the main text and Supplementary text can be obtained from the following tutorial Figures for BULK data.
We provide the code for data preprocessing and the code for related programs in the project, and they are all saved in the folder
Please do not hesitate to contact Dr. Tu at tujiajuan@163.com to seek any clarifications regarding any content or operation of the archive.