AGR2-high cells drive a FOXM1-mediated pro-malignancy program in NF-PanNETs and informatively predict patient outcomes
Creators: name: Tu, Jia-Juan
Publisher: name: Zenodo
Year: 2024

description: NF-NET raw data and all preprocessed data.

The goal of NF-NET

 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.

 

There are three types of raw data:

BULK Data Overview:



Content: Contains raw counts from 77 samples, each annotated with clinical information. This dataset is essential for comprehensive clinical studies and analyses.

Examples of Data Files (BULK_raw_count.zip): 




BULK_raw_count_77_sample.csv: gene expression data, rows represent genes, columns represent samples.

BULK_raw_count_77_sample_metadata.csv: sample_metadata, rows represent samples, columns represent clinical information.


scRNA-seq Data Overview:



Content: Comprises raw counts from 10 samples. Each sample includes detailed annotations of major cell types and sub-types.

Format: The raw count data are saved in CSV format, ensuring compatibility and ease of use across various data analysis platforms.

Examples of Data Files: scRNA_raw_data.zip, This folder contains three files, including gene expression data and annotation information and gene information.


scATAC-seq Data Overview:



Sample Count: Includes data from 4 individual samples.

Details: For each sample, the peak counts and gradients obtained post-processing are provided.

Formats:  (1) Peak counts are saved in Hierarchical Data Format (`.h5`), optimized for handling and managing large volumes of data.   (2) fragments are documented in tab-separated values format (`.tsv`), facilitating easy data parsing and integration. (3) the other data

Examples of Data Files:




ATAC_NF_2_RAW_DATA.zip

ATAC_NF_7_RAW_DATA.zip

ATAC_NF_22_RAW_DATA.zip

ATAC_NF_23_RAW_DATA.zip


Downstream analysis for each data type

scRNA-seq data analysis:

 The outputs of scRNA-seq data processed using the Seurat framework are encapsulated in two key files:

10NewPatients_merged_fastmnn_annotation.rds and 10NF_endoc_filter_tumor_anno.rds.



The former contains integrated scRNA-seq data for all cells from 10 new patients, utilizing fastMNN for batch correction, and is pivotal for reproducing the main study results concerning overall cellular analysis. 

The latter file is specifically focused on endocrine cells within tumor samples, essential for detailed cell-type-specific investigations. Both files, stored in RDS format, are instrumental in validating and extending the findings of our research on cellular dynamics in clinical settings.


For CNV analysis, normal tissue is used as a reference to differentiate between tumor and normal endocrine cells. 



The scRNA-seq data from normal tissues from 14 samples are saved in PCsamples_normal_tissue.zip .

For the InferCNV analysis results are contained within cnv.zip. This folder contains results based on the entire genome of endocrine cells, with results for cell division genes removed. For more details please see the main text of our work.


scATAC data analysis:













The processed outputs of scATAC data using the Signac framework are organized into two main folders.



 The ATAC_RAW_Signc.zip folder contains individual scATAC-seq data for each sample, processed with Signac.

Subsequently, all four samples are integrated, correcting for batch effects and annotated, with the results stored in ATAC_res.zip.








For comprehensive details, please refer to our work, NF-NET, and the corresponding code available on our GitHub repository: [TJJjiajuan](https://github.com/TJJjiajuan/NF-NET).