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Establishment of a reference single-cell RNA sequencing dataset for human pancreatic adenocarcinoma.

ABSTRACT: Single-cell RNA sequencing (scRNAseq) has been used to assess the intra-tumor heterogeneity and microenvironment of pancreatic ductal adenocarcinoma (PDAC). However, previous knowledge is not fully universalized. Here, we built a single cell atlas of PDAC from six datasets containing over 70 samples and >130,000 cells, and demonstrated its application to the reanalysis of the previous bulk transcriptomic cohorts and inferring cell-cell communications. The cell decomposition of bulk transcriptomics using scRNAseq data showed the cellular heterogeneity of PDAC; moreover, high levels of tumor cells and fibroblasts were indicative of poor-prognosis. Refined tumor subtypes signature indicated the tumor cell dynamics in intra-tumor and their specific regulatory network. We further identified functionally distinct tumor clusters that had close interaction with fibroblast subtypes via different signaling pathways dependent on subtypes. Our analysis provided a reference dataset for PDAC and showed its utility in research on the microenvironment of intra-tumor heterogeneity.

SUBMITTER: Chijimatsu R

PROVIDER: S-EPMC9283889 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Establishment of a reference single-cell RNA sequencing dataset for human pancreatic adenocarcinoma.

Chijimatsu Ryota R Kobayashi Shogo S Takeda Yu Y Kitakaze Masatoshi M Tatekawa Shotaro S Arao Yasuko Y Nakayama Mika M Tachibana Naohiro N Saito Taku T Ennishi Daisuke D Tomida Shuta S Sasaki Kazuki K Yamada Daisaku D Tomimaru Yoshito Y Takahashi Hidenori H Okuzaki Daisuke D Motooka Daisuke D Ohshiro Takahito T Taniguchi Masateru M Suzuki Yutaka Y Ogawa Kazuhiko K Mori Masaki M Doki Yuichiro Y Eguchi Hidetoshi H Ishii Hideshi H

iScience 20220622 8

Single-cell RNA sequencing (scRNAseq) has been used to assess the intra-tumor heterogeneity and microenvironment of pancreatic ductal adenocarcinoma (PDAC). However, previous knowledge is not fully universalized. Here, we built a single cell atlas of PDAC from six datasets containing over 70 samples and >130,000 cells, and demonstrated its application to the reanalysis of the previous bulk transcriptomic cohorts and inferring cell-cell communications. The cell decomposition of bulk transcriptomi ...[more]

PMID: 35847558

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Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is an extremely deadly neoplasm, with only a 5-year survival rate of around 9%. The tumor and its microenvironment are highly heterogeneous, and it is still unknown which cell types influence patient outcomes.MethodsWe used single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) to identify differences in cell types. We then applied the scRNA-seq data to decompose the cell types in bulk RNA sequencing (bulk RNA-seq) data from the Cancer Genome Atlas (TCGA) cohort. We employed unbiased machine learning integration algorithms to develop a prognosis signature based on cell type makers. Lastly, we verified the differential expression of the key gene LY6D using immunohistochemistry and qRT-PCR.ResultsIn this study, we identified a novel cell type with high proliferative capacity, Prol, enriched with cell cycle and mitosis genes. We observed that the proportion of Prol cells was significantly increased in PDAC, and Prol cells were associated with reduced overall survival (OS) and progression-free survival (PFS). Additionally, the marker genes of Prol cell type, identified from scRNA-seq data, were upregulated and associated with poor prognosis in the bulk RNA-seq data. We further confirmed that mutant KRAS and TP53 were associated with an increased abundance of Prol cells and that these cells were associated with an immunosuppressive and cold tumor microenvironment in PDAC. ST determined the spatial location of Prol cells. Additionally, patients with a lower proportion of Prol cells in PDAC may benefit more from immunotherapy and gemcitabine treatment. Furthermore, we employed unbiased machine learning integration algorithms to develop a Prol signature that can precisely quantify the abundance of Prol cells and accurately predict prognosis. Finally, we confirmed that the LY6D protein and mRNA expression were markedly higher in pancreatic cancer than in normal pancreatic tissue.ConclusionsIn summary, by integrating bulk RNA-seq and scRNA-seq, we identified a novel proliferative cell type, Prol, which influences the OS and PFS of PDAC patients.

Project description:Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, with limited biomarkers identified to predict its prognosis and treatment response of immune checkpoint blockade (ICB). This study aimed to explore the predictive ability of T cell marker genes score (TMGS) to predict their overall survival (OS) and treatment response to ICB by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. Multi-omics data of PDAC were applied in this study. The uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification. The non-negative matrix factorization (NMF) algorithm was applied to molecular subtypes clustering. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was adopted for TMGS construction. The prognosis, biological characteristics, mutation profile, and immune function status between different groups were compared. Two molecular subtypes were identified via NMF: proliferative PDAC (C1) and immune PDAC (C2). Distinct prognoses and biological characteristics were observed between them. TMGS was developed based on 10 T cell marker genes (TMGs) through LASSO-Cox regression. TMGS is an independent prognostic factor of OS in PDAC. Enrichment analysis indicated that cell cycle and cell proliferation-related pathways are significantly enriched in the high-TMGS group. Besides, high-TMGS is related to more frequent KRAS, TP53, and CDKN2A germline mutations than the low-TMGS group. Furthermore, high-TMGS is significantly associated with attenuated antitumor immunity and reduced immune cell infiltration compared to the low-TMGS group. However, high TMGS is correlated to higher tumor mutation burden (TMB), a low expression level of inhibitory immune checkpoint molecules, and a low immune dysfunction score, thus having a higher ICB response rate. On the contrary, low TMGS is related to a favorable response rate to chemotherapeutic agents and targeted therapy. By combining scRNA-seq and bulk RNA-seq data, we identified a novel biomarker, TMGS, which has remarkable performance in predicting the prognosis and guiding the treatment pattern for patients with PDAC.

Dataset Information

Establishment of a reference single-cell RNA sequencing dataset for human pancreatic adenocarcinoma.

Publications

Establishment of a reference single-cell RNA sequencing dataset for human pancreatic adenocarcinoma.

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