Single-cell RNA sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma

Bicheng Ye; Qi Wang; Xiaofeng Zhu; Lingling Zeng; Huiyuan Luo; Yan Xiong; Qin Li; Qinmei Zhu; Songyun Zhao; Ting Chen; Jingen Xie

doi:10.3389/fonc.2023.1236435

Single-cell RNA sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma

Front Oncol. 2023 Aug 2:13:1236435. doi: 10.3389/fonc.2023.1236435. eCollection 2023.

Authors

Bicheng Ye¹, Qi Wang², Xiaofeng Zhu³, Lingling Zeng⁴, Huiyuan Luo¹, Yan Xiong¹, Qin Li¹, Qinmei Zhu¹, Songyun Zhao⁵, Ting Chen⁶, Jingen Xie⁷

Affiliations

¹ Medical School, Yangzhou Polytechnic College, Yangzhou, China.
² Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, China.
³ Department of Neurology, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.
⁴ Department of Gastroenterology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China.
⁵ Department of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
⁶ Department of Oncology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China.
⁷ Department of General Medicine, Huai'an Cancer Hospital, Huai'an, China.

Abstract

Background: Pancreatic 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.

Methods: We 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.

Results: In 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.

Conclusions: In 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.

Keywords: KRAS; TP53; immunotherapy; pancreatic ductal adenocarcinoma; proliferative cells; single-cell sequencing; spatial transcriptome.