Single-cell RNA sequencing analysis revealed malignant ductal cell heterogeneity and prognosis signatures in pancreatic cancer

Haiyang Du; Gao Si; Jiqing Si; Xuejie Song; Fuchun Si

doi:10.1016/j.clinre.2023.102200

Single-cell RNA sequencing analysis revealed malignant ductal cell heterogeneity and prognosis signatures in pancreatic cancer

Clin Res Hepatol Gastroenterol. 2023 Oct;47(8):102200. doi: 10.1016/j.clinre.2023.102200. Epub 2023 Aug 27.

Authors

Haiyang Du¹, Gao Si², Jiqing Si³, Xuejie Song¹, Fuchun Si⁴

Affiliations

¹ Traditional Chinese Medicine (Zhong Jing) school, Henan University of Chinese Medicine, Zhengzhou 450046, China; Henan Key Laboratory of TCM Syndrome and Prescription Signaling, Henan International Joint Laboratory of TCM Syndrome and Prescription Signaling, Academy of Chinese Medical Sciences, Henan University of Chinese Medicine, Zhengzhou 450046, China.
² Department of Orthopedic, The Third Hospital of Peking University, Beijing 100029, China.
³ Henan Hospital of TCM, The Second Affiliated Hospital of Henan University of traditional Chinese Medicine, Zhengzhou 450046, China.
⁴ Traditional Chinese Medicine (Zhong Jing) school, Henan University of Chinese Medicine, Zhengzhou 450046, China; Henan Key Laboratory of TCM Syndrome and Prescription Signaling, Henan International Joint Laboratory of TCM Syndrome and Prescription Signaling, Academy of Chinese Medical Sciences, Henan University of Chinese Medicine, Zhengzhou 450046, China. Electronic address: [email protected].

PMID: 37643692
DOI: 10.1016/j.clinre.2023.102200

Abstract

Pancreatic cancer (PAC) remains one of the most lethal malignant neoplasms, which is diagnosed at an advanced stage and thus lose the chance for curative resection. Here, we further probed PAC with a comprehensive multi-omics approach. Using single-cell RNA sequencing, we provided an integrated analysis of ductal cell subpopulations over the Leiden algorithm to identify two mian subcluster: S100A6 + cells and FXYD2 + cells. The gene set enrichment analysis results show that the two subtypes focused on different pathways related to tumor development. Furthermore, we integrated bulk and single-cell RNA sequencing datasets to generate and validate the prognostic signatures of the overall survival (OS) in PAC patients and S100A6 + cells were significantly enriched in high-risk groups which had a poor prognosis. Collectively, this research expands our understanding of ductal cell and provides a new reliable prognosis signature in PAC.

Keywords: Ductal cell; Machine learning; Pancreatic cancer; S100A6 + cells; Singe-cell RNA sequencing.