Evidence map›Paper›PMID 37601684›Full record

ArticleFrontiers in oncology2023

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 and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
7.8field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 34 citations in OpenAlex.

  1. Article
  2. Review
  3. The role of CSTF2 in gastric cancer: implications for therapy.European journal of medical research · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 5 institutions in 1 country.

Bicheng YeMedical School, Yangzhou Polytechnic College, Yangzhou, China.
Qi WangDepartment of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, China.
Xiaofeng ZhuDepartment of Neurology, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.
Lingling ZengDepartment of Gastroenterology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China.
Huiyuan LuoMedical School, Yangzhou Polytechnic College, Yangzhou, China.
Yan XiongMedical School, Yangzhou Polytechnic College, Yangzhou, China.
Qin LiMedical School, Yangzhou Polytechnic College, Yangzhou, China.
Qinmei ZhuMedical School, Yangzhou Polytechnic College, Yangzhou, China.
Songyun ZhaoDepartment of Neurosurgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Ting ChenDepartment of Oncology, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huai'an, China.
Jingen XieDepartment of General Medicine, Huai'an Cancer Hospital, Huai'an, China.
Yangzhou Polytechnic Institute · CNSecond People’s Hospital of Huai’an · CNJiangsu University · CNNanjing Medical University · CNXuzhou Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

immunotherapyKRASpancreatic ductal adenocarcinomaproliferative cellssingle-cell sequencingspatial transcriptomeTP53

Identifiers

PMID37601684
PMCPMC10433893
OpenAlexW4385497760

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.