Evidence map›Paper›PMID 41050411›Full record

ArticleFrontiers in pharmacology2025

Constructing Scissor+ risk model to predict prognosis and immunotherapy responses in PAAD by integrating bulk and single-cell RNA sequencing data.

Gaofei Zhang, Jiao Yu, Fan Zhang, Fang Wang, Congya Zhou

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. A prognostic model based on ScissorTranslational cancer research · 2026
    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

5 authors.

Gaofei ZhangDepartment of Radiation Oncology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Jiao YuDepartment of Radiation Oncology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Fan ZhangDepartment of Oncology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Fang WangDepartment of Oncology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Congya ZhouDepartment of Radiation Oncology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study focused on epithelial cells to construct a prognostic risk model and provide targeted insights into responses to immunotherapy. Methods: Single-cell RNA sequencing (scRNA-seq) was clustered using Uniform Manifold Approximation and Projection (UMAP) and a risk model was developed through Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Kaplan-Meier analysis was performed to evaluate the prognosis of PAAD. The biological characteristics of LIPH were assessed using CCK-8, colony formation and Transwell assays. Results: Eight major cell clusters were identified, revealing two developmental trajectories for malignant epithelial cells from primary to metastases. Epithelial cells were categorized into Scissor+ and Scissor- subtypes, with Scissor+ epithelial cells exhibiting more complex cellular communication with TME cells. Furthermore, we successfully developed a risk model for PAAD patients based on the Scissor findings. The prognosis for PAAD patients in the high-risk group was significantly poorer within both the TCGA and ICGC cohorts. Differences were observed in the populations of naïve B cells, CD8 T cells, M0 macrophages, and activated dendritic cells in different groups. Knockdown of LIPH significantly inhibited the growth and invasion of PAAD cells. Conclusion: These findings underscore the significance of this risk model in predicting prognosis and immunotherapy responses, and enhancing understanding of tumor microenvironment (TME) heterogeneity in PAAD metastases.

Indexed as

Epithelial cellsPAADPrognosisScissorSingle-cell RNA sequencingTumor microenvironment

Identifiers

PMID41050411
PMCPMC12491314

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