Evidence map›Paper›PMID 39789613›Full record

ArticleBiology direct2025

OSBPL3 modulates the immunosuppressive microenvironment and predicts therapeutic outcomes in pancreatic cancer.

Qihui Sun, Xiaoqi Zhu, Qi Zou, Yang Chen, Tingting Wen, Tingting Jiang, Xiaojia Li, Fang Wei, Keping Xie, Jia Liu

Abstract read
In one paragraph

Article in Biology direct, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. 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

10 authors.

Qihui Sun *Center for Pancreatic Cancer Research, Department of Immunology, The South China University of Technology School of Medicine, Guangzhou, China.
Xiaoqi Zhu *Center for Pancreatic Cancer Research, Department of Immunology, The South China University of Technology School of Medicine, Guangzhou, China.
Qi Zou *Center for Pancreatic Cancer Research, Department of Immunology, The South China University of Technology School of Medicine, Guangzhou, China.
Yang ChenCenter for Pancreatic Cancer Research, Department of Immunology, The South China University of Technology School of Medicine, Guangzhou, China.
Tingting WenCenter for Pancreatic Cancer Research, Department of Immunology, The South China University of Technology School of Medicine, Guangzhou, China.
Tingting JiangCenter for Pancreatic Cancer Research, Department of Immunology, The South China University of Technology School of Medicine, Guangzhou, China.
Xiaojia LiCenter for Pancreatic Cancer Research, Department of Immunology, The South China University of Technology School of Medicine, Guangzhou, China.
Fang WeiCenter for Pancreatic Cancer Research, Department of Immunology, The South China University of Technology School of Medicine, Guangzhou, China. eyfangwei@scut.edu.cn.
Keping XieCenter for Pancreatic Cancer Research, Department of Immunology, The South China University of Technology School of Medicine, Guangzhou, China. scutmedicine@scut.edu.cn.
Jia LiuSchool of Medicine, South China University of Technology, Guangzhou, 510006, China. mcliujia@scut.edu.cn.

Funding

National Natural Science Foundation of China 82072632
6 · The paper itself

Abstract

backgroundPancreatic cancer is characterized by a complex tumor microenvironment that hinders effective immunotherapy. Identifying key factors that regulate the immunosuppressive landscape is crucial for improving treatment strategies.

methodsWe constructed a prognostic and risk assessment model for pancreatic cancer using 101 machine learning algorithms, identifying OSBPL3 as a key gene associated with disease progression and prognosis. We integrated multi-dataset analyses, single-cell transcriptomic data, and functional experiments to explore the role of OSBPL3 in pancreatic cancer.

resultsOur risk prediction model, developed using machine learning algorithms, demonstrated high predictive accuracy across multiple datasets. Notably, the "rf" algorithm model showed an AUC of 1 in the training set and AUCs of 0.887 and 0.977 in two validation datasets. Ridge regression analysis identified OSBPL3 as a core prognostic feature gene. High OSBPL3 expression in pancreatic cancer samples was associated with immunosuppressive characteristics, including reduced CD8 + T cell infiltration and increased immunosuppressive cell populations such as Treg cells and M2 macrophages. Transcriptomic sequencing following OSBPL3 knockdown revealed enrichment of immune-related pathways, suggesting OSBPL3's influence on the immune microenvironment. Single-cell analyses further confirmed OSBPL3's role in shaping the immunosuppressive landscape by modulating Treg cells and M2 macrophages. Additionally, OSBPL3 expression was linked to resistance to immunotherapy, with high OSBPL3 expression associated with lower Immunophenoscore (IPS) scores, indicating poor responsiveness to immunotherapy.

conclusionsOur study reveals OSBPL3 as a critical regulator of the immunosuppressive microenvironment in pancreatic cancer and a potential therapeutic target. Targeting OSBPL3 may enhance the efficacy of immunotherapy and improve patient outcomes. Further research is warranted to explore OSBPL3 as a biomarker for predicting immunotherapy response and as a potential therapeutic target in combination with anti-PD1 therapy.

Indexed as

Pancreatic NeoplasmsTumor MicroenvironmentHumansImmunotherapyMachine LearningPrognosis

Identifiers

PMID39789613
PMCPMC11716069

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.