Evidence map›Paper›PMID 41561750›Full record

ArticleFrontiers in oncology2025

The Identification of LA-tumor associated macrophages in immune modulation via amyloid-beta precursor protein/CD74 signal pathway in gastric cancer: a predictive module and machine learning.

Jian-Peng Wang, Chen-Chen An, Zi-Ning Wang, Zhi-Jian Wei, Ying Dai

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Jian-Peng Wang *Department of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Chen-Chen An *Innovation and Entrepreneurship Laboratory for College Students, Anhui Medical University, Hefei, Anhui, China.
Zi-Ning WangInnovation and Entrepreneurship Laboratory for College Students, Anhui Medical University, Hefei, Anhui, China.
Zhi-Jian WeiDepartment of Gastrointestinal Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Ying DaiDepartment of Medical Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Tumor-associated macrophages (TAMs) play a critical role in cancer immune microenvironment, modulating immune evasion. The prognostic role of TAMs gives insights into the immune landscape and therapeutic targets in gastric cancer (GC). Methods: GC microenvironment was analyzed via single-cell and bulk RNA-seq data from public databases. TAM subtypes were then identified via dimensionality reduction and annotation under quality control. TAM differentiation and function were evaluated by pseudo-time analysis, cell communication, molecular docking, and key gene enrichment. A predictive model based on LA-TAM was established. Amyloid-β precursor protein (APP) expression level and its effect on macrophage programmed death-1 (PD-1) expression was validated Results: In GC microenvironment, epithelial cells and fibroblasts were downregulated, while B cells, CD8 Conclusion: LA-TAM plays a key role in immune suppression and metabolic regulation in GC. Its key genes form a high-precision prognosis model, and endothelial cell-expressed APP may promote immune evasion by enhancing macrophage PD-1 expression, suggesting a potential target for immunotherapy.

Indexed as

gastric cancerimmune evasionmachine learningpredictive biomarkerstumor-associated macrophages

Identifiers

PMID41561750
PMCPMC12812576

What Socratic holds

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Registered trials

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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.