Evidence map›Paper›PMID 41523912›Full record

ArticleHuman mutation2026

Integrated Multiomics Analysis Reveals a Migrasome-Related Signature for Prognosis and Immunotherapy Response in Lung Adenocarcinoma.

Jiayu Zhou, Tianye Song, Nengzheng Wang, Ce Liang, Ming Jiang, Xu Zhang, Hong Gao, Qingqing Feng

Abstract read
In one paragraph

Article in Human mutation, 2026. 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. 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

8 authors.

Jiayu ZhouDepartment of Thoracic Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China, srrsh.com.
Tianye SongDepartment of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China, hebmu.edu.cn.
Nengzheng WangSchool of Life Sciences, Fudan University, Shanghai, China, fudan.edu.cn.
Ce LiangDepartment of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China, hebmu.edu.cn.
Ming JiangCenter for Genetic Medicine and Department of Otolaryngology-Head and Neck Surgery, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China, zju.edu.cn.
Xu ZhangDepartment of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China, hebmu.edu.cn.ORCID https://orcid.org/0009-0004-4086-9541
Hong GaoDepartment of Oncology, The Second Hospital of Hebei Medical University, Shijiazhuang, China, hebmu.edu.cn.ORCID https://orcid.org/0009-0002-5692-4627
Qingqing FengDepartment of Pulmonary and Critical Care Medicine, Shanxi Provincial People's Hospital, Taiyuan, China, spph-sx.com.ORCID https://orcid.org/0009-0003-2679-7660

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Migrasomes, a newly identified subtype of extracellular vesicles generated during cell migration, play crucial roles in tumor microenvironment modulation. However, their systematic characterization in lung adenocarcinoma (LUAD) remains unexplored. This study is aimed at deciphering migrasome-related molecular features and their clinical significance through multiomics integration. Methods: We integrated bulk transcriptomes (541 LUAD samples from TCGA/GEO) with single-cell RNA-seq (GSE156632). Migrasome-related genes (MIGgenes) were identified through WGCNA and differential expression analysis. A machine learning framework incorporating 10 algorithms generated 101 combinatorial models, with the optimal prognostic signature (MIGsig) selected via 10-fold cross-validation. Biological mechanisms were investigated through ssGSEA, TME analysis, and in vitro validation. Results: Our analysis revealed significant migrasome activity enrichment in endothelial cells and fibroblasts, with 115 cross-omics MIGgenes identified including 31 prognostic markers. The Lasso-Cox-derived 3-gene signature (GSTM5/DNASE1L3/PDGFB) demonstrated robust predictive performance (training set Conclusions: This study establishes the first migrasome-based prognostic model for LUAD, demonstrating both independent survival prediction capability and clinical utility for identifying immunotherapy beneficiaries. The MIGsig signature provides novel biological insights into migrasome-mediated tumor-immune interactions and represents a promising tool for precision oncology applications in LUAD management.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorImmunotherapyLung NeoplasmsCell MovementComputational BiologyGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMachine LearningMultiomicsPrognosisTranscriptomeTumor MicroenvironmentBiomarkers, Tumorimmunotherapy predictionlung adenocarcinomamachine learningmigrasomestumor microenvironment

Identifiers

PMID41523912
PMCPMC12781864

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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 OpenQuestion graph.