ArticleScientific reports2025
Construction of a prognostic risk model for acute myeloid leukemia based on exosomal genes and analysis of immune microenvironment characteristics.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Integrated Clinical-Molecular Risk Stratification in Diffuse Large B-Cell Lymphoma: Machine Learning Survival Analysis.JMIR medical informatics · 2026Article
- Immune monitoring for relapse of acute myeloid leukemia after allogeneic stem cell transplantation in clinical laboratories.Frontiers in immunology · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Acute myeloid leukemia (AML) exhibits significant heterogeneity in disease progression and therapeutic response, highlighting the urgent need for novel biomarkers to improve risk stratification and therapeutic targeting. In this study, we integrated multi-omics data from The Cancer Genome Atlas (TCGA, n = 151) and Genotype-Tissue Expression (GTEx, n = 337) cohorts to systematically analyze dynamic expression patterns of exosome-related genes in AML. Using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) algorithms, we identified 13 exosome-associated genes (EXOSC4, TMEM109, THBS1, MYH9, HLA-DRA, CAPZB, ITGA4, MYL6, CYB5R1, PSMA2, MPO, NDST2, and CANX) and constructed a prognostic risk model. The model demonstrated superior predictive accuracy compared to traditional clinical parameters, with area under the curve (AUC) values of 0.819, 0.825, and 0.832 for 1-, 2-, and 3-year survival predictions in the training set, and 0.909 in the independent GEO validation cohort (GSE71014). Kaplan-Meier analysis revealed significantly shorter overall survival in the high-risk group (log-rank P < 0.001, hazard ratio = 0.22, 95% CI = 0.13-0.36). Immune microenvironment characterization using CIBERSORTx identified increased infiltration of regulatory T cells (Tregs, P < 0.01) in high-risk patients. Functional enrichment analysis revealed enrichment of PI3K-Akt signaling pathways and TP53 transcriptional networks in high-risk groups. Molecular docking studies confirmed strong binding affinity of verteporfin (ITGA4 inhibitor, docking score=-16.0 kcal/mol) and ebselen (MPO inhibitor) to their respective targets, suggesting potential therapeutic strategies to overcome chemotherapy resistance. This study establishes a robust 13-gene exosome-based prognostic signature for AML risk stratification and identifies novel immunomodulatory mechanisms mediated by exosome-driven Treg polarization.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.