ArticleTranslational cancer research2025
Identification and functional analysis of
Article in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Mesothelin promotes acute myeloid leukemia progression through LYN-dependent signaling.The Journal of biological chemistry · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Immunotherapy is a pivotal approach in combating acute myeloid leukemia (AML), with the identification of immunomarkers being imperative. This investigation aimed to delineate biomarkers linked with immune-related genes (IRGs) in AML, thereby providing a theoretical framework for AML therapeutics. Methods: This research utilized AML-specific datasets [GSE9476 and The Cancer Genome Atlas (TCGA)-AML] alongside 1,793 IRGs. Initially, weighted gene co-expression network analysis (WGCNA) was employed to identify module genes using an integrative and systematic methodology. Differential gene expression analyses were conducted on GSE9476 and aggregated AML data from the University of California Santa Cruz (UCSC) Xena platform, alongside the Genotype-Tissue Expression (GTEx) database, to identify differentially expressed genes (DEGs). These DEGs were then intersected with WGCNA module genes and IRGs to isolate potential candidate genes. Kaplan-Meier (K-M) survival curves were subsequently utilized to identify pivotal genes with significant survival disparities. The prognostic significance of these genes was further assessed through both univariate and multivariate Cox regression analyses to pinpoint biomarkers. Finally, analyses focusing on functional enrichment associated with the identified biomarkers. Results: Using WGCNA, a cohort of 3,611 modular genes was identified. Intersection analysis involving WGCNA, DEGs, and IRGs led to the identification of eight promising candidate genes. Subsequent K-M survival assessments distilled these to six paramount genes, all of which underwent rigorous independent prognostic evaluation. Notably, Conclusions:
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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.