ArticleTranslational pediatrics2025
Article in Translational pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Exploring hub genes related to adipocytokines in keloids: a combined analysis integrating single-cell, Mendelian randomization and bulk transcriptome data with experimental verification.Frontiers in molecular biosciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Osteosarcoma (OS), the most common pediatric bone tumor, faces challenges with frequent relapse despite treatment advances. Identifying early diagnostic biomarkers and therapeutic targets is critical. The purpose of this study was to investigate the novel biomarkers for OS, and we also aimed to explore whether these biomarkers could potentially serve as the therapy targets. Methods: Integrated analysis combined three Gene Expression Omnibus (GEO) datasets (GSE42352, GSE126209, GSE12865) and TARGET-OS clinical-transcriptomic data (n=88). Immune-related genes from ImmPort (1,793 genes) were analyzed alongside differentially expressed genes (DEGs) identified via sva batch correction. Functional enrichment used clusterProfiler, while machine learning [eXtreme Gradient Boosting (XGB), random forest (RF), generalized linear model (GLM), support vector machine (SVM)] models were built with caret, xgboost, and kernlab. Prognostic genes were screened via univariate Cox regression (P<0.05). Key genes intersecting SVM and Cox results were validated via package for receiver operating characteristic (pROC), survival analysis, competing endogenous RNA (ceRNA) network (Cytoscape), immune infiltration (CIBERSORT), drug sensitivity (GDSC), and quantitative polymerase chain reaction (qPCR). Results: Differential analysis identified 1,370 DEGs (748 upregulated, 622 downregulated), intersecting with immune-related genes to yield 174 OS-linked immune-DEGs. Enrichment highlighted cytokine-PI3K-Akt pathways. Machine learning prioritized 10 genes, with Conclusions:
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.