ArticleDiscover oncology2025
Fatty acid metabolism-related risk signature revealing the immune landscape of neuroblastoma and predicting overall survival in pediatric neuroblastoma patients.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
backgroundTumor metabolic reprogramming is a hallmark in cancer cells, wherein fatty acid metabolism assumes a pivotal role in energy supply and the provision of diverse biosynthetic precursors. However, there is a lack of systematic analysis regarding the impact of fatty acid metabolism on prognosis in neuroblastoma (NB) patients and its influence on the immune microenvironment.
methodsWe acquired RNA expression profiles and corresponding clinical-pathological information for NB patients from the Gene Expression Omnibus, ArrayExpress, and TARGET databases. The GSE49710 cohort was utilized as a training set, whereas E-MTAB-8248 and the TARGET cohorts served as testing sets. Consensus clustering was employed to identify molecular subtypes based on fatty acid metabolism. Independent prognostic genes were pinpointed using LASSO-Cox analysis, which facilitated the development of a novel risk signature that was subsequently validated using the testing sets. We then proceeded to analyze the predictive power of the risk signature for prognosis, its correlation with clinical-pathological features, the immune landscape, and drug sensitivity.
resultsIn the consensus clustering analysis, patients in the training set were segregated into two clusters. Cluster 2 exhibiting significantly poorer overall survival (OS) compared to cluster 1. Moreover, cluster 2 was markedly associated with clinical-pathological features indicative of poor prognosis. Following this, univariate Cox regression analysis revealed 207 fatty acid metabolism genes (FMGs) correlated with patient OS. A risk signature based on 35 FMGs was constructed using LASSO-Cox regression analysis, demonstrating significant predictive accuracy and discrimination in both the training and testing sets. The risk signature emerged as an independent prognostic factor and was integrated with multiple clinical-pathological features to develop a nomogram. In the immune landscape analysis, the high-risk group displayed a compromised antigen presentation mechanism, reduced infiltration levels of various immune cells, and escaping of CD8 + T cells and NK cells. Additionally, different risk groups could exhibit different responsiveness to immune checkpoint inhibitors. Lastly, potential chemotherapeutic agents for each risk group were predicted.
conclusionThe novel risk signature, derived from FMGs, demonstrated promising efficacy in predicting the prognosis of NB patients, elucidating their immune landscape, and guiding therapeutic strategies.
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.