ArticleFrontiers in pharmacology2025
Machine learning-driven SLC prognostic signature for glioma: predicting survival and immunotherapy response.
Article in Frontiers in pharmacology, 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Gliomas are the most common and aggressive primary brain tumors, characterized by significant heterogeneity and poor prognosis. Despite advancements in treatment, therapeutic resistance and tumor recurrence remain major challenges. Identifying novel molecular biomarkers is essential for improving prognosis and developing more effective therapies. Methods: In this study, we developed a solute carrier family prognostic signature (SLCFPS) for gliomas using univariate Cox regression and machine learning algorithms across five independent glioma cohorts. Prognostic performance was evaluated through Kaplan-Meier survival analysis, concordance index (C-index), and receiver operating characteristic (ROC) curve analysis. The immune landscape, immunotherapy response, and drug sensitivity were further analyzed using bioinformatics tools such as ESTIMATE, xCell, TIDE, and drug response correlation analysis. Results: SLCFPS effectively stratified glioma patients into high- and low-risk groups, with higher scores associated with poorer survival outcomes. The model demonstrated superior predictive performance compared to existing glioma prognostic models. Additionally, SLCFPS was linked to an immunosuppressive tumor microenvironment and upregulated immune checkpoints, indicating potential implications for immunotherapy response. Furthermore, SLCFPS correlated with drug sensitivity, suggesting potential therapeutic options for glioma treatment. Discussion: Our findings highlight SLCFPS as a robust biomarker for glioma prognosis and treatment response. By providing insights into tumor immunity, this model may aid in the development of personalized therapeutic strategies. Further validation in clinical settings is necessary to explore its full potential in guiding glioma management.
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.