ArticleiScience2025
A tumor microenvironment model for glioma diagnosis and therapeutic evaluation based on the analysis of tissues and biological fluids.
Article in iScience, 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
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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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional glioma diagnostic methods have limitations, while liquid biopsy is a promising non-invasive option. This study developed the glioma-related cell signature (GRCS), a prediction model that integrates machine learning with biological insights. Trained on tumor-educated platelet samples, the GRCS model demonstrated consistent performance across validation cohorts comprising platelet, extracellular vesicle, and tumor tissue specimens. The GRCS score showed significant associations with patient age, histological grade, survival outcome, and mutational landscape. Moreover, the GRCS model effectively distinguished responses to bevacizumab and immunotherapy and identified potential candidates for combination therapies. Furthermore, a miRNA-based simplified GRCS model (GRCSS) was developed and validated across different specimen cohorts, demonstrating its robust diagnostic and prognostic capabilities in glioma. This work highlights the potential of GRCS as a versatile tool for personalized glioma management across multiple biopsy specimen types.
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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.