ArticleFrontiers in immunology2022
Prognostic Features of the Tumor Immune Microenvironment in Glioma and Their Clinical Applications: Analysis of Multiple Cohorts.
Article in Frontiers in immunology, 2022. 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, 3 citations in OpenAlex.
- Machine learning-based construction of Immunogenic cell death-related score for improving prognosis and personalized treatment in glioma.Scientific reports · 2025Article
- 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
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioma is the most common malignant tumor of the central nervous system. Tumor purity is a source of important prognostic factor for glioma patients, showing the key roles of the microenvironment in glioma prognosis. In this study, we systematically screened functional characterization related to the tumor immune microenvironment and constructed a risk model named Glioma MicroEnvironment Functional Signature (GMEFS) based on eight cohorts. The prognostic value of the GMEFS model was also verified in another two glioma cohorts, glioblastoma (GBM) and low-grade glioma (LGG) cohorts, from The Cancer Genome Atlas (TCGA). Nomograms were established in the training and testing cohorts to validate the clinical use of this model. Furthermore, the relationships between the risk score, intrinsic molecular subtypes, tumor purity, and tumor-infiltrating immune cell abundance were also evaluated. Meanwhile, the performance of the GMEFS model in glioma formation and glioma recurrence was systematically analyzed based on 16 glioma cohorts from the Gene Expression Omnibus (GEO) database. Based on multiple-cohort integrated analysis, risk subpathway signatures were identified, and a drug-subpathway association network was further constructed to explore candidate therapy target regions. Three subpathways derived from Focal adhesion (path: 04510) were identified and contained known targets including platelet derived growth factor receptor alpha (PDGFRA), epidermal growth factor receptor (EGFR), and erb-b2 receptor tyrosine kinase 2 (ERBB2). In conclusion, the novel functional signatures identified in this study could serve as a robust prognostic biomarker, and this study provided a framework to identify candidate therapeutic target regions, which further guide glioma patients' clinical decision.
Indexed as
Identifiers
What Socratic 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 Socratic graph.