Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

Nassani, Rayan; Bokhari, Yahya; Alrfaei, Bahauddeen M.

doi:10.1371/journal.pone.0287448

Cited by 5 publications

(1 citation statement)

References 103 publications

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“…Previously, Nassani et al (2023) emphasized the proposition of molecular signatures for predicting GBM prognosis, aiming to enhance clinical decisions, GBM management, and drug development. 47 Conversely, Zheng et al (2023) highlighted the unveiling of transcriptional heterogeneity in glioblastoma and established a significant connection between spatial cellular architecture and clinical outcomes. 48 In contrast, our Study identifies central memory CD8 + T cell-related genes and establishes a robust signature with predictive value for glioblastoma patients.…”

Section: Discussionmentioning

confidence: 99%

A novel immune cell signature for predicting glioblastoma after radiotherapy prognosis and guiding therapy

Huang,

Lu,

Sun

et al. 2024

Int J Immunopathol Pharmacol

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Background: Glioblastoma, a highly aggressive brain tumor, poses a significant clinical challenge, particularly in the context of radiotherapy. In this study, we aimed to explore infiltrating immune cells and identify immune-related genes associated with glioblastoma radiotherapy prognosis. Subsequently, we constructed a signature based on these genes to discern differences in molecular and tumor microenvironment immune characteristics, ultimately informing potential therapeutic strategies for patients with varying risk profiles. Methods: We leveraged UCSC Xena and CGGA gene expression profiles from post-radiotherapy glioblastoma as verification cohorts. Infiltration ratios were stratified into high and low groups based on the median value. Differential gene expression was determined through Limma differential analysis. A signature comprising four genes was constructed, guided by Gene Ontology (GO) functional enrichment results and Kaplan–Meier survival analysis. We evaluated differences in cell infiltration levels, Immune Score, Stromal Score, and ESTIMATE Score and their Pearson correlations with the signature. Spearman’s correlation was computed between the signature and patient drug sensitivity (IC50), predicted using Genomics of Drug Sensitivity in Cancer (GDSC) and CCLE databases. Results: Notably, the infiltration of central memory CD8+T cells exhibited a significant correlation with glioblastoma radiotherapy prognosis. Samples were dichotomized into high- and low-risk groups based on the optimal signature threshold (2.466642). Kaplan–Meier (K-M) survival analysis revealed that the high-risk group experienced a significantly poorer prognosis ( p = .0068), with AUC values exceeding 0.82 at 1, 3, and 5 years, underscoring the robust predictive potential of the signature scoring system. Independent validation sets substantiated the validity of the signature. Statistically significant differences in tumor microenvironments (p < .05) were observed between high- and low-risk groups, and these differences were significantly correlated with the signature ( p < .05). Furthermore, there were significant correlations between high and low-risk groups regarding immune checkpoint expressions, Immune Prognostic Score (IPS), and Tumor Immune Dysfunction and Exclusion (TIDE) scores. Conclusion: The immune cell signature, comprising SDC-1, PLAUR, FN1, and CXCL13, holds promise as a predictive tool for assessing glioblastoma prognosis following radiotherapy. This signature also offers valuable guidance for tailoring treatment strategies, emphasizing its potential clinical relevance in improving patient outcomes.

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Section: Discussionmentioning

confidence: 99%

A novel immune cell signature for predicting glioblastoma after radiotherapy prognosis and guiding therapy

Huang,

Lu,

Sun

et al. 2024

Int J Immunopathol Pharmacol

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Advances in AI-based genomic data analysis for cancer survival prediction

Deepali,

Goel,

Padmavati Khandnor

2024

Multimed Tools Appl

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Exploring the therapeutic mechanisms and prognostic targets of Biochanin A in glioblastoma via integrated computational analysis and in vitro experiments

Ge,

Yuan,

Wang

et al. 2024

Sci Rep

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Glioblastoma (GBM) is the most aggressive brain tumor and is characterized by a poor prognosis and high recurrence and mortality rates. Biochanin A (BCA) exhibits promising clinical anti-tumor effects. In this study, we aimed to explore the pharmacological mechanisms by which BCA acts against GBM. Network pharmacology was employed to identify overlapping target genes between BCA and GBM. Differentially expressed genes from the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database were visualized using VolcaNose. Interactions among these overlapping genes were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. Protein–protein interaction networks were constructed using Cytoscape 3.8.1. The Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology enrichment analyses were conducted using the Database for Annotation, Visualization, and Integrated Discovery. Survival analyses for these genes were performed using the GEPIA2 database. The Chinese Glioma Genome Atlas database was used to study the correlations between key prognostic genes. Molecular docking was confirmed using the DockThor database and visualized with PyMol software. Cell viability was assessed via the CCK-8 assay, apoptosis and the cell cycle stages were examined using flow cytometry, and protein expression was detected using western blotting. In all, 63 genes were initially identified as potential targets for BCA in treating GBM. Enrichment analysis suggested that the pharmacological mechanisms of BCA primarily involved cell cycle inhibition, induction of cell apoptosis, and immune regulation. Based on these findings, AKT1, EGFR, CASP3, and MMP9 were preliminarily predicted as key prognostic target genes for BCA in GBM treatment. Furthermore, molecular docking analysis suggested stable binding of BCA to the target protein. In vitro experiments revealed the efficacy of BCA in inhibiting GBM, with an IC50 value of 98.37 ± 2.21 μM. BCA inhibited cell proliferation, induced cell apoptosis, and arrested the cell cycle of GBM cells. Furthermore, the anti-tumor effects of BCA on U251 cells were linked to the regulation of the target protein. We utilized integrated bioinformatics analyses to predict targets and confirmed through experiments that BCA possesses remarkable anti-tumor activities. We present a novel approach for multi-target treatment of GBM using BCA.

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Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

Cited by 5 publications

References 103 publications

A novel immune cell signature for predicting glioblastoma after radiotherapy prognosis and guiding therapy

A novel immune cell signature for predicting glioblastoma after radiotherapy prognosis and guiding therapy

Advances in AI-based genomic data analysis for cancer survival prediction

Exploring the therapeutic mechanisms and prognostic targets of Biochanin A in glioblastoma via integrated computational analysis and in vitro experiments

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