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PubMed2026

Linking MRI radiomics to transcriptomics-based radiosensitivity in lower-grade glioma: A radiogenomic framework.

BACKGROUND: RSI is a transcriptomics-based biomarker associated with radiotherapy outcomes, but its clinical application is constrained by the requirement for tumor tissue and RNA sequencing. This study investigates whether MRI-derived radiomic features can reflect RSI-defined intrinsic radiosensitivity in lower-grade glioma.This addresses a critical gap arising from the limited availability of matched imaging and genomic data in routine clinical practice. METHODS: MRI-derived radiomic features were extracted from FLAIR images of lower-grade glioma patients obtained from TCIA and matched with transcriptomic data from TCGA. A total of 107 patients with both MRI and RNA sequencing data were included in the radiogenomic analysis. Radiomic features were ranked using a Borda-based ensemble feature selection strategy. Five supervised machine-learning classifiers were trained to predict RSI-based radiosensitivity classification, and model interpretability was assessed using SHAP within radiogenomic framework. RESULTS: Classification performance increased with feature number and stabilized at compact subset of 13 radiomic features. Logistic regression showed stable performance with an AUC of 0.82 (95 % CI: 0.71-0.93). SHAP analysis indicated that heterogeneity-related texture features were dominant contributors to model predictions, with many associated with the RR phenotype, while others were linked to the RS phenotype. CONCLUSION: An MRI-based radiomic signature enables non-invasive prediction of RSI-defined radiosensitivity in lower-grade glioma. Rather than offering an immediately deployable clinical tool, this study establishes a proof-of-concept radiogenomic framework demonstrating that intrinsic radiosensitivity, traditionally assessed through invasive molecular assays, can be approximated using quantitative imaging features. These findings highlight the potential of imaging-based radiosensitivity assessment and provide a foundation for future radiogenomic investigations.

Lower-grade glioma↗

PubMed2026

ERP44 Is Associated With Poor Prognosis and Promotes Proliferation and Temozolomide Resistance in Lower-grade Glioma.

BACKGROUND/AIM: Endoplasmic reticulum resident protein 44 (ERP44), a protein disulfide isomerase family member, has been implicated in tumor biology, but its role in lower-grade glioma (LGG) remains unclear. This study investigated the prognostic significance and biological function of ERP44 in LGG, focusing on proliferation and temozolomide (TMZ) resistance. MATERIALS AND METHODS: ERP44 expression, clinicopathological associations, and prognostic value were analyzed using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) datasets. Time-dependent receiver operating characteristic (ROC) curves, Cox regression, and a prognostic nomogram were constructed. Differential expression, Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) enrichment, immune infiltration, and drug sensitivity analyses were performed. Functional validation was conducted in SW1088 and SW1783 cells using shRNA-mediated ERP44 knockdown, followed by RT-qPCR, western blotting, CCK-8, colony formation, and TMZ IC50 assays. Subcutaneous xenograft models with or without TMZ treatment were used for in vivo validation. RESULTS: ERP44 was markedly upregulated in LGG and associated with higher WHO grade, IDH wildtype status, 1p/19q non-codeletion, and poor survival in TCGA and CGGA cohorts. ERP44 showed strong prognostic performance and improved risk stratification in a multivariable nomogram. Enrichment analyses linked high ERP44 expression to immune/inflammatory pathways and reduced neuronal functional signatures. ERP44 positively correlated with immune infiltration, proliferation/stemness markers, and predicted TMZ resistance, while its knockdown inhibited proliferation and colony formation, reduced TMZ IC50, suppressed xenograft growth, enhanced TMZ efficacy, and decreased Ki67 positivity. CONCLUSION: ERP44 is a prognostic biomarker that promotes LGG proliferation and TMZ resistance, suggesting its potential as a therapeutic target.

Humans↗

PubMed2024

Comparative genomic landscape of lower-grade glioma and glioblastoma.

Biomarkers for classifying and grading gliomas have been extensively explored, whereas populations in public databases were mostly Western/European. Based on public databases cannot accurately represent Chinese population. To identify molecular characteristics associated with clinical outcomes of lower-grade glioma (LGG) and glioblastoma (GBM) in the Chinese population, we performed whole-exome sequencing (WES) in 16 LGG and 35 GBM tumor tissues. TP53 (36/51), TERT (31/51), ATRX (16/51), EFGLAM (14/51), and IDH1 (13/51) were the most common genes harboring mutations. IDH1 mutation (c.G395A; p.R132H) was significantly enriched in LGG, whereas PCDHGA10 mutation (c.A265G; p.I89V) in GBM. IDH1-wildtype and PCDHGA10 mutation were significantly related to poor prognosis. IDH1 is an important biomarker in gliomas, whereas PCDHGA10 mutation has not been reported to correlate with gliomas. Different copy number variations (CNVs) and oncogenic signaling pathways were identified between LGG and GBM. Differential genomic landscapes between LGG and GBM were revealed in the Chinese population, and PCDHGA10, for the first time, was identified as the prognostic factor of gliomas. Our results might provide a basis for molecular classification and identification of diagnostic biomarkers and even potential therapeutic targets for gliomas.

Humans↗

PubMed2026

MRI-based radiomics model for predicting VEGFA expression and prognosis in lower-grade glioma.

BACKGROUND: Gliomas are the most common primary tumors of the central nervous system. Their treatment remains highly challenging, with high rates of associated disability and mortality. Conventional prognostic indicators no longer adequately satisfy the clinical demands of precision medicine. Therefore, it is essential to further explore novel prognostic biomarkers to enable accurate risk stratification and to provide new reference indicators for personalized precision therapy. PURPOSES: This study aimed to investigate the prognostic significance of vascular endothelial growth factor A (VEGFA) in patients diag nosed with lower-grade gliomas (LGGs) using an MRI based radiomics model. METHODS: Data regarding VEGFA expression and clinical records of LGG patients were retrieved from The Cancer Genome Atlas (TCGA). Corresponding preoperative MRI data were obtained from The Cancer Imaging Archive (TCIA) for radiomic feature extraction. Patients were stratified into high- and low- VEGFA expression groups based on survival information from the current cohort using the survminer package. The overall survival (OS) was assessed using Kaplan-Meier analysis and Cox proportional hazards regression. Predictive models were developed using logistic regression (LR), and model performance was evaluated via receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) values reported. An optimized model incorporating the Akaike information criterion (AIC) was also constructed (AIC-LR). RESULTS: VEGFA expression was significantly associated with OS (P = 0.002). Multivariate Cox regression confirmed VEGFA as an independent prognostic factor (hazard ratio [HR] = 2.545, 95% confidence interval: 1.422-4.555). Furthermore, VEGFA expression correlated with immune infiltration levels, particularly of M1 and M2 macrophages and T follicular helper cells, and was associated with enrichment in Wnt signaling and B cell receptor signaling pathways. The LR and AIC-LR models demonstrated acceptable predictive performance, with AUCs of 0.728 (95% CI: 0.612-0.843) and 0.725(95% CI: 0.612-0.839) in the training cohort, and 0.704 (95% CI: 0.562-0.847) and 0.718(95% CI: 0.576-0.861) in the validation cohort, respectively. CONCLUSIONS: The MRI based radiomics model showed potential for noninvasive assessment of VEGFA expression and may provide auxiliary information for prognostic evaluation in LGG. Further validation in larger samples and independent external cohorts is required before clinical application.

Radiomics↗

PubMed2026

Mutual Information-based Prognostic Biomarker Discovery in Cancer Genomics: Conceptual Framework and Representative Applications of MI-POG.

Mutual information (MI)-based approaches have increasingly been applied to cancer genomics; however, their use for genome-wide prognostic biomarker discovery remains relatively underexplored. The present article summarizes the conceptual workflow of Mutual Information-based Prognostic Omics Gene (MI-POG) based on previously published applications in breast cancer, lower-grade glioma, and other cancer datasets. The framework consists of clinical endpoint discretization, genome-wide MI-based screening, candidate ranking, and downstream validation using conventional survival-analysis approaches. Previous MI-POG applications identified solute carrier family 20 member 1 (SLC20A1) as a prognostic biomarker in hormone receptor-positive breast cancer. Elevated SLC20A1 expression was associated with unfavorable survival outcomes and was independently validated in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Methodological analyses demonstrated how survival endpoints can be integrated into an information-theoretic framework through fixed-time outcome discretization, enabling model-independent assessment of molecular-clinical dependencies. Applications across multiple cancer datasets suggested the potential applicability of the framework across biologically distinct tumor types, although further validation will be required to establish its robustness and generalizability. In conclusion, MI-POG can be formalized as an information-theoretic framework for genome-wide identification of prognostic biomarkers by quantifying molecular-clinical dependencies using mutual information. Representative applications from previously published studies suggest that MI-POG may complement conventional survival-analysis approaches and provide a useful strategy for biomarker discovery, although additional benchmarking and prospective validation will be required.

Humans↗

PubMed2025

Long-term epidemiological trends in (primary) pediatric central nervous system tumors: a 25-year cohort analysis in Western Mexico.

BACKGROUND: Central nervous system tumors (CNSTs) represent a significant oncological challenge in pediatric populations, particularly in developing regions where access to diagnostic and therapeutic resources is limited. METHODS: This research investigates the epidemiology, histological classifications, and survival outcomes of CNST in a cohort of pediatric patients aged 0 to 19 years within a 25-year retrospective study at the Civil Hospital of Guadalajara, Mexico, from 1999 to 2024. RESULTS: Data was analyzed from 273 patients who met inclusion criteria, revealing a higher incidence in males (51.6%) with a mean age at diagnosis of 8.2 years. Histological analysis revealed gliomas as the most common type (52.7%), followed by embryonal tumors (28.6%). High-grade tumors (WHO grade 4) comprised 49.8% of cases, demonstrating significantly poorer survival outcomes (median overall survival of 13.5 months) compared to lower-grade tumors (up to 57 months). The predominance of tumors in the supratentorial region and the notable differences in survival outcomes by tumor type underscore the varied impact of geographical and socioeconomic factors on pediatric oncology in Mexico. CONCLUSION: This study highlights the critical need for improved healthcare infrastructure and early diagnosis initiatives, as well as the importance of targeted research to address disparities in treatment and outcomes for pediatric CNST in this region.

Humans↗