ArticleJournal of translational medicine2024
Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 25 citations in OpenAlex.
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- A novel cuproptosis-related prognostic gene signature is identified by machine learning and integrative analyses in gliomas.Molecular and cellular biochemistry · 2026Article
- Glioblastoma radiomics can delineate systemic immune activity states like blood abundance of T cell populations or transcription factors.Journal of neuroinflammation · 2026Article
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- MRI semantic features as prognostic indicators and biological mechanism insights in glioblastoma multiforme.Neurosurgical review · 2026Article
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- CT-based deep learning signatures associated with transcriptomic heterogeneity and combined with nutritional biomarkers improve prediction of 3-year overall survival in esophageal squamous cell carcinoma.Insights into imaging · 2026Article
- Long non-coding RNAs in glioblastoma: from molecular drivers to therapeutic targets.Frontiers in immunology · 2026Review
- Integrated Analysis of the Immune Infiltration Pattern and Novel Diagnostic Biomarkers in Septic Cardiomyopathy.Immunity, inflammation and disease · 2026Article
- Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis.Annals of medicine · 2025Article
- Targeting non-coding RNAs to overcome resistance and improving outcomes in glioblastoma.Global medical genetics · 2025Review
- Immune-related long noncoding RNAs in predicting the prognosis and immune landscape of intrahepatic cholangiocarcinoma: a bioinformatics analysis with experimental verification.Translational cancer research · 2025Article
- Deep Neural Network-Based Risk Prediction of Glioblastoma Multiforme Recurrence.Journal of molecular neuroscience : MN · 2025Article
- Immunophenotype-guided interpretable radiomics model for predicting neoadjuvant anti-PD-1 response in stage III-IV d-MMR/MSI-H colorectal cancer.Journal for immunotherapy of cancer · 2025Article
- Deep Learning-Based DCE-MRI Automatic Segmentation in Predicting Lesion Nature in BI-RADS Category 4.Journal of imaging informatics in medicine · 2025Article
- Aberrant DNA hypermethylation-regulated long non-coding RNA RYR3-DT affects ferroptosis and progression of glioma cell through the CoQ10/FSP1 axis.Discover oncology · 2025Article
- Roles and Impacts of Integrative Medical Interventions in Central Nervous System Tumor Treatment: Multi-Technology Convergence and the Paradigm Shift Toward Functional Reconstruction.CNS neuroscience & therapeutics · 2025Review
- The Integrated Transcriptome Bioinformatics Analysis of Energy Metabolism-Related Profiles for Dorsal Root Ganglion of Neuropathic Pain.Molecular neurobiology · 2025Article
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundGlioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients.
methodsWe downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA). Then, we conducted a cox regression analysis to establish the immune-related lncRNAs signature and radiomics signature. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNAs signature, radiomics signature and immune checkpoint genes. Finally, we constructed a multifactors prognostic model and compared it with the clinical prognostic model.
resultsWe identified four immune-related lncRNAs and two radiomics features, which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The risk score curves and Kaplan-Meier curves confirmed that the immune-related lncRNAs signature and radiomics signature were a novel independent prognostic factor in GBM patients. The GSEA suggested that the immune-related lncRNAs signature were involved in L1 cell adhesion molecular (L1CAM) interactions and the radiomics signature were involved signaling by Robo receptors. Besides, the two signatures was associated with the infiltration of immune cells. Furthermore, they were linked with the expression of critical immune genes and could predict immunotherapy's clinical response. Finally, the area under the curve (AUC) (0.890,0.887) and C-index (0.737,0.817) of the multifactors prognostic model were greater than those of the clinical prognostic model in both the training and validation sets, indicated significantly improved discrimination.
conclusionsWe identified the immune-related lncRNAs signature and tradiomics signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with GBM.
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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.