ArticleJournal of cellular and molecular medicine2025
Integrative Machine Learning of Glioma and Coronary Artery Disease Reveals Key Tumour Immunological Links.
Article in Journal of cellular and molecular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Proteomic signatures and machine learning based-prediction models for cardiovascular risk in survivors of myocardial infarction.BMC cardiovascular disorders · 2026Article
- Exosomal lncRNA FENDRR Orchestrates Immune Remodelling and Ferroptosis in the Comorbidity of Lung Cancer and Type 1 Myocardial Infarction.Human mutation · 2026Article
- Expression and prognostic value of SEC13 across multiple tumour types and its association with the regulation by Pulsatilla chinensis: a multi-omics and Mendelian Randomization Study.Discover oncology · 2025Article
- Development of a machine learning model to predict overall survival for large hepatocellular carcinoma at BCLC stage A or B after curative hepatectomy.Frontiers in immunology · 2025Article
- Multi-omics analysis of the dynamic role of STAR+ cells in regulating platinum-based chemotherapy responses and tumor microenvironment in serous ovarian carcinoma.Frontiers in pharmacology · 2025Article
- Glucokinase Regulatory Protein (GCKR) Links Metabolic Reprogramming With Immune Exclusion: Insights From a Pan-Cancer Analysis and Gastric Cancer Validation.Human mutation · 2025Article
- Integrative Multiomics Analysis Identifies HK2 as a Key Regulator of Metabolic Reprogramming in Hepatic Stellate Cells.Human mutation · 2025Article
- Integrative Transcriptomic and Machine Learning Analysis Identifies Key Senescence-Associated Secretory Phenotype Genes Associated With Immune Dysregulation in Periodontitis.Human mutation · 2025Article
- Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.Frontiers in immunology · 2025Article
- Multi-omics integration identifies key biomarkers in retinopathy of prematurity through 16S rRNA sequencing and metabolomics.Frontiers in microbiology · 2025Article
- The functional and clinical significance of nucleoporin NUP153 across human cancers: a systematic study based on multi-omics analysis and bench work validation.Frontiers in immunology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
It is critical to appreciate the role of the tumour-associated microenvironment (TME) in developing strategies for the effective therapy of cancer, as it is an important factor that determines the evolution and treatment response of tumours. This work combines machine learning and single-cell RNA sequencing (scRNA-seq) to explore the glioma tumour microenvironment's TME. With the help of genome-wide association studies (GWAS) and Mendelian randomization (MR), we found genetic variants associated with TME elements that affect cancer and cardiovascular disease outcomes. Using machine learning techniques high dimensional data was analysed to obtain new molecular sub-types and biomarkers that are important for prognosis and treatment response. F3 was identified as a top regulator and revealed potential angiogenic and immunogenic characteristics within the TME that could be harnessed in immunotherapy. These results demonstrate the potential of machine-learning approaches in identifying and dissecting TME heterogeneity and informing treatment in precision oncology. This work proposes improving the immunotherapeutic response through targeted modulation of relevant cellular and molecular interactions.
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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.