Integrative Machine Learning of Glioma and Coronary Artery Disease Reveals Key Tumour Immunological Links DOI Creative Commons
Youfu He, Ganhua You, Yu Zhou

et al.

Journal of Cellular and Molecular Medicine, Journal Year: 2025, Volume and Issue: 29(2)

Published: Jan. 1, 2025

ABSTRACT It is critical to appreciate the role of tumour‐associated microenvironment (TME) in developing strategies for effective therapy cancer, as it an important factor that determines evolution and treatment response tumours. This work combines machine learning single‐cell RNA sequencing (scRNA‐seq) explore glioma tumour microenvironment's TME. With help genome‐wide association studies (GWAS) Mendelian randomization (MR), we found genetic variants associated with TME elements affect cancer cardiovascular disease outcomes. Using techniques high dimensional data was analysed obtain new molecular sub‐types biomarkers are prognosis response. F3 identified a top regulator revealed potential angiogenic immunogenic characteristics within could be harnessed immunotherapy. These results demonstrate machine‐learning approaches identifying dissecting heterogeneity informing precision oncology. proposes improving immunotherapeutic through targeted modulation relevant cellular interactions.

Language: Английский

Integrative Machine Learning of Glioma and Coronary Artery Disease Reveals Key Tumour Immunological Links DOI Creative Commons
Youfu He, Ganhua You, Yu Zhou

et al.

Journal of Cellular and Molecular Medicine, Journal Year: 2025, Volume and Issue: 29(2)

Published: Jan. 1, 2025

ABSTRACT It is critical to appreciate the role of tumour‐associated microenvironment (TME) in developing strategies for effective therapy cancer, as it an important factor that determines evolution and treatment response tumours. This work combines machine learning single‐cell RNA sequencing (scRNA‐seq) explore glioma tumour microenvironment's TME. With help genome‐wide association studies (GWAS) Mendelian randomization (MR), we found genetic variants associated with TME elements affect cancer cardiovascular disease outcomes. Using techniques high dimensional data was analysed obtain new molecular sub‐types biomarkers are prognosis response. F3 identified a top regulator revealed potential angiogenic immunogenic characteristics within could be harnessed immunotherapy. These results demonstrate machine‐learning approaches identifying dissecting heterogeneity informing precision oncology. proposes improving immunotherapeutic through targeted modulation relevant cellular interactions.

Language: Английский

Citations

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