ArticleFrontiers in immunology2021
A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy.
Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Combination Immunotherapy as a Promising Strategy to Overcome Immunotherapy Resistance: From Emergence to Next-Generation Approaches.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Priming with DNMT Inhibitors Potentiates PD-1 Immunotherapy by Triggering Viral Mimicry in Relapsed/Refractory NK/T-cell Lymphoma.Cancer discovery · 2025Article
- The contribution of methylation profiling in neuropathological diagnosis of central nervous system tumors in children, adolescent and young adults.Pathologica · 2025Article
- Integrated single-cell RNA-seq analysis reveals that EZH2 regulates the MIF-CD74 axis to modulate T cell activation and exhaustion in hepatocellular carcinoma.Journal of translational medicine · 2025Article
- Towards the Prediction of Responses to Cancer Immunotherapy: A Multi-Omics Review.Life (Basel, Switzerland) · 2025Review
- A review on trends in development and translation of omics signatures in cancer.Computational and structural biotechnology journal · 2024Review
- Epigenetics and immunotherapy in colorectal cancer: progress and promise.Clinical epigenetics · 2024Review
- The Immune Landscape of Pheochromocytoma and Paraganglioma: Current Advances and Perspectives.Endocrine reviews · 2024Review
- Identification of DNA methylation characteristics associated with metastasis and prognosis in colorectal cancer.BMC medical genomics · 2024Article
- Immunotherapy and Cancer: The Multi-Omics Perspective.International journal of molecular sciences · 2024Review
- DNA methylation signatures provide novel diagnostic biomarkers and predict responses of immune therapy for breast cancer.Frontiers in genetics · 2024Article
- Application progress of artificial intelligence in tumor diagnosis and treatment.Frontiers in artificial intelligence · 2024Review
- Apolipoprotein E is a Potential Biomarker for Predicting Cancer Prognosis and is Correlated with Immune Infiltration.OncoTargets and therapy · 2024Article
- PIK3CD correlates with prognosis, epithelial-mesenchymal transition and tumor immune infiltration in breast carcinoma.Discover oncology · 2023Article
- COL19A1 is a predictive biomarker for the responsiveness of esophageal squamous cell carcinoma patients to immune checkpoint therapy.Thoracic cancer · 2023Article
- Construction of a serum diagnostic signature based on m5C-related miRNAs for cancer detection.Frontiers in endocrinology · 2023Article
- Immunological function and prognostic value of lymphoid-specific helicase in liver hepatocellular carcinoma.Cancer biomarkers : section A of Disease markers · 2023Article
- Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review.Biomedicines · 2022Review
- Identification of adenylate cyclase 2 methylation in bladder cancer with implications for prognosis and immunosuppressive microenvironment.Frontiers in oncology · 2022Article
- mFrontiers in pharmacology · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, tumor immunotherapy based on immune checkpoint inhibitors (ICI) has been introduced and widely adopted for various tumor types. Nevertheless, tumor immunotherapy has a few drawbacks, including significant uncertainty of outcome, the possibility of severe immune-related adverse events for patients receiving such treatments, and the lack of effective biomarkers to determine the ICI treatments' responsiveness. DNA methylation profiles were recently identified as an indicator of the tumor immune microenvironment. They serve as a potential hot spot for predicting responses to ICI treatment for their stability and convenience of measurement by liquid biopsy. We demonstrated the possibility of DNA methylation profiles as a predictor for responses to the ICI treatments at the pan-cancer level by analyzing DNA methylation profiles considered responsive and non-responsive to the treatments. An SVM model was built based on this differential analysis in the pan-cancer levels. The performance of the model was then assessed both at the pan-cancer level and in specific tumor types. It was also compared to the existing gene expression profile-based method. DNA methylation profiles were shown to be predictable for the responses to the ICI treatments in the TCGA cases in pan-cancer levels. The proposed SVM model was shown to have high performance in pan-cancer and specific cancer types. This performance was comparable to that of gene expression profile-based one. The combination of the two models had even higher performance, indicating the potential complementarity of the DNA methylation and gene expression profiles in the prediction of ICI treatment responses.
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