ArticleAging2023
Thyroid cancer risk prediction model using m6A RNA methylation regulators: integrated bioinformatics analysis and histological validation.
Article in Aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Immunotherapy in Thyroid Cancer: Current Strategies and Challenges.Cancer medicine · 2026Review
- A Comprehensive Review of Robinetin: Distribution, Biological Activity and Pharmacokinetic Parameters.International journal of molecular sciences · 2025Review
- Development and validation of a necroptosis-related gene signature for predicting prognosis and immune infiltration in papillary thyroid cancer.Translational cancer research · 2025Article
- The role of epigenetic methylations in thyroid Cancer.World journal of surgical oncology · 2024Review
- Decoding the molecular symphony: interactions between the mNAR cancer · 2024Review
- RNA modifications in cancer immune therapy: regulators of immune cells and immune checkpoints.Frontiers in immunology · 2024Review
- Development of an m6A subtype classifier to guide precision therapy for patients with bladder cancer.Journal of Cancer · 2024Article
- The emerging roles of N6-methyladenosine RNA modifications in thyroid cancer.European journal of medical research · 2023Review
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8 authors.
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Abstract
backgroundEpigenetic reprogramming has been reported to play a critical role in the progression of thyroid cancer. RNA methylation accounts for more than 60% of all RNA modifications, and N6-methyladenosine (m6A) is the most common modification of RNAs in higher organisms. The purpose of this study was to explore the related modification mode of m6A regulators construction and its evaluation on the clinical prognosis and therapeutic effect of thyroid cancer.
methodsThe levels of 23 m6A regulators in The Cancer Genome Atlas (TCGA) were analyzed. Differentially expressed genes (DEGs) and survival analysis were performed based on TCGA-THCA clinicopathological and follow-up information, and the mRNA levels of representative genes were verified using clinical thyroid cancer data. In order to detect the effects of m6A regulators and their DEGs, consensus cluster analysis was carried out, and the expression of different m6A scores in Tumor Mutation Burden (TMB) and immune double antibodies (PD-1 antibody and CTLA4 antibody) were evaluated to predict the correlation between m6A score and thyroid cancer tumor immunotherapy response.
resultsDifferent expression patterns of m6A regulatory factors were detected in thyroid cancer tumors and normal tissues, and several prognoses related m6A genes were obtained. Two different m6A modification patterns were determined by consensus cluster analysis. Two different subgroups were established by screening overlapping DEGs between two m6A clusters, with cluster A having the best prognosis. According to the m6A score extracted from DEGs, thyroid cancer patients can be divided into high and low score subgroups. Patients with lower m6A score have longer survival time and better clinical features. The relationship between m6A score and Tumor Mutation Burden (TMB) and its correlation with the expression of PD-1 antibody and CTLA4 antibody proved that m6A score could be used as a potential predictor of the efficacy of immunotherapy in thyroid cancer patients.
conclusionsWe screened DEGs from cluster m6A and constructed a highly predictive model with prognostic value by dividing TCGA-THCA into two different clusters and performing m6A score analysis. This study will help clarify the overall impact of m6A modification patterns on thyroid cancer progression and formulate more effective immunotherapy strategies.
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