2025年2月18日 · This study aimed to establish a prognostic framework centered on genes associated with m6A and ferroptosis to enhance the accuracy of prognosis predictions for neuroblastoma (NB) patients, thereby improving targeted therapeutic strategies.

m6A能够加速mRNA前体的加工时间，加快mRNA在细胞中的转运速度和出核速度。 除了m6A，RNA上还存在以下常见的几种修饰，包括m1A，m5C等（图1）。 已知tRNA上发生碱基修饰的比例较高，会有各种各样的碱基修饰行为。

鉴于m6A在维持体内平衡方面的广泛作用，当m6A的调节出错时，就会发生癌症等疾病。 该综述重点讨论m6A的三个新角色:神经元突触功能和神经行为;T细胞稳态与免疫应答以及癌症干细胞的建立。

2024年11月15日 · Our results suggest a mechanism for the interplay between m 6 A modification and SG through YTHDF2 regulation. N6 -methyladenosine (m 6 A) is the most abundant internal modification of various types of RNAs and regulates RNA transcription (1, 2), splicing (3), transport (4), decay (5), and translation (6, 7).

2024年2月15日 · N6-methyladenosine (m6A) is the most abundant mRNA modification, and controls mRNA stability. m6A distribution varies considerably between and within species. Yet, it is unclear to what extent this variability is driven by changes in genetic sequences (‘cis’) or cellular environments (‘trans’) and via which mechanisms.

2021年2月21日 · N 6-methyladenosine (m6A) is the most prevalent, abundant and conserved internal cotranscriptional modification in eukaryotic RNAs, especially within higher eukaryotic...

2025年1月18日 · Here, we introduce pum6a, an innovative attention-based framework that integrates positive and unlabeled multi-instance learning (MIL) to address the challenges of incomplete labeling and missing...

2024年12月3日 · Sites of N6-methyladenosine (m6A) in the coding region of mRNAs can induce a distinct, translation-dependent decay pathway involving mRNA translocation to P-bodies.

2024年12月11日 · 本研究首先揭示了m6A RNA reader蛋白YTHDF2在早期效应或效应样CD8 T细胞中高表达。 分析结果表明YTHDF2促进了新生RNA（nascent RNA）合成，而m6A识别对于蛋白质特异性核功能至关重要，也加强了其在RNA水平上的自我调控。 T细胞中YTHDF2缺失会加剧肿瘤进展，并导致对PD-1阻断在小鼠和人类中的无反应性。 除了启动对线粒体健康必要的RNA衰变外，YTHDF2还协调染色质变化，促进T细胞的多功能性。 YTHDF2与IKZF1/3互作，促进目 …

2024年12月11日 · In this study, we developed an m6A site predictor called deepSRAMP, which integrates sequence-based and genome-derived features into the combination structure of a Transformer encoder and a recurrent neural network (RNN) for learning contextual representatives of proximal regions around m6A sites.