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Received — 26 March 2026 ⏭

Omics in Hepatocellular
The dual regulatory role of METTL14-mediated m<sup>6</sup>A modification in tumorigenesis and its underlying mechanisms Yulan Yang · Lemuge Chao · Xudong Ao · Junqing Liang
Front Oncol. 2026 Mar 4;16:1771313. doi: 10.3389/fonc.2026.1771313. eCollection 2026.ABSTRACTN6-methyladenosine (m6A), as the most abundant RNA epitranscriptional modification in eukaryotes, its key component of the methyltransferase complex, METTL14, not only cooperates in catalyzing m6A deposition but also has functions independent of methyltransferase activity. This article systematically reviews the dual regulatory role of METTL14 in tumors and its molecular mechanisms, mainly organizing the
20 March 2026 at 18:00

The dual regulatory role of METTL14-mediated m<sup>6</sup>A modification in tumorigenesis and its underlying mechanisms

Omics in Hepatocellular

By: Yulan Yang · Lemuge Chao · Xudong Ao · Junqing Liang

20 March 2026 at 18:00

Front Oncol. 2026 Mar 4;16:1771313. doi: 10.3389/fonc.2026.1771313. eCollection 2026.

ABSTRACT

N⁶-methyladenosine (m⁶A), as the most abundant RNA epitranscriptional modification in eukaryotes, its key component of the methyltransferase complex, METTL14, not only cooperates in catalyzing m⁶A deposition but also has functions independent of methyltransferase activity. This article systematically reviews the dual regulatory role of METTL14 in tumors and its molecular mechanisms, mainly organizing the relevant research in a logical sequence of "tumor suppressive effect - tumor promoting effect - controversial or context-dependent". Studies have shown that METTL14 often plays a tumor suppressive role in tumors such as hepatocellular carcinoma and colorectal cancer, while in pancreatic cancer and nasopharyngeal carcinoma, it mostly promotes malignant progression, showing a high degree of context dependence. This article focuses on two key mechanisms: on the one hand, METTL14 precisely regulates the processing, stability, and function of non-coding RNAs (including miRNAs, lncRNAs, and circRNAs) through m⁶A modification, reshaping the competitive endogenous RNA (ceRNA) network; on the other hand, it shapes an immunosuppressive tumor microenvironment by directly upregulating immune checkpoints such as PD-L1, mediating metabolism-immune interactions, and regulating the function of immune cells. Its functional duality also stems from the selective regulation of key pathways such as PI3K/AKT, as well as the differential interpretation by different m⁶A readers (such as YTHDF2 and IGF2BPs). Given the close association of these mechanisms with clinical prognosis, the expression level of METTL14 shows significant potential as a prognostic marker and therapeutic target; in the future, it is necessary to combine single-cell multi-omics and other technologies to analyze its dynamic regulatory network in specific tumor contexts and explore precise treatment strategies based on synthetic lethality or targeting downstream effector molecules.

PMID:41858346 | PMC:PMC12995618 | DOI:10.3389/fonc.2026.1771313

Received — 5 March 2026 ⏭

cs.AI, q-bio.NC updates on arXiv.org
TTSR: Test-Time Self-Reflection for Continual Reasoning Improvement Haoyang He · Zihua Rong · Liangjie Zhao · Yunjia Zhao · Lan Yang · Honggang Zhang
arXiv:2603.03297v1 Announce Type: cross Abstract: Test-time Training enables model adaptation using only test questions and offers a promising paradigm for improving the reasoning ability of large language models (LLMs). However, it faces two major challenges: test questions are often highly difficult, making self-generated pseudo-labels unreliable, and existing methods lack effective mechanisms to adapt to a model's specific reasoning weaknesses, leading to inefficient learning. To address the
5 March 2026 at 13:00

TTSR: Test-Time Self-Reflection for Continual Reasoning Improvement

cs.AI, q-bio.NC updates on arXiv.org

By: Haoyang He · Zihua Rong · Liangjie Zhao · Yunjia Zhao · Lan Yang · Honggang Zhang

5 March 2026 at 13:00

arXiv:2603.03297v1 Announce Type: cross Abstract: Test-time Training enables model adaptation using only test questions and offers a promising paradigm for improving the reasoning ability of large language models (LLMs). However, it faces two major challenges: test questions are often highly difficult, making self-generated pseudo-labels unreliable, and existing methods lack effective mechanisms to adapt to a model's specific reasoning weaknesses, leading to inefficient learning. To address these issues, we propose \textbf{TTSR}, a self-reflective test-time self-evolving training framework. TTSR employs a single pretrained language model that alternates between the roles of a \textit{Student} and a \textit{Teacher} at test time. The Student focuses on solving problems and learning from synthesized variant questions, while the Teacher analyzes the Student's failed reasoning trajectories, summarizes recurring reasoning weaknesses, and synthesizes targeted variant questions accordingly. This process guides the model to improve within a learnable regime through a continual self-evolving loop. Experimental results on multiple challenging mathematical reasoning benchmarks show that TTSR consistently improves reasoning performance and generalizes well across different model backbones and general-domain reasoning tasks. These findings suggest that teacher-mediated self-reflection provides an effective pathway for stable and continual reasoning improvement at test time.