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MCAT-mediated mitochondrial fatty acid metabolism regulates Lauren subtype divergence and suppresses gastric cancer progression through ROS/P53-dependent mitophagy and ferroptosis

Cell Death Differ. 2026 Sep 8. doi: 10.1038/s41418-026-01867-7. Online ahead of print.

ABSTRACT

Gastric cancer (GC) displays marked heterogeneity under the Lauren classification, yet the metabolic determinants of subtype divergence remain unclear. Here, we identify Malonyl-CoA:ACP transacylase (MCAT), a Lauren subtype-associated gene encoding a key mitochondrial fatty acid synthesis (mtFAS) enzyme, as a subtype-specific tumor suppressor in GC. Integrative multi-omics profiling revealed that MCAT expression is enriched in intestinal-type GC and correlates with favorable prognosis. Mechanistically, MCAT overexpression drives metabolic reprogramming through mitochondrial free fatty acid overload, suppressing β-oxidation while elevating mitochondrial reactive oxygen species (ROS), which triggers P53 phosphorylation at Ser15. This event concurrently activates PINK1/Parkin-mediated mitophagy and suppresses the SLC7A11/GPX4 axis to induce ferroptosis. Genetic rescue experiments confirmed that P53-Ser15 phosphorylation is essential for both mitophagy and ferroptosis induction. Endogenous MCAT levels are sufficient to determine basal ROS/P53/mitophagy/ferroptosis axis activity, and knockdown in high-expressing cells reverses these phenotypes, supporting a physiological, threshold-dependent role. In vivo, MCAT overexpression suppresses tumor growth and enhances mitophagy and ferroptosis markers. Collectively, these findings establish MCAT as a metabolic switch that links mtFAS to ROS/P53-dependent cell death, providing a potential biomarker and therapeutic target for GC.

PMID:42711380 | DOI:10.1038/s41418-026-01867-7

MCAT-mediated mitochondrial fatty acid metabolism regulates Lauren subtype divergence and suppresses gastric cancer progression through ROS/P53-dependent mitophagy and ferroptosis

Cell Death Differ. 2026 Sep 8. doi: 10.1038/s41418-026-01867-7. Online ahead of print.

ABSTRACT

Gastric cancer (GC) displays marked heterogeneity under the Lauren classification, yet the metabolic determinants of subtype divergence remain unclear. Here, we identify Malonyl-CoA:ACP transacylase (MCAT), a Lauren subtype-associated gene encoding a key mitochondrial fatty acid synthesis (mtFAS) enzyme, as a subtype-specific tumor suppressor in GC. Integrative multi-omics profiling revealed that MCAT expression is enriched in intestinal-type GC and correlates with favorable prognosis. Mechanistically, MCAT overexpression drives metabolic reprogramming through mitochondrial free fatty acid overload, suppressing β-oxidation while elevating mitochondrial reactive oxygen species (ROS), which triggers P53 phosphorylation at Ser15. This event concurrently activates PINK1/Parkin-mediated mitophagy and suppresses the SLC7A11/GPX4 axis to induce ferroptosis. Genetic rescue experiments confirmed that P53-Ser15 phosphorylation is essential for both mitophagy and ferroptosis induction. Endogenous MCAT levels are sufficient to determine basal ROS/P53/mitophagy/ferroptosis axis activity, and knockdown in high-expressing cells reverses these phenotypes, supporting a physiological, threshold-dependent role. In vivo, MCAT overexpression suppresses tumor growth and enhances mitophagy and ferroptosis markers. Collectively, these findings establish MCAT as a metabolic switch that links mtFAS to ROS/P53-dependent cell death, providing a potential biomarker and therapeutic target for GC.

PMID:42711380 | DOI:10.1038/s41418-026-01867-7

RubricBench: Aligning Model-Generated Rubrics with Human Standards

arXiv:2603.01562v2 Announce Type: replace Abstract: As Large Language Model (LLM) alignment evolves from simple completions to complex, highly sophisticated generation, Reward Models are increasingly shifting toward rubric-guided evaluation to mitigate surface-level biases. However, the community lacks a unified benchmark to assess this evaluation paradigm, as existing benchmarks lack both the discriminative complexity and the ground-truth rubric annotations required for rigorous analysis. To bridge this gap, we introduce RubricBench, a curated benchmark with 1,147 pairwise comparisons specifically designed to assess the reliability of rubric-based evaluation. Our construction employs a multi-dimensional filtration pipeline to target hard samples featuring nuanced input complexity and misleading surface bias, augmenting each with expert-annotated, atomic rubrics derived strictly from instructions. Comprehensive experiments reveal a substantial capability gap between human-annotated and model-generated rubrics, indicating that even state-of-the-art models struggle to autonomously specify valid evaluation criteria, lagging considerably behind human-guided performance.
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