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

Chin J Cancer Res. 2026 Aug 30;38(4):480-500. doi: 10.21147/j.issn.1000-9604.2026.04.08.

ABSTRACT

OBJECTIVE: Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in the breast cancer immune microenvironment remains poorly understood.

METHODS: We integrated multi-omics data from six breast cancer cohorts (N=3,764) to develop a programmed cell death learning signature (PCDsig) using over 100 machine learning combinations. The model was benchmarked against 29 published signatures. Single-cell transcriptomic analysis decoded the immune landscape and cellular crosstalk. The role of adaptor-related protein complex 1 subunit sigma 1 (AP1S1) was validated through a clinical cohort, in vitro functional assays, and in vivo syngeneic mouse models.

RESULTS: PCDsig significantly stratified patient prognosis across all cohorts, consistently outperforming 29 existing models. High PCDsig scores correlated with immune-excluded phenotypes, reduced CD8+ T cell infiltration, and lower immunophenoscores. Single-cell analysis revealed that high-PCDsig tumors utilize vascular endothelial growth factor A (VEGFA) signaling to foster an immunosuppressive microenvironment. AP1S1 was identified as the core driver of immune exclusion. And our clinical cohort supported the immune exclusion effect of AP1S1. AP1S1 knockdown impaired tumor progression in vitro and fundamentally remodeled the tumor immune ecosystem in vivo. Combining AP1S1 inhibition with anti-programmed cell death ligand 1 (anti-PD-L1) therapy exerted profound synergistic effects, driven by massive infiltration and functional activation of cytotoxic Granzyme B (GZMB)+CD8+ T cells.

CONCLUSIONS: Our study establishes the PCDsig we developed is a potential prognostic and predictive biomarker for breast cancer. We provide the first evidence of AP1S1 as a core immunomodulatory oncogene that mediates immune exclusion. Targeting AP1S1 represents a highly promising strategy to sensitize cold breast tumors to ICB, offering a new perspective for precision immunotherapy.

PMID:42712842 | PMC:PMC13551362 | DOI:10.21147/j.issn.1000-9604.2026.04.08

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

Chin J Cancer Res. 2026 Aug 30;38(4):480-500. doi: 10.21147/j.issn.1000-9604.2026.04.08.

ABSTRACT

OBJECTIVE: Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in the breast cancer immune microenvironment remains poorly understood.

METHODS: We integrated multi-omics data from six breast cancer cohorts (N=3,764) to develop a programmed cell death learning signature (PCDsig) using over 100 machine learning combinations. The model was benchmarked against 29 published signatures. Single-cell transcriptomic analysis decoded the immune landscape and cellular crosstalk. The role of adaptor-related protein complex 1 subunit sigma 1 (AP1S1) was validated through a clinical cohort, in vitro functional assays, and in vivo syngeneic mouse models.

RESULTS: PCDsig significantly stratified patient prognosis across all cohorts, consistently outperforming 29 existing models. High PCDsig scores correlated with immune-excluded phenotypes, reduced CD8+ T cell infiltration, and lower immunophenoscores. Single-cell analysis revealed that high-PCDsig tumors utilize vascular endothelial growth factor A (VEGFA) signaling to foster an immunosuppressive microenvironment. AP1S1 was identified as the core driver of immune exclusion. And our clinical cohort supported the immune exclusion effect of AP1S1. AP1S1 knockdown impaired tumor progression in vitro and fundamentally remodeled the tumor immune ecosystem in vivo. Combining AP1S1 inhibition with anti-programmed cell death ligand 1 (anti-PD-L1) therapy exerted profound synergistic effects, driven by massive infiltration and functional activation of cytotoxic Granzyme B (GZMB)+CD8+ T cells.

CONCLUSIONS: Our study establishes the PCDsig we developed is a potential prognostic and predictive biomarker for breast cancer. We provide the first evidence of AP1S1 as a core immunomodulatory oncogene that mediates immune exclusion. Targeting AP1S1 represents a highly promising strategy to sensitize cold breast tumors to ICB, offering a new perspective for precision immunotherapy.

PMID:42712842 | PMC:PMC13551362 | DOI:10.21147/j.issn.1000-9604.2026.04.08

Curr Med Imaging. 2026 Jun 22. doi: 10.2174/0115734056460566260609044755. Online ahead of print.

ABSTRACT

Lung cancer is the most common and fatal malignant tumour. Early detection and treatment are likely to reduce mortality, but most pulmonary nodules identified during routine health checks are harmless. Consequently, a clear distinction between benign and malignant nodules is vital to improve early detection and reduce unnecessary interventions. Radiomics, a new omics technology, can be used to extract high-dimensional quantitative features from medical images, providing a profound understanding of tumour pathophysiology. Radiomics has attracted the attention of medical researchers since its formal definition by the Dutch researcher Lambin et al. in 2012. The number of research papers on radiomics has grown tremendously over the past few years. At present, it is used to predict pulmonary nodule malignancy, for noninvasive risk stratification, for integration with genomics to identify genetic mutations associated with lung cancer, and for evaluation of therapeutic responses. With this review, we summarise the literature on radiomics of pulmonary nodules, discuss how it could be used in nodule management, and address the current challenges and future directions for improving precision oncology.

PMID:42333843 | DOI:10.2174/0115734056460566260609044755