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Targeting FTO shows therapeutic potential in esophageal squamous cell carcinoma by modulating microRNA biogenesis

Oncogene, Published online: 01 April 2026; doi:10.1038/s41388-026-03754-4

Targeting FTO shows therapeutic potential in esophageal squamous cell carcinoma by modulating microRNA biogenesis
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Targeting sialic acid metabolism: a therapeutic strategy against gastric cancer driven by WZ35

Cell Oncol (Dordr). 2026 Mar 23;49(2):60. doi: 10.1007/s13402-026-01194-6.

ABSTRACT

Glycolytic reprogramming is closely associated with the occurrence and progression of gastric cancer. Specifically, the energy derived from glucose metabolism and the cellular proteins by its intermediate products influence gastric cancer development. However, as an important branch of glucose metabolism, sialic acid metabolism and its mediated sialylation modifications remain insufficiently studied in gastric cancer, and their specific relationship with malignant tumor progression requires further exploration. This study employed a multi‑omics approach, integrating metabolomics, single‑cell RNA sequencing, and bulk RNA sequencing analyses, to investigate the metabolic landscape of gastric cancer and its associated alterations. The results indicated that sialic acid is a characteristic metabolite in malignant gastric cancer tissues. It modulates biological functions such as immune response, proliferative activity, and metabolic remodeling within gastric cancer tissues by influencing sialylation modifications. Furthermore, we identified the drug WZ35, which can inhibit the malignant proliferation of gastric cancer by targeting both sialic acid metabolism and sialylated protein modifications. We put forward a conjecture that the metabolism and modification of sialic acid promote the malignant development of gastric cancer, and we discovered that the drug WZ35 has an inhibitory effect on the sialic acid metabolism of gastric cancer.

GRAPHICAL ABSTRACT:

PMID:41870836 | PMC:PMC13009457 | DOI:10.1007/s13402-026-01194-6

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RFC4 drives temozolomide resistance in glioblastoma by activating STK38-BECN1-dependent autophagy

Nat Commun. 2026 Mar 23. doi: 10.1038/s41467-026-70798-1. Online ahead of print.

ABSTRACT

Glioblastoma (GBM) remains a lethal brain tumor due to therapy resistance. While autophagy contributes to temozolomide (TMZ) resistance, its regulation is incompletely understood. This study investigates the role of replication factor RFC4, which is associated with poor prognosis and TMZ resistance in GBM. Multi-omics analyses and molecular experiments reveal that TMZ-induced chromatin accessibility enables transcription factor YY1 to bind the RFC4 promoter and upregulate its expression. RFC4, in turn, stabilizes the kinase STK38, which is essential for autophagosome formation. The RFC4-STK38 interaction facilitates BECN1 recruitment, thereby activating autophagy. Phosphorylation of STK38 at T444 stabilizes this complex, whereas a phospho-deficient mutant impairs autophagy. In vivo, RFC4 overexpression confers TMZ resistance, reversible by autophagy inhibition. Thus, our findings identify the RFC4-STK38-BECN1 axis as a mechanism underlying TMZ resistance and a potential target for precision therapy in GBM.

PMID:41872171 | DOI:10.1038/s41467-026-70798-1

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<i>KRAS</i>-extrachromosomal DNA drives intratumoral heterogeneity in gastric cancer

Oncogene, Published online: 05 March 2026; doi:10.1038/s41388-026-03713-z

KRAS-extrachromosomal DNA drives intratumoral heterogeneity in gastric cancer
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PROSPECT: Unified Streaming Vision-Language Navigation via Semantic--Spatial Fusion and Latent Predictive Representation

arXiv:2603.03739v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have advanced zero-shot end-to-end Vision-Language Navigation (VLN), yet robust navigation requires not only semantic understanding but also predictive modeling of environment dynamics and spatial structure. We propose PROSPECT, a unified streaming navigation agent that couples a streaming Vision-Language-Action (VLA) policy with latent predictive representation learning. PROSPECT uses CUT3R as a streaming 3D foundation spatial encoder to produce long-context, absolute-scale spatial features, and fuses them with SigLIP semantic features via cross-attention. During training, we introduce learnable stream query tokens that query the streaming context and predict next-step 2D and 3D latent features (rather than pixels or explicit modalities), supervised in the latent spaces of frozen SigLIP and CUT3R teachers. The predictive branch shapes internal representations without inference overhead. Experiments on VLN-CE benchmarks and real-robot deployment demonstrate state-of-the-art performance and improved long-horizon robustness under diverse lighting. We will release code for the community soon.
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