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Received — 26 March 2026 ⏭

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

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Min Mao · Hang Ji · Wen-Qian Yu · Qu-Jing Gai · Qiang Sun · Qian Yan · Sen-Lin Xu · Meng-Li Zhu · Mei-Hua Qu · Han-Min Liu · Jean-Philipe Hugnot · Xi He · Xin Wang · Yan Wang
24 March 2026 at 18:00

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

Received — 11 March 2026 ⏭
Received — 5 March 2026 ⏭

PROSPECT: Unified Streaming Vision-Language Navigation via Semantic--Spatial Fusion and Latent Predictive Representation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zehua Fan · Wenqi Lyu · Wenxuan Song · Linge Zhao · Yifei Yang · Xi Wang · Junjie He · Lida Huang · Haiyan Liu · Bingchuan Sun · Guangjun Bao · Xuanyao Mao · Liang Xu · Yan Wang · Feng Gao
5 March 2026 at 13:00
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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