❌

Reading view

METTL16 enhances proteasome inhibitor resistance in multiple myeloma by inhibiting eIF2α-PERK interaction and promoting PSMB5 translation

Oncogene, Published online: 13 March 2026; doi:10.1038/s41388-026-03706-y

METTL16 enhances proteasome inhibitor resistance in multiple myeloma by inhibiting eIF2α-PERK interaction and promoting PSMB5 translation
  •  

Integrated multi-omics analysis reveals that MARCKS reprograms the immunosuppressive microenvironment to drive hepatocellular carcinoma progression

NPJ Precis Oncol. 2026 Mar 11. doi: 10.1038/s41698-026-01372-7. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide, and its progression is closely linked to the establishment of an immunosuppressive tumor microenvironment. Myristoylated alanine-rich C kinase substrate (MARCKS) has been implicated in tumor biology; however, its role in regulating immune interactions in HCC remains poorly defined. Here, we performed an integrated multi-omics analysis combining bulk transcriptomics, single-cell RNA sequencing, and spatial transcriptomics to systematically investigate the expression pattern and functional relevance of MARCKS in HCC. We found that MARCKS was significantly upregulated in HCC tissues and that high MARCKS expression was associated with aggressive clinicopathological features and unfavorable prognosis. Single-cell and spatial analyses revealed that MARCKS expression was enriched in myeloid cell populations within the tumor microenvironment. Functional annotation and mIF(Multiple immunofluorescence) validation demonstrated that MARCKS expression was associated with enhanced JAK/STAT3 signaling and M2-like macrophage polarization. Consistently, MARCKS silencing in HCC cell lines reduced STAT3 phosphorylation, suppressed malignant phenotypes in vitro, inhibited tumor growth in vivo, and diminished the capacity of tumor-derived conditioned media to promote macrophage M2 polarization. Together, these findings identify MARCKS as a key regulator of the immunosuppressive tumor microenvironment in HCC and highlight its potential as a therapeutic target for overcoming immune evasion.

PMID:41813922 | DOI:10.1038/s41698-026-01372-7

  •  

Assembly of helper NLR resistosome clusters upon activation of a coiled-coil NLR

Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10215-1

SUMM2, a coiled-coil NLR, promotes the assembly of higher-order resistosome clusters to initiate cell death in plants.
  •  

Risk-adaptive therapy guided by dynamic ctDNA in nasopharyngeal carcinoma

Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10244-w

A clinical trial testing whether monitoring ctDNA clearance during treatment for nasopharyngeal cancer could be used to inform decisions about an individual’s subsequent therapeutic programme shows promising results.
  •  

MAPK14/SLC7A11/GPX4 axis dysregulation drives podocyte ferroptosis via mediating glycerophospholipid metabolism

Cell Death Discovery, Published online: 11 March 2026; doi:10.1038/s41420-026-02990-7

MAPK14/SLC7A11/GPX4 axis dysregulation drives podocyte ferroptosis via mediating glycerophospholipid metabolism
  •  

Machine Learning for Stress Testing: Uncertainty Decomposition in Causal Panel Prediction

arXiv:2603.07438v1 Announce Type: new Abstract: Regulatory stress testing requires projecting credit losses under hypothetical macroeconomic scenarios -- a fundamentally causal question typically treated as a prediction problem. We propose a framework for policy-path counterfactual inference in panels that transparently separates what can be learned from data from what requires assumptions about confounding. Our approach has four components: (i) observational identification of path-conditional means via iterated regression, enabling continuous macro-path contrasts without requiring a control group; (ii) causal set identification under bounded confounding, yielding sharp identified sets with interpretable breakdown values that communicate robustness in a single number; (iii) an oracle inequality showing that recursive rollout error is governed by a horizon-dependent amplification factor, providing a concrete answer to how far ahead one can reliably predict under stress; and (iv) importance-weighted conformal calibration bands with diagnostics that quantify extrapolation cost and trigger abstention when coverage guarantees degrade. The final output is a three-layer uncertainty decomposition that cleanly separates estimation uncertainty from confounding uncertainty. We validate all results through simulation and semi-synthetic experiments with real unemployment data, including a Covid retrospective demonstrating the framework's diagnostic value under extreme scenarios.
  •  

CMMR-VLN: Vision-and-Language Navigation via Continual Multimodal Memory Retrieval

arXiv:2603.07997v1 Announce Type: new Abstract: Although large language models (LLMs) are introduced into vision-and-language navigation (VLN) to improve instruction comprehension and generalization, existing LLM- based VLN lacks the ability to selectively recall and use relevant priori experiences to help navigation tasks, limiting their performance in long-horizon and unfamiliar scenarios. In this work, we propose CMMR-VLN (Continual Multimodal Memory Retrieval based VLN), a VLN framework that endows LLM agents with structured memory and reflection capabilities. Specifically, the CMMR-VLN constructs a multimodal experi- ence memory indexed by panoramic visual images and salient landmarks to retrieve relevant experiences during navigation, introduces a retrieved-augmented generation pipeline to mimick how experienced human navigators leverage priori knowledge, and incorporates a reflection-based memory update strategy that selectively stores complete successful paths and the key initial mistake in failure cases. Comprehensive tests illustrate average success rate improvements of 52.9%, 20.9% and 20.9%, and 200%, 50% and 50% over the NavGPT, the MapGPT, and the DiscussNav in simulation and real tests, respectively eluci- dating the great potential of the CMMR-VLN as a backbone VLN framework.
  •  

UIS-Digger: Towards Comprehensive Research Agent Systems for Real-world Unindexed Information Seeking

arXiv:2603.08117v1 Announce Type: new Abstract: Recent advancements in LLM-based information-seeking agents have achieved record-breaking performance on established benchmarks. However, these agents remain heavily reliant on search-engine-indexed knowledge, leaving a critical blind spot: Unindexed Information Seeking (UIS). This paper identifies and explores the UIS problem, where vital information is not captured by search engine crawlers, such as overlooked content, dynamic webpages, and embedded files. Despite its significance, UIS remains an underexplored challenge. To address this gap, we introduce UIS-QA, the first dedicated UIS benchmark, comprising 110 expert-annotated QA pairs. Notably, even state-of-the-art agents experience a drastic performance drop on UIS-QA (e.g., from 70.90 on GAIA and 46.70 on BrowseComp-zh to 24.55 on UIS-QA), underscoring the severity of the problem. To mitigate this, we propose UIS-Digger, a novel multi-agent framework that incorporates dual-mode browsing and enables simultaneous webpage searching and file parsing. With a relatively small $\sim$30B-parameter backbone LLM optimized using SFT and RFT training strategies, UIS-Digger sets a strong baseline at 27.27\%, outperforming systems integrating sophisticated LLMs such as O3 and GPT-4.1. This demonstrates the importance of proactive interaction with unindexed sources for effective and comprehensive information-seeking. Our work not only uncovers a fundamental limitation in current agent evaluation paradigms but also provides the first toolkit for advancing UIS research, defining a new and promising direction for robust information-seeking systems.
  •  
❌