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Received — 27 May 2026 ⏭

MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziyu Zheng · Yaming Yang · Ziyu Guan · Wei Zhao · Xinyan Huang
26 May 2026 at 12:00
arXiv:2605.25771v1 Announce Type: cross Abstract: Multi-domain graph pre-training is a crucial step in constructing foundational graph models with cross-domain generalization capabilities. However, existing methods predominantly rely on jointly training all source domain graphs, resulting in high computational costs. Furthermore, it remains unclear whether all source domain graph data contribute equally to effective transfer. This paper empirically reveals significant data redundancy in multi-domain graph pre-training. Based on this finding, we propose the Multi-domain Graph Pre-training Framework, MDGMIX, which combines boundary-aware subgraph mixing with hierarchical discrimination. By selecting boundary nodes to construct challenging mixed-domain subgraphs, MDGMIX employs coarse-grained domain discrimination and fine-grained domain decomposition losses to decouple shared patterns from domain-specific patterns. During adaptation, MDGMIX employs a lightweight prompt weighting mechanism to transfer source domain knowledge. Extensive experiments demonstrate that MDGMIX consistently outperforms strong baselines in few-shot classification tasks while exhibiting superior time and memory efficiency. The code is available at: https://github.com/zhengziyu77/MDGMIX.

Spatial transcriptomic-metabolic features of tumor foci and tumor capsule in microvascular invasion with hepatocellular carcinoma: A spatial multi-omics study

✇Omics in Hepatocellular
By: Zhi-Hui Luo · Na Wang · Jingwei Zhao · Fei Long · Si Wu · Wei Zhong · Wei-Ming Chen · Bicheng Wang · Kun Wang · Yufeng Yuan · Jingjiao Zhou · Chunhui Yuan · Fubing Wang
15 May 2026 at 18:00

PLoS Med. 2026 May 15;23(5):e1004703. doi: 10.1371/journal.pmed.1004703. eCollection 2026 May.

ABSTRACT

BACKGROUND: Microvascular invasion (MVI) is closely related to the recurrence and metastasis of hepatocellular carcinoma (HCC), but the underlying cellular mechanism remains largely elusive. This study aims to elucidate the regional cellular discrepancy between MVI-positive (MVI+) and MVI-negative (MVI-) HCC by integrating Spatial transcriptomics (ST) and spatial metabolomics (SM).

METHODS AND FINDINGS: ST and SM were performed on six tissue samples from four patients (including 2 MVI+, 2 MVI-, and 2 paratumor tissues), with the integration of 79 public single-cell RNA sequencing datasets of HCC. Patient identity was used as a covariate in the linear equation for regional differentially expressed gene analysis with the ST data. Clinical validation was conducted through multiplex immunofluorescence staining in 79 patients, together with external validation in the cancer genome atlas (TCGA)-liver hepatocellular carcinoma (LIHC) cohort (n = 299) and an independent microarray dataset (n = 62). For cell-type-specific metabolic profiling, spatial transcriptomic-metabolic registration was performed. The functional roles of key metabolites were further validated in vitro using inflammatory cancer-associated fibroblasts (iCAFs) derived from hepatic stellate cells (HSCs) and primary CAFs through co-culture models and various functional assays assessing cell proliferation, migration, and invasion. In the tumor lesion, a malignant STMN1+HMGN2+GPC3+ cell subtype enriched in MVI+ HCC was identified, which exhibited enhanced proliferative activity and was associated with poor prognosis. This finding was further confirmed in a local cohort of 79 patients, where multiplex immunofluorescence staining for the three genes (STMN1, HMGN2, and GPC3) showed significantly higher expression in the MVI+ group than in the MVI- group (p = 0.046). Integrated SM analysis further revealed that this cell population underwent metabolic reprogramming characterized by suppressed glycerolipid metabolism. In the tumor capsule, iCAFs-related genes were downregulated in MVI+ cases, and iCAFs were located distally from the tumor boundary. Spatial metabolite mapping showed a strong correlation between taurine and iCAFs, and functional assays demonstrated that taurine promotes HCC proliferation and migration by suppressing iCAF activity. One limitation of this study is the small sample size of spatial omics data, which hinders a more complete molecular functional analysis of the STMN1+HMGN2+GPC3+ cell subtype and iCAFs in MVI+ HCC. Larger-scale ST cohorts are required to further validate and expand the findings of this study.

CONCLUSIONS: This integrative spatial atlas proposes a hypothesis that there exists a highly proliferative and metabolically reprogrammed malignant cell subtype in the tumor lesion of MVI+ HCC, and that taurine in the tumor capsule modulates iCAF activity to influence tumor progression. The exploratory results provide mechanistic insights into MVI-related HCC progression and offer potential avenues for targeted therapeutic intervention of MVI+ HCC.

PMID:42139279 | PMC:PMC13178920 | DOI:10.1371/journal.pmed.1004703

Received — 8 April 2026 ⏭

Pancreatic cancer immunotherapy biomarkers: from traditional markers to multimodal integration and dynamic monitoring

✇(Multiomics OR Omics) AND (Pancreatic)
By: Wei-Yi Zhao · Jin-Wei Zhao
6 April 2026 at 18:00

Front Immunol. 2026 Mar 19;17:1686658. doi: 10.3389/fimmu.2026.1686658. eCollection 2026.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) remains an intractable cancer marked by delayed diagnosis, rapid progression, and significant resistance to current treatments. Conventional biomarkers, such as CA19-9, have insufficient sensitivity and specificity. Meanwhile, the practical use of newer markers such as the tumor mutational burden and microsatellite instability is limited by the absence of standardized testing protocols and definitive threshold values. Circulating tumor DNA and exosomal miRNA hold promise for continuously tracking tumor dynamics and effectiveness of immunotherapy, but additional validation is necessary before their routine clinical application. Recent advancements in multiomics, nanotechnology, and artificial intelligence have opened new possibilities for more accurate and comprehensive biomarkers. For instance, Shah et al. developed shortwave-infrared-emitting nanoprobes to specifically target CD8+ cytotoxic T cells, permitting high-sensitivity in vivo imaging in breast cancer models. Batool et al. utilized nanoplasmonic sensors to detect changes in serum programmed death-ligand 1 and cytokine levels within 1-2 weeks post-treatment, achieving picomolar sensitivity. Chang et al. combined fluorescence and photoacoustic imaging in the NanoTrackThera platform, facilitating the real-time monitoring of immunotherapy efficacy. This review highlights the evolution of PDAC biomarkers from traditional markers to multimodal integration and dynamic monitoring. The limitations of current markers and potential of emerging technologies, including metabolic reprogramming markers, epigenetic regulators, and AI-driven predictive models, are discussed. Future directions include multicenter prospective trials to validate multimodal models, standardize detection methods, and increase interdisciplinary collaboration. By integrating genomic, epigenetic, metabolic, and microbiome data, these models can better capture the complexity of PDAC, thereby improving patient outcomes through precision immunotherapy.

PMID:41939911 | PMC:PMC13044031 | DOI:10.3389/fimmu.2026.1686658

Received — 26 March 2026 ⏭
Received — 11 March 2026 ⏭

Backdoor4Good: Benchmarking Beneficial Uses of Backdoors in LLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yige Li · Wei Zhao · Zhe Li · Nay Myat Min · Hanxun Huang · Yunhan Zhao · Xingjun Ma · Yu-Gang Jiang · Jun Sun
10 March 2026 at 12:00
arXiv:2603.07452v1 Announce Type: cross Abstract: Backdoor mechanisms have traditionally been studied as security threats that compromise the integrity of machine learning models. However, the same mechanism -- the conditional activation of specific behaviors through input triggers -- can also serve as a controllable and auditable interface for trustworthy model behavior. In this work, we present \textbf{Backdoor4Good (B4G)}, a unified benchmark and framework for \textit{beneficial backdoor} applications in large language models (LLMs). Unlike conventional backdoor studies focused on attacks and defenses, B4G repurposes backdoor conditioning for Beneficial Tasks that enhance safety, controllability, and accountability. It formalizes beneficial backdoor learning under a triplet formulation $(T, A, U)$, representing the \emph{Trigger}, \emph{Activation mechanism}, and \emph{Utility function}, and implements a benchmark covering four trust-centric applications. Through extensive experiments across Llama3.1-8B, Gemma-2-9B, Qwen2.5-7B, and Llama2-13B, we show that beneficial backdoors can achieve high controllability, tamper-resistance, and stealthiness while preserving clean-task performance. Our findings demonstrate new insights that backdoors need not be inherently malicious; when properly designed, they can serve as modular, interpretable, and beneficial building blocks for trustworthy AI systems. Our code and datasets are available at https://github.com/bboylyg/BackdoorLLM/B4G.

Arginine methylation-dependent stabilization of SUV39H1 promotes breast cancer growth

✇Oncogene - Issue - nature.com science feeds
By: Wei Zhao · Ge Wang · Peng Wang · Bo Ma · Bo Liu · Yenan Fu · Youzhi Tang · Xinwei Duan · Kunhao Zhou · Jing Zhang · Wei-Guo Zhu · Hongquan Zhang · Yu Yu
7 March 2026 at 08:00

Oncogene, Published online: 07 March 2026; doi:10.1038/s41388-026-03712-0

Arginine methylation-dependent stabilization of SUV39H1 promotes breast cancer growth
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