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Integrative proteogenomics maps multifactorial aetiology, progression and therapeutic vulnerabilities in gastric cancer

✇Omics in Gastric
By: Ya-Hsuan Chang · Tzu-Chan Hong · Kuen-Tyng Lin · Yi-Jing Hsiao · Hsiang-En Hsu · Juanilita T Waniwan · Rodrigo Espinoza Silva · I-Rue Lai · Po-Chu Lee · Ming-Tsan Lin · Chia-Tung Shun · Min-Shu Hsieh · Yi-Ju Chen · Song-Wei Wang · Wen-Hung Hsu · I-Chen Wu · Yao-Kuang Wang · Ching-Chun Li · Jaw-Yuan Wang · Yin-Chen Hsu · Hao Fang · Ze-Shiang Lin · Wen-Hsin Chang · Jyun-Hong Lin · Yan-Si Chen · Ying-Chieh Ko · Chi-Ya Shen · Yan-Ming Chen · Chia-Yu Wang · Yu-Teng Jheng · Wen-Yi Liu · Yu-Tai Wang · Chang-Wei Yeh · Pei-Rong Huang · Jyh-Ming Liou · Li-Tzong Chen · Chia-Li Han · Deng-Chyang Wu · Hsuan-Yu Chen · Sung-Liang Yu · Ming-Shiang Wu · Yu-Ju Chen · Taiwan Cancer Moonshot Program

Gut. 2026 Jan 30:gutjnl-2025-337247. doi: 10.1136/gutjnl-2025-337247. Online ahead of print.

ABSTRACT

BACKGROUND: Gastric cancer, with disproportionately higher incidence in East Asia, arises from complex host-microbiome-environment interactions beyond Helicobacter pylori (HP) infection. However, the molecular architecture linking environmental carcinogens, microbial succession and host response remains unclear.

OBJECTIVE: To delineate multifactorial aetiologies and clinically actionable subtypes/biomarkers of gastric cancer through integrative proteogenomic, microbial and environmental exposure profiling.

DESIGN: We established a multiomics atlas of paired tumour, adjacent mucosa tissues and blood from 154 treatment-naïve Taiwanese patients, integrating whole-exome sequencing, RNA-seq, proteome and phosphoproteome profiling with carcinogen signatures, HP status, microbiome composition and refined anatomical mapping. Cell-based functional assays tested carcinogen effects. Microbial subtype was assessed in an independent cohort.

RESULTS: A polycyclic-aromatic-hydrocarbon signature, dibenz[a,h]acridine, emerged as a high-risk exposure promoting invasion, immune suppression and poor survival, significantly exceeding nitrosamine-linked risk in this cohort. Multilayer integration defined three initiation ecologies: HP-driven inflammatory, non-HP microbiome-enriched immune-silent and HP-free microbially depleted states. Among HP-negative tumours, a Streptococcus-enriched subtype associated with tight-junction (CLDN18.2/ZO-1/OCLN) disruption and epithelial-mesenchymal transition, whereas a subset of clinically aggressive cases retained CLDN18.2-high epithelial-stable subtype for therapeutic accessibility. An independent cohort revealed gastric juice-derived Streptococcus anginosus abundance inversely correlated with tight-junction proteins. Anatomical mapping reveals location-specific, sex-specific, subtype-specific oncogenic networks and kinase activity, including CDK4 activation in clinical biomarker-negative tumours. Decision-tree models combining exposure and proteome-immune states refined recurrence and survival prediction beyond stage.

CONCLUSION: This proteogenomic framework defines exposure-informed and microbiome-informed gastric cancer subtypes, providing a molecular schema for patient stratification, prevention and actionable therapeutic vulnerabilities.

PMID:41617485 | DOI:10.1136/gutjnl-2025-337247

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Heterogeneity in Multi-Agent Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianyi Hu · Zhiqiang Pu · Yuan Wang · Tenghai Qiu · Min Chen · Xin Yu
arXiv:2512.22941v1 Announce Type: cross Abstract: Heterogeneity is a fundamental property in multi-agent reinforcement learning (MARL), which is closely related not only to the functional differences of agents, but also to policy diversity and environmental interactions. However, the MARL field currently lacks a rigorous definition and deeper understanding of heterogeneity. This paper systematically discusses heterogeneity in MARL from the perspectives of definition, quantification, and utilization. First, based on an agent-level modeling of MARL, we categorize heterogeneity into five types and provide mathematical definitions. Second, we define the concept of heterogeneity distance and propose a practical quantification method. Third, we design a heterogeneity-based multi-agent dynamic parameter sharing algorithm as an example of the application of our methodology. Case studies demonstrate that our method can effectively identify and quantify various types of agent heterogeneity. Experimental results show that the proposed algorithm, compared to other parameter sharing baselines, has better interpretability and stronger adaptability. The proposed methodology will help the MARL community gain a more comprehensive and profound understanding of heterogeneity, and further promote the development of practical algorithms.
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Graph AI generates neurological hypotheses validated in molecular, organoid, and clinical systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ayush Noori · Joaqu\'in Polonuer · Katharina Meyer · Bogdan Budnik · Shad Morton · Xinyuan Wang · Sumaiya Nazeen · Yingnan He · I\~naki Arango · Lucas Vittor · Matthew Woodworth · Richard C. Krolewski · Michelle M. Li · Ninning Liu · Tushar Kamath · Evan Macosko · Dylan Ritter · Jalwa Afroz · Alexander B. H. Henderson · Lorenz Studer · Samuel G. Rodriques · Andrew White · Noa Dagan · David A. Clifton · George M. Church · Sudeshna Das · Jenny M. Tam · Vikram Khurana · Marinka Zitnik
arXiv:2512.13724v1 Announce Type: cross Abstract: Neurological diseases are the leading global cause of disability, yet most lack disease-modifying treatments. We present PROTON, a heterogeneous graph transformer that generates testable hypotheses across molecular, organoid, and clinical systems. To evaluate PROTON, we apply it to Parkinson's disease (PD), bipolar disorder (BD), and Alzheimer's disease (AD). In PD, PROTON linked genetic risk loci to genes essential for dopaminergic neuron survival and predicted pesticides toxic to patient-derived neurons, including the insecticide endosulfan, which ranked within the top 1.29% of predictions. In silico screens performed by PROTON reproduced six genome-wide $\alpha$-synuclein experiments, including a split-ubiquitin yeast two-hybrid system (normalized enrichment score [NES] = 2.30, FDR-adjusted $p
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AI Deception: Risks, Dynamics, and Controls

✇cs.AI, q-bio.NC updates on arXiv.org
By: Boyuan Chen (Jay) · Sitong Fang (Jay) · Jiaming Ji (Jay) · Yanxu Zhu (Jay) · Pengcheng Wen (Jay) · Jinzhou Wu (Jay) · Yingshui Tan (Jay) · Boren Zheng (Jay) · Mengying Yuan (Jay) · Wenqi Chen (Jay) · Donghai Hong (Jay) · Alex Qiu (Jay) · Xin Chen (Jay) · Jiayi Zhou (Jay) · Kaile Wang (Jay) · Juntao Dai (Jay) · Borong Zhang (Jay) · Tianzhuo Yang (Jay) · Saad Siddiqui (Jay) · Isabella Duan (Jay) · Yawen Duan (Jay) · Brian Tse (Jay) · Jen-Tse (Jay) · Huang · Kun Wang · Baihui Zheng · Jiaheng Liu · Jian Yang · Yiming Li · Wenting Chen · Dongrui Liu · Lukas Vierling · Zhiheng Xi · Haobo Fu · Wenxuan Wang · Jitao Sang · Zhengyan Shi · Chi-Min Chan · Eugenie Shi · Simin Li · Juncheng Li · Jian Yang · Wei Ji · Dong Li · Jinglin Yang · Jun Song · Yinpeng Dong · Jie Fu · Bo Zheng · Min Yang · Yike Guo · Philip Torr · Robert Trager · Yi Zeng · Zhongyuan Wang · Yaodong Yang · Tiejun Huang · Ya-Qin Zhang · Hongjiang Zhang · Andrew Yao
arXiv:2511.22619v2 Announce Type: replace Abstract: As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an empirically demonstrated risk across language models, AI agents, and emerging frontier systems. This project provides a comprehensive and up-to-date overview of the AI deception field, covering its core concepts, methodologies, genesis, and potential mitigations. First, we identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception. We then review existing empirical studies and associated risks, highlighting deception as a sociotechnical safety challenge. We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment. Deception emergence reveals the mechanisms underlying AI deception: systems with sufficient capability and incentive potential inevitably engage in deceptive behaviors when triggered by external conditions. Deception treatment, in turn, focuses on detecting and addressing such behaviors. On deception emergence, we analyze incentive foundations across three hierarchical levels and identify three essential capability preconditions required for deception. We further examine contextual triggers, including supervision gaps, distributional shifts, and environmental pressures. On deception treatment, we conclude detection methods covering benchmarks and evaluation protocols in static and interactive settings. Building on the three core factors of deception emergence, we outline potential mitigation strategies and propose auditing approaches that integrate technical, community, and governance efforts to address sociotechnical challenges and future AI risks. To support ongoing work in this area, we release a living resource at www.deceptionsurvey.com.
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RMethyMD: An integrated platform for exploring RNA methylation in pan-cancer via a multiomics analysis

✇Omics In Lung
By: Linjie Zhao · Yujie Ren · Chengjun Gong · Shizheng Xiong · Wenyuan Wang · Wanjie Zheng · Jiaxi Zhang · Jiarui Liu · Zhiheng He · Jiafeng Yu · Tingming Liang · Li Guo

Cancer Lett. 2025 Jan 12;612:217462. doi: 10.1016/j.canlet.2025.217462. Online ahead of print.

ABSTRACT

A user-friendly integrated database, RMethyMD (http://www.tmliang.cn/rnamethy), was developed to provide a comprehensive analysis of methylation regulators aimed at facilitating the exploration of molecular features in tumorigenesis and clinical implications in cancer diagnosis and treatment via a multiomics approach. Subsequently, molecular landscapes and a robust constructed m6A-based prognostic model using coxBoost + RSF algorithms in lung cancer highlighted m6A as a suitable marker to guide therapeutic strategy. RMethyMD provides a comprehensive resource and multiomics analysis to explore m6A-based prognostic and clinical values, thereby contributing to aiding personalized cancer therapy.

PMID:39809358 | DOI:10.1016/j.canlet.2025.217462

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