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Received — 10 February 2026 ⏭ Favourites

Spatial Multi-omics Analyses Reveal Diabetes Promotes Pancreatic Cancer Progression by Stimulating Cholesterol-Induced Neutrophil Extracellular Trap Formation

✇(Multiomics OR Omics) AND (Pancreatic)
By: Guanqun Li · Can Zhang · Tianqi Lu · Ziwei Zhang · Jiafu Wu · Rui Bai · Yan Luo · Fengyi Wang · Yiqin Song · Liwei Liu · Jisheng Hu · Yongwei Wang · Gang Wang · Hongtao Tan · Hua Chen · Rui Kong · Le Li · Bei Sun
9 February 2026 at 19:00

Cancer Res. 2026 Feb 9. doi: 10.1158/0008-5472.CAN-25-2854. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) patients with diabetes mellitus (DM) exhibit poor clinical outcomes. Metabolic reprogramming of both cancer cells and immune compartments plays a crucial role in shaping the anti-tumor immune response in PDAC. DM-induced metabolic alteration may disrupt the intricate crosstalk between immune cells and tumor-associated immune factors, profoundly influencing PDAC progression. Here, we performed an integrated, spatially resolved multi-omics study to investigate DM-associated, cell-specific metabolic remodeling within the PDAC tumor microenvironment. DM influenced interactions between tumor cells and immune cells, which accelerated PDAC growth in both humans and mice. PDAC patients with DM exhibited higher tumor-stage, poorer differentiation, and worse outcomes. Spatial metabolic and transcriptional profiling revealed that SREBP2-dependent cholesterol biosynthesis exacerbated PDAC progression. Increased cholesterol biosynthesis promoted neutrophil recruitment and accelerated formation of neutrophil extracellular traps (NETs) by stimulating the CXCL1-CXCR1/CXCR2 signaling axis, ultimately promoting PDAC growth. Inhibition of SREBP2, pharmacological blockade of CXCL1, or perturbation of NETs markedly reduced PDAC growth in diabetic mouse models. Together, these multi-omics analyses and follow-up mechanistic studies constitute an integrated approach that elucidates a metabolic mechanism by which diabetes promotes PDAC development by remodeling the tumor immune microenvironment and highlights a potential therapeutic strategy for PDAC with DM.

PMID:41661642 | DOI:10.1158/0008-5472.CAN-25-2854

Received — 4 February 2026 ⏭ Favourites

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
30 January 2026 at 19:00

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

Received — 25 January 2026 ⏭ Favourites
Received — 7 January 2026 ⏭ Favourites

Establishment and Optimization of a Patient-Reported Outcome–Based Electronic-Diary for Symptoms Evaluation in Patients With Gastroesophageal Reflux Disorder: Prospective Cohort Study

✇Journal of Medical Internet Research
By: Yun-Chun Chen · Yen-Po Wang · Jui-Hsuan Hung · Da-Wei Wang · Shang-Liang Wu · Li-Fen Chen · Yueh-Hsin Ping · Mei-Lien Pan · Ching-Liang Lu
7 January 2026 at 05:30
Background: Gastroesophageal reflux disease (GERD) symptoms significantly affect patients’ quality of life. Patient-reported outcome (PRO) instruments for symptoms measurement in GERD patients is advocated by regulatory authority. Current tools for GERD symptoms evaluation are limited and the results can be biased by the recall bias. To better characterize the GERD symptoms, an e-diary was developed for daily GERD symptom monitoring. Objective: To build up and optimize a PRO-based e-diary, and to investigate the effect of symptom frequency on adherence. Methods: The GERD e-diary evaluated 8 daytime (acid regurgitation, cough, heartburn, sour taste in the mouth, hiccups, hoarseness, dysphagia, and chest pain) and 2 nighttime symptoms (acid regurgitation and cough) for consecutive 8 weeks. The adherence of e-diary, defined as daily completing rate of e-diary, was evaluated and optimized from First Stage to Third Stage with no reminder implemented in First Stage, sending reminding SMS (Short Message Service) text messaging upon detecting missing data in Second Stage (no reminder during the first 3 to 5 days after enrollment), and immediate installation of reminding system at enrollment in Third Stage. GERD symptom frequency was obtained by summation of the symptomatic days in each week. A multiple regression analysis was performed to examine the effects of system optimization and GERD symptom frequency on patient adherence, while controlling for potential confounding variables. Results: 138 GERD patients (M/F=70/68; age: mean 52.9, SD 12.3 years) were recruited. At First Stage, the adherence was 47.2%, 40% and 57.6% for nighttime, daytime and overall symptom. System optimization significantly improved adherence with increased adherence of nighttime symptoms by 12.5% (P=.005) and 10.9% (P=.01), daytime symptom by 21.7% (P
Received — 28 December 2025 ⏭ Favourites

Context matching is not reasoning when performing generalized clinical evaluation of generative language models

✇npj Digital Medicine
By: Andrew Wen · Qiuhao Lu · Yu-Neng Chuang · Guanchu Wang · Jiayi Yuan · Jiamu Zhang · Liwei Wang · Sunyang Fu · Kurt D. Miller · Heling Jia · Steven D. Bedrick · William R. Hersh · Kirk E. Roberts · Xia Hu · Hongfang Liu
27 December 2025 at 08:00

npj Digital Medicine, Published online: 27 December 2025; doi:10.1038/s41746-025-02253-2

Context matching is not reasoning when performing generalized clinical evaluation of generative language models
Received — 12 November 2025 ⏭ Favourites

UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jigang Fan · Quanlin Wu · Shengjie Luo · Liwei Wang
12 November 2025 at 13:00
arXiv:2506.03237v3 Announce Type: replace-cross Abstract: The detection of ligand binding sites for proteins is a fundamental step in Structure-Based Drug Design. Despite notable advances in recent years, existing methods, datasets, and evaluation metrics are confronted with several key challenges: (1) current datasets and methods are centered on individual protein-ligand complexes and neglect that diverse binding sites may exist across multiple complexes of the same protein, introducing significant statistical bias; (2) ligand binding site detection is typically modeled as a discontinuous workflow, employing binary segmentation and subsequent clustering algorithms; (3) traditional evaluation metrics do not adequately reflect the actual performance of different binding site prediction methods. To address these issues, we first introduce UniSite-DS, the first UniProt (Unique Protein)-centric ligand binding site dataset, which contains 4.81 times more multi-site data and 2.08 times more overall data compared to the previously most widely used datasets. We then propose UniSite, the first end-to-end ligand binding site detection framework supervised by set prediction loss with bijective matching. In addition, we introduce Average Precision based on Intersection over Union (IoU) as a more accurate evaluation metric for ligand binding site prediction. Extensive experiments on UniSite-DS and several representative benchmark datasets demonstrate that IoU-based Average Precision provides a more accurate reflection of prediction quality, and that UniSite outperforms current state-of-the-art methods in ligand binding site detection. The dataset and codes will be made publicly available at https://github.com/quanlin-wu/unisite.
Received — 3 November 2025 ⏭ Favourites

HiF-DTA: Hierarchical Feature Learning Network for Drug-Target Affinity Prediction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Minghui Li · Yuanhang Wang · Peijin Guo · Wei Wan · Shengshan Hu · Shengqing Hu
3 November 2025 at 13:00
arXiv:2510.27281v1 Announce Type: cross Abstract: Accurate prediction of Drug-Target Affinity (DTA) is crucial for reducing experimental costs and accelerating early screening in computational drug discovery. While sequence-based deep learning methods avoid reliance on costly 3D structures, they still overlook simultaneous modeling of global sequence semantic features and local topological structural features within drugs and proteins, and represent drugs as flat sequences without atomic-level, substructural-level, and molecular-level multi-scale features. We propose HiF-DTA, a hierarchical network that adopts a dual-pathway strategy to extract both global sequence semantic and local topological features from drug and protein sequences, and models drugs multi-scale to learn atomic, substructural, and molecular representations fused via a multi-scale bilinear attention module. Experiments on Davis, KIBA, and Metz datasets show HiF-DTA outperforms state-of-the-art baselines, with ablations confirming the importance of global-local extraction and multi-scale fusion.
Received — 31 October 2025 ⏭ Favourites

Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yihe Deng · I-Hung Hsu · Jun Yan · Zifeng Wang · Rujun Han · Gufeng Zhang · Yanfei Chen · Wei Wang · Tomas Pfister · Chen-Yu Lee
31 October 2025 at 12:00
arXiv:2510.25992v1 Announce Type: cross Abstract: Large Language Models (LLMs) often struggle with problems that require multi-step reasoning. For small-scale open-source models, Reinforcement Learning with Verifiable Rewards (RLVR) fails when correct solutions are rarely sampled even after many attempts, while Supervised Fine-Tuning (SFT) tends to overfit long demonstrations through rigid token-by-token imitation. To address this gap, we propose Supervised Reinforcement Learning (SRL), a framework that reformulates problem solving as generating a sequence of logical "actions". SRL trains the model to generate an internal reasoning monologue before committing to each action. It provides smoother rewards based on the similarity between the model's actions and expert actions extracted from the SFT dataset in a step-wise manner. This supervision offers richer learning signals even when all rollouts are incorrect, while encouraging flexible reasoning guided by expert demonstrations. As a result, SRL enables small models to learn challenging problems previously unlearnable by SFT or RLVR. Moreover, initializing training with SRL before refining with RLVR yields the strongest overall performance. Beyond reasoning benchmarks, SRL generalizes effectively to agentic software engineering tasks, establishing it as a robust and versatile training framework for reasoning-oriented LLMs.
Received — 28 October 2025 ⏭ Favourites

RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuquan Xue · Guanxing Lu · Zhenyu Wu · Chuanrui Zhang · Bofang Jia · Zhengyi Gu · Yansong Tang · Ziwei Wang
27 October 2025 at 12:00
arXiv:2510.17640v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLAs) have demonstrated remarkable performance on complex robotic manipulation tasks through imitation learning. However, existing imitation learning datasets contain only successful trajectories and lack failure or recovery data, especially for out-of-distribution (OOD) states where the robot deviates from the main policy due to minor perturbations or errors, leading VLA models to struggle with states deviating from the training distribution. To this end, we propose an automated OOD data augmentation framework named RESample through exploratory sampling. Specifically, we first leverage offline reinforcement learning to obtain an action-value network that accurately identifies sub-optimal actions under the current manipulation policy. We further sample potential OOD states from trajectories via rollout, and design an exploratory sampling mechanism that adaptively incorporates these action proxies into the training dataset to ensure efficiency. Subsequently, our framework explicitly encourages the VLAs to recover from OOD states and enhances their robustness against distributional shifts. We conduct extensive experiments on the LIBERO benchmark as well as real-world robotic manipulation tasks, demonstrating that RESample consistently improves the stability and generalization ability of VLA models.
Received — 15 September 2025 ⏭ Favourites

Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs of type 1 diabetes progression

✇(Multiomics OR Omics) AND (Pancreatic)
By: Rebecca Melton · Sara Jimenez · Weston Elison · Luca Tucciarone · Abigail Howell · Gaowei Wang · Denise Berti · Elisha Beebe · Michael Miller · Chun Zeng · Carolyn McGrail · Kennedy VanderStel · Katha Korgaonkar · Ruth Elgamal · Hannah Mummey · Joshua Chiou · Emily Griffin · Irina Kusmartseva · Mark Atkinson · Sebastian Preissl · Fabian J Theis · Maike Sander · Kyle J Gaulton
10 September 2025 at 18:00

Sci Adv. 2025 Sep 12;11(37):eady0080. doi: 10.1126/sciadv.ady0080. Epub 2025 Sep 10.

ABSTRACT

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here, we performed single-nucleus multiomics and spatial transcriptomics in up to 32 nondiabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine subtypes. β, Acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in β cells. Last, single-cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting β cell regulation. Overall, these results revealed drivers of T1D in the pancreas, which form the basis for therapeutic targets for disease prevention.

PMID:40929272 | PMC:PMC12422192 | DOI:10.1126/sciadv.ady0080

Received — 5 March 2025 ⏭ Favourites

Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs driving type 1 diabetes progression

✇(Multiomics OR Omics) AND (Pancreatic)
By: Rebecca Melton · Sara Jimenez · Weston Elison · Luca Tucciarone · Abigail Howell · Gaowei Wang · Denise Berti · Elisha Beebe · Michael Miller · Chun Zeng · Carolyn McGrail · Kennedy VanderStel · Katha Korgaonkar · Ruth Elgamal · Hannah Mummey · Joshua Chiou · Emily Griffin · Irina Kusmartseva · Mark Atkinson · Sebastian Preissl · Fabian J Theis · Maike Sander · Kyle J Gaulton
3 March 2025 at 19:00

bioRxiv [Preprint]. 2025 Feb 17:2025.02.13.637721. doi: 10.1101/2025.02.13.637721.

ABSTRACT

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here we performed single nucleus multiomics and spatial transcriptomics in up to 32 non-diabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine sub-types. Beta, acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in beta cells. Finally, single cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting beta cell regulation. Overall, these results revealed drivers of T1D progression in the pancreas, which form the basis for therapeutic targets for disease prevention.

PMID:40027657 | PMC:PMC11870426 | DOI:10.1101/2025.02.13.637721

Received — 24 December 2024 ⏭ Favourites

Deep whole-genome analysis of 494 hepatocellular carcinomas

✇Nature - Issue - nature.com science feeds
By: Lei Chen · Chong Zhang · Ruidong Xue · Mo Liu · Jian Bai · Jinxia Bao · Yin Wang · Nanhai Jiang · Zhixuan Li · Wenwen Wang · Ruiru Wang · Bo Zheng · Airong Yang · Ji Hu · Ke Liu · Siyun Shen · Yangqianwen Zhang · Mixue Bai · Yan Wang · Yanjing Zhu · Shuai Yang · Qiang Gao · Jin Gu · Dong Gao · Xin Wei Wang · Hidewaki Nakagawa · Ning Zhang · Lin Wu · Steven G. Rozen · Fan Bai · Hongyang Wang
14 February 2024 at 08:00

Nature, Published online: 14 February 2024; doi:10.1038/s41586-024-07054-3

The Chinese Liver Cancer Atlas project depicts a panoramic genomic landscape of hepatocellular carcinoma, covering candidate coding and non-coding drivers, mutational signatures, extrachromosomal circular DNA, subclonal catastrophic events and detailed evolutionary history.

N1-Methyladenosine modification of mRNA regulates neuronal gene expression and oxygen glucose deprivation/reoxygenation induction

✇Cell Death Discovery nature.com science feeds
By: Zhangyang Qi · Chi Zhang · Huan Jian · Mengfan Hou · Yongfu Lou · Yi Kang · Wei Wang · Yigang Lv · Shenghui Shang · Chaoyu Wang · Xueying Li · Shiqing Feng · Hengxing Zhou
12 May 2023 at 08:00

Cell Death Discovery, Published online: 12 May 2023; doi:10.1038/s41420-023-01458-2

N1-Methyladenosine modification of mRNA regulates neuronal gene expression and oxygen glucose deprivation/reoxygenation induction
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