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PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaocheng Fang · Jiarui Jin · Haoyu Wang · Che Liu · Jieyi Cai · Yujie Xiao · Guangkun Nie · Bo Liu · Shun Huang · Hongyan Li · Shenda Hong
arXiv:2509.19774v2 Announce Type: replace-cross Abstract: Electrocardiography (ECG) is the clinical gold standard for cardiovascular disease (CVD) assessment, yet continuous monitoring is constrained by the need for dedicated hardware and trained personnel. Photoplethysmography (PPG) is ubiquitous in wearable devices and readily scalable, but it lacks electrophysiological specificity, limiting diagnostic reliability. While generative methods aim to translate PPG into clinically useful ECG signals, existing approaches are limited by the misalignment of physiological semantics in generative models and the complexity of modeling in high-dimensional signals. To address these limitations, we propose PPGFlowECG, a two-stage framework that aligns PPG and ECG in a shared latent space using the CardioAlign Encoder and then synthesizes ECGs with latent rectified flow. We further provide a formal analysis of this coupling, showing that the CardioAlign Encoder is necessary to guarantee stable and semantically consistent ECG synthesis under our formulation. Extensive experiments on four datasets demonstrate improved synthesis fidelity and downstream diagnostic utility. These results indicate that PPGFlowECG supports scalable, wearable-first CVD screening when standard ECG acquisition is unavailable.
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CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dongchen Li · Jitao Liang · Wei Li · Xiaoyu Wang · Longbing Cao · Kun Yu
arXiv:2507.22533v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) hold significant promise for improving clinical decision support and reducing physician burnout by synthesizing complex, longitudinal cancer Electronic Health Records (EHRs). However, their implementation in this critical field faces three primary challenges: the inability to effectively process the extensive length and fragmented nature of patient records for accurate temporal analysis; a heightened risk of clinical hallucination, as conventional grounding techniques such as Retrieval-Augmented Generation (RAG) do not adequately incorporate process-oriented clinical guidelines; and unreliable evaluation metrics that hinder the validation of AI systems in oncology. To address these issues, we propose CliCARE, a framework for Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records. The framework operates by transforming unstructured, longitudinal EHRs into patient-specific Temporal Knowledge Graphs (TKGs) to capture long-range dependencies, and then grounding the decision support process by aligning these real-world patient trajectories with a normative guideline knowledge graph. This approach provides oncologists with evidence-grounded decision support by generating a high-fidelity clinical summary and an actionable recommendation. We validated our framework using large-scale, longitudinal data from a private Chinese cancer dataset and the public English MIMIC-IV dataset. In these settings, CliCARE significantly outperforms baselines, including leading long-context LLMs and Knowledge Graph-enhanced RAG methods. The clinical validity of our results is supported by a robust evaluation protocol, which demonstrates a high correlation with assessments made by oncologists.
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PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer

✇MRD
By: Yu Wang · Yong-Bo Xiang · Xiao-Wei Chen · Tao Zhang · Jian-Yang Wang · Wen-Yang Liu · Lei Deng · Lu-Hua Wang · Shu-Geng Gao · Nan Bi

Mil Med Res. 2026 Jan 6;12(1):94. doi: 10.1186/s40779-025-00679-z.

ABSTRACT

BACKGROUND: Despite the predictive impact of circulating tumor DNA (ctDNA) minimal residual disease (MRD), accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer (NSCLC) patients to guide personalized therapy remains challenging. This study aimed to develop and validate an interpretable artificial intelligence-assisted model using global data resources.

METHODS: Liquid biopsy data, blood-based genomic alterations, clinicopathological features, and survival outcomes of stage I-III NSCLC patients who underwent surgery or definitive chemoradiotherapy were collected from 6 cohorts. PRIME (Progression Risk prediction by Interpretable Machine learning on ctDNA-MRD, Mutations, and clinical-therapeutic features) was trained by 6 machine learning algorithms across 4 cohorts and validated in 2 independent cohorts. Model performance was evaluated by the area under the curve (AUC) and interpreted by SHapley Additive exPlanations (SHAP). Whole-exome sequencing (WES) or whole-genome sequencing (WGS) of tumor tissue from 430 stage II-III NSCLC patients and RNA-sequencing (RNA-seq) data from 1149 subjects, sourced from The Cancer Genome Atlas, were used to validate the prognostic effect of mutations identified in peripheral blood and investigate the underlying mechanisms.

RESULTS: A global dataset encompassing 781 blood samples from 493 patients was analyzed. Clinical stage, pre-treatment ctDNA, post-treatment MRD, blood-based Kelch-like ECH-associated protein 1 (KEAP1), serine/threonine kinase 11 (STK11), and cyclin-dependent kinase inhibitor 2A (CDKN2A) mutations, and treatment modality were significantly associated with the risk of disease progression and were thereby included in the model training. WES/WGS and RNA-seq confirmed the poor prognostic effect of KEAP1, STK11, and CDKN2A mutations, which were characterized by the suppressive tumor microenvironment and attenuated humoral immunity. The neural network (NN) model exhibited optimal prediction of treatment failure risk in the training (AUC = 0.85, 95% CI 0.81-0.89) and validation sets (AUC = 0.82, 95% CI 0.74-0.89). SHAP analysis indicated that MRD (+0.306), treatment modality (+0.128), and pre-treatment ctDNA (+0.043) ranked in the top 3 contributions. NN-PRIME outperformed single liquid biopsy biomarkers and clinical-therapeutic signatures, and demonstrated consistent robustness across different clinical scenarios. High-risk patients identified by NN-PRIME had poorer prognoses but derived significant benefits from adjuvant therapy after surgery.

CONCLUSIONS: As an interpretable model integrating readily-accessible and crucial clinical-genomic predictors, PRIME achieves enhanced performance, allowing for early outcome prediction, refined risk stratification, and personalized clinical decision-making.

PMID:41491583 | PMC:PMC12771999 | DOI:10.1186/s40779-025-00679-z

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Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rong Zhou · Dongping Chen · Zihan Jia · Yao Su · Yixin Liu · Yiwen Lu · Dongwei Shi · Yue Huang · Tianyang Xu · Yi Pan · Xinliang Li · Yohannes Abate · Qingyu Chen · Zhengzhong Tu · Yu Yang · Yu Zhang · Qingsong Wen · Gengchen Mai · Sunyang Fu · Jiachen Li · Xuyu Wang · Ziran Wang · Jing Huang · Tianming Liu · Yong Chen · Lichao Sun · Lifang He
arXiv:2601.01321v1 Announce Type: new Abstract: Digital twins, as precise digital representations of physical systems, have evolved from passive simulation tools into intelligent and autonomous entities through the integration of artificial intelligence technologies. This paper presents a unified four-stage framework that systematically characterizes AI integration across the digital twin lifecycle, spanning modeling, mirroring, intervention, and autonomous management. By synthesizing existing technologies and practices, we distill a unified four-stage framework that systematically characterizes how AI methodologies are embedded across the digital twin lifecycle: (1) modeling the physical twin through physics-based and physics-informed AI approaches, (2) mirroring the physical system into a digital twin with real-time synchronization, (3) intervening in the physical twin through predictive modeling, anomaly detection, and optimization strategies, and (4) achieving autonomous management through large language models, foundation models, and intelligent agents. We analyze the synergy between physics-based modeling and data-driven learning, highlighting the shift from traditional numerical solvers to physics-informed and foundation models for physical systems. Furthermore, we examine how generative AI technologies, including large language models and generative world models, transform digital twins into proactive and self-improving cognitive systems capable of reasoning, communication, and creative scenario generation. Through a cross-domain review spanning eleven application domains, including healthcare, aerospace, smart manufacturing, robotics, and smart cities, we identify common challenges related to scalability, explainability, and trustworthiness, and outline directions for responsible AI-driven digital twin systems.
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A Survey on Cache Methods in Diffusion Models: Toward Efficient Multi-Modal Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiacheng Liu · Xinyu Wang · Yuqi Lin · Zhikai Wang · Peiru Wang · Peiliang Cai · Qinming Zhou · Zhengan Yan · Zexuan Yan · Zhengyi Shi · Chang Zou · Yue Ma · Linfeng Zhang
arXiv:2510.19755v3 Announce Type: replace-cross Abstract: Diffusion Models have become a cornerstone of modern generative AI for their exceptional generation quality and controllability. However, their inherent \textit{multi-step iterations} and \textit{complex backbone networks} lead to prohibitive computational overhead and generation latency, forming a major bottleneck for real-time applications. Although existing acceleration techniques have made progress, they still face challenges such as limited applicability, high training costs, or quality degradation. Against this backdrop, \textbf{Diffusion Caching} offers a promising training-free, architecture-agnostic, and efficient inference paradigm. Its core mechanism identifies and reuses intrinsic computational redundancies in the diffusion process. By enabling feature-level cross-step reuse and inter-layer scheduling, it reduces computation without modifying model parameters. This paper systematically reviews the theoretical foundations and evolution of Diffusion Caching and proposes a unified framework for its classification and analysis. Through comparative analysis of representative methods, we show that Diffusion Caching evolves from \textit{static reuse} to \textit{dynamic prediction}. This trend enhances caching flexibility across diverse tasks and enables integration with other acceleration techniques such as sampling optimization and model distillation, paving the way for a unified, efficient inference framework for future multimodal and interactive applications. We argue that this paradigm will become a key enabler of real-time and efficient generative AI, injecting new vitality into both theory and practice of \textit{Efficient Generative Intelligence}.
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Demystifying the Roles of LLM Layers in Retrieval, Knowledge, and Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinyuan Song · Keyu Wang · PengXiang Li · Lu Yin · Shiwei Liu
arXiv:2510.02091v2 Announce Type: replace Abstract: Recent studies suggest that the deeper layers of Large Language Models (LLMs) contribute little to representation learning and can often be removed without significant performance loss. However, such claims are typically drawn from narrow evaluations and may overlook important aspects of model behavior. In this work, we present a systematic study of depth utilization across diverse dimensions, including evaluation protocols, task categories, and model architectures. Our analysis confirms that very deep layers are generally less effective than earlier ones, but their contributions vary substantially with the evaluation setting. Under likelihood-based metrics without generation, pruning most layers preserves performance, with only the initial few being critical. By contrast, generation-based evaluation uncovers indispensable roles for middle and deeper layers in enabling reasoning and maintaining long-range coherence. We further find that knowledge and retrieval are concentrated in shallow components, whereas reasoning accuracy relies heavily on deeper layers -- yet can be reshaped through distillation. These results highlight that depth usage in LLMs is highly heterogeneous and context-dependent, underscoring the need for task-, metric-, and model-aware perspectives in both interpreting and compressing large models.
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Tongyi DeepResearch Technical Report

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tongyi DeepResearch Team · Baixuan Li · Bo Zhang · Dingchu Zhang · Fei Huang · Guangyu Li · Guoxin Chen · Huifeng Yin · Jialong Wu · Jingren Zhou · Kuan Li · Liangcai Su · Litu Ou · Liwen Zhang · Pengjun Xie · Rui Ye · Wenbiao Yin · Xinmiao Yu · Xinyu Wang · Xixi Wu · Xuanzhong Chen · Yida Zhao · Zhen Zhang · Zhengwei Tao · Zhongwang Zhang · Zile Qiao · Chenxi Wang · Donglei Yu · Gang Fu · Haiyang Shen · Jiayin Yang · Jun Lin · Junkai Zhang · Kui Zeng · Li Yang · Hailong Yin · Maojia Song · Ming Yan · Peng Xia · Qian Xiao · Rui Min · Ruixue Ding · Runnan Fang · Shaowei Chen · Shen Huang · Shihang Wang · Shihao Cai · Weizhou Shen · Xiaobin Wang · Xin Guan · Xinyu Geng · Yingcheng Shi · Yuning Wu · Zhuo Chen · Zijian Li · Yong Jiang
arXiv:2510.24701v1 Announce Type: cross Abstract: We present Tongyi DeepResearch, an agentic large language model, which is specifically designed for long-horizon, deep information-seeking research tasks. To incentivize autonomous deep research agency, Tongyi DeepResearch is developed through an end-to-end training framework that combines agentic mid-training and agentic post-training, enabling scalable reasoning and information seeking across complex tasks. We design a highly scalable data synthesis pipeline that is fully automatic, without relying on costly human annotation, and empowers all training stages. By constructing customized environments for each stage, our system enables stable and consistent interactions throughout. Tongyi DeepResearch, featuring 30.5 billion total parameters, with only 3.3 billion activated per token, achieves state-of-the-art performance across a range of agentic deep research benchmarks, including Humanity's Last Exam, BrowseComp, BrowseComp-ZH, WebWalkerQA, xbench-DeepSearch, FRAMES and xbench-DeepSearch-2510. We open-source the model, framework, and complete solutions to empower the community.
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PaperArena: An Evaluation Benchmark for Tool-Augmented Agentic Reasoning on Scientific Literature

✇cs.AI, q-bio.NC updates on arXiv.org
By: Daoyu Wang · Mingyue Cheng · Qi Liu · Shuo Yu · Zirui Liu · Ze Guo
arXiv:2510.10909v2 Announce Type: replace Abstract: Understanding and reasoning on the web-scale scientific literature is a crucial touchstone for large language model (LLM) based agents designed to support complex knowledge-intensive tasks. However, existing works are mainly restricted to tool-free tasks within isolated papers, largely due to the lack of a benchmark for cross-paper reasoning and multi-tool orchestration in real research scenarios. In this work, we propose PaperArena, an evaluation benchmark for agents to address real-world research questions that typically require integrating information across multiple papers with the assistance of external tools. Given a research question, agents should integrate diverse formats across multiple papers through reasoning and interacting with appropriate tools, thereby producing a well-grounded answer. To support standardized evaluation, we provide a modular and extensible platform for agent execution, offering tools such as multimodal parsing, context retrieval, and programmatic computation. Experimental results reveal that even the most advanced LLM powering a well-established agent system achieves merely 38.78% average accuracy. On the hard subset, accuracy drops to only 18.47%, highlighting great potential for improvement. We also present several empirical findings, including that all agents tested exhibit inefficient tool usage, often invoking more tools than necessary to solve a task. We invite the community to adopt PaperArena to develop and evaluate more capable agents for scientific discovery. Our code and data are available https://github.com/Melmaphother/PaperArena.
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Prompt Engineering in Clinical Practice: Tutorial for Clinicians

✇Journal of Medical Internet Research
By: Jialin Liu · Fang Liu · Changyu Wang · Siru Liu
Large language models (LLMs), such as OpenAI’s GPT series and Google’s PaLM, are transforming healthcare by improving clinical decision-making, enhancing patient communication, and simplifying administrative tasks. However, their performance relies heavily on prompt design, where small changes in wording or structure can greatly impact output quality. This poses a challenge for clinicians who are not experts in natural language processing (NLP). This tutorial combines prompt engineering techniques tailored for clinical use, covering methods like zero-shot, few-shot, chain-of-thought, and meta-prompting. We examine four critical dimensions (accuracy, bias mitigation, privacy protection, and workflow integration) through clinical case studies grounded in real-world practice. We provide actionable guidance on defining objectives, applying core principles, iteratively refining prompts, and integrating them into interoperable electronic health record (EHR) systems. This framework helps clinicians leverage LLMs to improve decision-making, streamline documentation, and enhance patient communication while maintaining ethical standards and ensuring patient safety.
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Thor: a platform for cell-level investigation of spatial transcriptomics and histology

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Pengzhi Zhang · Weiqing Chen · Tu N Tran · Minghao Zhou · Kaylee N Carter · Ibrahem Kandel · Shengyu Li · Xen Ping Hoi · Yuxing Sun · Li Lai · Keith Youker · Qianqian Song · Yu Yang · Fotis Nikolos · Zejuan Li · Keith Syson Chan · John P Cooke · Guangyu Wang

Nat Commun. 2025 Aug 5;16(1):7178. doi: 10.1038/s41467-025-62593-1.

ABSTRACT

Spatial transcriptomics links gene expression with tissue morphology, however, current tools often prioritize genomic analysis, lacking integrated image interpretation. To address this, we present Thor, a comprehensive platform for cell-level analysis of spatial transcriptomics and histological images. Thor employs an anti-shrinking Markov diffusion method to infer single-cell spatial transcriptome from spot-level data, effectively combining gene expression and cell morphology. The platform includes 10 modular tools for genomic and image-based analysis, and is paired with Mjolnir, a web-based interface for interactive exploration of gigapixel images. Thor is validated on simulated data and multiple spatial platforms (ISH, MERFISH, Xenium, Stereo-seq). Thor characterizes regenerative signatures in heart failure, screens breast cancer hallmarks, resolves fine layers in mouse olfactory bulb, and annotates fibrotic heart tissue. In high-resolution Visium HD data, it enhances spatial gene patterns aligned with histology. By bridging transcriptomic and histological analysis, Thor enables holistic tissue interpretation in spatial biology.

PMID:40764306 | PMC:PMC12325965 | DOI:10.1038/s41467-025-62593-1

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Integrative Multi-Omic and Immune Profiling of Lung Adenocarcinoma: Molecular Landscapes, Gene Expression, and Treatment Response Insights

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yahui Tian · Honghong Dong · Yujie Guo · Shaowei Xin · Suxin Jiang · Zitong Wan · Huaiyu Wang · Yong Han

Oncologist. 2025 Jun 24:oyaf191. doi: 10.1093/oncolo/oyaf191. Online ahead of print.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is a major cause of cancer death. Traditional histopathological classification overlooks molecular heterogeneity, limiting personalized treatment. This study used multiomic data to define LUAD subtypes, assess prognostic significance, and analyze immune features, aiming to improve targeted therapy and clinical outcomes.

METHODS: This study used Consensus Clustering and Gap Statistics to analyze LUAD multiomic data, including mRNA, lncRNA, miRNA, DNA methylation, and mutations. Clustering was validated by silhouette plots and heatmaps. Molecular characterization involved regulon activity, immune and metabolic profiling. Functional assays (qPCR, WB, CCK-8, flow cytometry) assessed NDNF's role in LUAD.

RESULTS: Two molecular LUAD subtypes showed distinct clustering and survival outcomes. One subtype had worse prognosis and unique immune features, including checkpoint expression and microenvironment differences. Gene signatures and metabolism varied by subtype. NDNF was downregulated in tumors; its overexpression suppressed LUAD cell viability and promoted apoptosis, suggesting tumor-suppressive function.

CONCLUSION: This study identifies two LUAD subtypes with distinct molecular and immune features linked to prognosis and therapy response. NDNF downregulation and its tumor-suppressive effects highlight its therapeutic potential. These findings support improved LUAD stratification and personalized treatment strategies.

IMPLICATIONS FOR PRACTICE: This study reveals two LUAD molecular subtypes with distinct prognoses and immune features, offering a basis for more precise treatment strategies. The identification of NDNF as a potential tumor suppressor suggests its value as both a biomarker and therapeutic target. These findings support improved LUAD stratification and personalized therapy.

PMID:40554675 | DOI:10.1093/oncolo/oyaf191

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Pan-cancer profiling of FZD2 as a prognostic biomarker: integrative multi-omics analysis with experimental validation and functional characterization in gastric cancer

✇Omics in Gastric
By: Sijiang Zhou · Da Li · Chao Quan · Zhu Yu · Yue Feng · Shengyu Wang · Yong Li · Tongtong Qi · Junqiang Chen

Front Pharmacol. 2025 May 15;16:1534974. doi: 10.3389/fphar.2025.1534974. eCollection 2025.

ABSTRACT

BACKGROUND: Frizzled class receptor 2 (FZD2), is a critical protein in the Wnt signaling pathway, which plays significant roles in various cancers. However, its role in cancer progression, prognosis, and diagnosis remains largely unexplored. This study investigates the correlation between FZD2 expression and clinical outcomes, as well as its underlying molecular mechanisms in pan-cancer.

METHODS: A comprehensive bioinformatic analysis was performed using pan-cancer data from The Cancer Genome Atlas (TCGA), which included 33 cancer types. Gene set enrichment analysis (GSEA) was conducted to explore functional pathways, while a protein-protein interaction (PPI) network was constructed to further elucidate the role of FZD2 in tumor biology. The relationship between FZD2 expression and immune cell infiltration across 22 categories was assessed using CIBERSORT. Additionally, single-cell analysis was employed to examine FZD2 expression levels across different cell types. To investigate the functional impact of FZD2, loss-of-function experiments were carried out in gastric cancer cell lines using siRNA-mediated knockdown. Subsequent assays, including Polymerase Chain Reaction (PCR), Western blotting (WB), Cell Counting Kit-8 (CCK8), Flow Cytometry, wound healing, and transwell migration and invasion assays, were performed to assess cellular responses. A subcutaneous gastric cancer xenograft model was established in nude mice to investigate the effect of FZD2 knockdown on tumor growth in vivo.

RESULTS: Our analysis revealed significant upregulation of FZD2 in multiple malignancies, including stomach adenocarcinoma (STAD), bladder cancer (BLCA), and cholangiocarcinoma (CHOL). FZD2 expression was correlated with various cancer characteristics, including stemness score, matrix score, immune score, tumor mutational burden (TMB), microsatellite instability (MSI), RNA modification genes, and drug sensitivity. Notably, FZD2 was associated with altered sensitivity to several anticancer agents, suggesting its role in modulating treatment responses. FZD2 knockdown was demonstrated by both in vitro and in vivo experiments to suppress tumor cell proliferation, migration, and invasion in gastric cancer cell lines, indicating its critical role in tumor progression. Furthermore, FZD2 exhibited significant correlations with other Wnt pathway genes (e.g., Wnt2, Wnt4, Wnt5B), indicating a complex interaction network contributing to tumorigenesis.

CONCLUSION: FZD2 is widely upregulated in various tumor types, with its expression closely associated with key clinical outcomes, including overall survival, disease-specific survival, disease-free interval, as well as tumor mutations, drug sensitivity, immune cell infiltration, and immunotherapy-related biomarkers such as TMB and MSI. These findings highlight the pivotal role of FZD2 in cancer prognosis and treatment, offering potential for novel therapeutic approaches and the development of personalized medicine strategies in oncology.

PMID:40444048 | PMC:PMC12120476 | DOI:10.3389/fphar.2025.1534974

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Strategies for discovering novel hepatocellular carcinoma biomarkers

✇Omics in Hepatocellular
By: Shi-Tao Wu · Li Zhu · Xiao-Ling Feng · Hao-Yu Wang · Fang Li

World J Hepatol. 2025 Feb 27;17(2):101201. doi: 10.4254/wjh.v17.i2.101201.

ABSTRACT

Liver cancer, particularly hepatocellular carcinoma (HCC), remains a significant global health challenge due to its high mortality rate and late-stage diagnosis. The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes. This review provides a comprehensive overview of current and emerging biomarkers for HCC, including alpha-fetoprotein, des-gamma-carboxy prothrombin, glypican-3, Golgi protein 73, osteopontin, and microRNAs. Despite advancements, the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages. The review emphasizes the importance of integrating multi-omics approaches, combining genomics, proteomics, and metabolomics, to develop more robust biomarker panels. Such integrative methods have the potential to capture the complex molecular landscape of HCC, offering insights into disease mechanisms and identifying targets for personalized therapies. The significance of large-scale validation studies, collaboration between research institutions and clinical settings, and consideration of regulatory pathways for clinical implementation is also discussed. In conclusion, while substantial progress has been made in biomarker discovery, continued research and innovation are essential to address the remaining challenges. The successful translation of these discoveries into clinical practice will require rigorous validation, standardization of protocols, and cross-disciplinary collaboration. By advancing the development and application of novel biomarkers, we can improve the early detection and management of HCC, ultimately enhancing patient survival and quality of life.

PMID:40027561 | PMC:PMC11866143 | DOI:10.4254/wjh.v17.i2.101201

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Interplay between gut microbial communities and metabolites modulates pan-cancer immunotherapy responses

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xiaoqiang Zhu · Muni Hu · Xiaowen Huang · Lingxi Li · Xiaolin Lin · Xiaoyan Shao · Jiantao Li · Xiaoyue Du · Xinjia Zhang · Rongrong Sun · Tianying Tong · Yanru Ma · Lijun Ning · Yi Jiang · Yue Zhang · Yuqi Shao · Zhenyu Wang · Yilu Zhou · Jinmei Ding · Ying Zhao · Baoqin Xuan · Hongyang Zhang · Youwei Zhang · Jie Hong · Jing-Yuan Fang · Xiuying Xiao · Bo Shen · Songbing He · Haoyan Chen

Cell Metab. 2025 Jan 28:S1550-4131(24)00495-9. doi: 10.1016/j.cmet.2024.12.013. Online ahead of print.

ABSTRACT

Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment but remains effective in only a subset of patients. Emerging evidence suggests that the gut microbiome and its metabolites critically influence ICB efficacy. In this study, we performed a multi-omics analysis of fecal microbiomes and metabolomes from 165 patients undergoing anti-programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) therapy, identifying microbial and metabolic entities associated with treatment response. Integration of data from four public metagenomic datasets (n = 568) uncovered cross-cohort microbial and metabolic signatures, validated in an independent cohort (n = 138). An integrated predictive model incorporating these features demonstrated robust performance. Notably, we characterized five response-associated enterotypes, each linked to specific bacterial taxa and metabolites. Among these, the metabolite phenylacetylglutamine (PAGln) was negatively correlated with response and shown to attenuate anti-PD-1 efficacy in vivo. This study sheds light on the interplay among the gut microbiome, the gut metabolome, and immunotherapy response, identifying potential biomarkers to improve treatment outcomes.

PMID:39909032 | DOI:10.1016/j.cmet.2024.12.013

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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

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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Author Correction: m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome

✇Nature Biotechnology - Issue - nature.com science feeds
By: Lulu Hu · Shun Liu · Yong Peng · Ruiqi Ge · Rui Su · Chamara Senevirathne · Bryan T. Harada · Qing Dai · Jiangbo Wei · Lisheng Zhang · Ziyang Hao · Liangzhi Luo · Huanyu Wang · Yuru Wang · Minkui Luo · Mengjie Chen · Jianjun Chen · Chuan He

Nature Biotechnology, Published online: 30 November 2022; doi:10.1038/s41587-022-01616-4

Author Correction: m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome
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Detection of ovarian cancer using plasma cell-free DNA methylomes

✇Most Recent Articles: Clinical Epigenetics
By: Huaiwu Lu， Yunyun Liu， Jingyu Wang， Shaliu Fu， Lingping Wang， Chunxian Huang， Jing Li， Lingling Xie， Dongyan Wang， Dan Li， Hui Zhou and Qunxian Rao
Ovarian cancer (OC) is a highly lethal gynecologic cancer, and it is hard to diagnose at an early stage. Clinically, there are no ovarian cancer-specific markers for early detection. Here, we demonstrate the u...
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