This review details a three‐stage paradigm shift for tumor‐reactive CD8+ T‐cell identification: decoding transcriptomic states, deciphering clonal functional efficacy, and molecular‐level therapeutic TCR design. Addressing translational hurdles and generative AI “scientific blind spots”—such as missing catch bonds—we present a visionary roadmap. By fusing explainable AI (XAI) with dynamic physical constraints via accelerated MD simulations, this framework enables force‐activated functional prediction to engineer programmable cytotoxic TCRs.
Integration of plasma proteomics and multi‐organ MRI in 666 UK Biobank participants, combined with a knockoff‐based causal inference and machine learning framework, reveals organ‐specific drivers of ectopic fat: PLA2G1B (pancreas), ERBB2/IGFBP2 (liver), CA14 (muscle), and NCAM2/TNFRSF10B (pericardium). These drivers engage distinct pathways (e.g., PI3K‐Akt in liver, BMP/TGFβ in muscle) and predict divergent disease outcomes—muscle fat broadly associates with cardiometabolic, neurological, and digestive diseases, whereas liver fat links to diabetes/hypertension and pancreatic fat to metabolic conditions. This causal atlas supports tissue‐targeted strategies.
This review systematically compares the size‐dependent toxicity of microplastics and nanoplastics in the intestine, liver, kidney, lung, and brain. Particle size determines bioavailability, barrier penetration, and injury mechanisms. Because of their small size and high surface reactivity, NPs readily cross the intestinal epithelium and blood–brain barrier, enter mitochondria and lysosomes, and trigger ferroptosis through iron overload and lipid peroxidation. They also disrupt mitochondrial membrane potential and activate inflammatory pathways. In contrast, larger MPs remain mainly extracellular, inducing YAP‐mediated metabolic reprogramming, chronic oxidative stress, and persistent immune activation via mechanical stress. These differences drive organ‐specific outcomes: NPs cause intestinal ferroptosis while MPs induce metabolic shifts; NPs damage liver mitochondria whereas MPs provoke chronic granulomas; and neurotoxicity occurs directly via NPs or indirectly through the gut–brain axis. The gut acts as a central hub, spreading local injury to distant organs via gut–liver, gut–brain, and gut–kidney axes. This size‐based framework supports precision toxicology and health risk asse…
Mendelian randomization (MR) leverages genetic variants to mitigate confounding biases in causal inference. This review systematically maps MR's methodological evolution, highlights its expanding applications in epidemiology and drug target validation, and outlines future directions for overcoming current biases through dynamic, multi‐omics, and cross‐ancestry integration.
A schematic diagram illustrating the KMT5C‐H4K20me3‐EWSR1‐ACADM signaling axis and its role in ccRCC progression. Key Outcomes: KMT5C/H4K20me3 are upregulated in ccRCC and predict poor prognosis. EWSR1 is a novel noncanonical H4K20me3 reader in ccRCC. KMT5C/EWSR1 co‐repress ACADM via transcription and m6A modification. A‐196 + sunitinib synergistically inhibits ccRCC growth in vivo.
ABSTRACT With the rapid advancement of genome‐wide association studies (GWAS), downstream analyses of GWAS data have become essential for elucidating the genetic mechanisms that underlie complex diseases. However, current post‐GWAS analyses face numerous challenges, including heterogeneous data formats, challenges in multi‐omics integration, and increasingly complex analytical workflows. As a comprehensive post‐GWAS analysis software platform, Omics GWAS provides researchers with an automated, end‐to‐end solution spanning data preparation through results’ visualization by integrating functional modules such as data standardization, Mendelian randomization, multi‐omics joint analysis, comorbidity mechanism exploration, and drug target discovery. The platform supports conversion of GWAS summary statistics across diverse data sources, integrates multidimensional omics data including expression quantitative trait loci (eQTLs), protein quantitative trait loci (pQTLs), and methylation quantitative trait loci (mQTLs) and systematically investigates causal relationships between genotypes and phenotypes using methods such as Mendelian randomization, colocalization analysis, and summary dat…
The tumor microenvironment (TME) is a critical factor in antitumor immunity and treatment outcome in cancer therapy. We have developed an analysis tool called the immuno‐oncology biological research (IOBR) to investigate the TME and its role in antitumor immunity. Leveraging multi‐omics data, IOBR facilitates comprehensive analysis of TME characteristics, immune interactions, and their impact on immunotherapy outcomes. IOBR features six modules for TME analysis, including transcriptomic data preprocessing, TME profiling, TME pattern identification, ligand–receptor interaction analysis, genome‐TME interaction assessment, and visualization, along with modeling. Since its release, this tool has been widely applied in many studies. In the future, IOBR will gradually integrate TCR/BCR repertoire analysis, enhance genomic functionalities, and develop spatial transcriptomics modules, which will further our understanding of TME dynamics and tumor immunity.
The progression of differentiated thyroid carcinoma (DTC) poses significant clinical challenges, especially in determining the optimal time for intervention. To capture early signals of disease progression, we employed an optimized dynamic network biomarker (DNB) method—a systems biology approach that detects abrupt molecular changes indicating a critical transition signal. This analysis revealed that Stage II marks a critical transition in the disease trajectory. We further developed a scoring system called TCPSLevel (Thyroid Carcinoma Progression Signature Level), which quantifies individual progression risk based on gene expression profiles. TCPSLevel showed strong associations with clinical features and prognosis across multiple datasets. Our ensemble consensus clustering approach uncovered three robust DTC molecular subtypes, which demonstrated distinct clinical outcomes, immune microenvironments, regulatory landscapes, and therapeutic agents. A clinically applicable classifier (miniPC) was constructed using machine learning to facilitate subtype prediction. We also identified ASPH as a key regulator driving progression and validated its expression and function experimentally. Together, these findings offer new insights and practical tools for early risk assessment and personalized management of thyroid cancer.
Gene set enrichment analysis (GSEA) is a widely used computational method for determining whether predefined sets of genes show statistically significant concordant differences between two biological states. Despite its popularity, effective visualization of GSEA results remains challenging particularly for users seeking to extract meaningful insights without extensive programming knowledge. Although several tools are available for visualizing GSEA results, many lack the flexibility and customization options necessary for a comprehensive exploration of the data. For instance, the desktop GSEA software generates basic plots that are not publication ready and offer limited options for editing or modification. Users often encounter difficulties adjusting graphical parameters to achieve the desired level of customization or visual quality. Furthermore, traditional tools often fail to meet the demands of emerging analytical needs. For instance, they will lack the capability to effectively compare pathway activity levels across multiple experimental conditions. To bridge this gap, we introduce GseaVis, a user‐friendly R package specifically designed to simplify and enhance the visualization of GSEA results. GseaVis provides a variety of highly customizable and publication‐ready plots including enrichment plots, ranked gene heatmaps, and other forms of graphic visualizations of enriched gene sets. With its simple interface and flexibility, our tool significantly lowers the barrier for biologists and bioinformaticians to explore and present their GSEA data clearly and effectively. The GseaVis package is available on GitHub and is integrated with well‐established R libraries, allowing easy data manipulation and seamless integration into existing bioinformatics workflows. The GseaVis is publicly available via GitHub ( https://github.com/junjunlab/GseaVis ) for users’ access. A complete description of the usages can be found on the manuscript’s GitHub page ( https://junjunlab.github.io/gseavis‐manual/ ).
The immune microenvironment includes immune cells, cytokines, extracellular matrix, vesicles, etc. The interactions between these components form a unique local immune microecology. Although immunity serves as the defense against external pathogens, aberrant immune activation often contributes to disease development. Chronic diseases, a broad category of noncommunicable conditions characterized by long latency and prolonged course, are increasingly recognized for their intricate relationship with the immune microenvironment. Herein, we comprehensively summarize how the immune microenvironment, through its complex regulatory network, influences the progression and manifestation of chronic diseases. We further explore the potential of targeting the immune microenvironment as a therapeutic strategy, aiming to provide new insights and directions for the prevention, diagnosis, and treatment of chronic diseases.
Intratumoral microorganisms within solid tumor TMEs significantly influence tumorigenesis and development by altering immune and metabolic patterns. Their diverse compositions and species contribute to the structural and functional heterogeneity of the TME, affecting tumor progression. Understanding the dual roles of these microbes in antitumor and protumor activities and their complex interactions with the TME enhances our knowledge of the mechanisms underlying tumorigenesis and development.
Genome‐wide association studies (GWAS) have identified over 50,000 disease‐associated genetic variants, yet traditional data acquisition and analysis workflows face critical limitations, including inefficient terabyte‐scale data downloading, prohibitive computational infrastructure requirements, and complex cross‐database integration challenges, that impede research accessibility and clinical translation. We developed a cloud‐based GWAS platform integrating data from major international databases (GWAS Catalog, UK Biobank, and FinnGen) encompassing 40,000+ phenotypes across neuroimaging, proteomics, microbiome, metabolomics, and immunology. The platform employs a Kubernetes‐based distributed architecture with hybrid storage systems and optimized indexing structures. We complemented this with FastGWASR, an R package providing seamless integration for Mendelian randomization, drug target validation, and multiomics analyses. Performance evaluations demonstrated second‐level data extraction capabilities with > 99% reduction in local storage requirements and significant hardware demand reduction compared to traditional methods. The platform successfully processed large‐scale analyses, including MR‐PheWAS studies and multiomics integration workflows. Case studies validated platform effectiveness in metabolite‐diabetes causal analysis, PCSK9 drug target validation, and gut microbiome‐inflammation network analysis, achieving comparable scientific accuracy with dramatically improved efficiency. This cloud‐based ecosystem addresses fundamental barriers in GWAS research by democratizing access to genomic data analysis capabilities. The platform's integration of comprehensive data resources with user‐friendly analytical tools accelerates genomic discovery translation into precision medicine applications, particularly benefiting resource‐limited institutions and facilitating collaborative research across disciplines.
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Quan ChengCentral South University, Changsha, ChinaPeng LuoSouthern Medical University, Guangzhou, China
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Yuting MaChinese Academy of Medical Science, Suzhou, ChinaLinhui WangNavy Medical University, Shanghai, ChinaKai MiaoUniversity of Macau, Macau, ChinaJian ZhangSouthern Medical University, Guangzhou, ChinaHailin TangSun Yat-Sen University Cancer Center, Guangzhou, ChinaGuangchuang YuSouthern Medical University, Guangzhou, ChinaUlf D. KahlertOtto-von-Guericke University, Germany
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