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This study aimed to establish the Japanese Cancer Genome Atlas (JCGA) using data from fresh frozen tumor tissues obtained from 5143 Japanese cancer patients, including those with colorectal cancer (31.6%), lung cancer (16.5%), gastric cancer (10.8%) and other cancers (41.1%). The results are part of a single‐center study called “High‐tech Omics‐based Patient Evaluation” or “Project HOPE” conducted at the Shizuoka Cancer Center, Japan. All DNA samples and most RNA samples were analyzed using whole‐exome sequencing, cancer gene panel sequencing, fusion gene panel sequencing and microarray gene expression profiling, and the results were annotated using an analysis pipeline termed “Shizuoka Multi‐omics Analysis Protocol” developed in‐house. Somatic driver alterations were identified in 72.2% of samples in 362 genes (average, 2.3 driver events per sample). Actionable information on drugs that is applicable in the current clinical setting was associated with 11.3% of samples. When including those drugs that are used for investigative purposes, actionable information was assigned to 55.0% of samples. Germline analysis revealed pathogenic mutations in hereditary cancer genes in 9.2% of samples, among which 12.2% were confirmed as pathogenic mutations by confirmatory test. Pathogenic mutations associated with non–cancerous hereditary diseases were detected in 0.4% of samples. Tumor mutation burden (TMB) analysis revealed 5.4% of samples as having the hypermutator phenotype (TMB ≥ 20). Clonal hematopoiesis was observed in 8.4% of samples. Thus, the JCGA dataset and the analytical procedures constitute a fundamental resource for genomic medicine for Japanese cancer patients. The present study aims to establish the Japanese Cancer Genome Atlas (JCGA) by analyzing fresh frozen tumor tissues obtained from 5143 Japanese cancer patients. Somatic driver and druggable alterations were detected in 72.2% and 11.3% of samples, respectively, and germline pathogenic mutations in hereditary cancer genes were identified in 9.2% of samples. The JCGA dataset and analytical procedures constitute a fundamental resource for genomic medicine for Japanese cancer patients.

Summary Salvia miltiorrhiza Bunge, a renowned medicinal herb in traditional Chinese medicine, displays distinctive root texture and high phenolic acid content, traits influenced by genetic and environmental factors. However, the underlying regulatory networks remain unclear. Here, we performed multi‐omics analyses on ecotypes from four major Chinese regions, focusing on environmental impacts on root structure, phenolic acid accumulation and lignin composition. Lower temperatures and increased UV‐B radiation were associated with elevated rosmarinic acid (RA) and salvianolic acid B (SAB) levels, particularly in the Sichuan ecotype. Structural models indicated that the radial arrangement of xylem conduits contributes to greater root hardness. Genomic assembly and comparative analysis of the Sichuan ecotype revealed a unique phenolic acid metabolism gene cluster, including SmWRKY40, a WRKY transcription factor essential for RA and SAB biosynthesis. Overexpression of SmWRKY40 enhanced phenolic acid levels and lignin content, whereas its knockout reduced root hardness. Integrating high‐throughput (DNA affinity purification sequencing) and point‐to‐point (Yeast One‐Hybrid, Dual‐Luciferase and Electrophoretic Mobility Shift Assay) protein‐DNA interaction detection platform further identified SmWRKY40 binding sites across ecotypes, revealing specific regulatory networks. Our findings provide insights into the molecular basis of root texture and bioactive compound accumulation, advancing breeding strategies for quality improvement in S. miltiorrhiza.

With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high‐throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high‐throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi‐omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed. Radiogenomics is an emerging interdisciplinary field with promising prospects in oncology. This review is dedicated to summarizing the current workflows, emphasizing the application of artificial intelligence within them, and engaging in discussions around crucial topics such as tumor heterogeneity and the immune microenvironment in the context of radiogenomics. Finally, it elaborates on the challenges faced by radiogenomics and future trends.

Accurately predicting pathological complete response (pCR) to neoadjuvant treatment (NAT) in breast cancer remains challenging due to tumor heterogeneity. This study enrolled 2279 patients across 12 centers and develops a novel multi‐modality model integrating longitudinal magnetic resonance imaging (MRI) spatial habitat radiomics, transcriptomics, and single‐cell RNA sequencing for predicting pCR. By analyzing tumor subregions on multi‐timepoint MRI, the model captures dynamic intra‐tumoral heterogeneity during NAT. It shows superior performance over traditional radiomics, with areas under the curve of 0.863, 0.813, and 0.888 in the external validation, immunotherapy, and multi‐omics cohorts, respectively. Subgroup analysis shows its robustness across varying molecular subtypes and clinical stages. Transcriptomic and single‐cell RNA sequencing analysis reveals that high model scores correlate with increased immune activity, notably elevated B cell infiltration, indicating the biological basis of the imaging model. The integration of imaging and molecular data demonstrates promise in spatial habitat radiomics to monitor dynamic changes in tumor heterogeneity during NAT. In clinical practice, this study provides a noninvasive tool to accurately predict pCR, with the potential to guide treatment planning and improve breast‐conserving surgery rates. Despite promising results, the model requires prospective validation to confirm its utility across diverse patient populations and clinical settings. This study develops a multi‐modality model integrating longitudinal magnetic resonance imaging, transcriptomic, and scRNA‐seq data to predict pathological complete response (pCR) in breast cancer. It outperforms traditional radiomics models, offering non‐invasive pCR prediction and help guide treatment decisions. The scRNA‐seq analysis reveals the biological basis of the model, specifically indicating that higher model scores are associated with increased B cell infiltration.

Acute respiratory distress syndrome (ARDS) is a severe condition with complex pathogenesis, and emerging evidence highlights the potential role of metabolic factors, though the exact mechanisms are not fully understood. In this study, we used Mendelian randomisation (MR) and multi‐omics approaches to investigate the causal relationship between plasma metabolites, immune cell profiles and ARDS risk. MR analysis of 1400 metabolites identified two causal metabolites linked to increased ARDS risk, primarily involved in pantothenate and CoA biosynthesis. Single‐cell RNA sequencing of ARDS samples revealed that monocytes exhibited the highest levels of pantothenate synthesis. Intercellular communication and pseudotime analysis suggested that the pantothenate synthesis pathway influenced monocyte differentiation and interactions with other cell types. Gene set enrichment analysis showed that monocytes with high pantothenate synthesis were significantly enriched in phagocytosis‐related pathways. Subsequent MR analysis demonstrated that CD33dim HLA DR+ CD11b+%CD33dim HLA DR+ were a risk factor against ARDS. Notably, monocytes with high pantothenate synthesis exhibited decreased expression of antigen presentation markers HLA‐DRB5, HLA‐DRB1 and HLA‐DRA, suggesting that the high pantothenate synthesis monocytes exhibit attenuated antigen presentation and enhanced phagocytic function. Moreover, we developed a diagnostic model using machine learning algorithms. Shapley Additive explanation (SHAP) was leveraged to evaluate the model performance, with CALM2 identified as the most influential feature across the CatBoost and XGBoost models. In summary, this study integrates genetic, multi‐omics and machine learning approaches to provide novel insights into the pathogenesis of ARDS and its potential therapeutic strategies targeting monocyte metabolism and function.

Ovarian cancer (OC) is a high‐mortality gynaecological malignancy, and the role of PANoptosis, a comprehensive cell death mechanism, in its prognosis remains unexplored. This study aims to clarify it, potentially guiding OC diagnosis and treatment. We analysed the ovarian data from TCGA and GTEx, and the GSE184880 scRNA‐seq dataset from GEO. Spatial data and pathological images were sourced from the 10X Genomics website and GDC Portal. Features were extracted using CellProfiler and ResNet‐50, and a PANoptosis‐related pathomics prognostic model (PANPM) powered by deep learning was developed. The PANoptosis‐related hub gene STAT4 potentially served as a protective factor for patients with OC. A better prognosis in OC was found linked to higher PANoptosis. The PANPM, manifesting distinct advantages for clinical application by accurately extracting pathological features, performed excellently in validation and the high‐risk group indicated a poor prognosis. Additionally, STAT4+ T cells may inhibit OC, by activating the PANoptosis of epithelial cells through TNFSF12‐TNFRSF12A and TNF‐TNFRSF1A, which sheds light on potential therapeutic interventions involving STAT4+ T cells.

Microbiome and resistome transmission from mother to child, as well as from animal to environment, has been widely discussed in recent years. Dairy cows mainly provide milk and meat. However, in the dairy production system, the characteristics and transmission trends of resistome assembly and the microbiome in the gastrointestinal tract (GIT) remain unclear. In this study, we sequenced the GIT (rumen fluid and feces) microbiome of dairy cow populations from two provinces in China (136 cows and 36 calves), determined the characteristics of their resistome profiles and the distribution of antibiotics resistance genes (ARGs) across bacteria and further tracked the temporal dynamics of the resistome in offspring during early life using multi‐omics technologies (16S ribosomal RNA [rRNA] sequencing, metagenome, and metatranscriptome). We characterized the GIT resistome in cows, distinguished by gut sites and regions. The abundance of ARGs in calves peaked within the first 3 days after birth, with Enterobacteriaceae as the dominant microbial host. As calves aged, resistome composition stabilized, and overall ARG abundance gradually decreased. Both diet and age influenced carbohydrate‐active enzymes and ARG profiles. Resistance profiles in ecological niches (meconium, colostrum, soil, and wastewater) were unique, resembling maternal sources. Mobile genetic elements (MGEs), mainly found in soil and wastewater, played an important role in mediating these interactions. Multidrug resistance consistently emerged as the most significant form of resistance at the both the metagenome and metatranscriptome levels. Several antibiotic classes showed higher proportions at the RNA level than at the DNA level, indicating that even low‐abundance gene groups can have a considerable influence through high expression. This study broadens our understanding of ARG dissemination in livestock production systems, providing a foundation for developing future preventive and control strategies. This study revealed spatio‐temporal characteristics of gastrointestinal resistome in a cow‐to‐calf model and the pattern of their spread to the environment and offspring in the dairy production system. The gastrointestinal tract of dairy cows is a natural reservoir of resistomes and distinguished by gut sites and regions. The resistance profiles of different ecological niches (meconium, colostrum, soil, and water) were unique, and most of the features were shared with the maternal source. In the early life, antibiotics resistance genes may acquire from the maternal source, and diet and age are the primary regulatory factors of the resistome. Mobile genetic elements are an important medium between various ecological niches interactions which may occur most frequently from the soil and waste water. Highlights The gastrointestinal tract of dairy cows is a natural reservoir of resistomes and distinguished by gut sites and regions. In the early life, antibiotics resistance genes may acquire from maternal source, and diet and age are the primary regulatory factors of the resistome. Mobile genetic elements are an important medium between various ecological niches interactions which may occur most frequently from the soil and wastewater. Multidrug resistance consistently holds paramount significance as the most crucial form of drug resistance at the both metagenome and metatranscriptome level.

The heterogeneity of cancer‐associated fibroblasts (CAFs) could affect the response to immune checkpoint inhibitor (ICI) therapy. However, limited studies have investigated the role of inflammatory CAFs (iCAFs) in ICI therapy using pan‐cancer single‐cell RNA sequencing (scRNA‐seq) and spatial transcriptomics sequencing (ST‐seq) analysis. We performed pan‐cancer scRNA‐seq and ST‐seq analyses to identify the subtype of GSN+ iCAFs, exploring its spatial distribution characteristics in the context of ICI therapy. The pan‐cancer scRNA‐seq and bulk RNA‐seq data are incorporated to develop the Caf.Sig model, which predicts ICI response based on CAF gene signatures and machine learning approaches. Comprehensive scRNA‐seq analysis, along with in vivo and in vitro experiments, investigates the mechanisms by which GSN+ iCAFs influence ICI efficacy. The Caf.Sig model demonstrates well performances in predicting ICI therapy response in pan‐cancer patients. A higher proportion of GSN+ iCAFs is observed in ICI non‐responders compared to responders in the pan‐cancer landscape and clear cell renal cell carcinoma (ccRCC). Using real‐world immunotherapy data, the Caf.Sig model accurately predicts ICI response in pan‐cancer, potentially linked to interactions between GSN+ iCAFs and CD8+ Tex cells. ST‐seq analysis confirms that interactions and cellular distances between GSN+ iCAFs and CD8+ exhausted T (Tex) cells impact ICI efficacy. In a co‐culture system of primary CAFs, primary tumour cells and CD8+ T cells, downregulation of GSN on CAFs drives CD8+ T cells towards a dysfunctional state in ccRCC. In a subcutaneously tumour‐grafted mouse model, combining GSN overexpression with ICI treatment achieves optimal efficacy in ccRCC. Our study provides the Caf.Sig model as an outperforming approach for patient selection of ICI therapy, and advances our understanding of CAF biology and suggests potential therapeutic strategies for upregulating GSN in CAFs in cancer immunotherapy. Pan‐cancer scRNA‐seq and ST‐seq analyses identify GSN+ inflammatory cancer‐associated fibroblasts (CAFs) and their spatial distribution in immune checkpoint inhibitor (ICI) therapy. The Caf.Sig model accurately predicts ICI response across cancers using CAF gene signatures and machine learning. Higher GSN+ inflammatory CAF proportions correlate with ICI non‐response in pan‐cancer and clear cell renal cell carcinoma (ccRCC). Spatial transcriptome analysis confirms interactions between GSN+ inflammatory CAFs and CD8+ Tex cells impact ICI efficacy. GSN downregulation in CAFs drives CD8+ T cells towards a dysfunctional state in ccRCC.

Atherosclerosis (AS) is a prevalent cardiovascular disease, and emerging evidence highlights the critical role of gut microbiota in its development. Trimethylamine‐N‐oxide (TMAO), a metabolite derived from gut microbiota, is thought to promote AS progression by regulating smooth muscle protein 22‐alpha (SM22α)‐mediated inflammation in vascular smooth muscle cells. This study aims to explore the molecular mechanisms of TMAO in AS through multi‐omics analysis, particularly its effects on SIRT1 inhibition and SM22α modulation. 16S ribosomal RNA sequencing revealed an altered gut microbiota composition in AS mice, characterized by increased Bacteroides and decreased Firmicutes. Metabolomics analysis indicated elevated levels of TMAO in AS mice. Transcriptomic data and cell experiments further confirmed that TMAO promotes AS by regulating SM22α‐mediated inflammation via SIRT1 regulation. These findings suggest that TMAO accelerates progression through the SIRT1 and SM22α‐related pathways, offering novel therapeutic targets for AS intervention. Gut microbiota‐derived metabolite TMAO aggravates atherosclerosis by inhibiting SIRT1, leading to the downregulation of SM22α and promoting smooth muscle cell inflammation via the NF‐κB pathway. This study integrates multi‐omics and in vivo validation to uncover novel therapeutic targets in TMAO–SIRT1–SM22α signaling, offering new insights into microbiota‐driven cardiovascular disease progression.

A better understanding of the characteristic serum metabolites and microbiota from the gut and oral cavity in centenarians could contribute to elucidating the mutual connections among them and would help provide information to achieve healthy longevity. Here, we have recruited a total of 425 volunteers, including 145 centenarians in Suixi county — the first certified “International Longevity and Health Care Base” in China. An integrative analysis for the serum metabolites, gut, and oral microbiota of centenarians (aged 100–120) was compared with those of centenarians' lineal relatives (aged 24–86), the elderly (aged 65–88) and young (aged 23–54). Strikingly distinct metabolomic and microbiological profiles were observed within the centenarian signature, longevity family signature, and aging signature, underscoring the metabolic and microbiological diversity among centenarians and their lineal relatives. Within the centenarian between healthy and frail individuals, significant differences in metabolite profiles and microbiota compositions are observed, suggesting that healthy longevity is associated with unique metabolic and microbiota patterns. Through an integrative analysis, the tryptophan pathway has been revealed to be an important potential mechanism for individuals to achieve healthy longevity. Specifically, a key tryptophan metabolite, 5‐methoxyindoleacetic acid (5‐MIAA), was revealed to be associated with the genus Christensenellaceae R‐7 group, and it exhibited effects of delaying cell senescence, promoting lifespan, and alleviating inflammation. Our characterization of the extensive metabolomic and microbiota remodeling in centenarians may offer new scientific insights for achieving healthy longevity. Schematic representation of the design. Tryptophan metabolism is enriched in serum metabolites and gut microbiota of centenarians, potentially contributing to healthy longevity. A key tryptophan metabolite 5‐methoxyindoleacetic acid (5‐MIAA) is linked to the Christensenellaceae R‐7 group and exhibits effects of delaying cell senescence, promoting lifespan, and alleviating inflammation. Highlights Distinct metabolomic and microbiological profiles are observed in centenarian and longevity family. Healthy centenarians exhibit unique metabolic and microbiota patterns compared to frail counterparts. Tryptophan metabolism is enriched in both serum metabolites and gut microbiota of centenarians, potentially contributing to healthy longevity. A key tryptophan metabolite 5‐methoxyindoleacetic acid (5‐MIAA) is linked to the Christensenellaceae R‐7 group, and exhibits effects of delaying cell senescence, promoting lifespan and alleviating inflammation.