Abstract Background Growing evidence from multi‐cancer cohort studies has positioned the oral pathobiont Fusobacterium nucleatum (F. nucleatum) as an emerging microbial contributor to cancer progression. Increased intratumoral abundance of F. nucleatum has been reported in colorectal, breast, esophageal, pancreatic, oral, and gastric cancers and is frequently associated with adverse clinicopathological features, treatment resistance, metastatic behavior, and poor prognosis. Advances in microbiome profiling, spatial analysis, and single‐cell technologies have begun to reveal how F. nucleatum colonizes tumors and interacts with host cells and tumor‐associated microbial communities. Main body This review summarizes current evidence regarding the tumor‐associated activities of F. nucleatum, with emphasis on its routes of tumor entry, spatiotemporal colonization patterns, adhesion‐ and glycan‐dependent tropism, polymicrobial niche formation, and crosstalk with cancer cells and immune components. We discuss how F. nucleatum promotes oncogenic signaling, inflammatory amplification, genomic and epigenetic reprogramming, epithelial–mesenchymal transition, metastatic dissemination, immune evasion, and therapy adaptation. Particular attention is given to its context‐dependent effects on chemotherapy, radiotherapy, and immunotherapy responses, as well as emerging strategies aimed at detecting or selectively targeting intratumoral F. nucleatum. Conclusion F. nucleatum represents both a biomarker‐associated organism and a potentially modifiable component of the tumor microenvironment. Defining its strain‐level heterogeneity, spatial ecology, and therapy‐specific functions will be essential for translating microbiome‐guided precision oncology from mechanistic insight into clinical application. Key points F. nucleatum colonises tumours through mucosal translocation, adjacent‐tissue migration and hematogenous dissemination. It promotes cancer progression via adhesion, inflammation, immune evasion, epigenetic remodelling and metastasis. Its effects on therapy response are tumour‐context dependent. Microbiome‐guided targeting may enable precision oncology.

Breast cancer metastasis claims the majority of breast cancer-related deaths. Anoikis resistance is a key prerequisite for CTCs survival and metastasis. Previous studies have demonstrated that Nicotinamide N-methyltransferase (NNMT) plays a crucial role in cancer metastasis and apoptosis resistance. However, whether NNMT participates in breast cancer CTCs anoikis remains unexplored. In this study, the upregulation of NNMT was observed in CTCs from breast cancer patients and mouse CTCs models. NNMT in detached breast cancer cells is induced by FAK-STAT3 axis and resists anoikis through FAO activation, promoting CTCs survival. Mechanistically, NNMT promotes the expression of CPT1A and CD36 by suppressing PP2A methylation to enhance FAO. Furthermore, NNMT-induced FAO accelerates ROS clearance by maintaining NADP+/NADPH balance. In vivo experiments show that NNMT-knockdown, NNMT inhibitors and FAO inhibitors can all reduce lung metastases formation, suggesting that targeting NNMT-FAO suppresses the metastatic potential of breast cancer. Our study revealed that the upregulation of NNMT is induced by FAK-STAT3 axis, which contributes to CTCs anoikis resistance in breast cancer by activating FAO. Targeting NNMT may provide new therapeutic targets for metastatic breast cancer.

This study investigated the expression profile and prognostic significance of nicotinamide N-methyltransferase (NNMT) in pan-cancer stroma, with focused validation in lung adenocarcinoma (LUAD) and breast carcinoma (BC). Integrated analysis of Genotype-Tissue Expression and The Cancer Genome Atlas databases revealed tumor-specific NNMT mRNA upregulation in these malignancies. Subsequently, the relationship between NNMT mRNA expression levels and clinical parameters across multiple cancer types was examined, with particular emphasis on LUAD, BC, and CRC. Single-cell RNA sequencing data further revealed elevated NNMT expression specifically within stromal compartments, with fibroblasts exhibiting the highest NNMT expression levels among stromal cell populations. Protein-level validation using the Human Protein Atlas database, the National Cancer Institute's Proteomics Data Commons, and the quantitative immunohistochemistry of our independent clinical cohorts demonstrated elevated stromal NNMT protein expression, particularly in cancer-associated fibroblasts. Cox regression analysis and survival curve analysis established stromal NNMT expression as an independent prognostic factor for overall survival in LUAD and BC patients (P < .05). In conclusion, high stromal NNMT levels correlated with poor prognosis, establishing it as a promising biomarker for risk stratification in LUAD and BC.

OBJECTIVES:Carbapenem-resistant Enterobacteriaceae (CRE) pose a major public health threat due to extensive drug resistance and nosocomial spread. This study characterised the genome sequence of a multidrug-resistant Escherichia coli strain EC6030 carrying blaNDM-5 recovered from a 10-month-old patient, and investigated its relationship to global blaNDM-5-positive E. coli. METHODS:Whole-genome sequencing was performed to analyses antimicrobial resistance genes, virulence determinants, plasmid content and the genetic context of blaNDM-5 in EC6030. A total of 7606 blaNDM-5-carrying E. coli genomes were retrieved from the NCBI RefSeq database. Core-genome multilocus sequence typing, minimum-spanning tree analysis and SNP-based phylogenetic analysis were used to assess global distribution and genetic relatedness. RESULTS:EC6030 was resistant to multiple antimicrobial agents, including β-lactams, carbapenems, fluoroquinolones, trimethoprim-sulfamethoxazole, chloramphenicol and tetracycline, but remained susceptible to amikacin and gentamicin. Genomic analysis assigned EC6030 to ST410 and phylogroup C. The blaNDM-5 gene was located on a 46,161 bp IncX3 plasmid flanked by multiple insertion sequences. This plasmid showed high sequence similarity to previously reported blaNDM-carrying plasmids among E. coli and Klebsiella pneumoniae isolates in the public database. Globally, blaNDM-5-carrying E. coli strains are most prevalent in Asia, primarily from clinical specimens, and occur sporadically. The closest relative to EC6030 is a ST410 strain P24_WM1_05.20, recovered from the United Kingdom in 2020 which differing by 61 SNPs. CONCLUSION:These findings highlight the clinical significance and potential clonal and plasmid-mediated dissemination of blaNDM-5-carrying E. coli ST410 lineage and support strengthened genomic surveillance, antimicrobial stewardship and infection control strategies globally.

Abstract Prognostic models in oncology are developed one cancer at a time, from that cancer’s own labelled outcomes, and fail where prognostic information is scarcest. Rare cancers account for roughly a fifth of diagnoses and most paediatric malignancies, yet seldom supply enough events for a reliable time-to-event model. We therefore asked whether a representation learned without outcome labels can supply what those cohorts cannot. A Transformer encoder was pretrained by masked field-value modelling on 9,425,135 tumour records from the SEER 17 registries, diagnosed in 2000–2023. Only diagnosis-time fields passing a fail-closed coding-verification gate were admitted, and each record was emitted as an era-specific and a harmonised view, keeping two decades of recoding auditable. The encoder was then frozen and read by a linear Cox head for overall survival. Nine rare cancers were removed from the pretraining corpus entirely, each requiring an independent pretraining run. On a sealed test partition, all nine exceeded an architecture-identical random frozen encoder in Harrell concordance by +0.0034 to +0.0368, every lower confidence limit above zero. At 256 labelled patients, all 67 cancers favoured the pretrained representation over budget-matched Cox regression, median difference +0.0283. The advantage was bounded: given the entire training set, Cox regression was favoured in seven of nine rare cancers. The encoder did not outperform a field-frequency baseline on its own objective, so upstream reconstruction did not predict downstream transfer. Outcome-agnostic registry pretraining carries prognostic signal into cancers it has never seen, and is most useful where labels are fewest, without establishing clinical utility.

Tumour-associated microbiota are integral components of the tumour microenvironment (TME). However, previous studies on intratumoral microbiota primarily rely on bulk tissue analysis, which may obscure their spatial distribution and localized effects. In this study, we applied in situ spatial-profiling technology to investigate the spatial distribution of intratumoral microbiota in breast cancer and their interactions with the local TME. Using 5R 16S rRNA gene sequencing and RNAscope FISH/CISH on patients' tissue, we identified significant spatial heterogeneity in intratumoral microbiota, with Fusobacterium nucleatum (F. nucleatum) predominantly localized in tumour cell-rich areas. GeoMx digital spatial profiling (DSP) revealed that regions colonized by F. nucleatum exhibit significant influence on the expression of RNAs and proteins involved in proliferation, migration and invasion. In vitro studies indicated that co-culture with F. nucleatum significantly stimulates the proliferation and migration of breast cancer cells. Integrative spatial multi-omics and co-culture transcriptomic analyses highlighted the MAPK signalling pathways as key altered pathways. By intersecting these datasets, VEGFD and PAK1 emerged as critical upregulated proteins in F. nucleatum-positive regions, showing strong positive correlations with MAPK pathway proteins. Moreover, the upregulation of VEGFD and PAK1 by F. nucleatum was confirmed in co-culture experiments, and their knockdown significantly reduced F. nucleatum-induced proliferation and migration. In conclusion, intratumoral microbiota in breast cancer exhibit significant spatial heterogeneity, with F. nucleatum colonization markedly altering tumour cell protein expression to promote progression and migration. These findings provide novel perspectives on the role of microbiota in breast cancer, identify potential therapeutic targets, and lay the foundation for future cancer treatments.Key points Intratumoral Fusobacterium nucleatum exhibits significant spatial heterogeneity within breast cancer tissues. F. nucleatum colonization alters the expression of key proteins involved in tumour progression and migration. The MAPK signalling pathway is a critical mediator of F. nucleatum-induced breast cancer cell proliferation and migration. VEGFD and PAK1 are potential therapeutic targets to mitigate F. nucleatum-induced tumour progression.

OBJECTIVES:The emergence of plasmid mediated tet(X4) gene compromises the clinical utility of tigecycline and underscores growing concerns regarding its environmental reservoirs and potential for interspecies transmission, particularly within Klebsiella species. The aim of this study is to elucidate the dissemination patterns and evolutionary relationships of tet(X4)-harbouring plasmids across clinical and environmental Klebsiella isolates. METHODS:We conducted a comprehensive phylogenetic analysis integrating both newly sequenced plasmids and publicly available datasets from the NCBI Plasmid database. Conjugation assays were performed to assess the horizontal transfer potential of tet(X4)-harbouring plasmids. Furthermore, globally sourced genomic data of tet(X4)-carrying Klebsiella strains were subjected to infer their spatiotemporal distribution, transmission dynamics, and the time to the most recent common ancestor (tMRCA) using BEAST. RESULTS:The tet(X4) gene was located on conjugative plasmids ranging from 5.7 kb to 19.3 kb, predominantly embedded within a conserved abh-tet(X4)-ISCR2 structure flanked by mobile genetic elements such as IS26 and IS1, which likely facilitate horizontal gene transfer and plasmid integration. These plasmids commonly co-harboured multiple ARGs, including aadA1, floR, and tet(A). The tet(X4)-carrying Klebsiella isolates exhibited substantial genetic diversity, with ST534 and ST3393 identified as the most prevalent lineages. The tet(X4)-carrying K. pneumoniae strains exhibited clonal dissemination across clinical and environmental reservoirs, with the estimated tMRCA dating back to 1873. Moreover, the co-occurrence of tet(X4) with carbapenemase or colistin resistance genes highlights the significant public health threat posed by these high-risk strains. CONCLUSIONS:These findings highlight the urgent need for coordinated genomic surveillance under a One-Health framework to monitor and mitigate the global spread of multidrug-resistant tet(X4)-carrying Klebsiella isolates.

Recent data suggest that vascular endothelial growth factor receptor inhibitor (VEGFRi) can enhance the anti-tumor activity of the anti-programmed cell death-1 (anti-PD-1) antibody in colorectal cancer (CRC) with microsatellite stability (MSS). However, the comparison between this combination and standard third-line VEGFRi treatment is not performed, and reliable biomarkers are still lacking. We retrospectively enrolled MSS CRC patients receiving anti-PD-1 antibody plus VEGFRi (combination group, n=54) or VEGFRi alone (VEGFRi group, n=32), and their efficacy and safety were evaluated. We additionally examined the immune characteristics of the MSS CRC tumor microenvironment (TME) through single-cell and spatial transcriptomic data, and an MSS CRC immune cell-related signature (MCICRS) that can be used to predict the clinical outcomes of MSS CRC patients receiving immunotherapy was developed and validated in our in-house cohort. Compared with VEGFRi alone, the combination of anti-PD-1 antibody and VEGFRi exhibited a prolonged survival benefit (median progression-free survival: 4.4 vs. 2.0 months, P=0.0024; median overall survival: 10.2 vs. 5.2 months, P=0.0038) and a similar adverse event incidence. Through single-cell and spatial transcriptomic analysis, we determined ten MSS CRC-enriched immune cell types and their spatial distribution, including naive CD4+ T, regulatory CD4+ T, CD4+ Th17, exhausted CD8+ T, cytotoxic CD8+ T, proliferated CD8+ T, natural killer (NK) cells, plasma, and classical and intermediate monocytes. Based on a systemic meta-analysis and ten machine learning algorithms, we obtained MCICRS, an independent risk factor for the prognosis of MSS CRC patients. Further analyses demonstrated that the low-MCICRS group presented a higher immune cell infiltration and immune-related pathway activation, and hence a significant relation with the superior efficacy of pan-cancer immunotherapy. More importantly, the predictive value of MCICRS in MSS CRC patients receiving immunotherapy was also validated with an in-house cohort. Anti-PD-1 antibody combined with VEGFRi presented an improved clinical benefit in MSS CRC with manageable toxicity. MCICRS could serve as a robust and promising tool to predict clinical outcomes for individual MSS CRC patients receiving immunotherapy.

BACKGROUND:Metabolic dysregulation was closely associated with cancers. However, there is a lack of studies to explore the relationship between blood metabolites, related proteins, and different types of cancer. METHODS:Two-sample Mendelian randomization (MR) analysis was used to assess the causal effects of genetically determined metabolites and metabolite ratios on solid cancers. we analyzed 1400 metabolites/metabolite ratios as exposures and 16 cancers from UK Biobank/FinnGen as outcomes. Protein-metabolite interactions were mapped via MR and visualized with Cytoscape, followed by Gene Ontology enrichment. Clinical validation included metabolomic profiling of 75 breast cancer patients and 20 controls. RESULTS:MR analysis identified 11 metabolites or metabolite ratios causally associated with cancer risk. Moreover, 48 proteins were demonstrated to be involved in the regulation of these metabolites, which are predominantly enriched in 5 significant metabolic pathways in cancers. Clinically, elevated lignoceroylcarnitine (C24) reduced breast cancer risk, while high glucose-to-mannose and alanine-to-asparagine ratios increased risk. CONCLUSIONS:Our study revealed a causal effects of metabolites and its related proteins/pathways on various types of cancers.

This study investigates key microscopic regions involved in colorectal cancer liver metastasis (CRLM), focusing on the crucial role of cancer-associated fibroblasts (CAFs) in promoting tumor progression and providing molecular- and metabolism-level insights for its diagnosis and treatment using multi-omics. We followed 12 fresh surgical samples from 2 untreated CRLM patients. Among these, 4 samples were used for spatial transcriptomics (ST), 4 for spatial metabolomics, and 4 for single-cell RNA sequencing (scRNA-seq). Additionally, 92 frozen tissue samples from 40 patients were collected. Seven patients were used for immunofluorescence and RT-qPCR, while 33 patients were used for untargeted metabolomics. ST revealed that the spatial regions of CRLM consists of 7 major components, with fibroblast-dominated regions being the most prominent. These regions are characterized by diverse cell-cell interactions, and immunosuppressive and tumor growth-promoting environments. scRNA-seq identified that SPP1+ fibroblasts interact with CD44+ tumor cells, as confirmed through immunofluorescence. Spatial metabolomics revealed suberic acid and tetraethylene glycol as specific metabolic components of this structure, which was further validated by untargeted metabolomics. In conclusion, an SPP1+ fibroblast-rich spatial region with metabolic reprogramming capabilities and immunosuppressive properties was identified in CRLM, which potentially facilitates metastatic outgrowth through interactions with tumor cells.

Emerging evidence suggests that the APOBEC family is implicated in multiple cancers and might be utilized as a new target for cancer detection and treatment. However, the dysregulation and clinical implication of the APOBEC family in clear cell renal cell cancer (ccRCC) remain elusive. TCGA multiomics data facilitated a comprehensive exploration of the APOBEC family across cancers, including ccRCC. Remodeling analysis classified ccRCC patients into two distinct subgroups: APOBEC family pattern cancer subtype 1 (APCS1) and subtype 2 (APCS2). The study investigated differences in clinical parameters, tumor immune microenvironment, therapeutic responsiveness, and genomic mutation landscapes between these subtypes. An APOBEC family-related risk model was developed and validated for predicting ccRCC patient prognosis, demonstrating good sensitivity and specificity. Finally, the overview of APOBEC3B function was investigated in multiple cancers and verified in clinical samples. APCS1 and APCS2 demonstrated considerably distinct clinical features and biological processes in ccRCC. APCS1, an aggressive subtype, has advanced clinical stage and a poor prognosis. APCS1 exhibited an oncogenic and metabolically active phenotype. APCS1 also exhibited a greater tumor mutation load and immunocompromised condition, resulting in immunological dysfunction and immune checkpoint treatment resistance. The genomic copy number variation of APCS1, including arm gain and loss, was much more than that of APCS2, which may help explain the tired immune system. Furthermore, the two subtypes have distinct drug sensitivity patterns in clinical specimens and matching cell lines. Finally, we developed a predictive risk model based on subtype biomarkers that performed well for ccRCC patients and validated the clinical impact of APOBEC3B. Aberrant APOBEC family expression patterns might modify the tumor immune microenvironment by increasing the genome mutation frequency, thus inducing an immune-exhausted phenotype. APOBEC family-based molecular subtypes could strengthen the understanding of ccRCC characterization and guide clinical treatment. Targeting APOBEC3B may be regarded as a new therapeutic target for ccRCC.

Human Ureaplasma species are being increasingly recognized as opportunistic pathogens in human genitourinary tract infections, infertility, adverse pregnancy, neonatal morbidities, and other adult invasive infections. Although some general reviews have focused on the detection and clinical manifestations of Ureaplasma spp., the molecular epidemiology, antimicrobial resistance, and pathogenesis of Ureaplasma spp. have not been adequately explained. The purpose of this review is to offer valuable insights into the current understanding and future research perspectives of the molecular epidemiology, antimicrobial resistance, and pathogenesis of human Ureaplasma infections. This review summarizes the conventional culture and detection methods and the latest molecular identification technologies for Ureaplasma spp. We also reviewed the global prevalence and mechanisms of antibiotic resistance for Ureaplasma spp. Aside from regular antibiotics, novel antibiotics with outstanding in vitro antimicrobial activity against Ureaplasma spp. are described. Furthermore, we discussed the pathogenic mechanisms of Ureaplasma spp., including adhesion, proinflammatory effects, cytotoxicity, and immune escape effects, from the perspectives of pathology, related molecules, and genetics.

Objectives Esophageal squamous cell carcinoma (ESCA) is a challenging disease characterized by a high mortality rate. Understanding the prognostic relationship between G protein-coupled receptors (GPR) and ESCA is critical for improving patient outcomes, yet this connection remains to be fully explored.Methods In this study, we examined the roles of GPR genes and the tumor microenvironment (TME) in ESCA development and progression. Cox regression and Kaplan-Meier analysis demonstrated the predictive value of these genes. Our analysis of TME cell-cell communication revealed extensive interactions, particularly involving neutrophils. We also assessed the combined predictive value of GPR genes, TME score, and tumor mutation burden (TMB) for patient prognosis in ESCA, ultimately constructing a GPR-TME-TMB classifier for prognosis prediction.Results We identified significant differences in GPR gene expression between normal and tumor tissues, with four genes (GPER1, GPR82, FFAR2, and HCAR3) correlating with patient prognosis. Single-cell RNA sequencing analysis revealed 10 major cell types in the TME, with GPR gene expression highly enriched in neutrophils. Our findings indicate that the GPR-TME-TMB classifier is strongly associated with patient prognoses. Additionally, our results align with previous studies on the roles of GPR genes and the TME in ESCA.Conclusions Our results suggest that GPR-related genes play a role in ESCA progression and are strongly associated with TME in ESCA. We constructed a GPR-TME classifier for ESCA to provide new directions for the treatment and prognosis of ESCA patients.

Breast cancer (BC) is currently the most prevalent malignancy worldwide, and finding effective non-invasive biomarkers for routine clinical detection of BC remains a significant challenge. Here, we performed non-targeted and targeted metabolomics analysis on the screening, training and validation cohorts of serum samples from 1,947 participants. A metabolite biomarker model including glutamate, erythronate, docosahexaenoate, propionylcarnitine, and patient’s age was established for detecting BC. This model demonstrated better diagnostic performance than carbohydrate antigen 15-3 (CA15-3) and carcinoembryonic antigen (CEA) alone in discriminating BC from healthy controls both in the training and validation cohorts [area under the curve (AUC), 0.954; sensitivity, 87.1% and specificity, 93.5% for the training cohort and 0.834, 68.3%, and 85.2%, respectively, for the validation cohort 1]. This study has established a noninvasive approach for the detection of BC, which shows potential as a suitable supplement to the clinical screening methods currently employed for BC.

Whole genome sequencing (WGS) potentially represents a rapid approach for antimicrobial resistance genotype-to-phenotype prediction. However, the challenge still exists to predict fully minimum inhibitory concentrations (MICs) and antimicrobial susceptibility phenotypes based on WGS data. This study aimed to establish an artificial intelligence-based computational approach in predicting antimicrobial susceptibilities of multidrug-resistant Acinetobacter baumannii from WGS and gene expression data. Antimicrobial susceptibility testing (AST) was performed using the broth microdilution method for 10 antimicrobial agents. In silico multilocus sequence typing (MLST), antimicrobial resistance genes, and phylogeny based on cgSNP and cgMLST strategies were analyzed. High-throughput qPCR was performed to measure the expression level of antimicrobial resistance (AMR) genes. Most isolates exhibited a high level of resistance to most of the tested antimicrobial agents, with the majority belonging to the IC2/CC92 lineage. Phylogenetic analysis revealed undetected transmission events or local outbreaks. The percentage agreements between AMR phenotype and genotype ranged from 70.08% to 89.96%, with the coefficient of agreement (kappa) extending from 0.025 and 0.881. The prediction of AST employed by deep neural network models achieved an accuracy of up to 98.64% on the testing data set. Additionally, several linear regression models demonstrated high prediction accuracy, reaching up to 86.15% within an error range of one gradient, indicating a linear relationship between certain gene expressions and the corresponding antimicrobial MICs. In conclusion, neural network-based predictions could be used as a tool for the surveillance of antimicrobial resistance in multidrug-resistant A. baumannii.