Biomark Res. 2025 Aug 11;13(1):101. doi: 10.1186/s40364-025-00812-z.

ABSTRACT

BACKGROUND: Early identification of high-risk individuals is crucial for optimizing cancer screening, particularly when considering expensive and invasive methods such as multi-omics technologies and endoscopic procedures. However, developing a robust, practical multi-cancer risk prediction model that integrates diverse, multi-scale data and with proper validation remains a significant challenge.

METHODS: We initialized the FuSion study by recruiting 42,666 participants from Taizhou, China, with a discovery cohort (n = 16,340) and an independent validation cohort (n = 26,308) after exclusion criteria. We integrated multi-scale data from 54 blood-derived biomarkers and 26 epidemiological exposures to develop a risk prediction model for five common cancers, including lung, esophageal, liver, gastric, and colorectal cancer. Employing five supervised machine learning approaches, we used a LASSO-based feature selection strategy to identify the most informative predictors. The model was trained and internally validated in the discovery cohort, externally applied in the validation cohort, and further evaluated through a prospective clinical follow-up to assess cancer events via clinical examinations.

RESULTS: The final model comprising four key biomarkers along with age, sex, and smoking intensity, achieving an AUROC of 0.767 (95% CI: 0.723-0.814) for five-year risk prediction. High-risk individuals (17.19% of the cohort) accounted for 50.42% of incident cancer cases, with a 15.19-fold increased risk compared to the low-risk group. During follow-up of 2,863 high-risk subjects, 9.64% were newly diagnosed with cancer or precancerous lesions. Notably, cancer detection in the high-risk group was 5.02 times higher than in the low-risk group and 1.74 times higher than in the intermediate-risk group. In particular, the incidence of esophageal cancers in the high-risk group was 16.84 times that of the low-risk group.

CONCLUSIONS: This is the first population-based prospective study in a large Chinese cohort that leverage multi-scale data including biomarkers for multi-cancer risk prediction. Our effective risk stratification model not only enhances early cancer detection but also lays the foundation for the targeted application of advanced screening methods, including but not limited to multi-omics technologies and endoscopy. These findings support precision prevention strategies and the optimal allocation of healthcare resources.

PMID:40790537 | PMC:PMC12341305 | DOI:10.1186/s40364-025-00812-z

J Transl Med. 2025 Aug 7;23(1):877. doi: 10.1186/s12967-025-06725-7.

ABSTRACT

BACKGROUND: Gastric cancer (GC) is characterized by significant intertumoral heterogeneity, which often leads to the development of resistance to platinum-based chemotherapy. Combining platinum drugs with other therapeutic strategies may improve treatment efficacy; however, the mechanisms underlying platinum resistance in GC remain unclear.

METHODS: Key genes related to platinum resistance in GC were selected from the platinum resistance gene database and GC resistance datasets. The Similarity Network Fusion (SNF) algorithm was employed, along with prognosis-related methylation data and somatic mutation data, to classify the molecular subtypes of GC based on GC platinum resistance genes. Gene expression profiles, prognosis, immune cell infiltration, chemotherapy sensitivity, and immunotherapy responsiveness were comprehensively evaluated for each subtype. Localization and functional evaluation were conducted at the single-cell and spatial transcriptomics levels, and predictive models were developed using machine learning techniques. These functional differences in platinum resistance gene models were further explored in GC. Moreover, experimental validation was conducted to elucidate the mechanisms of key genes involved in platinum resistance in GC.

RESULTS: Stomach adenocarcinoma (STAD) patients were classified into three subtypes using the SNF algorithm and multiomics data. Patients with subtype CS2 exhibited a significantly poorer prognosis than those with subtypes CS1 and CS3 (p < 0.05). Subtype CS1 was characterized as immune-deprived, CS2 as stroma-enriched, and CS3 as immune-enriched. Patients with subtype CS2 also exhibited the most adverse therapeutic responses to docetaxel, cisplatin, and gemcitabine. Single-cell analysis revealed high enrichment of M1 module cells with elevated expression of resistance genes, including the transcription factor KLF9. Spatial transcriptomic analysis further confirmed the independent spatial distribution of malignant cells with high expression of drug resistance genes (DRGs). Predictive models based on machine learning demonstrated excellent prognostic performance. Patients in the high DRG group also exhibited poorer responses to immunotherapy. Cellular experiments revealed that KLF9 overexpression significantly inhibited the proliferation of AGS cells (p < 0.05), reduced their resistance to platinum-based drugs, and markedly decreased the levels of inflammatory cytokines in them.

CONCLUSION: KLF9 was identified as a promising therapeutic target for overcoming platinum resistance in GC, warranting further investigation into its role and potential clinical applications.

PMID:40775648 | PMC:PMC12330134 | DOI:10.1186/s12967-025-06725-7

Future Sci OA. 2025 Dec;11(1):2527598. doi: 10.1080/20565623.2025.2527598. Epub 2025 Aug 7.

ABSTRACT

AIM: This study aims to present a comprehensive international analysis of the existing techniques used in liquid biopsies and their use in isolating tumor markers to detect, predict, and monitor the results of cancer treatment.

MATERIALS AND METHODS: We conducted a narrative review using a scoping review model based on three databases, including PubMed/Medline, Scopus, and Cochrane. The search criteria included all articles on liquid biopsy of the last five years (June 30th, 2023-Oct 30, 2024) ((liquid Biopsy) AND (("2023/06/30"[Date - Publication]: "2024/10/30"[Date - Publication]))). We also approached gray literature on this topic. We focused on review articles as an eligibility criterion for this narrative review, but we also carried out a United States registered clinical trials review targeting immunotherapy and liquid biopsy with the limitation "recruiting" and/or "not yet recruiting" (updated on March 31, 2025).

RESULTS: We screened 2645 articles from PubMed/Medline, Scopus, and Cochrane and 45 articles from the gray literature. We retrieved the full text for 325 articles. Liquid biopsies involve the extraction of tumor-derived components such as circulating tumor cells, circulating tumor DNA, and tumor extracellular vesicles from the bodily fluids of cancer patients. We found 25 United States registered governmental clinical trials targeting immunotherapy and liquid biopsy, of which 20 trials are recruiting and five trials are not yet recruiting.

DISCUSSION: Developments in medicine have led to a more comprehensive understanding of tumor features, including tumor load, tumor staging, heterogeneity, gene mutations, and clonal evolution. The utilization of liquid biopsies from cancer patients has provided novel opportunities for detection and ongoing monitoring, precision medicine-based therapy, and identification of markers for therapeutic resistance.

PMID:40772765 | PMC:PMC12333414 | DOI:10.1080/20565623.2025.2527598

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

Eur Urol Focus. 2025 Aug 1:S2405-4569(25)00178-6. doi: 10.1016/j.euf.2025.06.009. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Urinary tumor DNA (utDNA) has emerged as a promising biomarker in the care, diagnosis, early detection, recurrence monitoring, and prognosis of bladder cancer (BCa). Its noninvasive nature, ease of access, and cost effectiveness make it an attractive option for both patients and health care providers. This review describes the current state of utDNA as a marker of BCa.

METHODS: Articles published between 2015 and 2025 on current utDNA-based techniques in BCa were identified and analyzed for relevance and insight into utDNA research and usage.

KEY FINDINGS AND LIMITATIONS: Recent investigations underscore the noninvasiveness and superior tumor detection capabilities of utDNA, particularly in the detection of minimal residual disease. Moreover, utDNA provides actionable information, such as tumor grade and staging information, to support precise treatment decisions, including targeted immunotherapy regimens and bladder preservation strategies. Although utDNA has shown promising results in small studies, larger studies must be performed before it can be considered as a standard procedure in clinical practice.

CONCLUSIONS AND CLINICAL IMPLICATIONS: Urinary tumor DNA has demonstrated great potential to improve on most, if not all, stages of detection, treatment, and monitoring of BCa. By preserving the low cost and noninvasiveness of urine cytology, and by replacing its suboptimal accuracy with a precision rivaling and often exceeding cystoscopy and circulating tumor DNA-based methods, utDNA offers patients a more comfortable, repeatable, and accurate way of detecting BCa. With increased sensitivity and accuracy, everything from low-grade tumors to the earliest signs of recurrence can be detected more effectively, optimizing patient treatment courses and improving outcomes.

PMID:40753029 | DOI:10.1016/j.euf.2025.06.009

Cancer Cell. 2025 Aug 11;43(8):1399-1422. doi: 10.1016/j.ccell.2025.07.008. Epub 2025 Jul 31.

ABSTRACT

Circulating tumor cells (CTCs) are cancer cells, shed from primary tumors or metastases into the bloodstream. The first non-invasive "liquid biopsy" for cancer monitoring, CTCs have been largely surpassed by circulating tumor DNA (ctDNA) for clinical applications, given the ease of DNA sequencing without specialized cell isolation methods. However, emerging rare cell capture technologies that can process larger blood volumes and enable advanced single-cell analyses may enhance the range and potential of CTC-based biomarkers. CTCs are increasingly valuable for assessing tumor heterogeneity, guiding protein biomarker-driven cancer immune therapies, and assessing heterogeneous drug resistance, as well as for detecting minimal disease. CTCs, thus, remain central to understanding cancer dissemination and are poised to offer complementary diagnostic roles in the application of minimally invasive liquid biopsies for cancer. Here, we review recent advances in the study of these rare circulating cancer cells and discuss current limitations and future directions.

PMID:40749671 | DOI:10.1016/j.ccell.2025.07.008

J Proteome Res. 2025 Aug 2. doi: 10.1021/acs.jproteome.5c00385. Online ahead of print.

ABSTRACT

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors have transformed lung adenocarcinoma (LUAD) treatment in EGFR-mutant (MT) patients, but strategies targeting wild-type (WT) EGFR tumors remain necessary. This study analyzed a diverse LUAD patient cohort with EGFR mutation statuses and wild-type profiles for ALK and KRAS to identify stage-specific biomarkers. Using quantitative proteomics and multiomics, we discovered 21 dysregulated proteins in early-stage EGFR-WT LUAD, identifying myeloid-derived growth factor (MYDGF) as a key candidate biomarker. Elevated MYDGF levels in tissue (n = 117) and serum (n = 196) correlated significantly with cancer stage in EGFR-WT patients but not EGFR-MT cases. Notably, a higher tumor-to-normal MYDGF ratio predicted a favorable prognosis in early-stage EGFR-WT LUAD. Functional studies demonstrated that MYDGF exerts distinct roles in cell viability and migration depending on its cellular localization and the invasive potential of cancer cells. Specifically, secreted MYDGF promoted a protumorigenic phenotype, whereas excess intracellular MYDGF appeared to suppress the oncogenic capacity of aggressive cancer cells. MYDGF knockdown and subsequent proteomic analysis provided further insights into these context-dependent functions. These findings highlight EGFR status- and stage-specific proteomic profiles in LUAD, emphasizing the importance of context-dependent biomarker assessment for personalized treatment strategies.

PMID:40752010 | DOI:10.1021/acs.jproteome.5c00385

Eur Respir J. 2025 Jul 31:2402537. doi: 10.1183/13993003.02537-2024. Online ahead of print.

ABSTRACT

BACKGROUND: Allograft injury in the early post-transplant period is a known risk factor of death after lung transplantation. However, the recipient tissue injury profile and its association with outcomes remain unexplored. This study leverages cell-free DNA (cfDNA) to test this association.

METHODS: The prospective cohort multicenter study included lung transplant recipients (GRAfT, NCT02423070) with serial plasma measurements of recipient-derived (rd)-cfDNA using digital droplet PCR. Non-transplant healthy controls were recruited as the comparator. Whole-genome bisulfite sequencing identified tissue sources of cfDNA. Mean rd-cfDNA levels within 30 days post-transplant was computed. Multivariable regression models were used to assess the association between rd-cfDNA tertiles and the primary outcome of death and secondary outcomes.

RESULTS: The study included 215 patients with 2530 cfDNA values, including 675 cfDNA assessments in the first 30 days. Median rd-cfDNA levels in the first 30 days post-transplant were ∼16-fold higher than cfDNA for healthy controls. Patients in the highest tertile rd-cfDNA group had lower lung function post-transplant, and increased risk of death (HR: 3.15, 95% CI: 1.59-6.24, p<0.001) and acute rejection (HR 2.33, 95% CI: 1.33-4.08, p=0.03), compared to the low/middle tertile group. Tissue-specific cfDNA sources were also distinct cfDNA in the highest versus lowest rd-cfDNA tertiles, with cfDNA from innate immune cells serving as the strongest predictor of mortality.

CONCLUSION: Post-transplant recipient tissue injury varies between lung transplant patients and is associated with increased risk of acute rejection and mortality.

PMID:40744691 | DOI:10.1183/13993003.02537-2024

Eur J Radiol. 2025 Jul 22;191:112327. doi: 10.1016/j.ejrad.2025.112327. Online ahead of print.

ABSTRACT

The NAVIGATOR project established an Italian regional imaging biobank and interactive research platform designed to support precision oncology through the integration of multimodal imaging, clinical, and omics data. The platform goes beyond a static repository, offering a secure Virtual Research Environment (VRE) where users can upload data, test AI algorithms, and execute complete analytical pipelines. The platform incorporates artificial intelligence (AI)-driven radiomics and deep learning methodologies to enable biomarker extraction, disease stratification, and predictive modeling. This manuscript presents the development and implementation of the NAVIGATOR infrastructure, including its data governance framework, ethical and legal considerations, and application to three oncological use cases: prostate, rectal, and gastric cancers. To date, the biobank has collected imaging and clinical data from over 700 patients across these cohorts. AI models were deployed within a dedicated VRE to facilitate image analysis, feature extraction, and classification tasks. The project addresses critical challenges related to data harmonization, regulatory compliance, privacy safeguards and fairness in AI systems. NAVIGATOR demonstrates the feasibility of integrating AI methodologies within imaging biobanks and provides a scalable framework to advance oncological research and support clinical decision-making.

PMID:40743874 | DOI:10.1016/j.ejrad.2025.112327

J Liq Biopsy. 2025 Jul 16;9:100312. doi: 10.1016/j.jlb.2025.100312. eCollection 2025 Sep.

ABSTRACT

Breast cancer (BC) is the most frequent cancer and the leading cause of cancer-related death among women worldwide. It represents a heterogeneous group of diseases with distinct morphological, immunophenotypic, and molecular profiles, which significantly impact clinical behavior and therapeutic response. Moreover, under treatment pressure, tumor cells may undergo molecular changes and phenotypic plasticity, leading to resistance and therapeutic failure. Although tissue biopsy remains the gold standard for diagnosis and molecular characterization, it has several limitations, including invasiveness, sampling bias, and the inability to dynamically capture tumor evolution over time. Hence, a non-invasive and repeatable approach capable of real-time monitoring is increasingly needed. Liquid biopsy (LB), through the analysis of circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), has emerged as a powerful tool to complement tissue biopsy. It allows for longitudinal assessment of tumor burden, detection of minimal residual disease, and identification of molecular alterations relevant to targeted therapies. Despite promising results, the integration of LB into clinical practice is still limited by methodological heterogeneity, standardization gaps, and regulatory issues. Nonetheless, LB represents a key advancement toward precision oncology and may become essential in the personalized management of BC patients. In this review, we explore the current applications, benefits, and technical limitations of LB in different BC settings. We provide a comprehensive overview of the biological and clinical significance of CTCs and ctDNA, emphasizing their diagnostic, prognostic, and predictive roles. Finally, we present an updated summary of ongoing clinical trials that incorporate LB for clinical decision-making.

PMID:40740670 | PMC:PMC12308030 | DOI:10.1016/j.jlb.2025.100312

World J Clin Oncol. 2025 Jul 24;16(7):106981. doi: 10.5306/wjco.v16.i7.106981.

ABSTRACT

Cancer remains a major global health concern, with escalating incidence and mortality rates underscoring the urgent need for novel diagnostic and therapeutic strategies. Increasing evidence has identified the oral microbiota as a critical contributor to tumorigenesis, thereby expanding the understanding of cancer pathogenesis beyond conventional risk factors such as tobacco use and genetic predisposition. This review summarizes recent progress in elucidating the complex relationship between the oral microbiota and various malignancies, particularly oral squamous cell carcinoma, esophageal adenocarcinoma, and pancreatic ductal adenocarcinoma. Pathogenic bacteria, including Porphyromonas gingivalis and Fusobacterium nucleatum, have been implicated in promoting tumor progression through mechanisms involving chronic inflammation, the production of metabolic toxins, and immune evasion. The dysbiosis of the oral microbiota, often driven by lifestyle factors such as poor diet, tobacco use, and alcohol consumption, further exacerbates these carcinogenic processes. Emerging therapeutic approaches including probiotics, oral microbiota transplantation, and CRISPR-based bacterial editing are under investigation for their potential to restore microbial homeostasis and suppress pathogenic species. Additionally, saliva-based microbial biomarkers have shown promise for non-invasive cancer screening. The integration of multi-omics technologies and artificial intelligence-driven platforms is further advancing the development of precision oncology. This review aims to consolidate fragmented findings concerning the oral microbiota-cancer axis and address existing gaps in mechanistic understanding. The review's significance lies in the translational potential of microbial research to clinical applications, offering opportunities to reduce the global cancer burden through early detection and microbiota-targeted therapies.

PMID:40741186 | PMC:PMC12304933 | DOI:10.5306/wjco.v16.i7.106981

Front Mol Biosci. 2025 Jul 15;12:1614288. doi: 10.3389/fmolb.2025.1614288. eCollection 2025.

ABSTRACT

Recent advances in spatial transcriptomics (ST) and spatial proteomics (SP) technologies have enabled high-dimensional molecular profiling at single-cell resolution, providing deeper insights into the tumour-immune microenvironment. However, these modalities are typically applied to separate tissue sections, limiting direct comparisons across molecular layers. We developed a wet-lab and computational framework to perform and integrate ST and SP from the same tissue section, as demonstrated on human lung cancer samples. Applying ST, SP, and hematoxylin and eosin (H&E) staining from the same section ensured consistency in tissue morphology and spatial context. Computational registration using Weave software allowed accurate alignment and annotation transfer across modalities. This co-registered dataset enabled single-cell level comparisons of RNA and protein expression, revealed segmentation accuracy and transcript-protein correlation analyses within individual cells. Notably, we observed systematic low correlations between transcript and protein levels-consistent with prior findings-now resolved at cellular resolution. Our approach highlights the feasibility and utility of performing spatially-resolved multi-omics analysis on the same section without compromising data quality, facilitating concordance studies and region-specific analysis of immune and tumour markers, and ultimately advancing our understanding of disease heterogeneity at the molecular level.

PMID:40735471 | PMC:PMC12304548 | DOI:10.3389/fmolb.2025.1614288

Cell Rep. 2025 Jul 29;44(8):116065. doi: 10.1016/j.celrep.2025.116065. Online ahead of print.

ABSTRACT

Genome-wide association studies (GWASs) have identified over 100 signals associated with type 1 diabetes (T1D). However, it has been challenging to translate any given T1D GWAS signal into mechanistic insights, such as causal variants, their target genes, and the specific cell types involved. Here, we present a comprehensive multi-omic integrative analysis of single-cell/nucleus resolution profiles of gene expression and chromatin accessibility in human pancreatic islets under baseline and T1D-stimulating conditions. We nominate effector cell types for all T1D GWAS signals and the regulatory elements and genes for three independent T1D signals acting through β cells at the DLK1/MEG3, RASGRP1, and TOX loci. Subsequently, we validated the functional impact of these genes and regulatory regions using isogenic human embryonic stem cells (hESCs). We found that loss of RASGRP1 or DLK1, as well as disruption of their corresponding regulatory regions, led to increased β cell apoptosis. Furthermore, β cells derived from isogenic hESCs carrying the T1D risk allele of rs3783355 associated with DLK1 showed elevated β cell death. Through additional RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) analyses, we identified five genes upregulated in both RASGRP1^-/- and DLK1^-/- β-like cells, four of which are near T1D GWAS signals. This integrative approach combining single-cell multi-omics, GWASs, and isogenic human pluripotent stem cell (hPSC)-derived β-like cells illuminates cell type context, genes, single nucleotide polymorphisms (SNPs), and regulatory elements underlying T1D-associated signals, providing insights into the biological functions and molecular mechanisms involved.

PMID:40737125 | DOI:10.1016/j.celrep.2025.116065

Diabetologia. 2025 Jul 30. doi: 10.1007/s00125-025-06462-y. Online ahead of print.

ABSTRACT

This article summarises the current understanding of the heterogeneity of type 1 diabetes from a June 2024 international Expert Forum organised by the editors of Diabetes, Diabetes Care, and Diabetologia. The Forum reviewed key factors contributing to the development and progression of type 1 diabetes and outlined specific, high-priority research questions. Knowledge gaps were identified and, notably, opportunities to harness disease heterogeneity to develop personalised therapies were outlined. Herein, we summarise our discussions and review the heterogeneity of genetic risk and immunologic and metabolic phenotypes that influence and characterise type 1 diabetes progression (presented as a palette of risk factors). We discuss how these age-related factors determine disease aggressiveness (along gradients) and describe how variable immunogenetic pathways aggregate (into networks) to affect beta cell and other pancreatic pathologies to cause clinical disease at different ages and with variable severity (described as disease-related thresholds). Heterogeneity of pathogenesis and clinical severity opens avenues to prevention and intervention, including the potential of disease-modifying immunotherapy and islet cell replacement. We conclude with a call for (1) continued research to identify more factors contributing to the disease, both overall and in specific subgroups; (2) investigations focusing on both individuals who surpass metabolic and immune thresholds and develop diabetes and those who remain disease free with the same level of immunogenetic risk; and (3) efforts to identify where the current type 1 diabetes staging system may fall short and determine how it can be improved to capture and leverage heterogeneity in prevention and intervention strategies.

PMID:40736750 | DOI:10.1007/s00125-025-06462-y

bioRxiv [Preprint]. 2025 Jun 8:2025.06.08.658506. doi: 10.1101/2025.06.08.658506.

ABSTRACT

Advances in spatial profiling have resulted in the generation of multi-omic atlases that span biological scales. In general, multiple workflows are required for image registration, coordinate registration, and spot deconvolution to integrate modalities. To improve the throughput of registration of multi-omic cohorts, we introduce PIVOT, a user-friendly and open-source interface for streamlined nonlinear registration. We demonstrate PIVOT's strengths through registration of three multi-omic datasets, and show comparison of its performance to existing workflows.

PMID:40661390 | PMC:PMC12259011 | DOI:10.1101/2025.06.08.658506