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

Biomedicines. 2025 Jul 14;13(7):1717. doi: 10.3390/biomedicines13071717.

ABSTRACT

Background: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet severe immune-related adverse events (irAEs) often necessitate immunotherapy discontinuation and cause life-threatening complications. Circulating plasma proteins, dynamically accessible and functionally linked to immunity, may predict and offer novel targets for irAEs. Methods: Leveraging multi-omics integration, we conducted bidirectional two-sample Mendelian randomization (MR) using protein quantitative trait loci (pQTLs) from 4998 plasma proteins and genome-wide association data of irAE phenotypes. A causal inference framework combining colocalization analysis, multivariable MR (MVMR) adjusting for body mass index (BMI) confounding, and mediation MR elucidated BMI-independent pathways. Systems biology approaches including tissue-specific expression profiling, pathway enrichment, and protein interaction network analysis revealed spatial and functional drivers of irAE pathogenesis. Results: Proteome-wide MR mapping identified eight plasma proteins (CCL20, CSF1, CXCL9, CD40, TGFβ1, CLSTN2, TNFSF12, TGFα) causally associated with all-grade irAEs, and five (CCL20, CCL25, CXCL10, ADA, TGFα) with high-grade irAEs. Colocalization prioritized CD40/TNFSF12 (all-grade) and ADA/CCL25 (high-grade) as therapeutic targets (PPH4 > 0.7). CXCL9/TNFSF12 (all-grade) and CCL25 (high-grade) exerted BMI-independent effects, suggesting intrinsic immune dysregulation mechanisms. Tissue-specific gene expression patterns, CSF1, TGFβ1 in lung, TNFSF12 in the ileum may explain organ-specific irAE vulnerabilities. High-grade irAEs correlated with compartmentalized immune dysregulation and IL-17/immunodeficiency pathway activation. Conclusions: This study establishes the causal atlas of plasma proteins in irAE pathogenesis, bridging biomarker discovery with actionable therapeutic targets. These advances align with next-generation immunotherapy goals: maximizing efficacy while taming the immune storm.

PMID:40722787 | PMC:PMC12293052 | DOI:10.3390/biomedicines13071717

World J Clin Cases. 2025 Jul 26;13(21):105066. doi: 10.12998/wjcc.v13.i21.105066.

ABSTRACT

BACKGROUND: Circulating tumor DNA (ctDNA)-based liquid biopsy has been found to be effective for the detection of minimal residual disease and the evaluation of prognostic risk in various solid tumors, with good sensitivity and specificity for identifying patients at high risk of recurrence. However, use of its results as a biomarker for guiding the treatment and predicting the prognosis of nasopharyngeal carcinoma (NPC) has not been reported.

CASE SUMMARY: In this case study of a patient with stage IVb NPC, we utilized ctDNA as an independent biomarker to guide treatment. Chemotherapy was administered in the early stages of the disease, and local intensity-modulated radiation therapy was added when the patient tested positive for ctDNA, while radiation therapy was stopped and the patient was observed when the ctDNA test was negative. During the follow-up period, ctDNA signals became positive before tumor progression and became negative again at the end of treatment. We also explored the potential of ctDNA in combination with Epstein-Barr virus (EBV) DNA status to predict the prognosis of NPC patients, as well as the criteria for selecting genetic mutations and the testing cycle for ctDNA analysis.

CONCLUSION: The results of ctDNA-based liquid biopsy can serve as an independent biomarker, either independently or in conjunction with EBV DNA status, to guide the treatment and predict the prognosis of NPC.

PMID:40726932 | PMC:PMC12068179 | DOI:10.12998/wjcc.v13.i21.105066

Int J Surg. 2025 Jul 15. doi: 10.1097/JS9.0000000000003049. Online ahead of print.

ABSTRACT

Fatty acid-binding proteins (FABPs) are intracellular lipid chaperones with molecular weights of approximately 14-15 kDa. By binding and transporting fatty acids and lipid-related molecules, FABPs precisely regulate metabolic pathways, signal transduction, and gene expression, playing a central role in cancer initiation and progression. The 11 identified subtypes (FABP1-FABP12; FABP11 is identical to FABP3) exhibit tissue-specific expression and influence tumor progression through metabolic reprogramming, immune microenvironment modulation, and therapy resistance. Metabolically, FABPs enhance fatty acid uptake, β-oxidation, and synthesis, meeting the high proliferative demands of tumors. In immune regulation, FABP4+ macrophages secrete IL-6 to suppress T cell activity, while FABP6 downregulates MHC-I molecule expression to reduce CD8+ T cell infiltration, fostering an immunosuppressive microenvironment. Regarding therapy resistance, FABP4 enhances mitochondrial β-oxidation to reduce apoptosis in ovarian cancer, and FABP5 promotes chemoresistance in HCC via the HIF-1α pathway. Functional heterogeneity exists among subtypes: FABP7 drives glioblastoma stem cell migration via RXRα signaling, while FABP5 exhibits context-dependent roles, promoting HCC progression but suppressing colorectal cancer (CRC) through mTOR-mediated autophagy. Clinically, FABPs serve as diagnostic biomarkers and therapeutic targets. However, challenges such as insufficient target specificity, cross-cancer heterogeneity, and normal tissue toxicity remain. Future studies should integrate multi-omics and single-cell technologies to elucidate cell-specific mechanisms and develop precise combination therapies for clinical translation.

PMID:40717587 | DOI:10.1097/JS9.0000000000003049

Iran J Pharm Res. 2025 May 25;24(1):e159954. doi: 10.5812/ijpr-159954. eCollection 2025 Jan-Dec.

ABSTRACT

CONTEXT: Gastric cancer (GC) is a major global health burden, with drug resistance representing a critical barrier to effective treatment. Understanding the mechanisms underlying drug resistance and leveraging advanced technologies, such as artificial intelligence (AI), are essential for developing innovative therapeutic strategies.

EVIDENCE ACQUISITION: This review systematically examines the primary mechanisms of drug resistance in GC, organized into eight categories: Reduced drug uptake, enhanced drug efflux, impaired pro-drug activation or increased inactivation, molecular target alterations, enhanced DNA damage repair, imbalance in apoptotic regulation, tumor microenvironment modifications, and phenotypic changes. Additionally, the role of AI in addressing these challenges is explored, with a focus on omics-driven insights, pathway analysis, biomarker discovery, and modeling drug-response relationships.

RESULTS: The review highlights the transformative potential of AI in advancing precision therapy for GC. Key applications include therapeutic stratification, optimization of drug combinations, adaptive therapy design, and integration with clinical workflows. Challenges such as data quality, model interpretability, and the need for interdisciplinary collaboration are identified, along with strategies to address these barriers. Future directions emphasize the development of explainable AI models, integration of multi-omics and real-time patient data, and AI-driven drug discovery targeting resistance pathways.

CONCLUSIONS: By bridging research and clinical practice, AI offers a promising path to more effective, personalized, and adaptive therapeutic strategies for GC. Overcoming existing challenges and leveraging AI's potential can significantly improve treatment outcomes and address the pressing issue of drug resistance in GC.

PMID:40708930 | PMC:PMC12285678 | DOI:10.5812/ijpr-159954

STAR Protoc. 2025 Jul 24;6(3):103980. doi: 10.1016/j.xpro.2025.103980. Online ahead of print.

ABSTRACT

Single-cell nucleosome, methylome, and transcriptome (scNMT) sequencing is a recently developed method that allows multiomics profiling of single cells. In this scNMT protocol, we describe profiling of cells from mouse brain and pancreatic organoids, using liquid handling platforms to increase throughput from 96-well to 384-well plate format. Our approach miniaturizes reaction volumes and incorporates the latest Smart-seq3 protocol to obtain higher numbers of detected genes and genomic DNA (gDNA) CpGs per cell. We outline normalization steps to optimally distribute per-cell sequencing depth. For complete details on the use and execution of this protocol, please refer to Kremer et al. and other works.^{1,2,3,4,5,6,7} This protocol is an update to Cerrizuela et al.⁷.

PMID:40711871 | DOI:10.1016/j.xpro.2025.103980

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

bioRxiv [Preprint]. 2025 May 5:2025.05.01.651678. doi: 10.1101/2025.05.01.651678.

ABSTRACT

Pancreatic cancer remains as one of the most challenging malignancies to diagnose and treat due to the late development of symptoms and limited early diagnostic options. Intraductal papillary mucinous neoplasms (IPMNs) are non-invasive precursors to invasive pancreatic ductal adenocarcinoma (PDAC)and an understanding of the changes in patterns of protein expression that accompany the progression from normal ductal (ND) cell, to IPMN to PDAC may provide avenues for improved earlier detection. In this study, we present an optimized spatial tissue proteomics workflow, termed SP-Max (Spatial Proteomics Optimized for Maximum Sensitivity and Reproducibility in Minimal Sample), designed to maximize protein recovery and quantification from limited laser micro dissected (LMD) samples. Our workflow enabled the identification of more than 6,000 proteins and the quantification of over 5,200 protein groups from FFPE tissue contours of pancreatic tissues. Comparative analyses across ND, IPMN, and PDAC revealed critical molecular differences in protein pathways and potential markers of progression. SP-Max provides a systematic, reproducible approach that significantly enhances our ability to study precancerous lesions and cancer progression in pancreatic tissues at unprecedented resolution.

PMID:40654937 | PMC:PMC12247709 | DOI:10.1101/2025.05.01.651678

Discov Oncol. 2025 Jul 23;16(1):1391. doi: 10.1007/s12672-025-03243-2.

ABSTRACT

We developed the first multi-omics prognostic signature integrating 19 programmed cell death (PCD) pathways and organelle functions (mitochondria, lysosomes, Golgi apparatus) to predict prognosis and immunotherapy response in non-small cell lung cancer (NSCLC). (2) Methods: By combining single-cell RNA-seq, bulk transcriptomics, and deep neural networks (DNN), we identified a 23-gene signature validated across four cohorts (AUC 0.696–0.812). Conducted MR analysis to explore causal links between signature genes and NSCLC incidence, providing biological insights. (3) Results: A prognostic signature was developed, including 23 prognostic genes related to 19 PCD patterns and three organelle functions. The signature demonstrated powerful performance in predicting NSCLC prognosis, immune in-filtration, and therapeutic response. Established DNN models showed high value in predicting risk score groupings of NSCLC. MR analysis for combined SNP information of the 23 prognostic genes suggested a link to the high incidence of NSCLC. Individual MR analysis showed that HIF1A and SQLE expression had a causal effect on NSCLC incidence. (4) Conclusion: This signature stratifies high-risk patients with immunosuppressive microenvironments and predicts enhanced sensitivity to gemcitabine and PD-1 inhibitors, offering a roadmap for personalized NSCLC management.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-025-03243-2.

PMID:40699399 | PMC:PMC12287486 | DOI:10.1007/s12672-025-03243-2

Int J Surg. 2025 Jul 22. doi: 10.1097/JS9.0000000000002999. Online ahead of print.

ABSTRACT

BACKGROUND: Breast cancer remains the most prevalent malignancy among women, and patients presenting with both breast and lung cancer pose significant challenges in clinical diagnosis and treatment. Currently, comprehensive multi-omics analyses for such multiple malignancies are lacking.

METHODS: An integrated multi-omics analysis was performed, incorporating quantitative proteomics and radiomics data from patients with single primary breast cancer as well as those with multiple primary tumors (breast and lung cancer).

RESULTS: Quantitative proteomics analysis revealed four distinct molecular signatures (Types I-IV). Patients with single breast cancer exhibited driving pathways primarily linked to cell proliferation (e.g., HER2), whereas those with multiple breast cancers showed enrichment in ER-related and proliferative pathways. In contrast, patients with multiple lung cancers displayed pathways associated with immune response and immune escape. Additionally, immune subtyping identified three distinct immune landscapes (Types I-III). Radiomic analysis demonstrated strong correlations between these molecular/immune subtypes and imaging findings. Patients with high imaging information scores exhibited pronounced tumor heterogeneity and reduced immune infiltration.

CONCLUSIONS: This study provides new insights into the molecular pathogenesis of multiple primary malignancies, particularly breast and lung cancer.

PMID:40694032 | DOI:10.1097/JS9.0000000000002999

J Liq Biopsy. 2025 Jul 8;9:100310. doi: 10.1016/j.jlb.2025.100310. eCollection 2025 Sep.

ABSTRACT

Bladder cancer is the ninth most common malignancy worldwide, with two clinically distinct forms: non-muscle-invasive disease, characterized by high recurrence and excellent long-term survival, and muscle-invasive disease, associated with poorer outcomes. Current surveillance-cystoscopy and urine cytology-offers high specificity but is invasive, costly, and insensitive to low-grade tumors, underscoring the need for reliable, non-invasive biomarkers. Liquid biopsy approaches in urine and blood have demonstrated promise for real-time assessment of tumor burden, molecular heterogeneity, and early recurrence. Circulating tumor DNA (ctDNA) assays detect tumor-derived genetic and epigenetic alterations, enabling dynamic monitoring of minimal residual disease and treatment response. Methylation-based tests and CpG-targeted sequencing in urine achieve high diagnostic accuracy, potentially reducing dependence on cystoscopy. Molecular classification of bladder tumors into luminal and basal subtypes has refined therapeutic strategies: FGFR inhibitors for luminal-papillary tumors, EGFR-targeted and chemotherapy approaches for basal/squamous cases, and immune-checkpoint inhibitors guided by immune-infiltration profiles. Integration of artificial intelligence with multi-omic liquid biopsy data further enhances predictive modeling for recurrence, treatment response, and minimal residual disease detection. Despite these advances, clinical implementation faces challenges including pre-analytical variability, lack of standardized assays, limited prospective validation, and unclear cost-effectiveness. Harmonized protocols, large multicenter trials, and health-economic evaluations are essential to translate liquid biopsy technologies into routine practice. Future integration with advanced imaging, tissue biopsy, and digital pathology-supported by multidisciplinary collaboration and formal guideline endorsement-holds the potential to personalize bladder cancer management, reduce invasive procedures, and improve patient outcomes.

PMID:40698358 | PMC:PMC12281373 | DOI:10.1016/j.jlb.2025.100310

J Hepatocell Carcinoma. 2025 Jul 17;12:1453-1469. doi: 10.2147/JHC.S493907. eCollection 2025.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) necessitates novel immunotherapeutic targets. PBK, a cancer/testis antigen (CTA), was identified as a pivotal hub gene influencing prognosis, tumor mutation burden (TMB), and immune microenvironment remodeling.

METHODS: PBK was prioritized using weighted gene co-expression network analysis (WGCNA) and differential expression screening in the TCGA-LIHC cohort, intersected with curated CTAs. Analyses assessed correlations with clinicopathological features (TNM stage, survival), genomic characterization (mutation frequencies), and functional validation via siRNA-mediated PBK knockdown in Huh7 cells (migration assay). Single-cell RNA sequencing (scRNA-seq) profiled of the tumor immune microenvironment.

RESULTS: PBK overexpression was significantly correlated with advanced TNM stage (P < 0.05) and poor survival (log-rank P = 0.003). Genomic analysis revealed distinct mutation profiles: high-PBK tumors exhibited increased TP53 mutation frequency (39% vs 17%) but decreased CTNNB1 mutations (20% vs 31%). Patients exhibiting with combined PBK overexpression and high TMB demonstrated the poorest prognosis. Functional validation confirmed that PBK knockdown significantly inhibited Huh7 cell migration capacity (P < 0.05). scRNA-seq analysis showed PBK-enriched tumors contained elevated proportions of immunosuppressive SPP1(+) macrophages (22.33% vs 6.6%, FDR corrected P < 0.001) and CD8(+) SLC4A10(+) MAIT cells (9.82% vs 4.7%, FDR corrected P < 0.001).

CONCLUSION: PBK synergistically drives HCC progression through three synergistic mechanisms: (1) promoting oncogenic mutation accumulation (eg, TP53), (2) increasing metastatic potential, and (3) reprogramming an immune-suppressive microenvironment enriched for SPP1(+) macrophages and CD8(+)SLC4A10(+) MAIT cells. This establishes PBK as a dual-purpose biomarker for prognostic stratification and immunotherapy resistance prediction, providing a mechanistic rationale for developing PBK-targeted therapies in HCC.

PMID:40697330 | PMC:PMC12279550 | DOI:10.2147/JHC.S493907