Mol Cancer. 2025 Dec 3. doi: 10.1186/s12943-025-02521-5. Online ahead of print.

ABSTRACT

Gut microbiota-derived metabolites are emerging as systemic "remote immunoregulators" that shape tumor immunity across tissues. Integrating evidence across short-chain fatty acids, tryptophan derivatives, secondary bile acids, polyamines and other metabolites, we advance a metabolite-immune pathway-cancer framework that links receptor-mediated signaling, epigenetic remodeling and metabolic reprogramming to context-dependent, bidirectional immune effects. Importantly, in addition to the g protein-coupled receptor / aryl hydrocarbon receptor pathway, the selected microbial small molecule metabolites are the true T-cell receptor ligands of unconventional T cells, directly shaping the tissue resident immune and tumor microenvironment, supplementing the receptor signaling and epigenetic programs in our framework. We synthesize how these metabolites recalibrate the tumor immune microenvironment-modulating antigen presentation, T-cell effector fitness and exhaustion, regulatory T-cell activity, and myeloid polarization-and why the same metabolite can either potentiate immune surveillance or entrench immunosuppression depending on ligand-receptor pairing, dose and tissue niche. We compare tumor-type specific patterns (e.g., colorectal, liver, lung, breast and prostate cancers) to highlight common circuits and organ-restricted idiosyncrasies. Methodologically, we outline how single-cell and spatial multi-omics, imaging mass spectrometry and functional biosensors now enable co-registration of metabolite exposure with immune-cell states in human tumors, providing an actionable basis for biomarker discovery. Given ongoing debate about signals attributed to intratumoral microbiota in low-biomass tumor tissues, we foreground quantifiable, spatially mappable and pharmacologically tractable metabolite-receptor pathways, using microbe-associated molecular patterns / translocation as comparators to judge when chemical signals should be prioritized as intervention targets. Finally, we evaluate precision intervention avenues-including fecal microbiota transplantation, rational bacterial consortia, engineered microbes and nanoparticle-enabled metabolite delivery-and propose stratification rules that pair metabolite/receptor signatures with fit-for-purpose delivery. Together, mapping tissue-specific metabolite-immune circuits and embedding them in robust biomarker frameworks may convert microbial metabolites from correlative markers into therapeutic targets and tools, improving the efficacy and durability of cancer immunotherapy.

PMID:41339918 | DOI:10.1186/s12943-025-02521-5

Acta Pharm Sin B. 2025 Sep;15(9):4411-4426. doi: 10.1016/j.apsb.2025.07.018. Epub 2025 Jul 16.

ABSTRACT

The translation of genetic findings from genome-wide association studies into actionable therapeutics persists as a critical challenge in Alzheimer's disease (AD) research. Here, we present PI4AD, a computational medicine framework that integrates multi-omics data, systems biology, and artificial neural networks for therapeutic discovery. This framework leverages multi-omic and network evidence to deliver three core functionalities: clinical target prioritisation; self-organising prioritisation map construction, distinguishing AD-specific targets from those linked to neuropsychiatric disorders; and pathway crosstalk-informed therapeutic discovery. PI4AD successfully recovers clinically validated targets like APP and ESR1, confirming its prioritisation efficacy. Its artificial neural network component identifies disease-specific molecular signatures, while pathway crosstalk analysis reveals critical nodal genes (e.g., HRAS and MAPK1), drug repurposing candidates, and clinically relevant network modules. By validating targets, elucidating disease-specific therapeutic potentials, and exploring crosstalk mechanisms, PI4AD bridges genetic insights with pathway-level biology, establishing a systems genetics foundation for rational therapeutic development. Importantly, its emphasis on Ras-centred pathways-implicated in synaptic dysfunction and neuroinflammation-provides a strategy to disrupt AD progression, complementing conventional amyloid/tau-focused paradigms, with the future potential to redefine treatment strategies in conjunction with mRNA therapeutics and thereby advance translational medicine in neurodegeneration.

PMID:41049755 | PMC:PMC12491700 | DOI:10.1016/j.apsb.2025.07.018

Cell Oncol (Dordr). 2025 Aug 26. doi: 10.1007/s13402-025-01103-3. Online ahead of print.

ABSTRACT

Lung cancer remains the leading cause of cancer-related mortality worldwide, with limited treatment efficacy and frequent resistance to conventional therapies. Recent advances have uncovered the critical influence of the human microbiota-complex communities of bacteria, viruses, fungi, and other microorganisms-on lung cancer pathogenesis and therapeutic responses. This review synthesizes current knowledge on the compositional and functional roles of microbiota across multiple body sites, including the gut, lung, tumor microenvironment, circulation, and oral cavity, highlighting their contributions to tumor initiation, progression, metastasis, and immune regulation. We emphasize the bidirectional communication between microbial metabolites and host immune pathways, particularly the gut-lung axis, which modulates systemic and local antitumor immunity. Importantly, microbiota composition has been linked to differential responses and toxicities in chemotherapy, radiotherapy, targeted therapy, and immune checkpoint blockade. Microbiota-targeted interventions, such as probiotics, fecal microbiota transplantation, and selective antibiotics, show promising potential to enhance treatment efficacy and mitigate adverse effects. However, challenges remain in clinical translation due to interindividual microbiome variability, mechanistic complexities, and limited longitudinal data. Future research integrating multi-omics, microbial functional profiling, and controlled clinical trials is essential to harness the microbiome as a precision medicine tool in lung cancer management. This review provides a comprehensive overview of the emerging role of microbiota in lung cancer development and therapy, offering new perspectives for innovative therapeutic strategies.

PMID:40856929 | DOI:10.1007/s13402-025-01103-3

J Am Soc Mass Spectrom. 2025 Aug 6;36(8):1598-1608. doi: 10.1021/jasms.4c00505. Epub 2025 Jul 2.

ABSTRACT

Gastric signet ring cell carcinoma (GSRC) is an aggressive subtype of gastric cancer (GC) with a poor prognosis. The lack of a systematic molecular and metabolic heterogeneity overview has led to slow progress in clinical practice. This study used mass spectrometry imaging (MSI) to investigate the metabolic landscape of GSRC in GC tissue with various differentiation grades. Our comprehensive spatial profiling of metabolites and lipids unveiled distinct metabolic signatures across different tissue subregions. A substantial number of lipidomic biomarkers associated with GSRC were identified, including phosphatidylethanolamine N-methyl (PE-NMe), phosphatidylethanolamine (PE), sphingomyelin (SM), diacylglycerol (DG), phosphatidic acid (PA), and phosphatidylcholine (PC), which may provide insights into its pathogenesis and potential therapeutic targets. Furthermore, multi-omics network analysis revealed intricate metabolic pathways involved in GSRC progression. Our findings highlight the importance of understanding the metabolic heterogeneity of GSRC and pave the way for future studies exploring its clinical implications and therapeutic strategies.

PMID:40600435 | DOI:10.1021/jasms.4c00505

Front Immunol. 2025 Jun 6;16:1606125. doi: 10.3389/fimmu.2025.1606125. eCollection 2025.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is characterized by metabolic and immune heterogeneity, driving tumor progression and therapy resistance. While G protein-coupled receptors (GPCR) signaling is known to regulate metabolism and immunity in cancers, its role in LUAD remains poorly defined. This study explores the influence of GPCR signaling on LUAD metabolism and immune landscape.

METHODS: We performed non-negative matrix factorization (NMF) clustering of GPCR signaling genes in TCGA-LUAD cohort to identify distinct molecular subgroups. A prognostic model was developed based on GPCR signaling genes using least absolute shrinkage and selection operator (LASSO) analysis and Cox regression. Differentially expressed genes were analyzed for metabolic pathway enrichment and immune infiltration. In addition, key genes within GPCR signaling were identified and validated through functional assays.

RESULTS: NMF clustering based on GPCR signaling identified three subgroups in LUAD, with cluster 3 exhibiting poorer overall survival and significant enrichment in multiple prognostic associated metabolism pathways including purine, pyrimidine, glyoxylate and dicarboxylate metabolism. Then, we developed a GPCRscore prognostic model and validated across multiple cohorts, which effectively stratified LUAD patients into distinct risk groups. High-risk LUAD patients had an immunosuppressive microenvironment and activated metabolic reprogramming. ADM was identified as a key gene in the high-risk group, correlating with tumor stage, immune suppression, and resistance to immunotherapy. Clinically, ADM was highly expressed in tumor tissues and shows elevated concentrations in the peripheral blood of patients with advanced-stage LUAD. Subsequently, we demonstrated that knock-down of ADM in LUAD cells impaired their proliferation, migration, and invasion, while also reducing the angiogenic potential of endothelial cells in vitro. Adrenomedullin promoted LUAD progression in a murine metastasis model. Further, adrenomedullin inhibited CD8⁺ T cells proliferation, induced exhaustion, and impaired cytotoxic function. Finally, drug sensitivity and cell viability analysis showed LUAD patients with high levels of ADM exhibited sensitivity to the treatment of Staurosporine and Dasatinib.

CONCLUSIONS: In summary, this study reveals the pivotal role of GPCR signaling particularly mediated by ADM in orchestrating metabolic reprogramming and immune modulation in LUAD. ADM emerges as a potential predictive biomarker and therapeutic target, offering valuable implications for optimizing strategies.

PMID:40547013 | PMC:PMC12179119 | DOI:10.3389/fimmu.2025.1606125

Ann Med. 2025 Dec;57(1):2494671. doi: 10.1080/07853890.2025.2494671. Epub 2025 May 3.

ABSTRACT

BACKGROUND: The combination of traditional Chinese medicine (TCM) with chemotherapy has been widely applied in the treatment of gastric cancer (GC). However, previous clinical studies have been constrained by small sample sizes and a lack of investigation into the molecular mechanisms of TCM. This study aims to assess the efficacy of TCM in treating GC by leveraging the strengths of meta-analysis and multi-omics approaches while also summarizing the underlying pharmacological mechanisms.

METHODS: A systematic literature review and meta-analysis were conducted using online databases to collect data before May 2024. This was to investigate the association between TCM combined with chemotherapy and the prognosis in GC. The molecular targets between the high-frequency TCMs and GC were identified through network pharmacology. The underlying mechanisms were investigated using multi-omics.

RESULTS: 9 studies with 2,158 patients were included. The meta-analysis results demonstrated that the combination of TCM and chemotherapy significantly improved the overall survival (OS) of GC patients (OR = 2.91; 95% CI: 2.70-3.12, p < 0.00001) and enhanced their quality of life (OR = 4.00; 95% CI: 1.99-8.03, p < 0.0001). Network pharmacology analysis identified 13 potential molecular targets of TCM in GC; additionally, multi-omics analysis highlighted the significant roles of MK, MIF, GALECTIN, and CypA signaling pathways in GC.

CONCLUSION: The combination of TCM with chemotherapy significantly improves the prognosis of GC; future research can focus on these key molecular targets and signaling pathways. This supports the application of precision medicine in cancer treatment and suggests the rational use of TCM in managing GC.

PMID:40317214 | PMC:PMC12051567 | DOI:10.1080/07853890.2025.2494671

Bioinformatics. 2024 Nov 1;40(11):btae623. doi: 10.1093/bioinformatics/btae623.

ABSTRACT

MOTIVATION: Enzymatic reaction play a pivotal role in regulating cellular processes with a high degree of specificity to biological functions. When enzymatic reactions are disrupted by gene, protein, or metabolite dysfunctions in diseases, it becomes crucial to visualize the resulting perturbed enzymatic reaction-induced multi-omics network. Multi-omics network visualization aids in gaining a comprehensive understanding of the functionality and regulatory mechanisms within biological systems.

RESULTS: In this study, we designed PhenoMultiOmics, an enzymatic reaction-based multi-omics web server designed to explore the scope of the multi-omics network across various cancer types. We first curated the PhenoMultiOmics database, which enables the retrieval of cancer-gene-protein-metabolite relationships based on the enzymatic reactions. We then developed the MultiOmics network visualization module to depict the interplay between genes, proteins, and metabolites in response to specific cancer-related enzymatic reactions. The biomarker discovery module facilitates functional analysis through differential omic feature expression and pathway enrichment analysis. PhenoMultiOmics has been applied to analyze the transcriptomics data of gastric cancer and the metabolomics data of lung cancer, providing mechanistic insights into interrupted enzymatic reactions and the associated multi-omics network.

AVAILABILITY AND IMPLEMENTATION: PhenoMultiOmics is freely accessed at https://phenomultiomics.shinyapps.io/cancer/ with a user-friendly and interactive web interface.

PMID:39418180 | PMC:PMC11549024 | DOI:10.1093/bioinformatics/btae623