TL;DR
The gut microbiota and its metabolites may be associated with the immune microenvironment and tumor progression in CESC, potentially involving the MIF signaling axis.
The gut microbiome has emerged as a potential modulator of the immune system, host metabolism, and cancer progression; however, the effects of microbiota-derived metabolites on distal lung cancer remain poorly understood. The current study adopts a systems biology approach integrating gut microbial metabolite annotation, metabolite-associated gene identification, transcriptomics validation, network topology analysis, immune infiltration profiling, and single-cell transcriptomic datasets to investigate potential metabolite-host interactions associated with lung adenocarcinoma (LUAD). The novelty of this study lies in integrating gut microbial metabolites with types of host molecular data to systematically prioritize and orient candidate metabolite-host genes and pathways interactions associated with LUAD. A total of 458 metabolites annotated as microbiota-associated and/or host-microbe co-metabolites were found to be enriched in amino acid, pyrimidine, and central carbon metabolic pathways. Integration of metabolite-associated genes with LUAD-related differentially expressed genes identified 603 overlapping targets enriched in cancer-associated pathways, including PI3K/Akt signaling, cytokine-cytokine receptor interaction, focal adhesion, and extracellular matrix receptor interaction. Network topology analysis prioritized SPP1 as a central hub gene. Independent validation in GSE31210 showed association of elevated SPP1 expression with reduced overall survival (log-rank p = 0.0007). Further, correlation analysis exhibited a positive association between SPP1 and GREM1 (ρ = 0.639, FDR = 1.76 × 10⁻¹⁷). Gene-metabolite interaction mapping identified multiple SPP1-associated metabolites, with oxalic acid prioritized for further investigation. Immune infiltration and single-cell analyses further associated SPP1 with macrophage-rich and stromal compartments of the LUAD tumor microenvironment (TME). Collectively, integration of 458 metabolites and 603 candidate genes prioritized an oxalic acid-SPP1-associated network with prognostic and TME relevance in LUAD, providing testable candidates for subsequent experimental validation.
Bladder urothelial carcinoma (BLCA) is a molecularly heterogeneous malignancy with substantial unmet needs in risk stratification and therapeutic optimization. While the urinary microbiome has emerged as a critical modulator of cancer biology, its systems-level integration with host genomic, transcriptomic, and immune multi-omics data remains poorly characterized. We performed an integrative in silico analysis of 412 muscle-invasive bladder cancers from The Cancer Genome Atlas (TCGA-BLCA), combining curated microbial abundance profiles with host transcriptomic, epigenomic, mutational, immune deconvolution, and clinical survival data. Differential abundance analysis, Spearman correlation networks, Gene Set Enrichment Analysis, and machine-learning-based prognostic modeling were employed to identify microbe-host interaction landscapes and evaluate clinical translational potential. We identified profound microbial dysbiosis in tumor tissues, with Paenibacillus (31.1-fold enrichment, P = 2.56 × 10-6) and Prevotella (19.0-fold enrichment, P = 2.60 × 10-3) dominating the tumor microenvironment, while commensal genera, including Lactobacillus, Arthrobacter, and Gemella, were significantly depleted. Paenibacillus exhibited strong negative correlations with oncogenic drivers MYC (Spearman Correlation Coefficient (SCC) = -0.506), ESR1 (SCC = -0.491), and AR (SCC = -0.458), suggesting tumor-suppressive mechanisms through metabolic and immune modulation. Conversely, Prevotella demonstrated bidirectional modulation of host genes, implicating pro-inflammatory and epithelial-mesenchymal transition pathways. Multi-omics integration revealed that microbial signatures stratified TCGA molecular subtypes, immune phenotypes, and clinical outcomes. A microbiome-informed prognostic model achieved superior predictive accuracy (AUC = 0.847) compared to clinical variables alone, with validation across four independent cohorts (combined HR = 0.65, 95% CI: 0.52-0.81, P < 0.001). This study establishes a comprehensive framework for microbiome-host interactions in BLCA, identifying Paenibacillus and Prevotella as opposing microbial orchestrators of tumor biology. These findings advance bladder cancer microbiome research from descriptive taxonomy toward the development of mechanistic, clinically actionable biomarkers for precision oncology.
BACKGROUND Colorectal cancer (CRC) exhibits pronounced biological diversity, a feature increasingly attributed to alterations in cellular metabolic reprograms. Serine metabolism supports nucleotide synthesis, redox balance, and epigenetic regulation via one-carbon metabolism, yet its role in shaping the tumor cellular interactions and immune landscape at single-cell level remains unclear. METHODS Single-cell transcriptomic profiles (GSE284449) were jointly analyzed with bulk expression datasets (GSE271719), together with Mendelian randomization (MR)-based inference, to dissect serine metabolism in CRC. High-serine-metabolism (HSM) cell populations were identified using scMetabolism, and robust marker genes were selected through Lasso, random forest, XGBoost, and SVM-RFE machine learning approaches. MR was applied to evaluate causal associations with CRC risk. Functional validation included IHC, qRT-PCR, western blot, and LC-MS metabolomics, while CellChat analysis characterized HSM cell interactions with immune and stromal cells. RESULTS ZBTB21 and TRPM2 were identified as core regulators of HSM cells, with ZBTB21 predominantly expressed in monocytes and pro-B cells. MR analysis suggested a potential inverse association between genetically predicted ZBTB21 expression and CRC risk, indicating that ZBTB21 may exert context-dependent effects at the population level. Cell-cell communication analysis suggested that HSM monocytes may interact with fibroblasts through signaling pathways including the MIF-SPP1 axis; however, these interactions are computationally inferred and require further experimental validation. Functional assays showed that ZBTB21 overexpression upregulated PHGDH and SHMT1, increased intracellular NADPH/NADP⁺ ratios, and influenced monocyte-related phenotypes. ZBTB21 levels were markedly increased in CRC samples relative to matched non-tumorous mucosal tissues. CONCLUSIONS At single-cell resolution, ZBTB21 emerges as a metabolic regulator that strengthens serine biosynthesis and redox homeostasis. While integrative analyses suggest a potential link between ZBTB21-associated metabolic states and immune interactions, further experimental validation is required to establish causal relationships. This integrative framework connects genetic causality, metabolism, and immune interactions, providing mechanistic insights and potential strategies for metabolic-targeted therapies in CRC. TRIAL REGISTRATION Not applicable.
Ulcerative colitis (UC) is an inflammatory bowel disease involving complex interactions between genetics, gut microbiota, metabolism, and immunity. This study aimed to systematically evaluate multi‐omics factors potentially associated with UC susceptibility and identify reliable diagnostic biomarkers. A two‐sample Mendelian randomization (MR) framework assessed potential causal associations between gut microbiome, circulating metabolites, immune cell phenotypes, and UC susceptibility. Significant MR findings were integrated with multiple transcriptomic datasets to identify differentially expressed candidate genes. Immune infiltration analysis, machine learning modeling, and external validation were subsequently performed. Single‐cell and spatial transcriptomics were used to localize key genes and to explore their potential cell type‐specific functions within the tissue microenvironment, followed by qRT‐PCR validation in independent clinical tissues and siRNA‐mediated IFITM2 knockdown in THP‐1‐derived macrophages. MR analyses identified potential causal associations for specific microbiota, sphingomyelin‐related metabolites, and immune cell phenotypes with UC susceptibility. Integrative analysis prioritized four core signature genes: SAG, WDR48, IFITM2, and SIRPA. A random forest model achieved an AUC of 0.964 and identified a four‐gene signature with strong diagnostic performance. Single‐cell and spatial transcriptomics localized IFITM2 upregulation mainly to myeloid cells, particularly Neutrophil_IFITM2. CellChat suggested a potential CD4_Tem_IL7R‐ANXA1‐FPR1‐Neutrophil_IFITM2 axis. qRT‐PCR supported the expression directions of the four genes, and IFITM2 knockdown in THP‐1‐derived macrophages reduced TNF‐α, IL‐6, and IL‐1β mRNA expression. This multi‐omics framework supports the potential roles of specific microbiota, sphingolipid metabolism, and immune phenotypes in UC pathogenesis. The four‐gene signature and characterization of Neutrophil_IFITM2, supported by independent qRT‐PCR validation and preliminary IFITM2 knockdown experiments, may provide a framework for precision diagnosis and future mechanistic studies in UC.
Colorectal cancer (CRC) arises via the stepwise adenoma-carcinoma sequence (ACS). Gut microbial dysbiosis and host metabolic reprogramming jointly correlate with CRC onset and advancement, yet their stage-specific crosstalk across ACS remains largely unclear. Limited multi-omics research on microbial-metabolic interactions throughout ACS hinders the development of early diagnostic biomarkers and preventive strategies. Here, we combined untargeted mucosal metabolomics and fecal shotgun metagenomic sequencing in 36 participants, covering healthy controls, ACS, and CRC patients. We systematically analyzed microbial composition, functions, differential metabolites, and enriched pathways and integrated multi-omics data to screen stage-specific signatures. Distinct gut microbial profiles and progressive functional shifts toward pathogenicity and abnormal carbohydrate metabolism were observed along ACS. Mucosal metabolism was continuously disrupted, with prominent alterations in taurine-hypotaurine, sphingolipid, and bile acid pathways. Core differential metabolites showed excellent diagnostic performance. Microbe-metabolite interactions were progressively enhanced to form a concerted pro-tumor axis. This study characterizes unique ACS-stage microbial-metabolic features. Dysregulated metabolic pathways and key microbe-metabolite crosstalk are closely associated with CRC progression, offering novel non-invasive biomarkers and premalignant intervention targets.
Background Endometrial cancer (EC) is a highly prevalent gynecological malignancy increasingly affecting younger populations. While the gut microbiota significantly modulates tumor progression via organ-to-organ networks, the specific molecular mechanisms and microbial co-metabolites driving EC remain poorly understood. This study profiles gut microbiota alterations in EC patients to identify correlations between microbial lipid/bile acid metabolites and EC pathogenesis. Methods A clinical cohort of 41 female participants (17 patients with histologically confirmed atypical endometrial hyperplasia or endometrioid adenocarcinoma, and 24 healthy controls) was prospectively enrolled. Fecal and serum samples underwent 16S rRNA gene sequencing (Illumina MiSeq) and LC-MS-based serum metabolomics, respectively. Spearman correlation analysis explored gut microbiota-metabolite associations. Results The EC and control groups shared structural similarities, but the Bacillota / Bacteroidota (B/B) ratio was higher in the EC group (1.35) than the control group (1.19). LEfSe analysis identified Megamonas, Megasphaera, Succinivibrio, and Veillonella as the most influential genera in the EC group (LDA > 4.0). Metabolomics revealed significantly elevated levels of hypoxanthine, 2,4-dihydroxyacetophenone-5-sulfate, inosine, benzoylcholine, 20-hydroxyeicosatetraenoic acid (20-HETE), and theobromine in EC patients. KEGG enrichment analysis showed that glycerophospholipid and purine metabolism were the most significantly dysregulated pathways (P < 0.05 ), with fine-scale network dissection revealing pathological remodeling of glycerophospholipid metabolism. Spearman correlation between the top 5 differential genera and top 20 serum metabolites demonstrated functional cross-talk between specific gut microbiota and circulating lipid and porphyrin homeostasis. Conclusions This study delineates distinct alterations in the gut microbiota and serum metabolomic profiles of EC patients, identifying Megamonas and Klebsiella as promising non-invasive predictive biomarkers. Targeting these specific taxa and their dysregulated downstream metabolites, such as protoporphyrin IX and 20-HETE, may offer novel avenues for early diagnosis and therapeutic intervention. Despite a modest sample size, these findings lay a foundation for future large-scale, mechanistic investigations into the gut-metabolism axis in EC.