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PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2026

Dietary Polyphenol Acteoside-Related Molecular Signatures in Clear Cell Renal Cell Carcinoma: Multi-Omics Profiling and Functional Validation of IMPDH1.

Clear cell renal cell carcinoma (ccRCC) is characterized by substantial metabolic and molecular heterogeneity, but the disease-relevant programs associated with acteoside, a dietary polyphenol, remain poorly understood. We integrated predicted acteoside targets with bulk, single-cell, and spatial transcriptomic data from ccRCC and combined molecular subtyping with cross-cohort machine-learning analysis. Acteoside-related signatures were preferentially enriched in malignant compartments and increased with tumor grade and stage. Consensus clustering identified two molecular subtypes with distinct biological and clinical features. C1 was associated with immune activation, metabolic activity, and more favorable survival, whereas C2 showed greater genomic instability, reduced renal epithelial differentiation, and poorer outcomes. We further benchmarked multiple machine-learning strategies and established a 10-gene prognostic model that retained predictive performance across independent cohorts, with IMPDH1 emerging as the strongest risk-associated feature. Functional experiments confirmed the biological relevance of IMPDH1: its knockdown suppressed ccRCC cell proliferation, DNA synthesis, colony formation, and migration, whereas overexpression produced the opposite effects. Together, these findings indicate that acteoside-related molecular signatures capture clinically relevant heterogeneity in ccRCC and provide a framework for linking dietary-polyphenol-related molecular space with tumor biology. The identification and functional validation of IMPDH1 further highlight its potential importance in ccRCC progression.

IMPDH1↗

PubMed2026

Integrative Multi-Omics Analysis Identifies Thrombosis-Associated Molecular Features Linked to Germline Susceptibility and Immune Cell Communication in Gastric Cancer.

Emerging evidence indicates that coagulation-related molecular programs are associated with thrombosis, tumor progression, and molecular dysregulation in gastric cancer (GC). However, thrombosis-associated molecular features in GC and their potential links to inherited susceptibility remain insufficiently understood. Integrated analyses of transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were performed to identify thrombosis-associated genes and establish a machine learning-based prognostic signature. Genome-wide association study (GWAS), expression quantitative trait loci (eQTL), transcriptome-wide association study (TWAS), and Mendelian randomization (MR) analyses were conducted to investigate susceptibility-associated transcriptional programs in GC. Functional assays were used to evaluate candidate genes associated with malignant phenotypes. Single-cell RNA sequencing (scRNA-seq) and cell-cell communication analyses were further performed to characterize cell-type-specific expression patterns and potential intercellular interactions. A total of 22 differentially expressed thrombosis-associated genes were identified, and a prognostic signature comprising 14 genes was established. The signature stratified patients into high- and low-risk groups and showed prognostic performance in both the training and validation cohorts. Integrative GWAS, eQTL, and TWAS analyses identified susceptibility-associated transcriptional programs that were positively correlated with the thrombosis-associated risk score. Silencing ACTN2 and CRYAB significantly reduced GC cell migration and invasion. scRNA-seq analysis revealed relatively high CRYAB expression in neutrophils, and CellChat analysis suggested potential neutrophil-B cell interactions involving COLLAGEN-related signaling. This integrative multi-omics study identified a thrombosis-associated molecular signature linked to prognosis and germline susceptibility-associated transcriptional programs in GC. ACTN2 and CRYAB may represent candidate genes associated with GC cell migration and invasion, while single-cell analysis suggested potential immune-related communication features.

Humans↗

PubMed2026

Integrative multi-omics analyses suggest a candidate microbial metabolite-associated host gene network in ulcerative colitis.

Ulcerative colitis (UC) is associated with gut microbial dysbiosis, but the host molecular alterations potentially linked to microbially derived metabolites remain incompletely understood. We integrated Mendelian randomization (MR), microbial metabolite annotation, computational target prediction, colonic transcriptomics, network analysis, and machine learning. MiBioGen microbiome GWAS data were used as exposures and FinnGen Release 12 ULCERENTER as the outcome. Metabolites linked to MR-prioritized taxa were retrieved from GutMGene, and human targets were predicted using SwissTargetPrediction and SEA. UC-related genes were defined by integrating differential expression analysis and WGCNA and then intersected with predicted metabolite targets. MR prioritized one family and eight genera showing nominal genetically supported associations with UC, but none remained significant after Benjamini-Hochberg FDR correction. Three prioritized genera were linked to 15 microbe-metabolite records, corresponding to 13 unique metabolites; nine were retained for target prediction, yielding 277 unique predicted human targets. Transcriptomic analysis identified 1,530 DEGs and a 312-gene MEgrey60 module, with 273 overlapping genes, producing 1,569 unique UC-related genes. Their intersection with the 277 predicted targets yielded 47 candidate genes. Enrichment analyses highlighted mainly metabolic and lipid-related processes. Random Forest showed the highest mean AUC across the two independent external benchmarking cohorts, and SHAP prioritized EPHX1, HSD17B2, IGFBP5, and MMP10. IBDome analysis showed inflammation-associated expression differences in these genes. This study provides a genomics-informed, hypothesis-generating framework that prioritizes candidate microbe-metabolite-host relationships in UC for future experimental validation.

Humans↗

PubMed2026

Epigenetics and In Silico Transcriptome Analysis of Pediatric Acute Myeloid Leukemia.

Pediatric acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy that accounts for about 15%-20% of childhood leukemias. Despite therapeutic advances, relapses remain common, and survival for high-risk patients is below 60%. Unlike adult AML, pediatric AML displays distinct genetic mutations, including FLT3-ITD, NPM1, KMT2A rearrangements, and core-binding factors (CBF) fusions, as well as extensive epigenetic dysregulation. Aberrant DNA methylation, histone modifications, and altered non-coding RNA expressions disrupt hematopoietic differentiation and activate oncogenic transcriptional networks. Recent advances in silico transcriptomic analysis have transformed the study of pediatric AML by integrating gene expression and epigenetic data to identify molecular drivers and regulatory networks. Computational RNA-seq pipelines and pathway analyses have highlighted key epigenetic regulators, including DNMT3A, TET2, and HDACs, as potential therapeutic targets. Multi-omics approaches combining transcriptomic, methylomic, and chromatin accessibility data are increasingly used to define biomarkers for diagnosis, prognosis, and therapeutic response. This review provides a comprehensive overview of the molecular and epigenetic landscape of pediatric AML, emphasizing the power of in silico transcriptome analysis to uncover disease mechanisms, refine patient stratification, and guide the development of precision-based epigenetic therapies aimed at improving long-term outcomes in children with AML.

Humans↗

PubMed2026

Cross-Kingdom Siderophores: Biosynthesis, Ecology, and Biotechnological Applications.

Microbial siderophores are high-affinity iron-binding compounds which are produced by bacteria, fungi, and actinomycetes to obtain iron and survive and interact with different species in an iron-deficient environment. While the conventional research on siderophore systems deals mainly with the study within the same taxa, modern researchers have increased their inclination toward cross-kingdom integration of siderophore behavior and their impact on host-associated environments. This can be largely attributed to differences in biosynthetic gene clusters, receptor systems, and regulatory networks, which produce distinct genotype-to-phenotype results determining microbial cooperation and competition. Current advancements in genomic research, together with omics studies like transcriptomics, proteomics, and metabolomics, have created newer insights into how siderophores function. However, the present literature evidences multiple major gaps in multi-omics data because the link between genomes and metabolomes remains weak due to inconsistent regulatory data sets and failure in identifying producer-consumer relationships in polymicrobial systems. Additionally, major constraints like molecular instability, delivery system limitations, host toxicity, limitations in upscaling, and regulatory issues delimit the use of siderophores in medical treatment, agricultural practices, and environmental biotechnology. This review aims to bridge the existing knowledge about siderophore biochemistry, biosynthesis, ecological functions, and genetic regulation across kingdoms while integrating multi-omics outlook with translational considerations. Thus, by connecting molecular mechanisms with evolutionary cross-talk, this study aims to provide a system-level framework in the world of siderophore-mediated iron uptake and therefore shapes future directions in emerging fields of microbial engineering, precision therapies, and sustainable biotechnology.

Fur regulation↗

PubMed2026

Integrative genomic and transcriptomic analyses identify key regulators of skin pigmentation in Larimichthys crocea.

The yellow body coloration of large yellow croaker (Larimichthys crocea) constitutes a crucial economic trait, yet its underlying genetic regulatory mechanisms remain poorly understood. This study systematically elucidated the molecular basis of body color variation by integrating genome resequencing and skin transcriptome analyses, combined with the contextual analysis of key pigmentation-related genes and phenotypic histological validation. 200 phenotyped individuals (including yellow-selected lines, F1 progeny, and normal control groups, all derived from a well-characterized aquaculture stock) identified 39 significantly associated SNPs (-log₁₀(P) ≥ 6), mapping to multiple candidate genes. These genes were significantly enriched in pathways related to pigment deposition (GO:0033059), melanosome organization (GO:0032438), melanogenesis, and tyrosine metabolism. Cross-developmental stage transcriptome analysis revealed 2395 differentially expressed genes (DEGs). Multi-omics integration identified eight overlapping candidate genes, including tyrp1, slc45a2, oca2, and dgat2, among which tyrp1 was prioritized for in-depth validation based on its core regulatory role in eumelanin synthesis, significant SNP association signal, and consistent downregulation in transcriptomic data. Experimental validation demonstrated that the g.895C > T mutation in exon 2 of tyrp1b was strongly significantly associated with the yellow phenotype: the frequency of mutant genotypes (TT/CT) reached 92.86%in the yellow-selected group, whereas the control group exclusively exhibited the wild-type genotype (CC). qPCR confirmed significantly downregulated tyrp1b expression in the skin of yellow individuals, consistent with the transcriptome trend. Histological and stereomicroscopic observations of skin tissues further validated the physiological basis of the yellow phenotype, revealing a significant reduction in melanophore number and abnormal melanosome morphology in yellow-phenotype individuals, accompanied by increased xanthophore density. These results suggest that tyrp1b mutation is strongly associated with the yellow phenotype. However, the presence of a wild-type CC individual in the yellow group indicates that this mutation is not strictly required for yellow coloration, suggesting that other genetic or environmental factors may also contribute to the phenotype, Additionally, downregulation of the carotenoid metabolism gene bco2 coupled with upregulation of xdh, together with the functional changes of slc45a2 and oca2, may synergistically promote xanthophore pigment deposition, contributing to the yellow phenotype. As melanin synthesis in large yellow croaker relies on the conserved tyrosinase pathway and transporter proteins, mutations in associated genes (tyrp1b, slc45a2, oca2) represent a primary underlying cause for the loss of melanin-based coloration and transition to a yellow phenotype in L. crocea. These findings provide key molecular targets and a theoretical foundation for molecular breeding of body color in this species, and also enrich the understanding of xanthism regulatory mechanisms in teleosts.

Animals↗

PubMed2026

Multi-omics integration uncovers epigenetic control of metabolic reprogramming in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by the absence of estrogen, progesterone, and HER2 receptors, limiting effective targeted therapies. Increasing evidence suggests that metabolic reprogramming, a hallmark of TNBC progression, is driven by underlying epigenetic mechanisms such as DNA methylation. The represented study performed an integrative analysis of transcriptomic (RNA-seq) and methylome data to uncover the metabolic-epigenetic interplay in TNBC. Differential gene expression analysis using DESeq2 revealed significant dysregulation of key metabolic genes, including upregulation of genes encoding glycolytic and serine biosynthesis enzymes and downregulation of metabolic tumor suppressors. Genome-wide methylation profiling identified extensive cytosine-phosphate-guanine (CpG) hypermethylation events associated with transcriptional repression, particularly in promoter regions. Integrative analysis pinpointed a subset of metabolism-related genes exhibiting both differential expression and methylation, such as FBP1, RASSF1A, and PHGDH. Pathway enrichment analysis highlighted aberrations in glycolysis/gluconeogenesis, fatty acid metabolism, and one-carbon pathways (adjusted p < 0.01). Importantly, TNBC patients with hypermethylated metabolic gene signatures displayed significantly shorter overall survival (log-rank p < 0.05). These findings reveal that DNA methylation-driven metabolic dysregulation contributes to TNBC aggressiveness and may provide novel biomarkers and therapeutic targets at the metabolic-epigenetic interface.

Humans↗

PubMed2026

Integrative multi-omics and machine learning identify the SPI1-METTL16-PLIN4 axis as a candidate driver of steatosis in HepG2 cells.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a prevalent metabolic disorder with limited therapeutic options. This study aimed to identify potential regulators and explore their functional roles in a cellular model of NAFLD. METHODS: WGCNA was performed on the hepatic transcriptomic dataset GSE126848 (31 NAFLD vs. 26 controls), followed by integration with serum proteomic data from 12 NAFLD patients and 12 healthy controls. Hub genes were prioritized using three machine learning algorithms. Functional validation was conducted in a HepG2 cellular steatosis model induced by high fructose (3.2 g/L) and oleic acid (400 μM) for 48 h. Lipid accumulation was assessed by Oil Red O staining and triglyceride/total cholesterol measurement. Inflammation was evaluated by TNF-α and IL-6 secretion (ELISA), and oxidative stress by ROS levels (flow cytometry). The binding interaction between METTL16 and PLIN4 mRNA was validated by RNA immunoprecipitation (RIP)-quantitative PCR. METTL16-mediated m6A modification of PLIN4 was assessed by Methylated RIP (MeRIP)-quantitative PCR. Transcriptional regulation of METTL16 by SPI1 was examined by chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays. RESULTS: Integrative analysis identified PLIN4 as a core hub gene. PLIN4 was upregulated in the HepG2 steatosis model (P < 0.001). PLIN4 knockdown alleviated lipid droplet accumulation (P < 0.001), reduced TNF-α and IL-6 secretion (P < 0.01), and decreased ROS levels (P < 0.001) in fructose/oleic acid-treated HepG2 cells. Mechanistically, METTL16 mediated its m6A modification to enhance PLIN4 mRNA stability. Furthermore, SPI1 was found to transcriptionally activate METTL16 by binding to its promoter (P < 0.001). PLIN4 re-expression partially reversed the protective effects of SPI1 knockdown on lipid accumulation (P = 0.01), inflammation (P < 0.05), and oxidative stress (P < 0.001). CONCLUSION: This study identifies the SPI1/METTL16/PLIN4 axis as a potential regulatory mechanism contributing to in vitro steatosis, inflammation, and oxidative stress in steatotic HepG2 cells.

Humans↗

PubMed2026

Integrated analysis reveals the impact of obesity on triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a highly aggressive and heterogeneous breast cancer subtype with limited therapeutic options. While the prevalence of overweight/obese (OW/OB) women continues to rise, the impact of obesity on molecular features of TNBC remains incompletely understood. We investigated clinicopathological and molecular data (including genomic, transcriptomic, proteomic and metabolomic profiling) using our original multi-omics database of TNBC (N = 465) for associations with patient body mass index (BMI). Multi-omics profiling revealed that OW/OB patients exhibited worse survival as well as elevated inflammation of tumor microenvironment, higher expression of immune checkpoints, and dysregulated lipid metabolism. Our in vivo experiments demonstrated that tumors in obese mice displayed faster growth rates, a higher proportion of PD-1+CD8+ T cells and enhanced responsiveness to anti-PD-1 treatment. In addition, we analyzed data from four independent clinical trials and discovered that OW/OB patients demonstrated higher pathological complete response rates and longer progression-free survival following anti-PD-1-based immunotherapy. In conclusion, our study systematically revealed that obesity is associated with coordinated immune-metabolic remodeling in TNBC, characterized by checkpoint enrichment and lipid dysregulation, which may help explain the enhanced anti-PD-1 responsiveness and should be taken into account in the field of precision medicine.

Immunity↗

PubMed2026

Integrated transcriptomic and metabolomic analysis of fluoride tolerance-related pathways and differentially expressed genes in silkworm strain XSKD.

XueSong KD (XSKD) silkworm strain exhibits prominent fluoride tolerance, yet the underlying molecular mechanisms of fluoride tolerance remains unclear. In the present study, fourth-instar pre-molting XSKD silkworms were used as experimental materials for integrated transcriptomic and untargeted metabolomic analyses. In total, 572 differentially expressed genes and 90 differential metabolites were screened. GO enrichment and KEGG enrichment based on the hypergeometric distribution model revealed that 13-Hydroxy-9Z,11E-octadecadienoic acid (13-(S)-HODE) acts as the core differential metabolite, which is significantly enriched in the linoleic acid metabolism pathway. Within this pathway, LOC101737302 and CYP338A1 display opposite expression trends and show correlations with pathway metabolites. Based on multi-omics data, this study preliminarily characterizes the lipid metabolic response under fluoride stress, providing omics dataset support for further in-depth exploration of the molecular mechanism of fluoride tolerance in silkworms.

Animals↗

PubMed2026

Markers of microvascular instability predict severity and survival in idiopathic pulmonary fibrosis.

INTRODUCTION: Most research on idiopathic pulmonary fibrosis (IPF) has focused on the interplay among fibroblasts, the immune system and epithelial cells. There is growing evidence that microvascular dysfunction also plays a role in disease progression, but large human translational studies are lacking. In this research, we aim to identify a proteomic signature of microvascular instability and assess the impact of current therapeutics on the microvasculature. METHODS: Olink proteomic data from patients with IPF were obtained from the Pulmonary Fibrosis Foundation Patient Registry (PFF-PR) (n=914) and an independent validation cohort (n=366). Among the PFF-PR, 640 patients also have whole-blood RNA sequencing data available. A subset of 79 microvascular-associated proteins was curated, and their associations with disease severity and transplant-free survival were examined. An adaptive least absolute shrinkage and selection operator was used to generate a novel microvascular risk score. RESULTS: Higher plasma levels of five microvascular-associated proteins (SDC1, MMP10, THBS2, HGF and SERPINA5) were associated with lung function and survival in both cohorts. Whole-blood RNA sequencing of patients with microvascular risk revealed enrichment of immune-mediated processes. Patients with higher microvascular risk who were subsequently put on nintedanib in the following year had significantly better 3-year transplant-free survival compared with patients who did not receive antifibrotic intervention (HR 0.56, 95% CI 0.35 to 0.89, p=0.0142). DISCUSSION: Integrative multi-omics analyses suggest that perturbations to microvascular remodelling contribute to disease severity and progression in IPF. This analysis offers a framework for a precision medicine approach for IPF.

Idiopathic pulmonary fibrosis↗

PubMed2026

Multi-omics analysis reveals Protein Kinase A-associated regulatory remodeling during adaptation of Trichoderma reesei to lignocellulosic substrate.

The filamentous fungus Trichoderma reesei is a major industrial source of holocellulolytic enzymes, and its response to complex carbon sources is regulated by nutrient-sensing mechanisms, including the cyclic adenosine monophosphate (cAMP)-protein kinase A (PKA) signaling pathway. Here, we integrated transcriptomics, quantitative proteomics, and phosphoproteomics to analyze PKAc1-associated responses in the parental strain QM9414 and a Δpkac1 strain cultivated under glucose or sugarcane bagasse conditions. Deletion of pkac1 was associated with altered growth-related phenotypes and reduced extracellular activities of selected biomass-depolymerizing enzymes. Multi-omics profiling revealed condition-dependent changes affecting subsets of carbohydrate-active enzymes (CAZymes) genes and proteins, nutrient transporters, stress-associated proteins, and regulatory factors. Phosphoproteomics identified phosphorylation-state changes associated with pkac1 deletion, including reduced phosphorylation at sites enriched for the PKA consensus motif. In silico peptide docking was used to prioritize candidate PKAc1-associated substrates for future validation, including a Sec 7-derived peptide with favorable docking behavior relative to the control peptide. Together, these data support a working model in which PKAc1 contributes to regulatory and phosphorylation-state remodeling during adaptation to sugarcane bagasse, with effects on the magnitude and/or timing of selected CAZyme-related outputs in T. reesei.

Trichoderma↗

PubMed2026

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (RCD) intersects with tumor metabolism, immune regulation, and therapeutic susceptibility, we built an RCD-centered framework for CRC stratification. Multi-cohort transcriptomic data were used to infer RCD subtypes with non-negative matrix factorization (NMF) and non-negative least squares (NNLS). Genomic, bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets were integrated to characterize subtype-associated biology. Machine-learning models were developed for immunotherapy response and survival-risk estimation. Candidate compounds were screened by GDSC2-based drug-sensitivity modeling and molecular docking, and FSTL3 was functionally assessed in vitro. The framework separated CRC samples into two RCD-related phenotypes resembling immune-hot and immune-cold states. RCD1 showed immune activation and higher mutational burden, whereas RCD2 showed immune-suppressed features, intratumoral heterogeneity, and aggressive biology. RCD-associated signatures showed potential for predicting immunotherapy response and survival risk. Dasatinib was prioritized for immune-cold, high-risk tumors, with preliminary evidence supporting its activity in CRC cells, while functional assays suggested a role for FSTL3 in growth, invasion, epithelial-mesenchymal transition, and apoptosis regulation. These findings suggest that RCD-based multi-omics analysis may refine CRC stratification and help generate therapeutic hypotheses.

Colorectal cancer↗

PubMed2026

Integrated single-cell and spatial transcriptomic analyses reveal malignant epithelial glycolytic heterogeneity and spatial niche remodeling during colorectal cancer progression.

Colorectal cancer (CRC) progression is shaped by metabolic reprogramming and complex interactions within the tumor microenvironment. However, the cellular heterogeneity, spatial organization, and clinical relevance of glycolytic activity in CRC remain incompletely understood. In this study, we integrated single-cell RNA sequencing, bulk transcriptomics, and spatial transcriptomics data to systematically characterize glycolytic heterogeneity in CRC. Glycolytic activity was quantified using five independent scoring methods, consistently showing that epithelial cells exhibited the highest glycolytic activity across the two single-cell cohorts. Stratification of CopyKAT-verified aneuploid malignant epithelial cells into high-glycolysis (HG) and low-glycolysis (LG) subgroups by glycolysis scores revealed that HG cells exhibited higher stemness scores and chromosomal copy number variations. Cell-cell communication analysis revealed that, compared with LG cells, HG cells exhibited increased interaction frequency and strength with immune and stromal populations, indicating enhanced malignant epithelial-microenvironment crosstalk. Spatial transcriptomics analyses further revealed that glycolytic activity varied across normal colorectal tissue, primary CRC, and colorectal liver metastases, accompanied by progressive remodeling of epithelial-associated spatial niches and MIF-mediated intercellular communication. Bulk transcriptomic analysis identified a glycolysis-related prognostic signature with robust predictive performance, which served as an independent prognostic factor for overall survival in CRC cohorts. Collectively, these findings indicate that glycolytic heterogeneity is a key feature of CRC malignant epithelial cells and is closely associated with tumor progression, microenvironmental remodeling, and clinical outcomes.

Humans↗

PubMed2026

Multi-omics integrative analysis provides insight into potential molecular responses to sustained high water flow in common carp (Cyprinus carpio) cultured in recirculating aquaculture.

To investigate the potential molecular responses by which water flow intensity affects the growth of common carp (Cyprinus carpio) in a recirculating aquaculture system (RAS), a control group (CG, actual water velocity 0.3 cm/s) and three sustained flow treatment groups were established, including a low-flow group (LF, 1 body length per second, bl/s), a medium-flow group (MF, 2 bl/s), and a high-flow group (HF, 3 bl/s). After 12 weeks of culture in the RAS, growth performance was compared among groups under different flow intensities. The best-performing group and the control group were then selected for the determination of intestinal digestive enzyme activities, as well as transcriptomic and whole-genome bisulfite sequencing analyses of muscle tissue. The results showed that the specific growth rate and feed intake of the HF group were significantly higher than those of the other groups (P < 0.05), whereas no significant difference in feed conversion ratio was observed among groups. Compared with the CG group, lipase activity was significantly higher in the HF group (P < 0.05), while α-amylase and trypsin activities showed increasing trends without significant differences. RNA-seq identified a total of 273 differentially expressed genes, including 72 upregulated genes and 201 downregulated genes in the HF group relative to the CG group. These genes were mainly enriched in glycolysis, pyruvate metabolism, ATP metabolism, the pentose phosphate pathway, the insulin signaling pathway, the PPAR signaling pathway, and the adipocytokine signaling pathway, indicating that sustained high water flow induced a muscle transcriptional response characterized by remodeling of energy metabolism and substrate utilization. Whole-genome bisulfite sequencing analysis showed that DNA methylation in common carp muscle occurred predominantly in the CpG context. Differentially methylated regions between the HF and CG groups were mainly distributed in transcription-related regulatory regions, including promoters, CpG islands, and CpG island shores. In promoter regions, the number of hypermethylated regions in the HF group relative to the CG group was markedly higher than that of hypomethylated regions. Integrated analysis further identified two candidate genes showing both promoter differential methylation and differential expression, namely LOC109094644 and bcorl1, suggesting that adaptation to high water flow may involve IGF-related growth regulation and remodeling of upstream transcriptional programs. The qPCR results were consistent with the transcriptomic data. Taken together, within the tested range, a sustained water flow of 3 bl/s was more conducive to the growth of common carp in the RAS, which may be associated with enhanced lipid digestion and utilization, remodeling of the muscle energy metabolic network, changes in promoter methylation, and the coordinated regulation of key candidate genes. This study provides a theoretical basis for clarifying the exercise adaptation mechanism of common carp in recirculating aquaculture and for optimizing flow velocity parameters.

Animals↗

PubMed2026

Multi-Omics Integration Identifies a Five-Gene Metabolic Signature With Experimental Validation in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is hallmarked by profound metabolic reprogramming; however, its intricate crosstalk with the tumor immune microenvironment (TIME) and its clinical ramifications remain inadequately elucidated. This study aims to systematically decipher the metabolic-immune interplay in ccRCC through multi-omics integration, with the goal of identifying robust prognostic biomarkers and actionable therapeutic vulnerabilities. AIMS: This study aims to systematically decipher the metabolic-immune interplay in clear cell renal cell carcinoma (ccRCC) through multi‑omics integration, and to identify robust prognostic biomarkers and actionable therapeutic vulnerabilities that can inform precision risk stratification and individualized treatment strategies. METHODS: We integrated bulk transcriptomic, genomic, and clinical data from multiple ccRCC cohorts. Differential expression and functional enrichment analyses were performed to characterize metabolic pathway alterations. Mendelian randomization (MR) was employed to infer causal relationships between metabolic disorders and ccRCC risk. A machine learning-based prognostic framework, incorporating SHAP (SHapley Additive exPlanations) for feature interpretability, was constructed and rigorously validated. TIME heterogeneity was dissected using deconvolution algorithms, while drug sensitivity, tumor mutation burden (TMB), and TIDE scores were utilized to assess therapeutic responses and immune evasion. Candidate gene function was evaluated through in vitro gain- and loss-of-function assays, with expression validated via TCGA, HPA, western blot, and qRT-PCR. RESULTS: Enrichment analysis identified coordinated dysregulation in lipid metabolism, energy homeostasis, and hypoxia response pathways. MR analysis confirmed lipid metabolism disorders as a causal risk factor for ccRCC. Our machine-learning model, centered on five core SHAP-identified features (SUCLA2, ACAT1, PC, SUCLG1, and HMGCS2), demonstrated superior predictive accuracy over conventional clinical staging. Immune profiling unveiled dichotomous TIME states: the low-risk group retained active immune surveillance, whereas the high-risk group was enriched with immunosuppressive subsets. Drug sensitivity screening pinpointed LY2109761 and carmustine as high-risk-specific candidate agents. Furthermore, TMB and TIDE analyses stratified high-risk patients displaying genomic instability and immune evasion phenotypes. Functionally, SUCLA2 knockdown significantly enhanced ccRCC cell proliferation and invasion, while its overexpression suppressed these malignant phenotypes, corroborating its tumor-suppressive role. Expression patterns of the hub genes were consistently validated across multi-level datasets and experimental assays. CONCLUSION: This study establishes a precision oncology framework for ccRCC by functionally linking metabolic biomarkers, immunophenotypes, and stratified therapeutic strategies. Importantly, we identify SUCLA2 as a potential functional tumor suppressor and a promising target for further mechanistic and translational investigation.

Humans↗

PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗