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PubMed2026

Non-coding RNAs in cancer: multi-omics insights, liquid biopsy advances, drug resistance mechanisms, and the road to clinical translation.

For most of the twentieth century, the transcriptional output of the human genome was thought to be biologically inert-a characterization that has been proven wrong in almost every important respect. Non-coding RNAs (ncRNAs) such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), small nucleolar RNAs (snoRNAs) and PIWI-interacting RNAs (piRNAs) are now thought of as vital regulators of gene expression in all the stages of cancer pathogenesis, including the initial epigenetic changes, metastatic spread and the development of therapeutic resistance. This review highlights four areas where the clinical potential of ncRNAs is most promising: reconstruction of ncRNA regulatory networks by multi-omics integration; circulating ncRNAs as minimally invasive cancer biomarkers; causal roles of ncRNAs in drug resistance through epithelial-mesenchymal plasticity, metabolic reprogramming, and stromal communication; and translation of ncRNA targeting strategies to clinical trials. We will need to invest equally in mechanistic rigor and translational infrastructure to move forward.

antisense oligonucleotides↗

PubMed2026

Graded Mulberry Leaf Supplementation Shapes Gut Microbiota, Reprograms Intestinal Metabolism, and Maintains Intestinal Chemical-Immune Barrier Homeostasis in Amur Sturgeon: A Multi-Omics Study.

Mulberry leaf contains abundant phytochemicals with antioxidant and immunomodulatory activities. However, systematic insight into its dose-dependent regulatory effects on the intestinal health of Amur sturgeon remains limited. In the present study, multi-omics approaches, including 16S rRNA gene sequencing, untargeted metabolomics, transcriptomics, together with RT-qPCR, were applied to investigate graded dietary mulberry leaf supplementation in Acipenser schrenckii. Juvenile sturgeons were fed four experimental diets containing 0%, 2%, 4% and 6% mulberry leaf over a 10-week feeding trial. Dietary mulberry leaf caused no adverse impacts on growth performance or intestinal digestive capacity. Although the overall structure of the intestinal microbiota remained stable, beneficial bacterial taxa were enriched in a dose-dependent manner. Intestinal metabolism underwent hierarchical remodelling: low inclusion levels supported basal nutrient metabolism, medium inclusion strengthened antioxidant capacity, and high inclusion reprogrammed lipid metabolism and immune function. Mulberry leaf reinforced the intestinal chemical barrier by balancing redox homeostasis and reducing mucosal epithelial permeability. Moreover, intestinal immunity was modulated through three sequential phases: initial innate immune priming, B-cell homing, and the establishment of sustained immune tolerance. In conclusion, mulberry leaf maintains intestinal chemical-immune barrier homeostasis in a dosage-tunable manner, supporting its potential application as a functional aquafeed ingredient.

Amur sturgeon (Acipenser schrenckii)↗

PubMed2004

The -omics era and its impact.

OBJECTIVE: To review the advances in clinically useful molecular biologic techniques and to identify their applications, as presented at the 12th Annual William Beaumont Hospital DNA Symposium. DATA SOURCES: The 7 manuscripts submitted were reviewed and their major findings were compared with literature on the same or related topics. STUDY SELECTION: Manuscripts address the use of molecular techniques in the detection of severe acute respiratory syndrome (SARS) and bacterial ribosome mutations, which may lead to ribosome-targeted drug resistance; pharmacogenomics as a clinical laboratory service and example of warfarin dosing using CYP2C9 mutation analysis; definition of the potential of cytosine arabinoside incorporation into DNA to disrupt transcription using an in vitro model of oligonucleotides; use of laser capture microdissection to isolate solid tumor cells free of nontumor cells; and molecular methods used to classify lymphomas. DATA SYNTHESIS: Two current issues related to the use of molecular tests in the clinical laboratories are (1) decentralization of molecular-based testing to a variety of nonmolecular laboratories and (2) need for wider acceptance of molecular-based testing through its incorporation in clinical practice guidelines. Molecular methods have had a major impact on infectious disease through the rapid identification of new infectious agents, SARS, and the characterization of drug resistance. Pharmacogenomics identifies the genetic basis for heritable and interindividual variation in response to drugs. The incorporation of the nucleoside analog, cytosine arabinoside, into DNA leads to local perturbation of DNA structure and reduces the ability of transcription factors to bind to their specific DNA binding elements as measured by electrophoretic mobility shift assays. Laser capture microdissection of tumor cells can provide an adequate number of cells for whole genome amplification. Gene expression microassay profiles of various lymphomas have modified classification systems and predict prognosis and response to therapy. CONCLUSIONS: The current -omics era will continue to emphasize the use of microarrays and database software for genomic, transcriptomic, and proteomic screening to search for a useful clinical assay. The number of molecular pathologic techniques will expand as additional disease-associated mutations are defined.

Animals↗

PubMed2026

Genome-wide association and multi-omics functional screens reveal the genetic architecture of foveal development.

Foveal hypoplasia causes visual impairment across congenital eye disorders, yet the genetic programmes governing foveal development remain poorly characterised and no tractable model exists for foveal disease. In the first genome-wide association study of foveal hypoplasia, we identified 42 sentinel variants mapping to 54 effector genes supported by ≥ 2 criteria from a variant-to-gene framework incorporating developmental multi-omics. Disruption of six effector genes using mutant lines and CRISPR knockouts in the zebrafish high acuity zone recapitulates structural, functional, and ultrastructural hallmarks of foveal hypoplasia, establishing the first vertebrate disease model. Integration with human foetal single-cell and spatial transcriptomics reveals two temporal waves of effector gene expression and identifies Müller glia as critical mediators of foveal patterning. Phenome-wide analyses reveal foveal variants are pleiotropic with refractive, lenticular, and metabolic traits, connecting foveal development to anterior segment and systemic disease biology. These findings should inform mechanistic studies of macular disease.

Journal Article↗

PubMed2026

Inclusion of Multi-Omic Biomarkers Improves Prediction Accuracy of Response, Relapse, and Overall Survival in Acute Myeloid Leukemia Patients Receiving High-Intensity Induction Chemotherapy.

BACKGROUND: Despite advancements in genetic markers for acute myeloid leukemia (AML) risk stratification, outcome prediction remains challenging due to disease heterogeneity and dynamic genetic changes, highlighting the need for reliable biomarkers to improve AML treatment strategies and patient outcomes. To refine outcome predictions, we investigated the use of microbial-derived biomarkers to predict composite complete remission (CRc), relapse, and survival for patients on high- and low-intensity regimens, and to integrate those variables into the widely clinically utilized European Leukemia Network (ELN-2022) genetic risk classification model for high-intensity-treated patients. METHODS: We first developed machine learning models that integrate baseline fecal metabolomics, 16S rRNA-based stool microbiome features, and clinical metadata (sex, antibiotic administration, AML somatic mutations, and cytogenetics) from two cohorts of AML patients (n = 83) undergoing remission induction chemotherapy. Univariate tests and sparse canonical correlation analysis were employed for variable selection and to explore fecal metabolite-microbe relationships. A robust machine learning approach using XGBoost was employed, with 100 stratified data splits (80% training, 20% testing) and coarse-to-fine hyperparameter optimization. Variable importance was aggregated across all models to select key predictors. RESULTS: For high-intensity-treated patients, XGBoost models achieved aggregated AUROC scores of 0.719, 0.729, and 0.65 for CRc, relapse, and overall survival, respectively. For low-intensity-treated patients, these models achieved aggregate AUROC scores of 0.945, 0.724, and 0.768 for these same outcomes, respectively. Integrating the biomarkers identified in the high-intensity machine-learning models with the current ELN-2022 AML risk stratification system effectively stratified patients into risk categories, which obtained higher concordance indices and likelihood ratios, demonstrating improved prognostic accuracy for each outcome compared to ELN-2022 alone. CONCLUSIONS: The inclusion of microbial-derived biomarkers serves as a robust prognostic tool to improve outcome prediction in AML patients, highlighting the potential of its integration into AML risk assessment and paving the way for personalized treatment strategies and improved patient outcomes.

Humans↗

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 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

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

Unveiling the molecular basis of gonadal development: Multi-omics uncovers sex-related genes and steroid pathways in Sinonovacula constricta.

The razor clam Sinonovacula constricta is an economically important cultured mollusk in China, but the molecular mechanism of its gonadal development and sexual differentiation remains unclear. This study integrated gonadal transcriptomic, proteomic, and metabolomic analysis to identify key sex-related molecules. Transcriptome analysis identified 2795 DELs and 6497 DEGs between sexes, including the sex-related genes Fem-1b, Fem-1c, GUCY1B2 and FAT4, as well as a regulatory network of 39 lncRNA-mRNA pairs involving Tektin-4, Ropporin-1, Histone H1, and FoxN4. Proteomic analysis revealed 3217 DEPs: Tektin family members, Ropporin-1 and Tssk proteins were upregulated in the testis, while histone H1 and FAT4 were upregulated in the ovary. Metabolomic analysis detected 409 DEMs, with uridine identified as a potential sex differential marker (upregulated in the ovary), and 23 gonadal development-related DEMs showed sex-specific upregulation. Integrative transcriptome-proteome analysis identified 1543 co-expressed DEGs/DEPs enriched in nucleosome assembly, oxidative phosphorylation, and carbon metabolism, including key sex-related genes AKAP14, Tektin/Tssk families, Histone H1, and FAT4. Transcriptome-metabolome integration identified 32 shared KEGG pathways (e.g., biosynthesis of unsaturated fatty acids, pyrimidine metabolism), while proteome-metabolome integration revealed 5 (positive ion) and 6 (negative ion) co-enriched pathways, with alanine, aspartate and glutamate metabolism and oxidative phosphorylation being functionally relevant to gonadal development. Collectively, these results reveal the molecular basis of gonadal development, highlight critical sex-related genes and steroid metabolic pathways, and provide valuable resources for future reproduction and breeding in S. constricta.

Animals↗

PubMed2026

AISP position statement: Standardising biological sample collection and handling for advanced diagnostics and multi-omic analyses in pancreatic cancer.

The quality of biological samples is a major determinant of analytical reliability and translational relevance in patients with pancreatic ductal adenocarcinoma (PDAC). However, variability in specimen procurement, handling, transport, processing, and storage can substantially affect tissue integrity and the robustness of downstream analyses. This paper, promoted by the Pathology and Basic Science Task Force of the Italian Association for the Study of the Pancreas (AISP), brings together experts in pathology, molecular biology, translational research, medical oncology, and gastroenterology to provide practical recommendations for the collection, handling, and pre-analytical management of biological samples. Draft recommendations were discussed during dedicated working group meetings and approved by consensus among all authors, supported by key literature. The document identifies the biological specimen as the critical link between patient care, pathology, and research, and provides guidance for clinicians and professionals involved in sample procurement and processing. By addressing the requirements of different analytical platforms, including genomics, organoid generation, immunophenotyping, pharmacogenomics, and multiplex/spatial analyses, this paper aims to reduce pre-analytical variability, improve diagnostic accuracy, and enhance the clinical and translational value of molecular investigations in pancreatic cancer. Standardised procedures across centres may facilitate comparable data collection, support multicentre studies, and strengthen collaboration between clinicians, pathologists, and research laboratories.

Biobanking↗

PubMed2024

Integrated multi-omics approaches reveal the neurotoxicity of triclocarban in mouse brain.

Triclocarban (TCC) is an antimicrobial ingredient that commonly incorporated in many household and personal care products, raising public concerns about its potential health risks. Previous research has showed that TCC could cross the blood-brain barrier, but to date our understanding of its potential neurotoxicity at human-relevant concentrations remains lacking. In this study, we observed anxiety-like behaviors in mice with continuous percutaneous exposure to TCC. Subsequently, we combined lipidomic, proteomic, and metabolic landscapes to investigate the underlying mechanisms of TCC-related neurotoxicity. The results showed that TCC exposure dysregulated the proteins involved in endocytosis and neurodegenerative disorders in mouse cerebrum. Brain energy homeostasis was also altered, as evidenced by the perturbation of pyruvate metabolism, TCA cycle, and oxidative phosphorylation, which in turn caused mitochondrial dysfunction. Meanwhile, the changing trends of sphingolipid signaling pathway and overproduction of mitochondrial reactive oxygen species (mROS) could enhance the neural apoptosis. The in vitro approach further demonstrated that TCC exposure promoted apoptosis, accompanied by the overproduction of mROS and alteration in the mitochondrial membrane potential in N2A cells. Together, dysregulated endocytosis, mROS-related mitochondrial dysfunction and neural cell apoptosis are considered to be crucial factors for TCC-induced neurotoxicity, which may contribute to the occurrence and development of neurodegenerative disorders. Our findings provide novel perspectives for the mechanisms of TCC-triggered neurotoxicity.

Animals↗

PubMed2024

Quantification of escape from X chromosome inactivation with single-cell omics data reveals heterogeneity across cell types and tissues.

Several X-linked genes escape from X chromosome inactivation (XCI), while differences in escape across cell types and tissues are still poorly characterized. Here, we developed scLinaX for directly quantifying relative gene expression from the inactivated X chromosome with droplet-based single-cell RNA sequencing (scRNA-seq) data. The scLinaX and differentially expressed gene analyses with large-scale blood scRNA-seq datasets consistently identified the stronger escape in lymphocytes than in myeloid cells. An extension of scLinaX to a 10x multiome dataset (scLinaX-multi) suggested a stronger escape in lymphocytes than in myeloid cells at the chromatin-accessibility level. The scLinaX analysis of human multiple-organ scRNA-seq datasets also identified the relatively strong degree of escape from XCI in lymphoid tissues and lymphocytes. Finally, effect size comparisons of genome-wide association studies between sexes suggested the underlying impact of escape on the genotype-phenotype association. Overall, scLinaX and the quantified escape catalog identified the heterogeneity of escape across cell types and tissues.

X Chromosome Inactivation↗

PubMed2026

Multi-omics analysis identifies key genes and functional loci affecting teat number in American Large White and Landrace pigs and their application in optimizing genomic selection models.

BACKGROUND: Teat number is a crucial economic trait in pigs. It directly affects the ability of sows to lactate, which in turn influences the survival and health of piglets. The teat number of French Large White pigs is close to 16, while the teat number of American Large White and Landrace pigs is about 14. In order to improve the teat number of American Landrace and Large White pigs through molecular approaches and precise breeding techniques, we genotyped 2,131 American Landrace and 4,564 American Large White with teat number phenotype using a 50 K SNP chip. Then, the SNP-chip data was imputed to the level of whole-genome sequencing (iWGS). Based on iWGS data, we conducted GWAS to identify novel, significant SNPs associated with teat number and to incorporate them into genomic selection. RESULTS: In Landrace pigs, significant SNPs for TTN mapped to SSC2, SSC7, SSC8, and SSC14; the SSC8 and SSC14 effects are novel. LTN mapped to SSC7, RTN to SSC7 and SSC8. The lead SSC7 SNP explained 2.60% of TTN phenotypic variance. In Large White pigs, significant SNPs were detected on SSC7 and SSC10 for TTN; SSC7, SSC10, and SSC12 for LTN; and SSC7 and SSC10 for RTN. The most significant locus on SSC7 accounted for 2.99% of the phenotypic variance in TTN. Additionally, a multi-population meta-analysis detected significant novel SNPs for LTN on SSC1 and SSC8. By utilizing Bayesian fine mapping, the most precise QTL confidence interval on SSC7 for both TTN and RTN in Large White pigs was reduced to 40 kb. By integrating functional gene annotation with RNA-seq and ATAC-seq data from Erhualian and Bamaxiang pigs mammary placodes at embryonic day 26, we prioritized PTPN13, TRPV3, ZDHHC13, and BRD2 as novel candidate genes for teat number. We then incorporated the significant SNPs to GBLUP and benchmarked genomic-selection accuracy. In both breeds, fitting the top SNP as fixed maximized prediction for TTN and RTN, whereas treating all significant loci as an additional random effect optimized LTN. CONCLUSIONS: Our findings provide a theoretical basis for dissecting new key genes affecting teat number and for advancing molecular breeding of teat number in pigs.

Animals↗

PubMed2026

COVID-19 multi-omics reveal organ-specific responses and biomarkers.

OBJECTIVE: Post-COVID-19 syndrome is characterised by persistent immune dysfunction and multi-organ sequelae. This study aimed to characterise the systemic blood molecular landscape induced by SARS-CoV-2 infection and identify prognostic markers linked to skeletal muscle mass loss, a key driver of poor outcomes. METHODS: We enrolled 30 healthy controls and 307 COVID-19 patients, collecting 422 plasma samples for integrated proteomic and metabolomic profiling to investigate organ-specific molecular alterations in COVID-19. RESULTS: We comprehensively mapped the molecular landscape of COVID-19, encompassing immune, tissue-specific, and metabolic perturbations, and delineated their interactions. Focusing on organ-damage-related molecular patterns associated with disease progression and mortality, we found that skeletal muscle mass loss contributed to poor clinical outcomes of COVID-19 (p < 0.0001). Dysregulated arginine metabolism emerged as a key metabolic signature in fatal COVID-19 cases, with GLUL, GOT1, and citrulline showing significant correlation with skeletal muscle mass loss. Longitudinal analyses further revealed that reduced citrulline levels underlie the poor outcome of COVID-19 patients with muscle mass loss. These findings were robustly supported through multiple approaches: Mendelian randomization confirmed causal relationships between citrulline depletion, sarcopenia/fat-free mass loss, and COVID-19 mortality (p < 0.05), transcriptomic analyses of SARS-CoV-2-infected golden hamsters (GSE231910) provided additional support in enrichment of arginine biosynthesis (FDR < 0.05), and in vitro experiments further demonstrated that citrulline depletion promotes pro-inflammatory M1 macrophage polarisation — a key immunological feature of critical COVID-19. Leveraging these insights, we developed a skeletal muscle loss-specific prognostic prediction model for COVID-19 using GLUL, GOT1, and citrulline. This model effectively stratified patients into high- and low-risk groups (p = 0.035). CONCLUSION: Our study advances the understanding of COVID-19-induced organ pathophysiology and provides a foundation for developing targeted therapeutic strategies for post-COVID sequelae.

COVID-19↗

PubMed2025

Multi-omics reveals cross-tissue regulatory mechanisms of autism risk loci via gut microbiota-immunity-brain axis.

Autism Spectrum Disorder (ASD) involves a multi-system interaction mechanism among genetics, immunity, and gut microbiota, yet its regulatory network remains undefined. This study conducted a meta-analysis on Genome-Wide Association Study data from four independent ASD cohorts to identify potential genetic loci. By integrating Polygenic Priority Score, brain region, and brain cell eQTL enrichment analyses, and combining summary-data-based Mendelian Randomisation (SMR) analyses of brain cis-eQTL and mQTL, bidirectional Mendelian Randomisation analyses of 473 gut microbiota, and SMR analysis of blood eQTL, SNPs such as rs2735307 and rs989134 with significant multi-dimensional associations were identified. These loci exert cross-tissue regulatory effects by participating in gut microbiota regulation, involving immune pathways such as T cell receptor signal activation and neutrophil extracellular trap formation, as well as cis-regulating neurodevelopmental genes (HMGN1 and H3C9P), or synergistically influencing epigenetic methylation modifications to regulate the expression of BRWD1 and ABT1. The cross-scale evidence chain constructed in this study provides a theoretical foundation for precision medicine research in ASD, holding promise to advance the development of innovative therapeutic strategies.

Autism spectrum disorder↗

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

Exposome influences: a multi-omics perspective on the combined toxic effects of pharmaceuticals and personal care products in Alzheimer's disease.

According to WHO data, approximately 57 million people worldwide were affected by dementia in 2021, with prevalence projected to rise. Alzheimer's disease (AD), responsible for 60%-80% of dementia cases, continues to be a leading cause of mortality, with current treatments offering limited efficacy and disease-modifying therapies lacking widespread adoption or conclusive safety evidence, shifting the focus toward prevention and risk modification. Risk factors for AD include both non-modifiable elements, such as age, genetics, and gender, and modifiable factors, like environmental pollution, health status, and diet. While age remains the primary non-modifiable risk factor, early-onset dementia represents only up to 9% of cases. Addressing modifiable factors is essential, as it could prevent or delay almost half of dementia cases, with interventions-such as increased physical activity, smoking cessation, alcohol limitation, and overall health management-being significantly associated with a reduced risk. In this context, the exposome approach offers a comprehensive, integrative framework in which both modifiable and non-modifiable risk factors interact to influence individual susceptibility. Within the neural exposome, chronic low-dose exposure to xenobiotics-such as industrial chemicals, pesticides, metals, pharmaceuticals and personal care products (PPCPs), and air pollutants-may induce neurodegeneration via mechanisms including oxidative stress, neuroinflammation, proteinopathies, and epigenetic modifications, although establishing causality remains challenging. Integration of genomics, transcriptomics, proteomics, metabolomics, and lipidomics, combined with artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL), provides promising avenues for biomarker discovery, enhanced preventive strategies, early non-invasive diagnosis, and therapeutic target identification by integrating multi-layered biological data with exposure profiles. This review highlights emerging AD risk factors-including PPCPs-underscoring complex, multifactorial nature of AD and exposome, and the requirement for an interdisciplinary research approach, while also addressing several critical research gaps and methodological limitations.

Alzheimer’s disease↗

PubMed2025

Multi-Omics Analysis of the Potential Mechanisms of Skin Albinism in Edangered Percocypris pingi: Abnormal Ubiquitination and Calcium Signal Inhibition.

Percocypris pingi is an endangered protected fish species in China. Its albino variants exhibit growth retardation and physiological abnormalities. Understanding its albinism mechanism holds significant scientific importance for molecular breeding programs and disease model development. This study integrated transcriptomic and proteomic analyses, combined with histopathological and molecular biological techniques, to systematically compare molecular differences in skin tissues between albino and wild-type P. pingi, with a focus on elucidating the multidimensional regulatory mechanisms underlying skin albinism. Our findings suggest that albinism in P. pingi is synergistically driven by hyperactivation of ubiquitin-mediated proteolysis (which suppressed TYR/TYRP1 enzymatic activity and disrupted the pH homeostasis of melanosomes), and inhibition of calcium signaling (which impeded melanin transport). This discovery provides novel insights into the mechanisms of pigment loss in fish species and offers a valuable reference for molecular breeding of endangered species as well as research on pigmentation-related disorders.

Animals↗