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PubMed2026

The domestication-associated WHP10 tandem cluster of amino acid transporter genes enhances whole-plant protein accumulation in maize.

Improving protein accumulation in maize is essential for sustainable agriculture, yet the regulatory mechanisms governing the intermediate "flow" of organic nitrogen remain elusive. Here, we show that the maize stem acts as a regulatory node for nitrogen allocation. By integrating spatial transcriptomics and metabolomics with quantitative genetics, we demonstrate that a transport-oriented stem program orchestrates the high-protein phenotype of the wild maize accession Ames21814. We identified a major locus, Whole-plant High Protein 10 (WHP10), that encodes a tandemly duplicated cluster of amino acid transporter genes. WHP10 exhibits strong vascular-biased expression, driven by promoter divergence that enhances the wild allele's activity. Functional assays and genetic validation support a model in which the WHP10 cluster facilitates the transport of multiple nitrogen-rich amino acids, thereby contributing to vascular-associated amino acid transport and post-uptake organic-nitrogen partitioning. Our findings establish stem flow as a regulatory layer for protein accumulation and identify WHP10 as a high-value target for precision breeding to enhance whole-plant protein accumulation without compromising grain yield.

Zea mays↗

PubMed2026

A CqbZIP55-CqPIF3 regulatory module associated with light-responsive flavonoid biosynthesis during quinoa seedling de-etiolation.

Quinoa (Chenopodium quinoa) is an emerging leafy vegetable and microgreen crop rich in health-promoting flavonoids, yet the regulatory mechanisms linking light perception to early metabolic adaptation remain unclear. Here, we integrated phenotypic, transcriptomic, metabolomic, and molecular analyses to investigate early de-etiolation responses in quinoa seedlings. Short-term light exposure rapidly promoted seedling establishment and induced transcriptional programs associated with photosynthesis, carbon metabolism, hormone signaling, and flavonoid biosynthetic gene expression, whereas metabolite changes were more limited, indicating temporal uncoupling between transcriptional activation and metabolic accumulation. Genome-wide bZIP analysis identified CqbZIP55 as a light-responsive regulator that directly binds and activates the CqCHS promoter. CqPIF3 also bound the CqCHS promoter and showed stronger transactivation activity than CqbZIP55 in transient reporter assays. Protein interaction and dual-luciferase assays showed that CqbZIP55 physically interacts with CqPIF3 and modulates CqPIF3-associated promoter activity. Exogenous quercetin upregulated CqbZIP55 and prolonged CqCHS expression, suggesting a candidate metabolite-associated reinforcement mechanism. Together, these findings support functional interplay between CqbZIP55 and CqPIF3 in light-responsive regulation of flavonoid biosynthetic gene expression in quinoa seedlings, while further quinoa-based perturbation and in vivo promoter-occupancy assays are required to establish their physiological role in planta. This study provides a framework for further investigation of photoprotective metabolic regulation in quinoa.

Chenopodium quinoa↗

PubMed2026

A stem-like chromatin program in small-cell lung cancer is associated with poor outcomes after chemoimmunotherapy.

Small-cell lung cancer (SCLC) is an aggressive malignancy with substantial tumor heterogeneity and limited clinically actionable biomarkers beyond established features such as liver metastases. We profile tumor-intrinsic chromatin accessibility in a patient-derived xenograft biobank and identify three recurrent chromatin programs: neuroendocrine, marked by ASCL1/NEUROD1 activity; immunogenic, marked by IRF-associated activity; and stem-like, marked by TEAD/OCT activity. These programs are reproduced at the cohort level across bulk and single-cell transcriptomic datasets comprising more than 800 tumors, including 300 extensive-stage samples. In patients treated with chemoimmunotherapy, the stem-like program is associated with inferior survival, including a median overall survival of 7.41 months versus 15.9 and 12.6 months for immunogenic and neuroendocrine groups, respectively. This association remains significant after adjustment for liver metastases, brain metastases, and elevated lactate dehydrogenase. These findings support a high-risk stem-like SCLC chromatin program for prospective biomarker refinement and therapeutic investigation.

ATAC-seq↗

PubMed2026

Integrated transcriptomic and metabolomic analyses reveal key regulators associated with lipid metabolic differences between subcutaneous and visceral adipose tissues in sheep.

The location of fat deposition has a significant impact on meat quality and body health, and different adipose tissues exhibit significant differences in lipid metabolism and immune regulation. This study aimed to systematically compare the phenotypic characteristics, transcriptome, and metabolome of subcutaneous adipose tissue (SAT) and two types of visceral adipose tissue (VAT) in sheep, in order to reveal the metabolic differences between SAT and VAT and their potential regulatory mechanisms. The results showed that compared with VAT, SAT had stronger triglyceride deposition ability and obvious cellular hypertrophy. Through integrative analysis, 15 key lipid metabolism genes and 12 differential metabolites were identified. Among them, ACACA, FASN, ELOVL6, SCD, as well as metabolites palmitic acid and glycerol-3-phosphate, may play a central role in SAT lipid synthesis and storage; whereas IGFBP2, ADRB3, LTA4H, and metabolites arachidonic acid and leukotriene B4 may be involved in the lipolysis regulation and inflammatory response of VAT. These findings may provide deeper insights into the regulatory mechanisms of fat deposition in sheep.

Animals↗

PubMed2026

Mechanistic analysis of rice caryopsis morphogenesis regulated by exogenous hormones and related precursor substances under blue light conditions.

Rice caryopsis morphogenesis is regulated by light signals and hormonal networks. However, the mechanism by which exogenous hormones and related precursor substances modulate rice caryopsis morphogenesis under blue light remains elusive. In the present study, we aimed to elucidate the molecular mechanisms underlying the regulatory effects of exogenous phytohormones and related precursor substances on caryopsis development at 10 days after pollination (10 DAP) in the japonica rice cultivar 'Chujing 27' under blue light conditions. Results showed that tryptamine treatment increased caryopsis cell volume, thereby significantly driving caryopsis expansion; meanwhile, it markedly enhanced the activities of TDC and TAA, the key rate-limiting enzymes mediating the conversion of tryptophan to auxin, leading to a significant elevation in endogenous auxin content (P < 0.05). In comparison, exogenous auxin treatment significantly boosted carbohydrate accumulation and the activities of associated metabolic enzymes (P < 0.05). Integrated transcriptomic and metabolomic analyses revealed that tryptamine treatment led to significant enrichment of the starch and sucrose metabolic pathway, and drove the coordinated enhancement of carbon metabolic flux and auxin biosynthesis by upregulating key auxin biosynthetic genes (e.g., TAA1) and repressing auxin oxidative degradation. Genes Os04g0531100, Os03g0266100 and Os11g0221200 identified by weighted gene co-expression network analysis (WGCNA) may serve as important candidate targets regulating rice caryopsis morphology and physiological traits under blue light conditions. This study first uncovers the critical function of the "tryptamine-auxin axis" in regulating rice caryopsis development under blue light, laying a theoretical foundation for regulating caryopsis morphogenesis via exogenous hormones and their precursors.

Oryza↗

PubMed2026

Omics signature of new-onset mild cognitive impairment and dementia in a population-based study.

Plasma proteomics and metabolomics snapshots reveal a molecular signature in circulation delineating pathophysiology of major and minor neurocognitive disorder. To identify new cues to disease aetiology and diagnostic approach, we applied plasma proteomics and metabolomics profiling platforms to samples collected in a population-based study of the Singapore Longitudinal Ageing Studies Wave 2 (SLAS-2). In this longitudinal study, blood samples were analysed with standard clinical chemistry, plasma proteomics (Sengenics) and metabolomics (Nightingale) panels. Participants were followed up for the development of mild cognitive impairment (MCI) and dementia for 3-5 years. Of the total 1,892 molecules in all assay types, 463 demonstrated significant associations with baseline prevalent MCI and dementia. We trained an automatic linear modelling of predictors for follow-up new-onset MCI and dementia. The best model consists of 10 variables including ZSCAN18, PRKD3, SPANXN4, DDX43, saturated fatty acids, PPP3CA, NFATC4, IL-8, PAK6, and PDGFB. In terms of molecular function, these molecular markers are involved in immunological dysfunction and inflammatory reaction, protein coding, lipids, DNA-binding transcription factor activity, and nervous system development. In conclusion, our current research has identified an omics signature linked to new-onset mild cognitive disorder and dementia, which we hope can help enhance the accuracy of their diagnosis using circulating blood samples.

Humans↗

PubMed2026

Clinical proteomics in inborn errors of metabolism: from biomarker discovery to implementation.

INTRODUCTION: Inborn errors of metabolism (IEMs) are rare, heterogeneous disorders traditionally diagnosed through genetic testing, enzyme assays, and metabolite measurements. However, these tools often do not fully explain phenotypic variability, organ involvement, disease progression, or treatment response. Clinical proteomics provides a complementary functional layer by capturing changes in protein abundance, proteoforms, post-translational modifications (PTM), and biological pathways, offering insights beyond genotype- and metabolite-based approaches. AREAS COVERED: This review examines the role of high-resolution mass spectrometry and computational proteomics in biomarker discovery and clinical decision-making for IEMs. It focuses on their contribution to diagnosis, variant interpretation, patient stratification, and treatment monitoring. Disease-specific applications are discussed, with the strongest evidence in lysosomal storage disorders, mitochondrial diseases, congenital disorders of glycosylation, and selected neurodegenerative or renal metabolic conditions. The literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar, covering peer-reviewed articles available up to 2026, with emphasis on methodological advances and translational applications in clinical proteomics for IEMs. EXPERT OPINION: Proteomics will not replace established diagnostic tools, but it can help address clinically actionable questions in selected contexts. Translation into clinical practice will require standardized workflows, multicenter validation, clinically anchored endpoints, and integration with other omics approaches.

Humans↗

PubMed2026

In silico analysis of SH3BP2 genomic alterations and expression profiles in CRC.

AIM: Colorectal cancer (CRC) is a widespread health issue that attains high mortality. The adaptor protein SH3BP2 amplification results in metabolic changes, oxidative stress, NK cell activity, and inflammation. The NK cells are capable of destroying tumor cells without prior activation, help prevent metastasis, and have prognostic value. Targeting SH3BP2 to regulate NK cell activity in the TME could enhance CRC-based immunotherapy. MATERIALS AND METHODS: The cancer hallmark tool helps in understanding SH3BP2 hallmark annotation. Utilizing the STRING tool and the KEGG pathway, protein functional enrichment and PPI networking were analyzed. TIMER 2.0 was used for immune cell infiltration correlation analysis, and UALCAN was used for CPTAC-based protein expression profiling. RESULTS AND CONCLUSIONS: The GEO (GSE9348) dataset showed SH3BP2 is upregulated in CRC (log2 fold change = 1.18). GEO, TCGA, and cBioPortal revealed SH3BP2 alterations in CRC cases, potentially aiding immune evasion. Mutations in SH3BP2 influence cancer growth, suppressing tumors or promoting them by activating NF-κB and affecting immune responses through WNT/β-catenin, PI3K, MAPK, and JAK-STAT pathways. Overall, SH3BP2 plays a key role in cancer growth and immune regulation, making it a promising target for CRC therapy. Further experimental validation is needed to demonstrate its diagnostic and therapeutic potency.

Humans↗

PubMed2026

Changes of DNA methylation and gene expression profile in placental villi and chorioamniotic membranes under preeclampsia.

BACKGROUND: Preeclampsia (PE) is a serious pregnancy complication with elusive pathogenesis. Although epigenetic dysregulation is implicated, its layer-specific placental roles are poorly defined. This study aimed to identify shared and layer-specific epigenetic alterations in PE by profiling DNA methylation and gene expression in placental villi (PV) and chorioamniotic membranes (CAM). RESEARCH DESIGN AND METHODS: PV and CAM samples were collected from 7 normal and 8 PE pregnancies, and three public DNA methylation datasets (GSE98224, GSE44667, GSE75196) were integrated. Differentially methylated genes (DMGs) and differentially expressed genes (DEGs) were identified based on whole-genome methylation and transcriptome sequencing. Layer-specific and shared gene sets were identified by cross-analysis, with functional annotation using Gene Ontology (GO). RESULTS: EM-seq revealed a hypermethylation-dominant, tissue-specific methylation landscape in PE placentas. Cross-tissue comparison identified shared DMGs between the two layers, including nine key genes consistently altered in public datasets. Integrated analysis in PV further identified 22 co-dysregulated genes, enriched in thermoregulation, maternal-fetal immunity, signal transduction, and cell differentiation. CONCLUSIONS: This study elucidates the shared and layer-specific dysregulation of gene networks at methylomic and transcriptomic levels in PE placenta. Comparing PV and CAM highlights placental epigenetic heterogeneity and dysfunction, offering novel clues for mechanistic research and layer-targeted therapies.

Humans↗

PubMed2026

Convergent methodologies in prosthetic joint infection research: integrating transdisciplinary approaches to understand and prevent biofilm-driven failure of orthopaedic prostheses.

Prosthetic joint infections (PJIs) remain among the most devastating complications of arthroplasty, imposing substantial clinical, economic and patient burdens. Although culture-based diagnostics underpin current clinical practice, PJIs are biofilm-driven infections shaped by taxonomic diversity, spatial organization, host responses and surface interactions, meaning conventional approaches provide only a partial and often decontextualized view of the infection process. We examine how convergent methodologies can transform PJI research by integrating approaches that have traditionally been studied in isolation, including sequencing, transcriptomics, metabolomics, advanced imaging and culture-based characterization. We discuss how whole-genome sequencing, shotgun metagenomics, transcriptomic and metabolomic approaches resolve pathogen identity, functional activity and adaptive persistence and how cross-scale imaging and spatial biology techniques reveal where microbes colonize, interact and survive across implant surfaces. We highlight emerging opportunities to unify these datasets into coherent frameworks that capture both the molecular and physical dimensions of PJIs. Integrating these complementary approaches will enable a multi-layered understanding of PJIs that link composition, function and spatial organization. Ultimately, this provides a foundation for predictive diagnostics, precision antimicrobial strategies and improved implant design and supports a shift towards more effective, mechanism-informed management of implant-associated infection.

Prosthesis-Related Infections↗

PubMed2026

Longitudinal development of infant oral ecosystem: salivary metabolomic, bacteriome, and virome dynamics in early infancy.

This prospective cohort study investigated the longitudinal development of the salivary bacteriome, virome, and metabolome during early infancy. We assessed the associations between oral bacteria, viruses, and metabolites from 10 mother-infant dyads, with oral samples collected at 1 and 2 years of age. Forty saliva and plaque samples underwent untargeted metabolomic analysis, and infant saliva samples underwent metagenomic sequencing. Maternal salivary and plaque metabolomic profiles remained largely stable, whereas infant profiles were clearly separated from maternal profiles and changed with age. Notably, infant dental plaque metabolism underwent more substantial changes from year 1 to year 2 than saliva, with age-dependent metabolite shifts mainly involving energy, amino acid, nucleotide, and lipid metabolic pathways. Our findings also revealed significant developmental shifts in salivary bacteriome, virome, and functional pathway profiles during early childhood. The most abundant oral bacteria in early life, comprising over 75% of total abundance, included Veillonella, Streptococcus, Rothia, Prevotella, Neisseria, and Actinomyces species. While human viruses like Roseolovirus were detected, bacteriophages constituted the majority of the virome. Comparing infants at year 1 and year 2, we identified differentially abundant bacteria, viruses, metabolic functional pathways, and specific metabolites. We observed associations between bacteria and viruses, noting that these cross-kingdom relationships attenuated as infants grew. The study results underscore the complex and dynamic development of the oral microbiome, virome, and metabolome during early childhood.IMPORTANCEThe human oral cavity undergoes substantial microbial and metabolic development during early childhood, yet the temporal changes in the infant oral ecosystem remain incompletely understood. In this study, we longitudinally profiled the salivary metabolome, bacteriome, and virome of infants at 1 and 2 years of age. We demonstrated that the infant oral metabolome undergoes substantial developmental shifts, particularly in pathways related to energy, amino acid, and lipid metabolism; whereas maternal metabolic profiles remained stable over the same period. Furthermore, our results revealed the dynamic assembly of infant salivary virome and bacteriome and their associations with the functional pathways and metabolites. These findings provide new insights into the complex and dynamic development of the oral microbiome, virome, and metabolome in early infancy.

bacteriome↗

PubMed2026

Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.

Efficient and accurate identification of functional genes is critical to biological research, yet traditional single-species approaches are often limited by low efficiency. Previously, we established a novel method for identifying key genes using cross-species protein domain features and machine learning. However, the high multiplicity of gene members associated with specific domains creates a substantial workload for subsequent experimental validation. To address this, this study proposes an enhanced approach that integrates EggNOG-based protein sequence annotation with domain analysis. Unannotated sequences are subsequently analyzed for protein domains, generating a comprehensive "direct gene annotation plus domain" hybrid feature matrix. While the hybrid matrix model yielded comparable predictive accuracy, it significantly enhanced feature resolution: the top 50 predicted features were all known motility-related genes or domains. Furthermore, among the top 100 ranked features, 58 are confirmed to be directly related to motility based on experimental evidence. Although strict genus-level control still yielded 51 confirmed features, excessive taxonomic restriction drastically reduces the number of training genomes, which may paradoxically impair identification efficiency. These results demonstrate that the new method effectively reduces the subsequent experimental workload and enables high-throughput identification of functional genes in a single analysis. With accuracy and efficiency far exceeding those of existing single-species identification methods, it provides a highly efficient solution for mining key genes underlying other complex bacterial phenotypes.

Machine Learning↗

PubMed2026

Thyroid-stimulating hormone receptor mediates peripheral-central neuroimmune crosstalk in autoimmune thyroid diseases.

BACKGROUND: Organ-specific autoimmune diseases, particularly Graves' disease (GD) and its extrathyroidal manifestation, Graves' orbitopathy (GO), are characterized by systemic autoimmunity that may extend its impact to the central nervous system (CNS). While thyroid-stimulating hormone receptor (TSHR) is the primary driver of pathological remodeling in the thyroid and orbital tissues, emerging evidence suggests it is also expressed in the brain and may participate in neuroimmune signaling. However, the molecular mechanisms linking peripheral TSHR-driven autoimmunity to these extended systemic features remain unclear. Thus, GD and GO provide a unique window to investigate how peripheral autoantibodies influence CNS involvement as part of its broader pathological spectrum. METHODS: Genome-wide association studies (GWAS) and post-GWAS analyses were integrated with bulk RNA sequencing, single-cell and spatial transcriptomics, and brain imaging phenotypes to comprehensively characterize peripheral and central alterations in GD and GO. Mendelian randomization was applied to test causal relationships between genetic variants and brain signatures. Structural biology analyses were further conducted including protein-protein docking, small-molecule docking, and normal mode dynamics to identify prospective modulators of TSHR. Immunofluorescence staining was performed in a GO mouse model to validate the colocalization of potential interacted proteins in the specific brain region. RESULTS: Brain imaging-derived phenotypes (IDPs) alterations in GO and GO were systematically analyzed to identify neuroanatomical and functional alterations. TSHR was further identified as a shared genetic driver across peripheral and central compartments. TSHR was expressed in spiny projection neurons, microglia, and peripheral T cells, with cell-cell communication analyses highlighting TSHR-mediated interactions among neurons, endothelial cells, and microglia. Immunofluorescence staining in a GO mouse model confirmed the colocalization of TSHR with FN1 and GNAS in the basal ganglia, providing tissue-level validation of the computationally predicted ligand-receptor interactions. Immune profiling further showed immune alterations in GD and GO. Structural modeling supported plausible physical interfaces between TSHR and interacting proteins, and small-molecule screening identified three repurposable compounds - venetoclax, irinotecan, and dutasteride - with predicted favorable docking scores and stable binding poses in our simulations. CONCLUSIONS: These findings demonstrate that TSHR acts as a molecular hub mediating peripheral-central neuroimmune crosstalk in GD and GO. The results support a broader "disease-molecule axis" framework that links genetic susceptibility with multi-level immune and neural mechanisms. This work provides mechanistic insights relevant to the development of TSHR-targeted therapies, with implications for both peripheral immune modulation and central regulation. However, the limited sample size, lack of longitudinal follow-up, and absence of in vivo validation warrant cautious interpretation and further investigation.

Receptors, Thyrotropin↗

PubMed2026

Systematic discovery of retina-enriched Rik genes identifies 1190005I06Rik as a novel modulator of visual signalling.

BACKGROUND: High‑throughput transcriptome projects have revealed thousands of mammalian genes with little or no functional annotation. Among these are hundreds of loci assigned provisional “Rik” identifiers following discovery in the RIKEN cDNA annotation effort. Although often dismissed as genomic dark matter, such genes may encode tissue‑restricted proteins that modulate physiologic functions and influence disease. The retina is a highly specialised neural tissue and a common site of inherited disorders; understanding its molecular repertoire could illuminate novel therapeutic avenues. METHODS: We integrated bulk RNA‑seq from ten adult mouse tissues, evolutionary and domain analysis, single‑cell RNA‑seq, and CRISPR/Cas9 gene disruption to systematically catalogue protein‑coding Rik genes enriched in the retina and test the function of a representative gene. RESULTS: A rigorous differential expression analysis identified 44 Rik genes with robust retina‑specific expression compared with nine non‑retinal tissues. Many of these genes lack orthologues beyond rodents, while others show broad conservation, illustrating a continuum from lineage‑restricted to conserved retinopathy candidates. Single‑cell transcriptomics revealed that these genes are expressed across retinal cell types, with the highest aggregate expression in cone photoreceptors and inner interneurons. To evaluate physiological significance, we generated a 1190005I06Rik knockout mouse. Although retinal architecture appeared normal, loss of 1190005I06Rik enhanced electroretinogram b‑wave amplitudes and altered light‑avoidance behaviour, indicating that this previously uncharacterised gene acts as a negative modulator of visual signalling. CONCLUSIONS: We present a curated atlas of retina‑enriched Rik genes and demonstrate that 1190005I06RIK modulates retinal circuit function. This resource expands the molecular landscape of the retina and provides new candidates for the genetic basis of inherited retinal disease. Our findings underscore that unannotated genes may exert measurable effects on sensory processing and warrant systematic exploration in the context of human ocular disorders.

Animals↗

PubMed2026

Reduced legacy precipitation decreases microbial community growth efficiency and alters soil organic carbon in a California grassland.

BACKGROUND: Changes in global patterns can leave a lasting legacy in semiarid grasslands by reshaping microbial growth dynamics and carbon cycling during the first wet-up in the autumn-a period known for intense microbial activity and significant carbon emissions. To study the lasting impacts of decreased winter rain, we implemented two precipitation regimes (100% vs. 50% mean annual precipitation) in California Mediterranean-climate grassland field plots. After the dry season, soils were rewetted in the laboratory with H218O and sampled at 0 h, 3 h, 24 h, 48 h, 72 h, and 168 h post rewet. We quantified CO2 efflux, measured microbial growth and mortality via quantitative 18O stable isotope probing and 16S rRNA gene amplicon sequencing, and characterized the soil organic carbon chemical composition, metagenomes, and metatranscriptomes. RESULTS: We found that reduced winter precipitation imposed a strong legacy effect on microbial turnover; despite maintaining similar respiration rates, microbial growth declined by ~1 order of magnitude, yielding decreased community growth efficiency (CGE = new biomass growth/respiration), and microbial mortality declined by ~2 orders of magnitude. Soil organic carbon also shifted from lipid-like, amino-sugar-like, and protein-like compounds (indicative of microbial necromass) to more oxidized lignin-like and tannin-like compounds (indicative of decomposing plant-derived compounds). Meta-omics revealed distinct metabolic strategies linked to CGE. At high-CGE, microbes appeared to consume more energetically favorable N-rich necromass (released via high microbial turnover); this allowed for increased amino acids and peptidoglycan biosynthesis and greater aromatic compound degradation, fueling further energy production and growth efficiency. At low CGE, communities had elevated carbohydrate metabolism and lipid turnover, consistent with increased investment in plant detritus degradation and membrane repair and maintenance rather than growth. CONCLUSIONS: Together, our findings demonstrate that reduced winter rainfall decreases microbial turnover following rewetting without a concurrent reduction in CO2 emissions. This shift results in persistently lower CGE, which has the potential to increase soil carbon loss as CO2. If such conditions are maintained over multiple years, these changes could reshape soil organic carbon stocks and alter the balance of grassland ecosystems under future climate scenarios. While our data suggest that sustained reductions in CGE may drive SOC decline, the magnitude and persistence of these effects depend on long-term environmental dynamics and warrant further investigation. Video Abstract.

Soil Microbiology↗

PubMed2026

Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.

Cancer remains a major global health burden, with approximately 20 million new cases and 9.7 million cancer-related deaths reported globally in 2022. While advances in radiological imaging, molecular profiling, and clinical data have enhanced the interpretation of disease progression, the availability of multiple such modalities still does not meet the needs of a large patient population. This narrative review focuses on the role of multimodal artificial intelligence and machine learning in bridging the gap in interpreting heterogeneous modalities to improve risk prediction, prognostic assessment, and treatment decision-making in precision oncology. Multimodal frameworks such as Pathomic Fusion illustrate how complementary histopathological and genomic information can be integrated for cancer diagnosis and prognostic modeling. Multimodal models have demonstrated potential in virtual biopsy, cancer screening, prognostic prediction, radiotherapy planning, intraoperative guidance, and clinical-trial design using digital twins and synthetic control arms. The major limitations of incorporating multimodal artificial intelligence and machine learning in oncology include data heterogeneity, demographic or institutional biases, and reproducibility challenges that hinder translation. Accordingly, appropriate data-governance strategies, fairness audits, and privacy-preserving approaches such as federated learning should be considered where appropriate. Future progress will depend on the development of standardized benchmarking datasets, robust external validation, seamless integration with electronic health records and picture archiving and communication systems, and the implementation of explainable, secure, and clinically validated multimodal artificial intelligence frameworks that support precision oncology in routine clinical practice.

deep learning↗

PubMed2026

Editorial: Nutrigenetics and nutrigenomics in livestock.

Explore the source record for details and available documents.

gene expression↗

PubMed2026

A regulatory network underlying idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease in which genetic susceptibility interacts with epithelial, immune, and mesenchymal remodeling. Although the chromosome 11p15.5 locus contains established IPF susceptibility signals near MUC5B and TOLLIP, the broader regulatory architecture of this region remains incompletely resolved. METHODS: We integrated IPF genome-wide association study summary statistics with methylation, expression, and protein quantitative trait loci using summary-data-based Mendelian randomization (SMR). SMR-prioritized candidates were evaluated in independent transcriptomic and methylation cohorts and further contextualized using microRNA, transcription-factor, protein-interaction, machine-learning, single-cell, and spatial transcriptomic analyses. Fibrosis-associated expression patterns were assessed in a bleomycin-induced pulmonary fibrosis rat model. RESULTS: The analyses recovered the established MUC5B and TOLLIP signals and prioritized BRSK2 as a comparatively underexplored candidate supported by eQTL-based SMR and independent molecular evidence. The BRSK2 pQTL association did not pass the HEIDI test and was therefore not interpreted as convergent protein-level genetic evidence. Network analyses linked BRSK2 to cell-cycle, metabolic-stress, and senescence-related programs, while cross-cohort machine learning prioritized FOXA2, CDC25B, and NFE2 as informative network features. Single-cell and spatial analyses localized BRSK2 preferentially to fibroblast and myofibroblast compartments and to regions with greater histological fibrosis severity. In fibrotic rat lungs, BRSK2 expression increased, whereas FOXA2 and CDC25B decreased at the transcript and protein levels. CONCLUSIONS: These findings refine the molecular landscape of the chromosome 11p15.5 IPF susceptibility locus and prioritize BRSK2 as a candidate component of an IPF-associated profibrotic fibroblast state. Its causal contribution, direct regulatory relationships, and therapeutic tractability require targeted mechanistic validation.

Idiopathic Pulmonary Fibrosis↗