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PubMed2026

Interpretable data integration for single-cell and spatial multi-omics.

Integrating single-cell or spatial transcriptomic and epigenomic data enables scrutinizing the transcriptional regulatory mechanisms controlling cell fate. Current integration methods usually align multi-omics data into a shared latent space but fail to reveal the underlying connections between genes and regulatory elements. The correlation- or regression-based regulatory inference methods cannot dissect different transcriptional regulation codes for cells under different spatial and temporal states. To address both problems, we develop a feature-guided optimal transport (FGOT) method, which simultaneously uncovers cellular heterogeneity and their associated transcriptional regulatory links. FGOT also provides post hoc interpretability for existing integration methods. FGOT is applicable for paired/unpaired single-cell multi-omics data and paired spatial multi-omics data. Benchmarking and validating via histone modification data or three-dimensional (3D) genomics data show good robustness and accuracy in integration and inference of regulatory links. The method allows systematic screening of cell-state and spatial-location-specific regulatory elements in diseases at the single-cell level. A record of this paper's transparent peer review process is included in the supplemental information.

Single-Cell Analysis↗

PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗

PubMed2025

HoloFoodR: a statistical programming framework for holo-omics data integration workflows.

SUMMARY: Holo-omics is an emerging research area that integrates multi-omic datasets from the host organism and its microbiome to study their interactions. Recently, curated and openly accessible holo-omic databases have been developed. The HoloFood database, for instance, provides nearly 10 000 holo-omic profiles for salmon and chicken under controlled treatments. However, bridging the gap between holo-omic data resources and algorithmic frameworks remains a challenge. Combining the latest advances in statistical programming with curated holo-omic data sets can facilitate the design of open and reproducible research workflows in the emerging field of holo-omics. AVAILABILITY AND IMPLEMENTATION: HoloFoodR R/Bioconductor package and the source code are available under the open-source Artistic License 2.0 at the package homepage https://doi.org/10.18129/B9.bioc.HoloFoodR.

Software↗

PubMed2006

Integration of omics data: how well does it work for bacteria?

In the current omics era, innovative high-throughput technologies allow measuring temporal and conditional changes at various cellular levels. Although individual analysis of each of these omics data undoubtedly results into interesting findings, it is only by integrating them that gaining a global insight into cellular behaviour can be aimed at. A systems approach thus is predicated on data integration. However, because of the complexity of biological systems and the specificities of the data-generating technologies (noisiness, heterogeneity, etc.), integrating omics data in an attempt to reconstruct signalling networks is not trivial. Developing its methodologies constitutes a major research challenge. Besides for their intrinsic value towards health care, environment and industry, prokaryotes are ideal model systems to further develop these methods because of their lower regulatory complexity compared with eukaryotes, and the ease with which they can be manipulated. Several successful examples outlined in this review already show the potential of the systems approach for both fundamental and industrial applications, which would be time-consuming or impossible to develop solely through traditional reductionist approaches.

Bacteria↗

PubMed2006

OmicBrowse: a browser of multidimensional omics annotations.

UNLABELLED: OmicBrowse is a browser to explore multiple datasets coordinated in the multidimensional omic space integrating omics knowledge ranging from genomes to phenomes and connecting evolutional correspondences among multiple species. OmicBrowse integrates multiple data servers into a single omic space through secure peer-to-peer server communications, so that a user can easily obtain an integrated view of distributed data servers, e.g. an integrated view of numerous whole-genome tiling-array data retrieved from a user's in-house private-data server, along with various genomic annotations from public internet servers. OmicBrowse is especially appropriate for positional-cloning purposes. It displays both genetic maps and genomic annotations within wide chromosomal intervals and assists a user to select candidate genes by filtering their annotations or associated documents against user-specified keywords or ontology terms. We also show that an omic-space chart effectively represents schemes for integrating multiple datasets of multiple species. AVAILABILITY: OmicBrowse is developed by the Genome-Phenome Superbrain Project and is released as free open-source software under the GNU General Public License at http://omicspace.riken.jp.

Chromosome Mapping↗

PubMed2026

Genetic and epigenetic underpinnings of biological aging: a multi-omics study integrating Mendelian randomization, spatial transcriptomics, and drug target discovery.

Inflammaging represents a hallmark of biological aging, yet the causal inflammatory mediators driving multi-dimensional epigenetic aging and their effector genes remain poorly characterized at the genetic level. We developed a four-tier analytical framework integrating causal screening, multi-omics effector gene mapping, spatial transcriptomics, and drug target evaluation. Two-sample Mendelian randomization (MR) of 91 circulating inflammatory proteins against six aging phenotypes identified IL-12B, IFNG, and IL-2 as the most robust pro-aging mediators with consistent effects across independent outcomes. Using multi-omics summary-based MR (SMR) as the core analytical engine, we integrated four-layer whole-blood molecular QTL resources eQTL (eQTLGen, n = 31,684), sQTL (GTEx, n = 755), pQTL (INTERVAL + SCALLOP, n = 34,232), and mQTL (McRae et al., n = 1,980) - with GWAS summary statistics for four epigenetic age acceleration measures. At a stringent threshold (P_SMR < 1×10⁻¹²), seven high-confidence effector genes were identified: NHLRC1, TPMT, SELP, and RIPPLY3 for IEAA; ZNF373A and PLDN for HannumAA; and EDARADD for PhenoAA. The chromosome 6p21 NHLRC1-TPMT locus, overwhelmingly driven by methylation QTL signals (-log₁₀P = 26.06), emerged as the dominant genetic node of epigenetic aging. Spatial projection via gsMap onto a mouse E16.5 embryo atlas (121,767 cells) revealed preferential enrichment in smooth muscle and lung, with EDARADD showing marked specificity in mucosal epithelium. Cross-database drug target mining classified TPMT and SELP as repurposable known targets and NHLRC1 as a high-priority novel druggable candidate. This study provides multi-omics convergent causal evidence for inflammation-driven epigenetic aging and delivers genetically anchored targets for precision anti-aging intervention.

Aging↗

PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗

PubMed2026

Improving recombinant protein productivity in CHO cells via multi-omics data integration.

Chinese hamster ovary (CHO) cells represent the dominant host system for the production of recombinant therapeutic proteins. In recent decades, extensive research has focused on process/media optimization and cell line engineering to improve both the productivity and quality of biopharmaceutical proteins produced in CHO cells. Nevertheless, the inherent complexity of biological pathways and the heterogeneous cellular responses to different environmental conditions have posed substantial challenges to traditional methodologies. Recent advances in omics technologies have enabled comprehensive characterization of CHO cell physiology, providing multidimensional molecular and phenotypic insights that facilitate the enhancement of recombinant protein production. This review first summarizes the methodologies and advances in CHO omics research, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics. It then examines contemporary approaches to integrate and analyze multi-omics data in CHO cells. The review further elucidates how these multi-omics datasets can be strategically applied across various developmental stages, including cell line selection, genetic engineering, expression vector design, and bioprocess optimization. Finally, we explore the transformative potential of integrating multi-omics with artificial intelligence and discuss promising future research directions in CHO cell studies. These emerging paradigms offer novel opportunities for data-driven cell engineering and bioprocess optimization in CHO-based biomanufacturing.

Bioprocessing↗

PubMed2026

Integrated analysis of gut microbiota, serum metabolomics, and proteomics reveals novel associations with clinical symptoms in patients with cerebral infarction.

BACKGROUND: Cerebral infarction (CI) is a major cause of adult disability and mortality worldwide. Mounting evidence supports the critical role of the gut-brain axis in cerebrovascular disease progression. This study aimed to characterize the alterations in gut microbiota, serum metabolome, and serum proteome in patients with CI, and to identify multi-omics signatures associated with clinical symptoms. METHODS: A total of 20 CI patients and 20 healthy controls (HC) were enrolled. Fecal microbiota was profiled using 16 S rRNA gene high-throughput sequencing. Serum metabolomics and proteomics were analyzed using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and data-independent acquisition (DIA) proteomics, respectively. Spearman correlation and multi-omics integration were applied to explore the associations among microbiota, metabolites, proteins, and clinical indicators. RESULTS: CI patients displayed significant gut microbiota dysbiosis, with a markedly lower gut microbiota health index (GMHI) and higher microbiota disorder index (MDI) compared with HC (P < 0.001). The genera g_norank_o_RF39 and Oxalobacter were significantly enriched in CI patients, whereas Clostridium_sensu_stricto_1 and Agathobacter were enriched in HC. Metabolomic analysis identified 445 differential metabolites, mainly involved in glycerophospholipid metabolism, phenylalanine metabolism, and caffeine metabolism. Proteomic analysis revealed 140 differentially expressed proteins linked to inflammatory responses, calcium signaling, and NF-κB signaling. Multi-omics integration showed that signature gut microbiota was strongly correlated (P < 0.005) with key serum metabolites and proteins implicated in CI pathogenesis. CONCLUSIONS: This integrated multi-omics study revealed distinct gut microbiota, serum metabolomic, and proteomic alterations in CI patients. The microbiota-metabolite-protein regulatory axes provide novel insights into the gut-brain axis in CI and may serve as potential diagnostic biomarkers or therapeutic targets.

Humans↗

PubMed2025

NOODAI: a webserver for network-oriented multi-omics data analysis and integration pipeline.

SUMMARY: Omics profiling has proven of great use for unbiased and comprehensive identification of key features that define biological phenotypes and underlie medical conditions. While each omics profile assists characterization of specific molecular components relevant for the studied phenotype, their joint evaluation can offer deeper insights into the overall mechanistic functioning of biological systems. Here, we introduce an approach where, starting from representative traits (e.g. differentially expressed elements) obtained for each omics profile, we construct and analyze joint interaction networks. The resulting networks rely on the existing knowledge of confident interactions among biological entities. We use these maps to identify and describe central elements, which connect multiple entities characteristic of the studied phenotypes and we leverage MONET network decomposition tool in order to highlight functionally connected network modules. In order to enable broad usage of this approach, we developed the NOODAI software platform, which enables integrative omics analysis through a user-friendly interface. The analysis outcomes are presented both as raw output tables as well as informative summary plots and written reports. Since the MONET tool enables the use of algorithms with strong performance in identifying disease-relevant modules, NOODAI software platform can be of a high value for analyzing clinical multi-omics datasets. AVAILABILITY AND IMPLEMENTATION: NOODAI is freely accessible at https://omics-oracle.com. Source code is available under GPL3 at: https://github.com/TotuTiberiu/NOODAI with the DOI: 10.5281/zenodo.17203984.

Software↗

PubMed2026

PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.

MOTIVATION: Integrating multi-omics data provides valuable insights into biological processes by capturing information across multiple molecular layers, enabling a comprehensive understanding of complex diseases and driving advancements in precision medicine. However, existing computational methods for multi-omics integration face significant challenges, such as low reliability and poor generalizability, due to the high dimensionality and low sample size nature of omics data. RESULTS: To address these challenges, we present PEARL (Pearson-Enhanced spectrAl gRaph convoLutional networks), a novel deep graph learning method for biomedical classification and functional important omics features identification. PEARL leverages a simple yet effective learning architecture to achieve superior and robust performance in high-dimensional, low-sample-size multi-omics settings. Our results demonstrate that PEARL significantly outperforms existing state-of-the-art methods on both synthetic and real biomedical datasets. Furthermore, applied to Alzheimer's disease (AD) brain multi-omics data, features prioritized by PEARL lead to functionally important genes that demonstrate significant enrichment in AD-related pathways. These findings highlight PEARL's practical utility in biomedical research and its potential to enhance biological interpretability in multi-omics studies. AVAILABILITY AND IMPLEMENTATION: The source code of our computational framework is available at https://github.com/zqq121017/PEARL.

Multiomics↗

PubMed2026

Integrated Multi-omics Profiling of 2,4-dinitrochlorobenzene (DNCB)-induced Atopic Dermatitis in Mice Reveals a Coordinated Network of Barrier Dysfunction, Immune Activation, and Metabolic Reprogramming.

Atopic dermatitis (AD) is caused by a combination of epidermal barrier defect and immune imbalance. However, the molecular networks between these structural abnormalities and metabolic variations are unclear. This study aim of this research was to examine the concurrent molecular alterations in skin barrier damage and metabolic disorders in an AD-like mouse model by a multi-omics strategy. A 2,4-dinitrochlorobenzene (DNCB)-induced AD-like mouse model was established and the skin tissues were examined through the combination of transcriptomic, quantitative proteomic, and metabolomic analyses. Cross-omics correlation and network analyses were performed to identify consistently abnormal molecular pathways and crucial regulatory molecules. DNCB treatment caused severe epidermal hyperplasia, and prominent infiltration of CD3⁺ T cells, F4/80⁺ macrophages, and mast cells. Transcriptomic and proteomic analysis indicated significant disruption in keratinocyte differentiation, extracellular matrix organization, and cornified envelope formation pathways. Combined analysis detected 171 molecules which were simultaneously altered at both mRNA and protein levels, and network analysis identified FLG2 and KRT6B as central barrier-related molecules. Pathway enrichment analysis consistently showed the participation of AMPK and PPAR signaling pathways. Metabolomic analysis also revealed coordinated changes in lipid and amino acid metabolism which were closely associated with cornified envelope-associated genes and collagen-modifying enzymes. These findings indicate a close relationship between barrier, immune and metabolic regulation in DNCB-induced dermatitis and provide a multi-omics resource for future mechanistic studies of atopic skin inflammation.

Animals↗

PubMed2026

Precision UV-B irradiation for flavonoid biofortification in indoor-cultivated Morus nigra: Integrated multi-omics and molecular docking insights.

Precision application of UV irradiation represents an effective strategy for improving the quality of functional food crops under controlled environmental conditions. Morus nigra serves as a significant functional food resource in Xinjiang, with its leaves being rich in diverse bioactive compounds with nutritional and health-promoting properties. In this study, a low-dose UV-B treatment regimen was developed to enhance flavonoids in indoor-cultivated M. nigra without growth penalty. Multi-omics revealed a hormone shift (suppressed auxin vs. activated JA signaling). This triggered transcriptional reprogramming of PAL/4CL and CHS/CYP75B1, redirecting carbon flux towards flavonoid biosynthesis. Transient overexpression of MYB, NAC, and TIFY variants validated this regulatory network. The UV-B-induced NAC upregulated key genes and flavonoids, while different TIFY members showed diverse regulatory effects. Molecular docking predicted that the induced flavonoids had hypoglycemic, antioxidant, and anti-browning potential. This study indicates targeted UV-B as a green biofortification strategy for high-value crops in controlled environments.

Flavonoid biosynthesis↗

PubMed2026

SeqUIaSCOPE: multi-omics data integration platform for single-patient clinical oncology pathway exploration.

SUMMARY: SeqUIaSCOPE is an open-source platform designed for routine clinical oncology diagnostics through case-centric integration and visualization of genomic variants, fusion events, and expression profiles. The platform combines molecular-level validation via embedded genome browsing with systems-level interpretation through dynamic pathway visualization, enabling geneticists to assess how alterations converge across biological networks. Flexible reporting with customizable templates accommodates diverse institutional requirements, while secure cluster-based or local deployment ensures compliance with data protection policies, making advanced multi-omics diagnostics accessible to academic and clinical institutions. AVAILABILITY AND IMPLEMENTATION: SeqUIaSCOPE is freely available on GitHub at https://github.com/BioIT-CEITEC/sequiascope under the MIT license and archived at Zenodo (https://zenodo.org/records/21338445). Due to the sensitive nature of patient data, the repository provides simulated datasets that mimic the structure of real clinical data for testing and exploration. Documentation and a live demo accompany these datasets, allowing users to explore the application without any prior setup. The repository also includes a Helm chart for Kubernetes deployment and Docker containers for local deployment, ensuring compatibility across Linux, macOS, and Windows. No user registration is required, and all data remains on local or institutional infrastructure.

Humans↗

PubMed2026

mmContext: an open framework for multimodal contrastive learning of omics and text data.

SUMMARY: Multimodal approaches are increasingly leveraged for integrating omics data with textual biological knowledge. Yet there is still no accessible, standardized framework that enables systematic comparison of omics representations with different text encoders within a unified workflow. We present mmContext, a lightweight and extensible multimodal embedding framework built on top of the open-source Sentence Transformers library. The software allows researchers to train or apply models that jointly embed omics and text data using any numeric representation stored in an AnnData.obsm layer and any text encoder available in Hugging Face. mmContext supports integration of diverse biological text sources and provides pipelines for training, evaluation, and data preparation. We train and evaluate models for a RNA-Seq and text integration task, and demonstrate their utility through zero-shot classification of cell types and diseases across four independent datasets. By releasing all models, datasets, and tutorials openly, mmContext enables reproducible and accessible multimodal learning for omics-text integration. AVAILABILITY AND IMPLEMENTATION: Pretrained checkpoints and full source code for our custom MMContextEncoder are available on Hugging Face huggingface.co/jo-mengr. The Python package github.com/mengerj/mmcontext provides the model implementation and training and evaluation scripts for custom training. The releases for the publication can be accessed via zenodo: adata_hf_datasets: doi.org/10.5281/zenodo.19185217 and mmContext: doi.org/10.5281/zenodo.19185493.

Computational Biology↗

PubMed2025

Transfer learning with multiomics integration and deep neural networks reveals drug resistance mechanisms in cancer.

Drug resistance remains one of the primary challenges in effective cancer therapy. In this study, we employed a deep neural network (DNN)-based transfer learning (TL) approach to predict drug response and uncover drug resistance mechanisms. We integrated gene expression, somatic mutation, and copy number aberration (CNA) data with drug response profiles using multi-omics integration (MI). We used the Genomics of Drug Sensitivity in Cancer (GDSC) data for training and incorporated drugs with same pathways into the training models. We then evaluated drug response predictions on independent in-vivo PDX Encyclopedia (PDX) and ex-vivo the Cancer Genome Atlas (TCGA) datasets. In addition, we conducted pathway enrichment analyses to elucidate the mechanisms underlying drug resistance for paclitaxel, 5-fluorouracil (5-FU), gemcitabine, and cetuximab. We also applied Fisher's exact test (FET) to assess potential associations between drug resistance and the presence of mutations or CNAs. Our pan-drug models outperformed other methods based on the area under the precision-recall curve (AUCPR). Our pathway enrichment analyses revealed LDHB-mediated pyruvate metabolism and FYN-mediated focal adhesion might have pivotal roles in paclitaxel resistance, while PINK1-mediated mitophagy might be critical in 5-FU resistance. In addition to transcriptional activation, FET suggested that CNAs in LDHB and PINK1 may also be associated with resistance to paclitaxel and 5-FU, respectively. Furthermore, enrichment results for paclitaxel and cetuximab indicated shared resistance mechanisms between the two drugs. Importantly, our findings are consistent with prior experimental studies, providing literature-based validation of our results. Overall, our DNN-based TL approach achieved strong predictive performance across PDX & TCGA datasets and enrichment analyses provided valuable biological insights into drug resistance mechanisms.

Humans↗

PubMed2025

Multi-omics Mendelian randomization integrating RNA-seq, eQTL and pQTL data revealed CPXM1 as a potential drug target for osteoporosis.

Osteoporosis, a prevalent skeletal disorder characterized by decreased bone mineral density and increased fracture risk, continues to be a major global health concern. Traditional treatments for osteoporosis have limited efficacy and safety profiles, highlighting the need for novel therapeutic targets. This study integrates multi-omics data, including RNA-seq, expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data, through Mendelian randomization (MR) to identify potential drug targets for osteoporosis. By leveraging bidirectional two-sample MR analysis, we identified CPXM1 (Carboxypeptidase X, M14 family member 1) as a novel gene that is causally linked to osteoporosis risk. Through transcriptomic and proteomic validation, we demonstrate that CPXM1 was upregulated in aged bone tissues and osteoporotic conditions in both human and murine models. Gene set enrichment analysis (GSEA) revealed significant dysregulation of bone homeostasis pathways, including increased extracellular matrix degradation and suppression of osteoblast differentiation in aged mice. Furthermore, phenome-wide association studies (PheWAS) confirmed minimal off-target effects of CPXM1, reinforcing its potential as a therapeutic target. Finally, computational drug repurposing predicted several promising drug candidates, including Doxorubicin, 5-Fluorouracil, and 2-Methylcholine, which may target CPXM1 pathways for osteoporosis treatment. These findings highlight CPXM1 as a potential biomarker and therapeutic target, offering new avenues for osteoporosis therapy.

Osteoporosis↗

PubMed2026

The metastatic spectrum in functional and non-functional NENs: mechanistic insights from multi-omics.

Neuroendocrine neoplasms (NENs) are biologically heterogeneous tumors in which differentiation/grade and hormonal functionality are intersecting but non-equivalent axes. This review focuses on functional and non-functional well-differentiated neuroendocrine tumors (NETs), principally gastroenteropancreatic and pancreatic NETs, and critically evaluates how site, lineage, stage, tumor burden, genomic and epigenetic alterations, immune-stromal remodeling, metabolic adaptation, microbiome-associated signals, and treatment pressure converge on metastasis and recurrence. Apparent outcome differences by functionality are inconsistent after clinicopathological adjustment: non-functional presentation is often enriched for delayed diagnosis and adverse features, whereas functional subtypes range from typically indolent insulinomas to clinically aggressive hormone-producing tumors. We reconcile these observations through a layered model in which lineage-defining alterations and chromatin/telomere programs establish cellular state; signaling and metabolic plasticity enable stress adaptation; and hypoxia, angiogenesis, immune cells, fibroblasts, extracellular matrix, and therapy create selective niches for dissemination and relapse. We also define computational strategies for heterogeneous multi-omics integration and a staged biomarker-validation pathway. Evidence remains dominated by pancreatic NETs, and causal support is weakest for microbiome-functionality relationships and several proposed cross-omic links. A spectrum-based framework is therefore most useful when it generates testable, site- and grade-specific hypotheses rather than treating functionality as an isolated prognostic variable.

Humans↗