Multi-omics Integration Research Articles

Advances in the application of colorectal cancer organoids in precision medicine

Colorectal cancer (CRC) ranks among the most prevalent gastrointestinal tumors globally and poses a significant threat to human health. In recent years, tumor organoids have emerged as ideal models for clinical disease research owing to their ability to closely mimic the original tumor tissue and maintain a stable phenotypic structure. Organoid technology has found widespread application in basic tumor research, precision therapy, and new drug development, establishing itself as a reliable preclinical model in CRC research. This has significantly advanced individualized and precise tumor therapies. Additionally, the integration of single-cell technology has enhanced the precision of organoid studies, offering deeper insights into tumor heterogeneity and treatment response, thereby contributing to the development of personalized treatment approaches. This review outlines the evolution of colorectal cancer organoid technology and highlights its strengths in modeling colorectal malignancies. This review also summarizes the progress made in precision tumor medicine and addresses the challenges in organoid research, particularly when organoid research is combined with single-cell technology. Furthermore, this review explores the future potential of organoid technology in the standardization of culture techniques, high-throughput screening applications, and single-cell multi-omics integration, offering novel directions for future colorectal cancer research.

Innovative Insights into Single-Cell Technologies and Multi-Omics Integration in Livestock and Poultry

In recent years, single-cell RNA sequencing (scRNA-seq) has marked significant strides in livestock and poultry research, especially when integrated with multi-omics approaches. These advancements provide a nuanced view into complex regulatory networks and cellular dynamics. This review outlines the application of scRNA-seq in key species, including poultry, swine, and ruminants, with a focus on outcomes related to cellular heterogeneity, developmental biology, and reproductive mechanisms. We emphasize the synergistic power of combining scRNA-seq with epigenomic, proteomic, and spatial transcriptomic data, enhancing molecular breeding precision, optimizing health management strategies, and refining production traits in livestock and poultry. The integration of these technologies offers a multidimensional approach that not only broadens the scope of data analysis but also provides actionable insights for improving animal health and productivity.

TMODINET: A trustworthy multi-omics dynamic learning integration network for cancer diagnostic

Multiple types of omics data contain a wealth of biomedical information which reflect different aspects of clinical samples. Multi-omics integrated analysis is more likely to lead to more accurate clinical decisions. Existing cancer diagnostic methods based on multi-omics data integration mainly focus on the classification accuracy of the model, while neglecting the interpretability of the internal mechanism and the reliability of the results, which are crucial in specific domains such as precision medicine and the life sciences. To overcome this limitation, we propose a trustworthy multi-omics dynamic learning framework (TMODINET) for cancer diagnostic. The framework employs multi-omics adaptive dynamic learning to process each sample to provide patient-centered personality diagnosis by using self-attentional learning of features and modalities. To characterize the correlation between samples well, we introduce a graph dynamic learning method which can adaptively adjust the graph structure according to the specific classification results for specific graph convolutional networks (GCN) learning. Moreover, we utilize an uncertainty mechanism by employing Dirichlet distribution and Dempster-Shafer theory to obtain uncertainty and integrate multi-omics data at the decision level, ensuring trustworthy for cancer diagnosis. Extensive experiments on four real-world multimodal medical datasets are conducted. Compared to state-of-the-art methods, the superior performance and trustworthiness of our proposed algorithm are clearly validated. Our model has great potential for clinical diagnosis.

Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0.

The use of large transcriptome datasets has greatly improved our understanding of the tumor microenvironment (TME) and helped develop precise immunotherapies. The growing application of multi-omics, single-cell RNA sequencing (scRNA-seq), and spatial transcriptome sequencing has led to many new insights, yet these findings still require clinical validation in large cohorts. To advance multi-omics integration in TME research, we have upgraded the Immuno-Oncology Biological Research (IOBR) package to IOBR 2.0, restructuring and standardizing its analytical workflow. IOBR 2.0 offers six modules for TME analysis based on multi-omics data, including data preprocessing, TME estimation, TME infiltration pattern identification, cellular interaction analysis, genome and TME interaction, and feature visualization, as well as modeling. Additionally, IOBR 2.0 enables constructing gene signatures and reference matrices from scRNA-seq data for TME deconvolution. The user-friendly pipeline provides comprehensive insights into tumor-immune interactions, and a detailed GitBook(https://iobr.github.io/book/) offers a complete manual and analysis guide for each module.

Multi-omics integration analysis revealed the regulatory network of ethylene and 1-MCP treatments on kiwifruit shelf life quality

Multi-omics integration analysis revealed the regulatory network of ethylene and 1-MCP treatments on kiwifruit shelf life quality

Integrative Multi-Omics Approach in Vascular Ehlers–Danlos Syndrome: Further Insights into the Disease Mechanisms by Proteomic Analysis of Patient Dermal Fibroblasts

Background: Dominant mutations in COL3A1 are known to cause vascular Ehlers–Danlos syndrome (vEDS) by impairing extracellular matrix (ECM) homeostasis. This disruption leads to the fragility of soft connective tissues and a significantly increased risk of life-threatening arterial and organ ruptures. Currently, treatments for vEDS are primarily symptomatic, largely due to a limited understanding of its underlying pathobiology and molecular mechanisms. Methods: In this study, we conducted a comprehensive analysis of the intracellular proteome of vEDS fibroblasts, integrating these findings with our previous transcriptome results to identify key molecular pathways that drive the disease. Additionally, we explored the therapeutic potential of inhibiting miR-29b-3p as a proof of concept. Results: Our integrative multi-omics analysis revealed complex pathological networks, emphasizing the critical role of miRNAs, particularly miR-29b-3p, in impairing ECM organization, autophagy, and cellular stress responses, all of which contribute to the pathogenesis of vEDS. Notably, the inhibition of miR-29b-3p in vEDS fibroblasts resulted in the upregulation of several differentially expressed target genes involved in these critical processes, as well as increased protein expression of essential ECM components, such as collagen types V and I. These changes suggest potential therapeutic benefits aimed at improving ECM integrity and restoring intracellular homeostasis. Conclusions: Overall, our findings advance our understanding of the complex biological mechanisms driving vEDS and lay a solid foundation for future research focused on developing targeted and effective treatment strategies for this life-threatening disorder.

Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids

Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids

Unlocking biological complexity: the role of machine learning in integrative multi-omics

Unlocking biological complexity: the role of machine learning in integrative multi-omics

MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification

BackgroundThe integration of multi-omics data through deep learning has greatly improved cancer subtype classification, particularly in feature learning and multi-omics data integration. However, key challenges remain in embedding sample structure information into the feature space and designing flexible integration strategies.ResultsWe propose MoAGL-SA, an adaptive multi-omics integration method based on graph learning and self-attention, to address these challenges. First, patient relationship graphs are generated from each omics dataset using graph learning. Next, three-layer graph convolutional networks are employed to extract omic-specific graph embeddings. Self-attention is then used to focus on the most relevant omics, adaptively assigning weights to different graph embeddings for multi-omics integration. Finally, cancer subtypes are classified using a softmax classifier.ConclusionsExperimental results show that MoAGL-SA outperforms several popular algorithms on datasets for breast invasive carcinoma, kidney renal papillary cell carcinoma, and kidney renal clear cell carcinoma. Additionally, MoAGL-SA successfully identifies key biomarkers for breast invasive carcinoma.

Supervised multiple kernel learning approaches for multi-omics data integration

BackgroundAdvances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learning (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi-omics inputs, despite being an underused tool in genomic data mining.ResultsWe provide novel MKL approaches based on different kernel fusion strategies. To learn from the meta-kernel of input kernels, we adapted unsupervised integration algorithms for supervised tasks with support vector machines. We also tested deep learning architectures for kernel fusion and classification. The results show that MKL-based models can outperform more complex, state-of-the-art, supervised multi-omics integrative approaches.ConclusionMultiple kernel learning offers a natural framework for predictive models in multi-omics data. It proved to provide a fast and reliable solution that can compete with and outperform more complex architectures. Our results offer a direction for bio-data mining research, biomarker discovery and further development of methods for heterogeneous data integration.

Proteogenomic analysis reveals non-small cell lung cancer subtypes predicting chromosome instability, and tumor microenvironment

Non-small cell lung cancer (NSCLC) is histologically classified into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC). However, some tumors are histologically ambiguous and other pathophysiological features or microenvironmental factors may be more prominent. Here we report integrative multiomics analyses using data for 229 patients from a Korean NSCLC cohort and 462 patients from previous multiomics studies. Histological examination reveals five molecular subtypes, one of which is a NSCLC subtype with PI3K-Akt pathway upregulation, showing a high proportion of metastasis and poor survival outcomes regardless of any specific NSCLC histology. Proliferative subtypes are present in LUAD and LSCC, which show strong associations with whole genome doubling (WGD) events. Comprehensive characterization of the immune microenvironment reveals various immune cell compositions and neoantigen loads across molecular subtypes, which predicting different prognoses. Immunological subtypes exhibit a hot tumor-enriched state and a higher efficacy of adjuvant therapy.

Machine learning-informed liquid-liquid phase separation for personalized breast cancer treatment assessment.

Breast cancer, characterized by its heterogeneity, is a leading cause of mortality among women. The study aims to develop a Machine Learning-Derived Liquid-Liquid Phase Separation (MDLS) model to enhance the prognostic accuracy and personalized treatment strategies for breast cancer patients. The study employed ten machine learning algorithms to construct 108 algorithm combinations for the MDLS model. The robustness of the model was evaluated using multi-omics and single-cell data across 14 breast cancer cohorts, involving 9,723 patients. Genetic mutation, copy number alterations, and single-cell RNA sequencing were analyzed to understand the molecular mechanisms and predictive capabilities of the MDLS model. Immunotherapy targets were predicted by evaluating immune cell infiltration and immune checkpoint expression. Chemotherapy targets were identified through correlation analysis and drug responsiveness prediction. The MDLS model demonstrated superior prognostic power, with a mean C-index of 0.649, outperforming 69 published signatures across ten cohorts. High-MDLS patients exhibited higher tumor mutation burden and distinct genomic alterations, including significant gene amplifications and deletions. Single-cell analysis revealed higher MDLS activity in tumor-aneuploid cells and identified key regulatory factors involved in MDLS progression. Cell-cell communication analysis indicated stronger interactions in high-MDLS groups, and immunotherapy response evaluation showed better outcomes for low-MDLS patients. The MDLS model offers a robust and precise tool for predicting breast cancer prognosis and tailoring personalized treatment strategies. Its integration of multi-omics and machine learning highlights its potential clinical applications, particularly in improving the effectiveness of immunotherapy and identifying therapeutic targets for high-MDLS patients.

Using Artificial Intelligence for Personalized Cancer Treatment

The rapid evolution of artificial intelligence and the tools it has created gives rise to significant opportunities for precision oncology and promises a brighter future for the development of the sector. Precision oncology is a modern and advancing branch of cancer treatment and it is a renovated approach which specializes on specific treatment plans rather than “one-size-fits”; with advanced technology including but not limited to genomics, proteomics and bioinformatics, precision oncology focuses on optimizing therapeutic efficacy by targeting the specific molecular and cellular characteristics of a patient's cancer. This review paper explores the sophisticated and complex potential roles of artificial intelligence in cancer treatment, emphasizing on its impact in personalized treatment plans, immunotherapy and diagnostic accuracy. The immense database that AI has access to enables medical professionals to analyze imaging results and detect similarities of symptoms, genetic patterns, offering enhanced prognostic and diagnostic capabilities by eliminating human error and increasing cost effectiveness. This paper also dives into applications such as immune checkpoint inhibitors, digital pathology and demonstrations of the remarkable capability of artificial intelligence in multi-omics integration. Nonetheless, the advent of such advanced artificial intelligence tools, although promising, have not yet become the “gold standard” in oncology due to many rising agitations regarding reliability, since treatment decisions made by opaque AI algorithms are prone to error due to potential biases in AI systems and design errors. By critically examining these points, this paper evaluates both the advantages and disadvantages of the future implementation of artificial intelligence in the field of precision oncology, aiming to devise optimal integration in clinical practice.

Why Symptoms Linger in Quiescent Crohn's Disease: Investigating the Impact of Sulfidogenic Microbes and Sulfur Metabolic Pathways.

Even in the absence of inflammation, persistent symptoms in patients with Crohn's disease (CD) are prevalent and worsen quality of life. We previously demonstrated enrichment in sulfidogenic microbes in quiescent Crohn's disease patients with (qCD + S) vs without persistent GI symptoms (qCD-S). Thus, we hypothesized that sulfur metabolic pathways would be enriched in stool while differentially abundant microbes would be associated with important sulfur metabolic pathways in qCD + S. We performed a multicenter observational study nested within SPARC IBD. Quiescent inflammation was defined by fecal calprotectin level < 150 mcg/g. Persistent symptoms were defined by CD-PRO2. Active CD (aCD) and non-IBD diarrhea-predominant irritable bowel syndrome (IBS-D) were included as controls. Thirty-nine patients with qCD + S, 274 qCD-S, 21 aCD, and 40 IBS-D underwent paired shotgun metagenomic sequencing and untargeted metabolomic profiling. The fecal metabolome in qCD + S was significantly different relative to qCD-S and IBS-D but not aCD. Patients with qCD + S were enriched in sulfur-containing amino acid pathways, including cysteine and methionine, as well as serine, glycine, and threonine. Glutathione and nicotinate/nicotinamide pathways were also enriched in qCD + S relative to qCD-S, suggestive of mitochondrial dysfunction, a downstream target of H2S signaling. Multi-omic integration demonstrated that enriched microbes in qCD + S were associated with important sulfur metabolic pathways. Bacterial sulfur metabolic genes, including CTH, isfD, sarD, and asrC, were dysregulated in qCD + S. Finally, sulfur metabolites with and without sulfidogenic microbes showed good accuracy in predicting the presence of qCD + S. Microbial-derived sulfur pathways and downstream mitochondrial function are perturbed in qCD + S, which implicate H2S signaling in the pathogenesis of this condition. Future studies will determine whether targeting H2S pathways results in improved quality of life in qCD + S.

Impact of Polyploidy on Crop Improvement and Plant Breeding Strategies: A Review

Polyploidy, the condition of possessing multiple sets of chromosomes, is a widespread phenomenon in plants that has had a profound impact on crop evolution and improvement. This review explores the role of polyploidy in plant breeding strategies, emphasizing its contributions to enhancing agronomic traits, overcoming genetic barriers, and expanding genetic diversity. Polyploid crops, such as wheat, cotton, and Brassica species, exhibit superior yield, increased biomass, and enhanced stress tolerance compared to their diploid counterparts. Polyploidy can arise naturally or be induced artificially, providing breeders with opportunities to create novel crop varieties through synthetic polyploidization. The complexity of polyploid genomes poses significant challenges, including fertility issues, meiotic instability, and difficulties in genome management. Advances in genomic and biotechnological tools, such as genomic selection and CRISPR-based genome editing, are beginning to address these obstacles, allowing for precise manipulation of polyploid genomes and improved breeding outcomes. Additionally, polyploidy plays a crucial role in overcoming reproductive barriers in interspecific hybrids, facilitating the transfer of desirable traits between species that would otherwise be genetically incompatible. The potential of polyploidy for developing climate-resilient crops is particularly noteworthy, as polyploid plants often exhibit greater tolerance to drought, salinity, and extreme temperatures. This makes polyploid breeding a promising approach for addressing the challenges of climate change and ensuring food security. Future research should focus on understanding the genetic and epigenetic mechanisms underlying polyploid genome stability and trait expression, as well as integrating advanced computational tools to predict and manipulate polyploid gene function. Emerging areas such as synthetic biology and multi-omics integration will further enhance our ability to engineer polyploid crops with complex trait architectures. Polyploidy remains a powerful and versatile tool for plant breeders, offering immense potential for crop improvement, genetic innovation, and the development of resilient agricultural systems. As the understanding and technological capabilities in polyploid breeding continue to advance, polyploid crops will play a central role in sustainable agricultural development and global food production.

Development of an HPPD-Inhibitor Resistant Wheat and Multiomics Integrative Analysis of Herbicide Toxicity and OsHIS1 Detoxification in Wheat.

Weed infestation in agricultural fields significantly diminishes crop yields. Herbicides are widely used as a primary method of weed control. Developing herbicide-resistant crops through the expression of resistant genes represents a sustainable approach. This study generated wheat germplasms highly resistant to 4-hydroxyphenylpyruvate dioxygenase (HPPD)-inhibiting herbicides by transforming the rice HPPD INHIBITOR SENSITIVE 1 (OsHIS1) gene into Xinong 511, conferring resistance to mesotrione at levels up to nine times the typical field application rate (1350 g ai ha-1). Agronomic trait evaluations under greenhouse and field conditions showed no additional effects on wheat. Herbicide susceptibility assays confirmed the specific resistance to different HPPD inhibitors. Transcriptome and metabolome analyses revealed regulation of flavonoid and photosynthesis-antenna protein pathways in the herbicide functional. Collectively, OsHIS1 could be applied in the production of herbicide-resistant wheat.

Integrative multiomic analysis identifies distinct molecular subtypes of NAFLD in a Chinese population.

Nonalcoholic fatty liver disease (NAFLD) has become a common health care burden worldwide. The high heterogeneity of NAFLD remains elusive and impairs outcomes of clinical diagnosis and pharmacotherapy. Several NAFLD classifications have been proposed on the basis of clinical, genetic, alcoholic, or serum metabolic analyses. Yet, accurately predicting the progression of NAFLD to cirrhosis or hepatocellular carcinoma (HCC) in patients remains a challenge. Here, on the basis of a Chinese cohort of patients, we classified NAFLD into three distinct molecular subtypes (NAFLD-mSI, NAFLD-mSII, and NAFLD-mSIII) using integrative multiomics including whole-genome sequencing (WGS), proteomics, phosphoproteomics, lipidomics, and metabolomics across a broad range of liver, blood, and urine specimens. We found that NAFLD-mSI had higher expression of CYP1A2 and CYP3A4, which alleviate hepatic steatosis through mediating free fatty acid/bile acid-mTOR-FXR/PPARα signaling. NAFLD-mSII displayed an elevated risk of liver cirrhosis along with increased hepatic infiltration of M1 and M2 macrophages because of lipid-triggered hepatic CCL2 and CRP production. NAFLD-mSIII exhibited a potential risk for HCC development by increased transcription of CEBPB- and ERCC3-regulated oncogenes because of activation of the EGF-EGFR/CHKA/PI3K-PDK1-AKT cascade. Next, we validated the existence of these three NAFLD molecular subtypes in an external cohort comprising 92 patients with NAFLD across three different Chinese hospitals. These findings may aid in understanding the molecular features underlying NAFLD heterogeneity, thereby facilitating clinical diagnosis and treatment strategies with the aim of preventing the development of liver cirrhosis and HCC.

Network medicine informed multiomics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. We developed a network medicine methodology via integrating human brain multi-omics data to prioritize drug targets and repurposable treatments for ALS. We leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on human brain expression quantitative trait loci (QTL) (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Using a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in known ALS pathobiological pathways. Applying network proximity analysis of predicted ALS-associated genes and drug-target networks under the human protein-protein interactome (PPI) model, we identified potential repurposable drugs (i.e., Diazoxide and Gefitinib) for ALS. Subsequent validation established preclinical evidence for top-prioritized drugs. In summary, we presented a network-based multi-omics framework to identify drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

GREMI: An Explainable Multi-Omics Integration Framework for Enhanced Disease Prediction and Module Identification.

Multi-omics integration has demonstrated promising performance in complex disease prediction. However, existing research typically focuses on maximizing prediction accuracy, while often neglecting the essential task of discovering meaningful biomarkers. This issue is particularly important in biomedicine, as molecules often interact rather than function individually to influence disease outcomes. To this end, we propose a two-phase framework named GREMI to assist multi-omics classification and explanation. In the prediction phase, we propose to improve prediction performance by employing a graph attention architecture on sample-wise co-functional networks to incorporate biomolecular interaction information for enhanced feature representation, followed by the integration of a joint-late mixed strategy and the true-class-probability block to adaptively evaluate classification confidence at both feature and omics levels. In the interpretation phase, we propose a multi-view approach to explain disease outcomes from the interaction module perspective, providing a more intuitive understanding and biomedical rationale. We incorporate Monte Carlo tree search (MCTS) to explore local-view subgraphs and pinpoint modules that highly contribute to disease characterization from the global-view. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods in seven different classification tasks, and our model effectively addresses data mutual interference when the number of omics types increases. We further illustrate the functional- and disease-relevance of the identified modules, as well as validate the classification performance of discovered modules using an independent cohort.

Metabolomic and microbiomic resilience of Hong Kong oysters to dual stressors: Zinc oxide nanoparticles and low salinity

Zinc oxide nanoparticles, increasingly used in industrial and consumer products, and low salinity, exacerbated by climate change-induced alterations in precipitation patterns, represent significant environmental pressures in estuarine and coastal environments. This study advances previous research on their impacts on Hong Kong oysters (Crassostrea hongkongensis) by integrating metabolomics of hepatopancreas and gills with intestinal microbiomics. Employing advanced multi-omics integration methods, our analysis reveals novel insights into metabolic resilience under combined stress conditions. This resilience is characterized by coordinated, organ-specific adjustments in energy metabolism (d-glucose 1-phosphate in hepatopancreas, cytidine in gills), antioxidant defenses (glutathione, meso-2,6-diaminoheptanedioate, pimelic acid in hepatopancreas; indole, 3-(3-hydroxyphenyl)propanoic acid in gills), immune function (l-glutamine, ergocalciferol in hepatopancreas; argininosuccinic acid in gills), and membrane stability (lanosterin in hepatopancreas, allantoin in gills). Notably, under dual stressors, we observed a previously undescribed stabilization of microbial alpha diversity and certain phyla, an absence of distinctive biomarkers, and certain metabolic activity stabilization within the intestinal microbiota. These findings suggest robust compensatory mechanisms that maintain physiological homeostasis and microbial balance under stress, contrasting with primarily negative impacts reported in previous studies. Integration of metabolomic and microbiomic data revealed coordinated responses between microbial community changes and metabolic adjustments, particularly in osmoregulation, energy metabolism and antioxidant defenses, under dual stressors. This comprehensive approach provides a more realistic model of environmental challenges, revealing sophisticated adaptive strategies in Hong Kong oysters. Our study offers critical insights for understanding bivalve resilience, informing conservation strategies, and managing marine ecosystems in the face of increasing anthropogenic pressures.