Multi-omics Data Research Articles

Integrating genetic and gene expression data in network-based stratification analysis of cancers

BackgroundCancers are complex diseases that have heterogeneous genetic drivers and varying clinical outcomes. A critical area of cancer research is organizing patient cohorts into subtypes and associating subtypes with clinical and biological outcomes for more effective prognosis and treatment. Large-scale studies have collected a plethora of omics data across multiple tumor types, providing an extensive dataset for stratifying patient cohorts. Network-based stratification (NBS) approaches have been presented to classify cancer tumors using somatic mutation data. A challenge in cancer stratification is integrating omics data to yield clinically meaningful subtypes. In this study, we investigate a novel approach to the NBS framework by integrating somatic mutation data with RNA sequencing data and investigating the effectiveness of integrated NBS on three cancers: ovarian, bladder, and uterine cancer.ResultsWe show that integrated NBS subtypes are more significantly associated with overall survival or histology. Specifically, we observe that integrated NBS subtypes for ovarian and bladder cancer were more significantly associated with patient survival than single-data type NBS subtypes, even when accounting for covariates. In addition, we show that integrated NBS subtypes for bladder and uterine are more significantly associated with tumor histology than single-data type NBS subtypes. Integrated NBS networks also reveal highly influential genes that span across multiple integrated NBS subtypes and subtype-specific genes. Pathway enrichment analysis of integrated NBS subtypes reveal overarching biological differences between subtypes. These genes and pathways are involved in a heterogeneous set of cell functions, including ubiquitin homeostasis, p53 regulation, cytokine and chemokine signaling, and cell proliferation, emphasizing the importance of identifying not only cancer-specific gene drivers but also subtype-specific tumor drivers.ConclusionsOur study highlights the significance of integrating multi-omics data within the NBS framework to enhance cancer subtyping, specifically its utility in offering profound implications for personalized prognosis and treatment strategies. These insights contribute to the ongoing advancement of computational subtyping methods to uncover more targeted and effective therapeutic treatments while facilitating the discovery of cancer driver genes.

CrossAttOmics: Multi-Omics data integration with CrossAttention.

Advances in high throughput technologies enabled large access to various types of omics. Each omics provides a partial view of the underlying biological process. Integrating multiple omics layers would help have a more accurate diagnosis. However, the complexity of omics data requires approaches that can capture complex relationships. One way to accomplish this is by exploiting the known regulatory links between the different omics, which could help in constructing a better multimodal representation. In this article, we propose CrossAttOmics, a new deep-learning architecture based on the cross-attention mechanism for multi-omics integration. Each modality is projected in a lower dimensional space with its specific encoder. Interactions between modalities with known regulatory links are computed in the feature representation space with cross-attention. The results of different experiments carried out in this paper show that our model can accurately predict the types of cancer by exploiting the interactions between multiple modalities. CrossAttOmics outperforms other methods when there are few paired training examples. Our approach can be combined with attribution methods like LRP to identify which interactions are the most important. The code is available at https://github.com/Sanofi-Public/CrossAttOmics and https://doi.org/10.5281/zenodo.15065928. TCGA data can be downloaded from the Genomic Data Commons Data Portal. CCLE data can be downloaded from the depmap portal. Supplementary data are available at Bioinformatics online.

Use of Artificial Intelligence in Current Fight Against Antimicrobial Resistance.

Antimicrobial resistance (AMR) poses a significant global health threat, with projections indicating it could surpass cancer in mortality rates by 2050 if left unaddressed. Optimizing antimicrobial dosing is critical to mitigate resistance and improve clinical outcomes. Traditional approaches, including population pharmacokinetics (PK) models and Bayesian estimation, are limited by mechanistic hypothesis requirements and complexity. Artificial intelligence (AI) and machine learning (ML) offer transformative solutions by leveraging large datasets to predict drug exposure accurately, refine sampling strategies, and enable real-time dose adjustments through therapeutic drug monitoring. This review highlights the role of ML models, in managing PK and pharmacodynamic variability across diverse patient populations. AI models often equal or outperform traditional methods in achieving therapeutic targets while minimizing toxicity, as demonstrated in some case studies involving ganciclovir, vancomycin, and daptomycin. Despite challenges such as data quality, interpretability, and integration with clinical workflows, AI's dynamic adaptability and precision underscore its potential. Future directions emphasize integrating multi-omics data, developing bedside decision-support tools, and expanding AI applications to broader drug categories and populations. Continued research and clinical validation are essential to harness AI's full potential in advancing precision medicine and combating AMR effectively.

Multi-omics identify ribosome related causal genes methylation, splicing, and expression in prostate cancer

BackgroundUnderstanding the molecular underpinnings of prostate cancer remains a critical challenge in oncology. Ribosomes, essential cellular organelles responsible for protein synthesis, have emerged as potential regulators in cancer development. Previous studies suggest that dysfunction in ribosomal processes may contribute significantly to prostate cancer progression. We used summary-data-based Mendelian randomization (SMR) and colocalization analysis, as well as single-cell analysis, to investigate the association between ribosome-related genes and prostate cancer by integrating multi-omics.MethodIn this study, we employed a multi-omics approach integrating genomics and transcriptomics data to investigate the role of ribosome-related genes in prostate cancer. Summary-level data for prostate cancer were obtained from The Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome and FinnGen studies. SMR analyses were performed to assess the relevance of ribosomal gene-related molecular signatures to prostate cancer. We further performed colocalization analysis to assess whether the identified signal pairs shared causal genetic variants. Genes were then validated with single-cell sequencing analysis.ResultsWe identified significant causal effects of ribosome gene methylation on prostate cancer. After integrating the multi-omics data of mQTL, sQTL and eQTL, we identified two ribosomal genes, NSUN4 and MPHOSPH6. Methylation and splicing at different sites on the NSUN4 gene showed increased and decreased risks for prostate cancer, indicating complex gene regulation mechanisms. For instance, NSUN4 methylation site of cg10215817 was genetically associated with the increased prostate cancer risk (OR 1.20, 95% CI 1.10,1.30), while NSUN4 methylation site of cg00937489 was genetically associated with the decreased prostate cancer risk (OR 0.84, 95% CI 0.74,0.94); NSUN4 chr1:46341497:46344801 splicing (OR 1.11, 95% CI 1.05–1.17) were positively associated with prostate cancer risk, while NSUN4 chr1:46340919:46344801 splicing (OR 0.95, 95% CI 0.92–0.97) were negatively associated with prostate cancer risk. Expression analysis indicated significant associations between prostate cancer risk and increased expression levels of NSUN4 (OR 1.06, 95% CI 1.03–1.09; PPH4 = 0.79) and MPHOSPH6 (OR 1.07, 95% CI 1.04–1.10; PPH4 = 0.70). In-depth single-cell analysis showed that NSUN4 highly expresses in epithelial cells, while MPHOSPH6 highly expresses in myeloid cells.ConclusionThe study found that ribosome NSUN4 and MPHOSPH6 genes were associated with prostate cancer risk. This integrative multi-omics study underscores the significance of ribosome-related genes in prostate cancer etiology. By elucidating the molecular mechanisms underlying ribosome dysfunction, our research identifies potential therapeutic targets for mitigating disease progression. These findings not only enhance our understanding of prostate cancer biology but also pave the way for personalized therapeutic strategies targeting ribosomal pathways to improve clinical outcomes.

Data- and knowledge-derived functional landscape of human solute carriers.

The human solute carrier (SLC) superfamily of ~460 membrane transporters remains the largest understudied protein family despite its therapeutic potential. To advance SLC research, we developed a comprehensive knowledgebase that integrates systematic multi-omics data sets with selected curated information from public sources. We annotated SLC substrates through literature curation, compiled SLC disease associations using data mining techniques, and determined the subcellular localization of SLCs by combining annotations from public databases with an immunofluorescence imaging approach. This SLC-centric knowledge is made accessible to the scientific community via a web portal featuring interactive dashboards and visualization tools. Utilizing this systematically collected and curated resource, we computationally derived an integrated functional landscape for the entire human SLC superfamily. We identified clusters with distinct properties and established functional distances between transporters. Based on all available data sets and their integration, we assigned biochemical/biological functions to each SLC, making this study one of the largest systematic annotations of human gene function and a potential blueprint for future research endeavors.

Ligand-receptor dynamics in heterophily-aware graph neural networks for enhanced cell type prediction from single-cell RNA-seq data

Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing structured data, particularly in domains where relationships and interactions between entities are key. By leveraging the inherent graph structure in datasets, GNNs excel in capturing complex dependencies and patterns that traditional neural networks might miss. This advantage is especially pronounced in the field of computational biology, where the intricate connections between biological entities play a crucial role. In this context, Our work explores the application of GNNs to single-cell RNA sequencing (scRNA-seq) data, a domain characterized by complex and heterogeneous relationships. By extracting ligand-receptor (L-R) associations from LIANA and constructing Cell-Cell association networks with varying edge homophily ratios, based on L-R information, we enhance the biological relevance and accuracy of depicting cellular communication pathways. While standard GNN models like Graph Convolutional Networks (GCN), GraphSAGE, Graph Attention Networks (GAT), and MixHop often assume homophily (similar nodes are more likely to be connected), this assumption does not always hold in biological networks. To address this, we explore advanced graph neural network methods, such as H2Graph Convolutional Networks and Gated Bi-Kernel GNNs (GBK-GNN), that are specifically designed to handle heterophilic data. Our study spans across six diverse datasets, enabling a thorough comparison between heterophily-aware GNNs and traditional homophily-assuming models, including Multi-Layer Perceptrons, which disregards graph structure entirely. Our findings highlight the importance of considering data-specific characteristics in GNN applications, demonstrating that heterophily-focused methods can effectively decipher the complex patterns within scRNA-seq data. By integrating multi-omics data, including gene expression profiles and L-R interactions, we pave the way for more accurate and insightful analyses in computational biology, offering a more comprehensive understanding of cellular environments and interactions.

Single-Cell Multiomic Analysis of Circadian Rhythmicity in Mouse Liver.

From bacteria to humans, most organisms showcase inherent 24-hour circadian rhythms, best exemplified by the sleep-wake cycle. These rhythms are remarkably widespread, governing hormonal, metabolic, physiological, and behavioral oscillations, and are driven by "molecular clocks" that orchestrate the rhythmic expression of thousands of genes throughout the body. Here, we generate single-cell RNA and ATAC multiomic data to simultaneously characterize gene expression and chromatin accessibility of ~33,000 mouse liver cells across the 24-hour day. Our study yields several key insights, including: (i) detecting circadian rhythmicity in both discretized liver cell types and transient sub-lobule cell states, capturing space-time RNA and ATAC profiles in a cell-type- and cell-state-specific manner; (ii) delving beyond mean cyclic patterns to characterize distributions, accounting for gene expression stochasticity due to transcriptional bursting; (iii) interrogating multimodal circadian rhythmicity, encompassing RNAs, DNA regulatory elements, and transcription factors (TFs), while examining priming and lagging effects across modalities; and (iv) inferring spatiotemporal gene regulatory networks involving target genes, TFs, and cis-regulatory elements that controls circadian rhythmicity and liver physiology. Our findings apply to existing single-cell data of mouse and Drosophila brains and are further validated by time-series single molecule fluorescence in situ hybridization, as well as vast amounts of existing and orthogonal high-throughput data from chromatin immunoprecipitation followed by sequencing, capture Hi-C, and TF knockout experiments. Altogether, our study constructs a comprehensive map of the time-series transcriptomic and epigenomic landscapes that elucidate the function and mechanism of the liver peripheral clocks.

Mini Review: Highlight of Recent Advances and Applications of MALDI Mass Spectrometry Imaging in 2024.

Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) is an emerging imaging tool that allows visualisation of hundreds of analytes unbiasedly in a single experiment. This paper highlights the adaptations of MALDI-MSI in different context in 2024, such as clinical diagnostic, pharmacology, forensics applications, plant metabolism and biology. Challenges and advancements were also discussed regarding sample preparation, instrumentations, data analysis, and integration of machine learning in the trend of single cell resolution and multi-omics. There are still rooms for improvements in sensitivity, spatial resolution, acquisition algorithm and data integration across multi-omics data to enable MALDI-MSI at subcellular level.

Expression and clinical significance of FANCI gene in pan-cancer: a comprehensive analysis based on multi-omics data

IntroductionThe FANCI gene, an essential element of the Fanconi anemia pathway, has been associated with a variety of cancer types. This investigation seeks to clarify the expression profiles, prognostic relevance, and diagnostic capabilities of FANCI across multiple malignancies, along with its links to immune cell infiltration, genetic alterations, protein-protein interactions, and functional roles.MethodsBy utilizing data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, we conducted a comprehensive analysis of FANCI mRNA expression using R software and visualized the results with the ggplot2 package. Prognostic and diagnostic evaluations were conducted using Xiantao tools to produce survival and receiver operating characteristic (ROC) curves. The examination of genetic variation was facilitated through cBioPortal, while DNA methylation and mRNA modifications were analyzed utilizing UALCAN and SangerBox 3.0. Correlations with immune responses were assessed via the EPIC platform and SangerBox 3.0. Additionally, we constructed protein-protein interaction networks employing the STRING database and Cytoscape software. Functional enrichment analyses encompassed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). The CancerSEA database was also utilized for single-cell level investigation of FANCI’s association with the functional states of cancer.ResultsOur findings reveal that FANCI is significantly upregulated in the majority of tumor types when compared to normal tissues, with increased protein levels observed in several cancers, including colorectal adenocarcinoma (COAD) and pancreatic adenocarcinoma (PAAD). Elevated FANCI expression is associated with unfavorable prognoses in cancers such as adrenocortical carcinoma (ACC) and liver hepatocellular carcinoma (LIHC). Methylation assessments demonstrated a robust inverse correlation between FANCI promoter methylation and its expression in LIHC. Moreover, FANCI expression was found to be connected to immune cell infiltration and tumor mutation burden in select cancers.DiscussionIn summary, FANCI presents as a promising biomarker for cancer prognosis and diagnosis, with potential implications for therapeutic interventions. Subsequent investigations should concentrate on elucidating the mechanistic functions of FANCI in cancer development and assessing its viability as a therapeutic target.

From Genome to Phenome: Opportunities and Challenges of Molecular Imaging.

The study of the human phenome is essential for understanding the complexities of wellness and disease and their transitions, with molecular imaging being a vital tool in this exploration. Molecular imaging embodies the 4 principles of human phenomics: precise measurement, accurate calculation or analysis, well-controlled manipulation or intervention, and innovative invention or creation. Its application has significantly enhanced the precision, individualization, and effectiveness of medical interventions. This article provides an overview of molecular imaging's technologic advancements and presents the potential use of molecular imaging in human phenomics and precision medicine. The integration of molecular imaging with multiomics data and artificial intelligence has the potential to transform health care, promoting proactive and preventive strategies. This evolving approach promises to deepen our understanding of the human phenome, lead to preclinical diagnostics and treatments, and establish quantitative frameworks for precision health management.

Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks

The rapid growth of multi-omics datasets and the wealth of biological knowledge necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying drug targets based on a limited number of samples. We propose a framework called GNNRAI for the supervised integration of multi-omics data with biological priors represented as knowledge graphs. Our framework leverages graph neural networks (GNNs) to model the correlation structures among features from high-dimensional ‘omics data, which reduces the effective dimensions in data and enables us to analyze thousands of genes simultaneously using hundreds of samples. Furthermore, our framework incorporates explainability methods to elucidate informative biomarkers. We apply our framework to Alzheimer’s disease (AD) multi-omics data, showing that the integration of transcriptomics and proteomics data with prior AD knowledge is effective, improving the prediction accuracy of AD status over single-omics analyses and highlighting both known and novel AD-predictive biomarkers.

ZAR1 and ZAR2 orchestrate the dynamics of maternal mRNA polyadenylation during mouse oocyte development

BackgroundDuring meiosis, the oocyte genome keeps dormant for a long time until zygotic genome activation. The dynamics and homeostasis of the maternal transcriptome are essential for maternal-to-zygotic transition. Zygotic arrest 1 (ZAR1) and its homolog, ZAR2, are RNA-binding proteins that are important for the regulation of maternal mRNA stability.ResultsSmart-seq2 analysis reveals drastically downregulated maternal transcripts. However, the detection of transcript levels by Smart-seq2 may be biased by the polyadenylated tail length of the mRNAs. Similarly, differential expression of maternal transcripts in oocytes with or without Zar1/2 differs when analyzed using total RNA-seq and Smart-seq2, suggesting an influence of polyadenylation. Combined analyses using total RNA-seq, LACE-seq, PAIso-seq2, and immunoprecipitation-mass spectrometry reveals that ZAR1 may target the 3’-untranslated regions of maternal transcripts, regulates their stability in germinal vesicle stage oocytes, and interacts with other proteins to control the polyadenylation of mRNAs.ConclusionsThe jointly analyzed multi-omics data highlight the limitations of Smart-seq2 in oocytes, clarify the dynamics of the maternal transcriptome, and uncover new roles of ZAR1 in regulating the maternal transcriptome.

Incorporating gene expression and environment for genomic prediction in wheat

IntroductionThe adoption of novel molecular strategies such as genomic selection (GS) in crop breeding have been key to maintaining rates of genetic gain through increased efficiency and shortening the cycle of evaluation relative to conventional selection. In the search for improved methodologies that incorporate novel sources of variation for the assessment of genetic merit, GS remains a focus of crop breeding research globally. Here we explored the role transcriptome data could play in enhancing GS in wheat.MethodsAcross 286 wheat lines, we integrated phenotype and multi-omic data from controlled environment and field experiments including ca. 40K single nucleotide polymorphisms (SNP), abundance data for ca. 50K transcripts as well as meta-data (e.g. categorical environments) to predict individual genetic merit for two agronomic traits, flowering time and height. We evaluated the performance of different model scenarios based on linear (GBLUP) and Gaussian/nonlinear (RKHS) regression in the Bayesian analytical framework. These models explored the relative contributions of different combinations of additive genomic (G), transcriptomic (T) and environment (E), with and without considering non-additive epistasis, dominance and genotype by environment (G × E) random effects.ResultsIn controlled environments, where traits were measured under contrasting daylength regimes (long and short days), transcriptome abundance outperformed other random effects when considered independently, while the model combining SNP, environment and G × E marginally outperformed the transcriptome. The best performing model for prediction of both flowering and height combined all data types, where the GBLUP framework showed slightly better performance overall compared with RKHS across all tests. Under field conditions, we found that models combining all variables were superior using the RKHS framework. However, the relative contribution of the transcriptome was reduced.DiscussionOur results show there is a predictive advantage to direct inclusion of the transcriptome for genomic evaluation in wheat breeding for traits where G × E is a factor. However, the complexity and cost of generating transcriptome data are likely to limit its feasibility for commercial breeding at this stage. We demonstrate that combining less costly environmental covariates with conventional genomic data provides a practical alternative with similar gains to the transcriptome when environments are well characterised.

Unraveling the COVID-19 Severity Hubs and Interplays in Inflammatory-Related RNA-Protein Networks.

The rapid worldwide transmission of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to severe cases of hypoxia, acute respiratory distress syndrome, multi-organ failure, and ultimately death. Small-scale molecular interactions have been analyzed by focusing on several genes/single genes, providing important insights; however, genome-wide multi-omics comprehensive molecular interactions have not yet been well investigated with the exception of GWAS and eQTLm, both of which show genetic risks. From April of 2020 until now, we have created a Japan-wide system, initially named the Japan COVID-19 Task Force. This system has collected more than 6500 COVID-19 patients' peripheral blood and as much associated clinical information as possible from a network of more than 120 hospitals. DNA, RNA, serum, and plasma were extracted and stored in this bank. This study unravels the interplay of inflammatory gene networks that induce different COVID-19 severity levels (mild, moderate, severe, and critical) by using multi-omics data from the Japan COVID-19 Task Force. We analyze RNA and protein expressions to estimate severity-specific inflammation networks that uncover the interplay between RNA and protein networks via ligand-receptor pairs. Our large-scale RNA/protein expression data analysis reveals that the atypical chemokine receptor 2 (ACKR2) acts as a key broker linking RNA and protein inflammation networks to induce COVID-19 critical severity. ACKR2 emerges in RNA and protein inflammation networks, showing active interplay in high-severity cases and weak interactions in mild cases. The results also show severity-specific molecular interactions between interleukin (IL), cytokine receptor activity, cell adhesion, and interactions involving the CC chemokine ligand (CCL) gene family and ACKR2.

Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes

The integration of artificial intelligence and personalized medicine is transforming HIV management by enhancing diagnostics, treatment optimization, and disease monitoring. Advances in machine learning, deep neural networks, and multi-omics data analysis enable precise prognostication, tailored antiretroviral therapy, and early detection of drug resistance. AI-driven models analyze vast genomic, proteomic, and clinical datasets to refine treatment strategies, predict disease progression, and pre-empt therapy failures. Additionally, AI-powered diagnostic tools, including deep learning imaging and natural language processing, improve screening accuracy, particularly in resource-limited settings. Despite these innovations, challenges such as data privacy, algorithmic bias, and the need for clinical validation remain. Successful integration of AI into HIV care requires robust regulatory frameworks, interdisciplinary collaboration, and equitable technology access. This review explores both the potential and limitations of AI in HIV management, emphasizing the need for ethical implementation and expanded research to maximize its impact. AI-driven approaches hold great promise for a more personalized, efficient, and effective future in HIV treatment and care.

Recent Trends in Food Quality and Authentication: The Role of Omics Technologies in Dairy and Meat Production.

The global demand for animal protein presents significant challenges in the production of nutritionally rich foods, such as milk and meat. Traditionally, the quality of these products is assessed using physicochemical, microbiological, and sensory methods. Although effective, these techniques are constrained by time limiting their widespread application. Furthermore, growing concerns regarding sustainability, animal welfare, and transparency have driven the development of technologies to enhance the rapid and precise assessment of food quality. In this context, omics technologies have transformed the characterization of animal-origin food by providing in-depth molecular understanding of their composition and quality. These tools enable the identification of biomarkers, adulteration detection, optimization of nutritional profiles, and enhancement of authentication and traceability, facilitating the development of functional foods. Despite their potential, several barriers persist, including high implementation cost, the need for specialized infrastructure, and the complexity of integrating multi-omics data. The main aim of this review was to provide information on advances in the application of omics technologies in dairy and meat production systems and studies that use them in food quality, authentication, and sustainability. It also outlines opportunities in areas such as fraud prevention and functional product development to support the transition to safer, healthier, and more transparent food systems.

Early prediction of preeclampsia from clinical, multi-omics and laboratory data using random forest model

BackgroundPredicting preeclampsia (PE) within the first 16 weeks of gestation is difficult due to various risk factors, poorly understood causes and likely multiple pathogenic phenotypes of preeclampsia. ObjectivesIn this study, we aimed to develop prediction models for early-onset preeclampsia (EPE) and late-onset preeclampsia (LPE) respectively using clinical data, metabolome and proteome analyses on plasma samples and laboratory data.MethodsWe retrospectively recruited 56 EPE, 50 LPE patients and 92 normotensive controls from three tertiary hospitals and used clinical and laboratory data in early pregnancy. Models for EPE and LPE were fitted with the use of patient’ clinical, multi-omics and laboratory data.ResultsBy comparing multi-omics and laboratory test variables between EPE, LPE and healthy controls, we identified sets of differentially expressed biomarkers, including 49 and 33 metabolites, 28 and 36 proteins as well as 5 and 7 laboratory variables associated with EPE and LPE respectively. Using the random forest algorithm, we developed a prediction model using seven clinical factors, seven metabolites, five laboratory test variables. The model yielded the highest accuracy for EPE prediction with good sensitivity (87.5%, 95% confidence interval [CI]: 67.64%-97.34%) and specificity (94.1%, 95% CI: 80.32%-99.28%). We also developed a prediction model that exhibited high accuracy in separating LPE from controls (sensitivity: 66.67%, 95% CI: 43.03%-85.41%; specificity: 94.12%, 95% CI: 80.32%-99.28%) using seven clinical factors, five metabolites and eight proteins.ConclusionOur study has identified a set of significant omics and laboratory features for PE prediction. The established models yielded high prediction performance for preeclampsia risk from clinical, multi-omics and laboratory information.

Single-cross prediction with imputed multi-omic data: A case study in rapeseed.

Advancements in sequencing technologies enabled the assembly and characterization of plant genomes with high resolution. In breeding programs, this data is combined with phenotypic information in genomic prediction to select genotypes based on their genetic profiles. Although SNP arrays are commonly used for genotyping, they capture only a fraction of the genomewide diversity. To address this, one approach involves genotyping the entire population with arrays, sequence a subset using whole-genome sequencing (WGS) or assessing gene expression profiles, followed by imputing the data across the entire population. This study evaluates the effect of imputed WGS markers (SNPs and structural variants) and expression data on genomic prediction in a rapeseed hybrid breeding population. A combination of SNP arrays, WGS, and RNA sequencing was employed, followed by imputation of marker and expression data. Genomic prediction was utilized to estimate general and specific combining ability effects in untested hybrids. However, while adding imputed whole-genome and expression data increased marker density and linkage disequilibrium, it didn´t enhance prediction accuracy compared to SNP array data. This is attributed to redundancy in relationship, imputation errors, or environmental influences on gene expressions. This suggests that SNP arrays continue to be reliable for genomic prediction in rapeseed hybrid breeding.

The immunosuppressive role of VSIG4 in colorectal cancer and its interaction with the tumor microenvironment

BackgroundThe tumor microenvironment in colorectal cancer (CRC) significantly influences disease progression and immune responses, particularly the role of macrophages in regulating immune evasion requires further investigation.MethodsThis study integrated data from the TCGA-COAD dataset with the GEO database, along with single-cell RNA sequencing data, to systematically analyze key genes in colorectal cancer. R software was utilized for data normalization and differential analysis, with criteria set at ∣log2FoldChange ∣ > 1 and adjusted p-value < 0.05 for gene selection. The Seurat package was employed for clustering single-cell data, while the “Monocle2” algorithm was used to perform pseudo-time analysis on the differentiation trajectory of macrophages. Additionally, non-negative matrix factorization (NMF) was applied for subtype classification of CRC patients, and various machine learning algorithms (such as LASSO and random forest models) were utilized to identify key pathogenic genes. Finally, PCR was employed to validate the expression of these key genes, and immune analysis software was used to assess their impact on immune cells, alongside pathway enrichment analysis.ResultsThrough the integration of multi-omics data, we identified significant differential expression of VSIG4, CYBBC3AR1, and FCGR1A in CRC patients. LASSO and random forest models selected these three genes as critical pathogenic factors for CRC, with AUC values exceeding 0.8 across multiple machine learning models, demonstrating their high diagnostic efficacy. PCR validation further supported the differential expression of VSIG4 and other genes in CRC. Single-cell transcriptomic analysis revealed that VSIG4 was highly enriched in specific macrophage subpopulations and significantly influenced the tumor microenvironment by regulating CD8 + T cell immune exhaustion. Pseudo-time analysis indicated that the differentiation trajectory of macrophages during tumor progression was closely associated with VSIG4 expression. Additionally, cell communication analysis.highlighted the important role of VSIG4 in the interactions between macrophages and endothelial cells. Pathway enrichment analysis demonstrated that VSIG4 expression was closely linked to the regulation of the JAK-STAT pathway and metabolic pathways such as the TCA cycle.ConclusionThis study provides the first evidence that VSIG4, CYBBC3AR1, and FCGR1A play critical roles in the immune microenvironment of colorectal cancer, particularly emphasizing the immunoregulatory function of VSIG4 in macrophage activity and CD8 + T cell immune exhaustion. PCR validation further confirmed the differential expression of these genes. These findings offer new insights into the molecular mechanisms of CRC and provide a potential theoretical basis for targeting VSIG4 in immunotherapy.

Integrating analysis of multi-omics summary data identifies novel plasma protein biomarkers and drug targets for bladder cancer

The plasma proteins are an important source of therapeutic targets. This study aims to address the diagnostic and therapeutic challenges of bladder cancer (BC) by using Mendelian randomization (MR) with a large sample size from multiple centers to identify the plasma proteins which are causally related to the pathogenesis of BC. Followed by merging nine plasma protein datasets from six studies, a total of 5538 plasma proteins and three BC datasets (ieu-b-4874, ukb-b-8193, FinnGen_R11_C3_ BLADDER_EXALL) were used to perform proteome‑wide MR to estimate the contribution of plasma proteins to BC, separately. To ensure the robustness of the results, Veen intersection operation on MR results revealed that 14 meaningful candidate pathogenic plasma proteins (ANKRD27, BIN1, FAHD1, IL17RB, MRPL21, PPT1, PSCA, SLC16A3, SLURP1, SPON2, TACSTD2, TMEM87B, YWHAB) were obtain from three datasets. Then, we validated these proteins through various methods, including meta-analysis, reverse MR, Bayesian co-localization analysis and summary-data-based MR (SMR), and pathogenic plasma proteins were divided into three layers according to the validation confidence. We then performed single-cell transcriptome analysis (Registration number: GSE222315), which showed that 13/14 candidate plasma proteins were expressed and 12 proteins were differentially expressed in at least one cell type. Finally, protein–protein interactions (PPI) analysis and druggability evaluation were performed to explore the relationship between the interaction of plasma protein markers and existing cancer drug targets. Summarily, our research uncovered 14 plasma protein biomarkers linked to BC risk, offering novel perspectives on the etiology and potential targets for developing screening biomarkers and therapeutic drugs for BC.