Integrative Analysis Of Multi-omics Data Research Articles

Subtypes identification on heart failure with preserved ejection fraction via network enhancement fusion using multi-omics data

Heart failure with preserved ejection fraction (HFpEF) is associated with multiple etiologic and pathophysiologic factors. HFpEF leads to significant cardiovascular morbidity and mortality. There are various reasons that fail to identify effective therapeutic interventions for HFpEF, primarily due to its clinical heterogeneity causing significant difficulties in determining physiologic and prognostic implications for this syndrome. Thus, identifying clinical subtypes using multi-omics data has great implications for efficient treatment and prognosis of HFpEF patients. Here we proposed to integrate mRNA, DNA methylation and microRNA (miRNA) expression data of HFpEF with a similarity network fusion (SNF) method following a network enhancement (ne-SNF) denoising technique to form a fused network. A spectral clustering method was then used to obtain clusters of patient subtypes. Experiments on HFpEF datasets demonstrated that ne-SNF significantly outperforms single data subtype analysis and other integrated methods. The identified subgroups were shown to have statistically significant differences in survival. Two HFpEF subtypes were defined: a high-risk group (16.8%) and a low-risk group (83.2%). The 5-year mortality rates were 63.3% and 33.0% for the high- and low-risk group, respectively. After adjusting for the effects of clinical covariates, HFpEF patients in the high-risk group were 2.43 times more likely to die than the low-risk group. A total of 157 differentially expressed (DE) mRNAs, 2199 abnormal methylations and 121 DE miRNAs were identified between two subtypes. They were also enriched in many HFpEF-related biological processes or pathways. The ne-SNF method provides a novel pipeline for subtype identification in integrated analysis of multi-omics data.

Statistical Approaches for the Integrative Analysis of Multi-omics Data

Statistical Approaches for the Integrative Analysis of Multi-omics Data

SNP-adjacent super enhancer network mediates enhanced osteogenic differentiation of MSCs in ankylosing spondylitis.

Ankylosing spondylitis (AS) is a rheumatic disease with pathological osteogenesis that causes bony ankylosis and even deformity over time. Mesenchymal stem cells (MSCs) are multipotent stem cells that are the main source of osteoblasts. We previously demonstrated that enhanced osteogenic differentiation of MSCs from AS patients (ASMSCs) is related to pathological osteogenesis in AS. However, the more concrete mechanism needs further exploration. Super enhancers (SEs) are dense clusters of stitched enhancers that control cell identity determination and disease development. Single-nucleotide polymorphisms (SNPs) regulate the formation and interaction of SEs and denote genes accounting for AS susceptibility. Via integrative analysis of multiomic data, including histone 3 lysine 27 acetylation (H3K27ac), chromatin immunoprecipitation sequencing (ChIP-seq), SNPs and RNA sequencing (RNA-seq) data, we discovered a transcription network mediated by AS SNP-adjacent SEs (SASEs) in ASMSCs and identified key genes, such as Toll-like receptor 4 (TLR4), interleukin 18 receptor 1 (IL18R1), insulin-like growth factor binding protein 4 (IGFBP4), transportin 1 (TNPO1) and proprotein convertase subtilisin/kexin type 5 (PCSK5), which are pivotal in osteogenesis and AS pathogenesis. The SASE-regulated network modulates the enhanced osteogenic differentiation of ASMSCs by synergistically activating the PI3K-Akt, NF-kappaB and Hippo signaling pathways. Our results emphasize the crucial role of the SASE-regulated network in pathological osteogenesis in AS, and the preferential inhibition of ASMSC osteogenic differentiation by JQ1 indicates that SEs may be attractive targets in future treatment for new bone formation in AS.

Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer\u2019s disease created by integrative analysis of multi-omics data

BackgroundMild cognitive impairment (MCI) is a precursor to Alzheimer’s disease (AD), but not all MCI patients develop AD. Biomarkers for early detection of individuals at high risk for MCI-to-AD conversion are urgently required.MethodsWe used blood-based microRNA expression profiles and genomic data of 197 Japanese MCI patients to construct a prognosis prediction model based on a Cox proportional hazard model. We examined the biological significance of our findings with single nucleotide polymorphism-microRNA pairs (miR-eQTLs) by focusing on the target genes of the miRNAs. We investigated functional modules from the target genes with the occurrence of hub genes though a large-scale protein-protein interaction network analysis. We further examined the expression of the genes in 610 blood samples (271 ADs, 248 MCIs, and 91 cognitively normal elderly subjects [CNs]).ResultsThe final prediction model, composed of 24 miR-eQTLs and three clinical factors (age, sex, and APOE4 alleles), successfully classified MCI patients into low and high risk of MCI-to-AD conversion (log-rank test P = 3.44 × 10−4 and achieved a concordance index of 0.702 on an independent test set. Four important hub genes associated with AD pathogenesis (SHC1, FOXO1, GSK3B, and PTEN) were identified in a network-based meta-analysis of miR-eQTL target genes. RNA-seq data from 610 blood samples showed statistically significant differences in PTEN expression between MCI and AD and in SHC1 expression between CN and AD (PTEN, P = 0.023; SHC1, P = 0.049).ConclusionsOur proposed model was demonstrated to be effective in MCI-to-AD conversion prediction. A network-based meta-analysis of miR-eQTL target genes identified important hub genes associated with AD pathogenesis. Accurate prediction of MCI-to-AD conversion would enable earlier intervention for MCI patients at high risk, potentially reducing conversion to AD.

Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components.

The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables.

Abstract PO-026: GLUER: integrative analysis of multi-omics data at single-cell resolution

Abstract Single-cell omics assays such as RNA-Seq, ATAC-Seq and methylome sequencing have been developed to identify cell types and/or states in heterogeneous tissues. However, it is challenging to integrate these different types of single-cell omics data. Moreover, the advent of spatially resolved single-cell imaging data represent another challenge for integrative analysis with omics data. Here, we present a new algorithm, inteGrative anaLysis of mUlti-omics at single-cEll Resolution (GLUER), for integration of single-cell multi-omics data as well as imaging data. We tested GLUER using multiple datasets generated using multiomics data generated on the same single cells, which was taken as the ground truth. Our results demonstrate that GLUER has significantly improved performance in terms of the accuracy of matching cells with different data modalities, which in turn enhances downstream analyses such as clustering and trajectory inference. GLUER provides a principled analytical framework for studying the heterogeneity of cell populations using multi-omics and imaging data. Citation Format: Tao Peng, Kamyar Esmaeili Pourfarhangi, Kai Tan. GLUER: integrative analysis of multi-omics data at single-cell resolution [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-026.

Integrative analysis of multi-omics data for discovering low-frequency variants associated with low-density lipoprotein cholesterol levels.

The abundance of omics data has facilitated integrative analyses of single and multiple molecular layers with genome-wide association studies focusing on common variants. Built on its successes, we propose a general analysis framework to leverage multi-omics data with sequencing data to improve the statistical power of discovering new associations and understanding of the disease susceptibility due to low-frequency variants. The proposed test features its robustness to model misspecification, high power across a wide range of scenarios and the potential of offering insights into the underlying genetic architecture and disease mechanisms. Using the Framingham Heart Study data, we show that low-frequency variants are predictive of DNA methylation, even after conditioning on the nearby common variants. In addition, DNA methylation and gene expression provide complementary information to functional genomics. In the Avon Longitudinal Study of Parents and Children with a sample size of 1497, one gene CLPTM1 is identified to be associated with low-density lipoprotein cholesterol levels by the proposed powerful adaptive gene-based test integrating information from gene expression, methylation and enhancer-promoter interactions. It is further replicated in the TwinsUK study with 1706 samples. The signal is driven by both low-frequency and common variants. Models are available at https://github.com/ytzhong/DNAm. Supplementary data are available at Bioinformatics online.

Integrated analysis of multi-omics data to identify transcriptional regulators of cancer progression

Integrated analysis of multi-omics data to identify transcriptional regulators of cancer progression

A general framework for integrative analysis of incomplete multiomics data

There is a tremendous current interest in measuring multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation profiles, metabolic profiles, protein expressions) on a large number of subjects. Although genotypes are typically available for all study subjects, other data types may be measured only on a subset of subjects due to cost or other constraints. In addition, quantitative omics measurements, such as metabolite levels and protein expressions, are subject to detection limits in that the measurements below (or above) certain thresholds are not detectable. In this article, we propose a rigorous and powerful approach to handle missing values and detection limits in integrative analysis of multiomics data. We relate quantitative omics variables to genetic variants and other variables through linear regression models and relate phenotypes to quantitative omics variables and other variables through generalized linear models. We derive the joint-likelihood for the two sets of models by allowing arbitrary patterns of missing values and detection limits for quantitative omics variables. We carry out maximum-likelihood estimation through computationally fast and stable algorithms. The resulting estimators are approximately unbiased and statistically efficient. An application to a major study on chronic obstructive lung disease yielded new biological insights.

Integrative Analysis of Multi-omics Data Identified EGFR and PTGS2 as Key Nodes in a Gene Regulatory Network Related to Immune Phenotypes in Head and Neck Cancer.

Malignant progression exhibits a tightly orchestrated balance between immune effector response and tolerance. However, underlying molecular principles that drive the establishment and maintenance of the tumor immune phenotype remain to be elucidated. We trained a novel molecular classifier based on immune cell subsets related to programmed death-ligand 1 (PD-L1) and interferon γ (IFNγ) expression, which revealed distinct subgroups with higher (cluster A) or lower (subcluster B3) cytotoxic immune phenotypes. Integrative analysis of multi-omics data was conducted to identify differences in genetic and epigenetic landscapes as well as their impact on differentially expressed genes (DEG) among immune phenotypes. A prognostic gene signature for immune checkpoint inhibition (ICI) was established by a least absolute shrinkage and selection operator (LASSO)-Cox regression model. Mutational landscape analyses unraveled a higher frequency of CASP8 somatic mutations in subcluster A1, while subcluster B3 exhibited a characteristic pattern of copy-number alterations affecting chemokine signaling and immune effector response. The integrative multi-omics approach identified EGFR and PTGS2 as key nodes in a gene regulatory network related to the immune phenotype, and several DEGs related to the immune phenotypes were affected by EGFR inhibition in tumor cell lines. Finally, we established a prognostic gene signature by a LASSO-Cox regression model based on DEGs between nonprogressive disease and progressive disease subgroups for ICI. Our data highlight a complex interplay between genetic and epigenetic events in the establishment of the tumor immune phenotype and provide compelling experimental evidence that a patient with squamous cell carcinoma of the head and neck at higher risk for ICI treatment failure might benefit from a combination with EGFR inhibition.

Construction of a Comprehensive Multiomics Map of Hepatocellular Carcinoma and Screening of Possible Driver Genes.

Objectives: The occurrence of hepatocellular carcinoma (HCC) is a complex process involving genetic mutations, epigenetic variation, and abnormal gene expression. However, a comprehensive multiomics investigation of HCC is lacking, and the available multiomics evidence has not led to improvements in clinical practice. Therefore, we explored the molecular mechanism underlying the development of HCC through an integrative analysis of multiomics data obtained at multiple levels to provide innovative perspectives and a new theoretical basis for the early diagnosis, personalized treatment and medical guidance of HCC.Methods: In this study, we collected whole-exome sequencing data, RNA (mRNA and miRNA) sequencing data, DNA methylation array data, and single nucleotide polymorphism (SNP) array data from The Cancer Genome Atlas (TCGA). We analyzed the copy number variation (CNV) in HCC using GISTIC2. MutSigCV was applied to identify significantly mutated genes (SMGs). Functional enrichment analyses were performed using the clusterProfiler package in R software. The prognostic values of discrete variables were estimated using Kaplan–Meier survival curves.Results: By analyzing the HCC data in TCGA, we constructed a comprehensive multiomics map of HCC. Through copy number analysis, we identified significant amplification at 29 loci and significant deletions at 33 loci. A total of 13 significant mutant genes were identified. In addition, we also identified three HCC-related mutant signatures, and among these, signature 22 was closely related to exposure to aristolochic acids. Subsequently, we analyzed the methylation level of HCC samples and identified 51 epigenetically silenced genes that were significantly associated with methylation. The differential expression analysis identified differentially expressed mRNAs and miRNAs in HCC samples. Based on the above-described results, we identified a total of 93 possible HCC driver genes, which are driven by mutations, methylation, and CNVs and have prognostic value.Conclusion: Our study reveals variations in different dimensions of HCC. We performed an integrative analysis of genomic signatures, single nucleotide variants (SNVs), CNVs, methylation, and gene expression in HCC. Based on the results, we identified HCC possible driver genes that might facilitate prognostic prediction and support decision making with regard to the choice of therapy.

Multi-omics network analysis reveals distinct stages in the human aging progression in epidermal tissue.

In recent years, reports of non-linear regulations in age- and longevity-associated biological processes have been accumulating. Inspired by methodological advances in precision medicine involving the integrative analysis of multi-omics data, we sought to investigate the potential of multi-omics integration to identify distinct stages in the aging progression from ex vivo human skin tissue. For this we generated transcriptome and methylome profiling data from suction blister lesions of female subjects between 21 and 76 years, which were integrated using a network fusion approach. Unsupervised cluster analysis on the combined network identified four distinct subgroupings exhibiting a significant age-association. As indicated by DNAm age analysis and Hallmark of Aging enrichment signals, the stages captured the biological aging state more clearly than a mere grouping by chronological age and could further be recovered in a longitudinal validation cohort with high stability. Characterization of the biological processes driving the phases using machine learning enabled a data-driven reconstruction of the order of Hallmark of Aging manifestation. Finally, we investigated non-linearities in the mid-life aging progression captured by the aging phases and identified a far-reaching non-linear increase in transcriptional noise in the pathway landscape in the transition from mid- to late-life.

Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling

BackgroundGlioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes.ResultsIn this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner.ConclusionsPRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.

Unraveling the Genomic-Epigenomic Interaction Landscape in Triple Negative and Non-Triple Negative Breast Cancer.

Background: The recent surge of next generation sequencing of breast cancer genomes has enabled development of comprehensive catalogues of somatic mutations and expanded the molecular classification of subtypes of breast cancer. However, somatic mutations and gene expression data have not been leveraged and integrated with epigenomic data to unravel the genomic-epigenomic interaction landscape of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC). Methods: We performed integrative data analysis combining somatic mutation, epigenomic and gene expression data from The Cancer Genome Atlas (TCGA) to unravel the possible oncogenic interactions between genomic and epigenomic variation in TNBC and non-TNBC. We hypothesized that within breast cancers, there are differences in somatic mutation, DNA methylation and gene expression signatures between TNBC and non-TNBC. We further hypothesized that genomic and epigenomic alterations affect gene regulatory networks and signaling pathways driving the two types of breast cancer. Results: The investigation revealed somatic mutated, epigenomic and gene expression signatures unique to TNBC and non-TNBC and signatures distinguishing the two types of breast cancer. In addition, the investigation revealed molecular networks and signaling pathways enriched for somatic mutations and epigenomic changes unique to each type of breast cancer. The most significant pathways for TNBC were: retinal biosynthesis, BAG2, LXR/RXR, EIF2 and P2Y purigenic receptor signaling pathways. The most significant pathways for non-TNBC were: UVB-induced MAPK, PCP, Apelin endothelial, Endoplasmatic reticulum stress and mechanisms of viral exit from host signaling Pathways. Conclusion: The investigation revealed integrated genomic, epigenomic and gene expression signatures and signing pathways unique to TNBC and non-TNBC, and a gene signature distinguishing the two types of breast cancer. The study demonstrates that integrative analysis of multi-omics data is a powerful approach for unravelling the genomic-epigenomic interaction landscape in TNBC and non-TNBC.

Integrative Analysis of Multi-omics Data Reveals AP-1 Is a Key Regulator in Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor with poor prognosis. Currently, its mechanism of pathogenesis remains elusive. In this study, we employed integrative -omics approaches (RNA-seq, ChIP-seq, and MNase-seq) to interrogate the epigenome in ICC. The results of ChIP-seq, differential gene expression, and MNase-seq analyses suggested that ICC cell lines exhibit less histone modification of cancer-related genes compared with a normal cell line. The region of different histone modification patterns is enrichment in sites of AP-1 motif. Subsequent analysis showed that ICC had different nucleosome occupancy in differentially expressed genes compared to a normal cell line. Furthermore, we found that AP-1 plays a key role in ICC and regulates ICC-related genes through its AP-1 binding site. This study is the first report showing the global features of histone modification, transcript, and nucleosome profiles in ICC; we also show that the transcription factor AP-1 might be a key target gene in ICC. Funding Statement: This work was supported by granted from the National Natural Science Foundation of China (No. 81071990, No. 81641110); Science and Technology Program of Guangzhou, China (No. 201707010305); Youth Science Foundation of Guangdong Second Provincial General Hospital (No. YQ2016-001). Declaration of Interests: The authors declare that they have no conflicts of interest.

Erratum to: Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations.

Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals: gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study.

Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke.

Despite many years of research, no biomarkers for stroke are available to use in clinical practice. Progress in high-throughput technologies has provided new opportunities to understand the pathophysiology of this complex disease, and these studies have generated large amounts of data and information at different molecular levels. The integration of these multi-omics data means that thousands of proteins (proteomics), genes (genomics), RNAs (transcriptomics) and metabolites (metabolomics) can be studied simultaneously, revealing interaction networks between the molecular levels. Integrated analysis of multi-omics data will provide useful insight into stroke pathogenesis, identification of therapeutic targets and biomarker discovery. In this Review, we detail current knowledge on the pathology of stroke and the current status of biomarker research in stroke. We summarize how proteomics, metabolomics, transcriptomics and genomics are all contributing to the identification of new candidate biomarkers that could be developed and used in clinical stroke management.

Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations.

Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals: gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study.

New insights into cheddar cheese microbiota-metabolome relationships revealed by integrative analysis of multi-omics data

Cheese microbiota and metabolites and their inter-relationships that underpin specific cheese quality attributes remain poorly understood. Here we report that multi-omics and integrative data analysis (multiple co-inertia analysis, MCIA) can be used to gain deeper insights into these relationships and identify microbiota and metabolite fingerprints that could be used to monitor product quality and authenticity. Our study into different brands of artisanal and industrial cheddar cheeses showed that Streptococcus, Lactococcus and Lactobacillus were the dominant taxa with overall microbial community structures differing not only between industrial and artisanal cheeses but also among different cheese brands. Metabolome analysis also revealed qualitative and semi-quantitative differences in metabolites between different cheeses. This also included the presence of two compounds (3-hydroxy propanoic acid and O-methoxycatechol-O-sulphate) in artisanal cheese that have not been previously reported in any type of cheese. Integrative analysis of multi-omics datasets revealed that highly similar cheeses, identical in age and appearance, could be distinctively clustered according to cheese type and brand. Furthermore, the analysis detected strong relationships, some previously unknown, which existed between the cheese microbiota and metabolome, and uncovered specific taxa and metabolites that contributed to these relationships. These results highlight the potential of this approach for identifying product specific microbe/metabolite signatures that could be used to monitor and control cheese quality and product authenticity.

New Insights into Aging and Obesity-Associated Aging Characteristics of Female Subcutaneous Adipose Tissue Revealed by Integrative Analysis of Multi-Omics Data

New Insights into Aging and Obesity-Associated Aging Characteristics of Female Subcutaneous Adipose Tissue Revealed by Integrative Analysis of Multi-Omics Data