Integrative Analysis of Multi-Omics Data for Biomarker Discovery.

During the past two decades, computational analysis has become paramount for biological research. Advancements in high-throughput methods and computational tools resulted in the generation of large amounts of data from different omics fields (multi-omics), such as genomics, epigenomics, transcriptomics, and metabolomics. This plethora of large scale and diverse omics data is being driven by the understanding that a single-omic type does not provide adequate information and integrative analysis of multi-omics data is optimal to gain sufficiently meaningful insights into the actual biological mechanisms. Although various open-source tools have been developed for this purpose, multi-omics data integration and analysis are still beset by a number of problems, including software compatibility, complex parameter selection and creation of functional pipelines with multiple steps of analyses. In this work we present GenOptics, a novel visual analytics platform that aims to facilitate the integration and subsequent analysis of diverse multi-omics datasets as well as meta-data (e.g., clinical data), through a fully interactive environment. The platform comprises of two separate parts. The first incorporates asynchronous analyses of Next-Generation Sequencing raw data, including RNA-, Whole exome-, and ChIP-Seq, using workflows implemented with the Common Workflow Language (doi:10.6084/m9.figshare.3115156.v2) and Docker containers to automate software installation and confer cross-platform portability. The second part constitutes the analytical platform itself, designed to facilitate the execution of robust bioinformatics analyses by life scientists with minimal or no knowledge of programming. GenOptics constitutes an open source (https://genoptics.github.io/), computational biology platform for novel pattern and biomarker detection.

With the development and improvement of high-throughput sequencing technologies, the acquisition and processing approaches of various biological omics data on different levels are becoming more mature. Despite several new disease-associated factors have been discovered based on single omics data analysis, identification of disease targets by integrative analysis of multi-omics data is still growing. Since life is a complex regulatory system in which the regulation of gene mutations, epigenetic alterations, abnormal gene expression as well as anomalous variations in signal pathway are related with the occurrence and development of diseases, it is obvious that finding therapeutic factors using single omics data analysis has its limitation. Systematical studies of clinical and pathological mechanisms and identification of optimal therapeutic targets through integrative analysis of multi-omics data from different levels and resources have become an important research direction of precision medicine, which would provide innovative perspectives on disease study and new theoretical basis for early diagnosis, personalized treatment and medicine guide. In this review, we introduce new technologies and research progresses in screening therapeutic targets using systematic omics such as genomics, transcriptomics and epigenomics, and also discusse new strategies and advantages of integrative analysis among them.

With the advent of high throughput genome-wide assays it has become possible to simultaneously measure multiple types of genomic data. Several projects like TCGA, ICGC, NCI-60 has generated comprehensive, multi-dimensional maps of the key genomic changes like MiRNA, MRNA, proteomics etc. from cancer samples[2,4]. These genomic data can be used for classifying tumour types[5]. Integrative analysis of these data from multiple sources can potentially provide additional biological insights, but methods to do any such analysis are lacking. One of the widely used solutions to handle high dimension data is by removing redundant information in the integrated sample. Most of the expressed genes are overlapped and can be projected onto lower dimension, and then be used to classify different tumor types, without the loss of any/much information. Sufficient dimension reduction (SDR) [1], a supervised dimension reduction approach, can be ideal to achieve such a goal. In this paper, we propose a novel integrative SDR method that can reduce dimensions of multiple data types simultaneously while sharing common latent structures to improve prediction and interpretation. In particular, we extend the sliced inverse regression (SIR) technique, a major SDR method, to integrate multiple omits data for simultaneous dimension reduction. SIR is a supervised dimension reduction method that assumes that the outcome variable Y depends on the predictor variable X through d unknown linear combinations of the predictor[3]. The predictor variable is replaced by its projection into a lower dimension subspace of the predictor space without the loss of information. The aim is to find the intersection of all the subspaces δ called the central susbspace (CS) of the predictor space satisfying the property Y ╨ X| Pδ X. To integrate multiple types of data, we propose and implement a new integrative sufficient dimension reduction method extending SIR[3], called integrative SIR. The main idea is that we take into account all the multi-omics data information simultaneously while finding a basis matrix for each data type with some sharing latent structures. Finally, we get d dimension data which is much smaller than the original data dimension. The reduced dimension d was achieved by cross validation. To demonstrate the integrated analysis of multi-omics data, we applied and compared conventional SIR and integrative SIR to analyze MRNA, MiRNA and proteomics expression profile of a subset of cell lines from the NCI-60 panel. The data used is taken from [6]. The outcomes we have to classify are CNS, Leukemia and Melanoma tumor types. We pre-screened 400 variables from each data type with the criteria of high variance. To find classification error, we performed random forest classification after we applied to each method with leave-one-out cross-validation. As a result, we found out that integrative SIR leads to less classification error as compared to conventional SIR. To summarize, we proposed a new integrative SIR method, a supervised dimension reduction technique for integrative analysis of multi-omics data types. Unlike conventional SDR methods, the new approach can reduce the dimensions of multiple omics data simultaneously while sharing common latent structures across data types without losing any information in prediction. By efficiently capturing the common information, our numerical study shows that integrative SIR classifies tumor types more accurately as compared to conventional SDR methods.

BackgroundCharacterizing cancer molecular subtypes is crucial for improving prognosis and individualized treatment. Integrative analysis of multi-omics data has become an important approach for disease subtyping, yielding better understanding of the complex biology. Current multi-omics integration tools and methods for cancer subtyping often suffer challenges of high computational efficiency as well as the problem of weight assignment on data types.ResultsHere, we present an efficient multi-omics integration via weighted affinity and self-diffusion (MOSD) to dissect cancer heterogeneity. MOSD first construct local scaling affinity on each data type and then integrate all affinities by weighted linear combination, followed by the self-diffusion to further improve the patients’ similarities for the downstream clustering analysis. To demonstrate the effectiveness and usefulness for cancer subtyping, we apply MOSD across ten cancer types with three measurements (Gene expression, DNA methylation, miRNA).ConclusionsOur approach exhibits more significant differences in patient survival and computationally efficient benchmarking against several state-of-art integration methods and the identified molecular subtypes reveal strongly biological interpretability. The code as well as its implementation are available in GitHub: https://github.com/DXCODEE/MOSD.

Colorectal cancer (CRC) exhibits substantial heterogeneity in molecular subtypes and clinical outcomes. We performed an integrative analysis of multi-omics data from 274 CRC patients to investigate the impact of gut microbiota composition on prognosis, identify novel subtypes, and develop a machine learning-based prognostic model. Our microbiome analysis revealed significant differences between CRC and normal tissues. Multi-omics clustering identified two major CRC subtypes, CS1 and CS2, with distinct molecular characteristics and survival outcomes. We developed the Multi-Omics Integrative Clustering and Machine Learning Score (MCMLS) model, which demonstrated strong prognostic value in predicting patient survival and outperformed existing models. The MCMLS low-score group exhibited higher immune cell infiltration, increased metabolic pathway activity, and potentially better immunotherapy response. In contrast, the MCMLS high-score group showed higher mutation burden, fibroblast infiltration, and enrichment of cell adhesion and migration pathways. Bacterial analysis revealed differentially abundant bacteria associated with prognosis. Importantly, MCMLS consistently predicted immunotherapy response across six independent datasets. Our findings highlight the complex interplay between the gut microbiome, tumor microenvironment, and immune landscape in CRC, providing valuable insights for improving patient stratification and personalized treatment strategies.

<div>Abstract<p><b>Background:</b> The effects of hepatocellular carcinoma on liver metabolism and circulating metabolites have been subjected to continuing investigation. This study compares the levels of selected metabolites in sera of hepatocellular carcinoma cases versus patients with liver cirrhosis and evaluates the influence of gender, race, and alcoholic cirrhosis on the performance of the metabolites as candidate biomarkers for hepatocellular carcinoma.</p><p><b>Methods:</b> Targeted quantitation of 15 metabolites is performed by selected research monitoring in sera from 89 Egyptian subjects (40 hepatocellular carcinoma cases and 49 cirrhotic controls) and 110 U.S. subjects (56 hepatocellular carcinoma cases and 54 cirrhotic controls). Logistic regression models are used to evaluate the ability of these metabolites in distinguishing hepatocellular carcinoma cases from cirrhotic controls. The influences of gender, race, and alcoholic cirrhosis on the performance of the metabolites are analyzed by stratified logistic regression.</p><p><b>Results:</b> Two metabolites are selected on the basis of their significance to both cohorts. Although both metabolites discriminate hepatocellular carcinoma cases from cirrhotic controls in males and Caucasians, they are insignificant in females and African Americans. One metabolite is significant in patients with alcoholic cirrhosis and the other in nonalcoholic cirrhosis.</p><p><b>Conclusions:</b> The study demonstrates the potential of two metabolites as candidate biomarkers for hepatocellular carcinoma by combining them with α-fetoprotein (AFP) and gender. Stratified statistical analyses reveal that gender, race, and alcoholic cirrhosis affect the relative levels of small molecules in serum.</p><p><b>Impact:</b> The findings of this study contribute to a better understanding of the influence of gender, race, and alcoholic cirrhosis in investigating small molecules as biomarkers for hepatocellular carcinoma. <i>Cancer Epidemiol Biomarkers Prev; 23(1); 64–72. ©2013 AACR</i>.</p></div>

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.

Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC. Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups. Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients. Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.

Endometrial cancer (EC) is a frequently diagnosed gynecologic cancer. Identifying reliable prognostic genes for predicting EC onset is crucial for reducing patient morbidity and mortality. Here, a comprehensive strategy with transcriptomic and proteomic data was performed to measure EC's characteristics. Based on the publicly available RNA-seq data, death-associated protein kinase 3, recombination signal-binding protein for the immunoglobulin kappa J region, and myosin light chain 9 were screened out as potential biomarkers that affect the EC patients' prognosis. A linear model was further constructed by multivariate Cox regression for the prediction of the risk of being malignant. From further integrative analysis, exosomes were found to have a highly enriched role that might participate in EC occurrence. The findings were validated by qRT-polymerase chain reaction (PCR) and western blotting. Collectively, we constructed a prognostic-gene-based model for EC prediction and found that exosomes participate in EC incidents, revealing significantly promising support for the diagnosis of EC.

With the rapid development of high-throughput experimental technologies, bioinformatics and computational modeling has been a rapid evolving science field concerned with the development of various analysis methods and tools for investigating these large biological data efficiently and rigorously. There are many methods and tools available for the analysis of single omics dataset. It is a great challenge that biological systems are being further investigated by integrating multiple heterogeneous and large omics data. Many powerful methods and algorithmic techniques have been developed to answer important biomedical questions through integrative analysis. In this chapter, in order to help the bench biologist analyze omics data, we introduced various methods from classical statistical techniques for single marker association and multivariate analysis to more recent advances from gene network analysis and integrative analysis of multi-omics data.

Humans are easily exposed to environmentally hazardous factors in industrial sites or daily life. In addition, exposure to various substances and not just one harmful substance is common. However, research on the effects of combined exposure on humans is limited. Therefore, this study examined the effects of combined exposure to volatile organic compounds (VOCs) on the human body. We separated 193 participants into four groups according to their work-related exposure (nonexposure, toluene exposure, toluene and xylene exposure, and toluene, ethylbenzene, and xylene exposure). We then identified the methylation level and long noncoding RNA (lncRNA) levels by omics analyses, and performed an integrated analysis to examine the change of gene expression. Thereafter, the effects of combined exposure to environmental hazards on the human body were investigated and analyzed. Exposure to VOCs was found to negatively affect the development and maintenance of the nervous system. In particular, the MALAT1 lncRNA was found to be significantly reduced in the complex exposure group, and eight genes were significantly downregulated by DNA hypermethylation. The downregulation of these genes could cause a possible decrease in the density of synapses as well as the number and density of dendrites and spines. In summary, we found that increased combined exposure to environmental hazards could lead to additional epigenetic changes, and consequently abnormal dendrites, spines, and synapses, which could damage motor learning or spatial memory. Thus, lncRNA MALAT1 or FMR1 could be novel biomarkers of neurotoxicity to identify the negative health effects of VOC complex exposure.

Hepatocellular carcinoma after sustained response to interferon in non-cirrhotic hepatitis C: flaws in the cure, or a clue to the flaws?

The medicinal plant Digitalis purpurea produces cardiac glycosides that are useful in the pharmaceutical industry. These bioactive compounds are in high demand due to ethnobotany's application to therapeutic procedures. Recent studies have investigated the role of integrative analysis of multi-omics data in understanding cellular metabolic status through systems metabolic engineering approach, as well as its application to genetically engineering metabolic pathways. In spite of numerous omics experiments, most molecular mechanisms involved in metabolic pathways biosynthesis in D. purpurea remain unclear. Using R Package Weighted Gene Co-expression Network Analysis, co-expression analysis was performed on the transcriptome and metabolome data. As a result of our study, we identified transcription factors, transcriptional regulators, protein kinases, transporters, non-coding RNAs, and hub genes that are involved in the production of secondary metabolites. Since jasmonates are involved in the biosynthesis of cardiac glycosides, the candidate genes for Scarecrow-Like Protein 14 (SCL14), Delta24-sterol reductase (DWF1), HYDRA1 (HYD1), and Jasmonate-ZIM domain3 (JAZ3) were validated under methyl jasmonate treatment (MeJA, 100 μM). Despite early induction of JAZ3, which affected downstream genes, it was dramatically suppressed after 48 hours. SCL14, which targets DWF1, and HYD1, which induces cholesterol and cardiac glycoside biosynthesis, were both promoted. The correlation between key genes and main metabolites and validation of expression patterns provide a unique insight into the biosynthesis mechanisms of cardiac glycosides in D. purpurea.

With the rapid development of multi-omics technologies and accumulation of large-scale bio-datasets, many studies have conducted a more comprehensive understanding of human diseases and drug sensitivity from multiple biomolecules, such as DNA, RNA, proteins and metabolites. Using single omics data is difficult to systematically and comprehensively analyze the complex disease pathology and drug pharmacology. The molecularly targeted therapy-based approaches face some challenges, such as insufficient target gene labeling ability, and no clear targets for non-specific chemotherapeutic drugs. Consequently, the integrated analysis of multi-omics data has become a new direction for scientists to explore the mechanism of disease and drug. However, the available drug sensitivity prediction models based on multi-omics data still have problems such as overfitting, lack of interpretability, difficulties in integrating heterogeneous data, and the prediction accuracy needs to be improved. In this paper, we proposed a novel drug sensitivity prediction (NDSP) model based on deep learning and similarity network fusion approaches, which extracts drug targets using an improved sparse principal component analysis (SPCA) method for each omics data, and construct sample similarity networks based on the sparse feature matrices. Furthermore, the fused similarity networks are put into a deep neural network for training, which greatly reduces the data dimensionality and weakens the risk of overfitting problem. We use three omics of data, RNA sequence, copy number aberration and methylation, and select 35 drugs from Genomics of Drug Sensitivity in Cancer (GDSC) for experiments, including Food and Drug Administration (FDA)-approved targeted drugs, FDA-unapproved targeted drugs and non-specific therapies. Compared with some current deep learning methods, our proposed method can extract highly interpretable biological features to achieve highly accurate sensitivity prediction of targeted and non-specific cancer drugs, which is beneficial for the development of precision oncology beyond targeted therapy.

The tumour suppressor p16/CDKN2A and the metabolic gene, methyl-thio-adenosine phosphorylase (MTAP), are frequently co-deleted in some of the most aggressive and currently untreatable cancers. Cells with MTAP deletion are vulnerable to inhibition of the metabolic enzyme, methionine-adenosyl transferase 2A (MAT2A), and the protein arginine methyl transferase (PRMT5). This synthetic lethality has paved the way for the rapid development of drugs targeting the MAT2A/PRMT5 axis. MAT2A and its liver- and pancreas-specific isoform, MAT1A, generate the universal methyl donor S-adenosylmethionine (SAM) from ATP and methionine. Given the pleiotropic role SAM plays in methylation of diverse substrates, characterising the extent of SAM depletion and downstream perturbations following MAT2A/MAT1A inhibition (MATi) is critical for safety assessment. We have assessed in vivo target engagement and the resultant systemic phenotype using multi-omic tools to characterise response to a MAT2A inhibitor (AZ’9567). We observed significant SAM depletion and extensive methionine accumulation in the plasma, liver, brain and heart of treated rats, providing the first assessment of both global SAM depletion and evidence of hepatic MAT1A target engagement. An integrative analysis of multi-omic data from liver tissue identified broad perturbations in pathways covering one-carbon metabolism, trans-sulfuration and lipid metabolism. We infer that these pathway-wide perturbations represent adaptive responses to SAM depletion and confer a risk of oxidative stress, hepatic steatosis and an associated disturbance in plasma and cellular lipid homeostasis. The alterations also explain the dramatic increase in plasma and tissue methionine, which could be used as a safety and PD biomarker going forward to the clinic.