Genome-wide multi-omics profiling of colorectal cancer identifies immune determinants strongly associated with relapse

Multi-OMICS approaches in cancer biology: New era in cancer therapy

Innovative multi-omics frameworks integrate diverse datasets from the same patients to enhance our understanding of the molecular and clinical aspects of cancers. Advanced omics and multi-view clustering algorithms present unprecedented opportunities for classifying cancers into subtypes, refining survival predictions and treatment outcomes, and unravelling key pathophysiological processes across various molecular layers. However, with the increasing availability of cost-effective high-throughput technologies (HTT) that generate vast amounts of data, analyzing single layers often falls short of establishing causal relations. Integrating multi-omics data spanning genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offers unique prospects to comprehend the underlying biology of complex diseases like cancer. This discussion explores algorithmic frameworks designed to uncover cancer subtypes, disease mechanisms, and methods for identifying pivotal genomic alterations. It also underscores the significance of multi-omics in tumor classifications, diagnostics, and prognostications. Despite its unparalleled advantages, the integration of multi-omics data has been slow to find its way into everyday clinics. A major hurdle is the uneven maturity of different omics approaches and the widening gap between the generation of large datasets and the capacity to process this data. Initiatives promoting the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for experts in data analysis and interpretation, are crucial for translating theoretical findings into practical applications.

GS-TCGA: Gene Set-Based Analysis of The Cancer Genome Atlas

Most tools for analyzing large gene expression datasets, including The Cancer Genome Atlas (TCGA), have focused on analyzing the expression of individual genes or inference of the abundance of specific cell types from whole transcriptome information. While these methods provide useful insights, they can overlook crucial process-based information that may enhance our understanding of cancer biology. In this study, we describe three novel tools incorporated into an online resource; gene set-based analysis of The Cancer Genome Atlas (GS-TCGA). GS-TCGA is designed to enable user-friendly exploration of TCGA data using gene set-based analysis, leveraging gene sets from the Molecular Signatures Database. GS-TCGA includes three unique tools: GS-Surv determines the association between the expression of gene sets and survival in human cancers. Co-correlative gene set enrichment analysis (CC-GSEA) utilizes interpatient heterogeneity in cancer gene expression to infer functions of specific genes based on GSEA of coregulated genes in TCGA. GS-Corr utilizes interpatient heterogeneity in cancer gene expression profiles to identify genes coregulated with the expression of specific gene sets in TCGA. Users are also able to upload custom gene sets for analysis with each tool. These tools empower researchers to perform survival analysis linked to gene set expression, explore the functional implications of gene coexpression, and identify potential gene regulatory mechanisms.

From Omic Layers to Personalized Medicine in Colorectal Cancer: The Road Ahead

Colorectal cancer is a major health concern since it is a highly diagnosed cancer and the second cause of death among cancers. Thus, the most suitable biomarkers for its diagnosis, prognosis, and treatment have been studied to improve and personalize the prevention and clinical management of colorectal cancer. The emergence of omic techniques has provided a great opportunity to better study CRC and make personalized medicine feasible. In this review, we will try to summarize how the analysis of the omic layers can be useful for personalized medicine and the existing difficulties. We will discuss how single and multiple omic layer analyses have been used to improve the prediction of the risk of CRC and its outcomes and how to overcome the challenges in the use of omic layers in personalized medicine.

Mechano-immunology in microgravity

Life on Earth has evolved to thrive in the Earth's natural gravitational field; however, as space technology advances, we must revisit and investigate the effects of unnatural conditions on human health, such as gravitational change. Studies have shown that microgravity has a negative impact on various systemic parts of humans, with the effects being more severe in the human immune system. Increasing costs, limited experimental time, and sample handling issues hampered our understanding of this field. To address the existing knowledge gap and provide confidence in modelling the phenomena, in this review, we highlight experimental works in mechano-immunology under microgravity and different computational modelling approaches that can be used to address the existing problems.

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach

Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.

Tendon and multiomics: advantages, advances, and opportunities

Tendons heal by fibrosis, which hinders function and increases re-injury risk. Yet the biology that leads to degeneration and regeneration of tendons is not completely understood. Improved understanding of the metabolic nuances that cause diverse outcomes in tendinopathies is required to solve these problems. 'Omics methods are increasingly used to characterize phenotypes in tissues. Multiomics integrates 'omic datasets to identify coherent relationships and provide insight into differences in molecular and metabolic pathways between anatomic locations, and disease stages. This work reviews the current literature pertaining to multiomics in tendon and the potential of these platforms to improve tendon regeneration. We assessed the literature and identified areas where 'omics platforms contribute to the field: (1) Tendon biology where their hierarchical complexity and demographic factors are studied. (2) Tendon degeneration and healing, where comparisons across tendon pathologies are analyzed. (3) The in vitro engineered tendon phenotype, where we compare the engineered phenotype to relevant native tissues. (4) Finally, we review regenerative and therapeutic approaches. We identified gaps in current knowledge and opportunities for future study: (1) The need to increase the diversity of human subjects and cell sources. (2) Opportunities to improve understanding of tendon heterogeneity. (3) The need to use these improvements to inform new engineered and regenerative therapeutic approaches. (4) The need to increase understanding of the development of tendon pathology. Together, the expanding use of various 'omics platforms and data analysis resulting from these platforms could substantially contribute to major advances in the tendon tissue engineering and regenerative medicine field.

MicroRNA Interactome Multiomics Characterization for Cancer Research and Personalized Medicine: An Expert Review

MicroRNAs (miRNAs) that are mutually modulated by their interacting partners (interactome) are being increasingly noted for their significant role in pathogenesis and treatment of various human cancers. Recently, miRNA interactome dissected with multiomics approaches has been the subject of focus since individual tools or methods failed to provide the necessary comprehensive clues on the complete interactome. Even though single-omics technologies such as proteomics can uncover part of the interactome, the biological and clinical understanding still remain incomplete. In this study, we present an expert review of studies involving multiomics approaches to identification of miRNA interactome and its application in mechanistic characterization, classification, and therapeutic target identification in a variety of cancers, and with a focus on proteomics. We also discuss individual or multiple miRNA-based interactome identification in various pathological conditions of relevance to clinical medicine. Various new single-omics methods that can be integrated into multiomics cancer research and the computational approaches to analyze and predict miRNA interactome are also highlighted in this review. In all, we contextulize the power of multiomics approaches and the importance of the miRNA interactome to achieve the vision and practice of predictive, preventive, and personalized medicine in cancer research and clinical oncology.

Comprehensive Volatilome and Metabolome Signatures of Colorectal Cancer in Urine: A Systematic Review and Meta-Analysis

To increase compliance with colorectal cancer screening programs and to reduce the recommended screening age, cheaper and easy non-invasiveness alternatives to the fecal immunochemical test should be provided. Following the PRISMA procedure of studies that evaluated the metabolome and volatilome signatures of colorectal cancer in human urine samples, an exhaustive search in PubMed, Web of Science, and Scopus found 28 studies that met the required criteria. There were no restrictions on the query for the type of study, leading to not only colorectal cancer samples versus control comparison but also polyps versus control and prospective studies of surgical effects, CRC staging and comparisons of CRC with other cancers. With this systematic review, we identified up to 244 compounds in urine samples (3 shared compounds between the volatilome and metabolome), and 10 of them were relevant in more than three articles. In the meta-analysis, nine studies met the criteria for inclusion, and the results combining the case-control and the pre-/post-surgery groups, eleven compounds were found to be relevant. Four upregulated metabolites were identified, 3-hydroxybutyric acid, L-dopa, L-histidinol, and N1, N12-diacetylspermine and seven downregulated compounds were identified, pyruvic acid, hydroquinone, tartaric acid, and hippuric acid as metabolites and butyraldehyde, ether, and 1,1,6-trimethyl-1,2-dihydronaphthalene as volatiles.

Omics Integration Analysis Unravel the Landscape of Driving Mechanisms of Colorectal Cancer

Colorectal cancer (CRC) is one of the most malignant cancers and results in a substantial rate of morbidity and mortality. Diagnosis of this malignancy in early stages increases the chance of effective treatment. High-throughput data analyses reveal omics signatures and also provide the possibility of developing computational models for early detection of this disease. Such models would be able to use as complementary tools for early detection of different types of cancers including CRC. In this study, using gene expression data, the Flux balance analysis (FBA) applied to decode metabolic fluxes in cancer and normal cells. Moreover, transcriptome and genome analyses revealed driver agents of CRC in a biological network scheme. By applying comprehensive publicly available data from TCGA, different aspect of CRC regulome including the regulatory effect of gene expression, methylation, microRNA, copy number aberration and point mutation profile over protein levels investigated and the results provide a regulatory picture underlying CRC. Compiling omics profiles indicated snapshots of changes in different omics levels and flux rate of CRC. In conclusion, considering obtained CRC signatures and their role in biological operating systems of cells, the results suggest reliable driver regulatory modules that could potentially serve as biomarkers and therapeutic targets and furthermore expand our understanding of driving mechanisms of this disease.
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Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data

The Cancer Genome Atlas (TCGA) is a publicly funded project that aims to catalog and discover major cancer-causing genomic alterations with the goal of creating a comprehensive ‘atlas’ of cancer genomic profiles. The availability of this genome-wide information provides an unprecedented opportunity to expand our knowledge of tumourigenesis. Computational analytics and mining are frequently used as effective tools for exploring this byzantine series of biological and biomedical data. However, some of the more advanced computational tools are often difficult to understand or use, thereby limiting their application by scientists who do not have a strong computational background. Hence, it is of great importance to build user-friendly interfaces that allow both computational scientists and life scientists without a computational background to gain greater biological and medical insights. To that end, this survey was designed to systematically present available Web-based tools and facilitate the use TCGA data for cancer research.

Disparate Metabolomics Data Reassembler: A Novel Algorithm for Agglomerating Incongruent LC-MS Metabolomics Datasets

In the past decade, the field of LC-MS-based metabolomics has transformed from an obscure specialty into a major "-omics" platform for studying metabolic processes and biomolecular characterization. However, as a whole the field is still very fractured, as the nature of the instrumentation and the information produced by the platform essentially creates incompatible "islands" of datasets. This lack of data coherency results in the inability to accumulate a critical mass of metabolomics data that has enabled other -omics platforms to make impactful discoveries and meaningful advances. As such, we have developed a novel algorithm, called Disparate Metabolomics Data Reassembler (DIMEDR), which attempts to bridge the inconsistencies between incongruent LC-MS metabolomics datasets of the same biological sample type. A single "primary" dataset is postprocessed via traditional means of peak identification, alignment, and grouping. DIMEDR utilizes this primary dataset as a progenitor template by which data from subsequent disparate datasets are reassembled and integrated into a unified framework that maximizes spectral feature similarity across all samples. This is accomplished by a novel procedure for universal retention time correction and comparison via identification of ubiquitous features in the initial primary dataset, which are subsequently utilized as endogenous internal standards during integration. For demonstration purposes, two human and two mouse urine metabolomics datasets from four unrelated studies acquired over 4 years were unified via DIMEDR, which enabled meaningful analysis across otherwise incomparable and unrelated datasets.

Data mining and machine learning approaches for the integration of genome-wide association and methylation data: methodology and main conclusions from GAW20

BACKGROUND

Multiple layers of genetic and epigenetic variability are being simultaneously explored in an increasing number of health studies. We summarize here different approaches applied in the Data Mining and Machine Learning group at the GAW20 to integrate genome-wide genotype and methylation array data.

RESULTS

We provide a non-intimidating introduction to some frequently used methods to investigate high-dimensional molecular data and compare the different approaches tried by group members: random forest, deep learning, cluster analysis, mixed models, and gene-set enrichment analysis. Group contributions were quite heterogeneous regarding investigated data sets (real vs simulated), conducted data quality control and assessed phenotypes (eg, metabolic syndrome vs relative differences of log-transformed triglyceride concentrations before and after fenofibrate treatment). However, some common technical issues were detected, leading to practical recommendations.

CONCLUSIONS

Different sources of correlation were identified by group members, including population stratification, family structure, batch effects, linkage disequilibrium and correlation of methylation values at neighboring cytosine-phosphate-guanine (CpG) sites, and the majority of applied approaches were able to take into account identified correlation structures. The ability to efficiently deal with high-dimensional omics data, and the model free nature of the approaches that did not require detailed model specifications were clearly recognized as the main strengths of applied methods. A limitation of random forest is its sensitivity to highly correlated variables. The parameter setup and the interpretation of results from deep learning methods, in particular deep neural networks, can be extremely challenging. Cluster analysis and mixed models may need some predimension reduction based on existing literature, data filtering, and supplementary statistical methods, and gene-set enrichment analysis requires biological insight.

CINdex: A Bioconductor Package for Analysis of Chromosome Instability in DNA Copy Number Data

The CINdex Bioconductor package addresses an important area of high-throughput genomic analysis. It calculates the chromosome instability (CIN) index, a novel measurement that quantitatively characterizes genome-wide copy number alterations (CNAs) as a measure of CIN. The advantage of this package is an ability to compare CIN index values between several groups for patients (case and control groups), which is a typical use case in translational research. The differentially changed cytobands or chromosomes can then be linked to genes located in the affected genomic regions, as well as pathways. This enables in-depth systems biology-based network analysis and assessment of the impact of CNA on various biological processes or clinical outcomes. This package was successfully applied to analysis of DNA copy number data in colorectal cancer as a part of multi-omics integrative study as well as for analysis of several other cancer types. The source code, along with an end-to-end tutorial, and example data are freely available in Bioconductor at http://bioconductor.org/packages/CINdex/.

Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer

The presence of tumor-infiltrating lymphocytes (TIL) is a favorable prognostic factor in breast cancer, but what drives immune infiltration remains unknown. Here we examine if clonal heterogeneity, total mutation load, neoantigen load, copy number variations (CNV), gene- or pathway-level somatic mutations, or germline polymorphisms (SNP) are associated with immune metagene expression in breast cancer subtypes. Thirteen published immune metagenes correlated separately with genomic metrics in the three major breast cancer subtypes. We analyzed RNA-Seq, DNA copy number, mutation and germline SNP data of 627 ER⁺, 207 HER2⁺, and 191 triple-negative (TNBC) cancers from The Cancer Genome Atlas. P-values were adjusted for multiple comparisons, and permutation testing was used to assess false discovery rates. Increased immune metagene expression associated significantly with lower clonal heterogeneity estimated by MATH score in all subtypes and with a trend for lower overall mutation, neoantigen, and CNV loads in TNBC and HER2⁺ cancers. In ER⁺ cancers, mutation load, neoantigen load, and CNV load weakly but positively associated with immune infiltration, which reached significance for overall mutation load only. No highly recurrent single gene or pathway level mutations associated with immune infiltration. High immune gene expression and lower clonal heterogeneity in TNBC and HER2⁺ cancers suggest an immune pruning effect and equilibrium between immune surveillance and clonal expansion. Thus, immune checkpoint inhibitors may tip the balance in favor of immune surveillance in these cancers. Cancer Res; 77(12); 3317-24. ©2017 AACR.

Meta-analytic support vector machine for integrating multiple omics data

BACKGROUND

Of late, high-throughput microarray and sequencing data have been extensively used to monitor biomarkers and biological processes related to many diseases. Under this circumstance, the support vector machine (SVM) has been popularly used and been successful for gene selection in many applications. Despite surpassing benefits of the SVMs, single data analysis using small- and mid-size of data inevitably runs into the problem of low reproducibility and statistical power. To address this problem, we propose a meta-analytic support vector machine (Meta-SVM) that can accommodate multiple omics data, making it possible to detect consensus genes associated with diseases across studies.

RESULTS

Experimental studies show that the Meta-SVM is superior to the existing meta-analysis method in detecting true signal genes. In real data applications, diverse omics data of breast cancer (TCGA) and mRNA expression data of lung disease (idiopathic pulmonary fibrosis; IPF) were applied. As a result, we identified gene sets consistently associated with the diseases across studies. In particular, the ascertained gene set of TCGA omics data was found to be significantly enriched in the ABC transporters pathways well known as critical for the breast cancer mechanism.

CONCLUSION

The Meta-SVM effectively achieves the purpose of meta-analysis as jointly leveraging multiple omics data, and facilitates identifying potential biomarkers and elucidating the disease process.

G-DOC Plus - an integrative bioinformatics platform for precision medicine

BACKGROUND

G-DOC Plus is a data integration and bioinformatics platform that uses cloud computing and other advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information.

RESULTS

G-DOC Plus currently holds data from over 10,000 patients selected from private and public resources including Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the recently added datasets from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT), caArray studies of lung and colon cancer, ImmPort and the 1000 genomes data sets. The system allows researchers to explore clinical-omic data one sample at a time, as a cohort of samples; or at the level of population, providing the user with a comprehensive view of the data. G-DOC Plus tools have been leveraged in cancer and non-cancer studies for hypothesis generation and validation; biomarker discovery and multi-omics analysis, to explore somatic mutations and cancer MRI images; as well as for training and graduate education in bioinformatics, data and computational sciences. Several of these use cases are described in this paper to demonstrate its multifaceted usability.

CONCLUSION

G-DOC Plus can be used to support a variety of user groups in multiple domains to enable hypothesis generation for precision medicine research. The long-term vision of G-DOC Plus is to extend this translational bioinformatics platform to stay current with emerging omics technologies and analysis methods to continue supporting novel hypothesis generation, analysis and validation for integrative biomedical research. By integrating several aspects of the disease and exposing various data elements, such as outpatient lab workup, pathology, radiology, current treatments, molecular signatures and expected outcomes over a web interface, G-DOC Plus will continue to strengthen precision medicine research. G-DOC Plus is available at: https://gdoc.georgetown.edu .

Upregulation of the growth arrest-specific-2 in recurrent colorectal cancers, and its susceptibility to chemotherapy in a model cell system

Colorectal cancer (CRC) is one of the most common life-threatening malignances worldwide. CRC relapse markedly decreases the 5-year survival of patients following surgery. Aberrant expression of genes involved in pathways regulating the cell cycle, cell proliferation, or cell death are frequently reported in CRC tumorigenesis. We hypothesized that genes involved in CRC relapse might serve as prognostic indicators. We first evaluated the significance of gene sequences in the feces of patients with CRC relapse by consulting a public database. Tumorigenesis of target tissues was tested through tumor cell growth, cell cycle regulation, and chemotherapeutic efficacy. We found a highly significant correlation between CRC relapse and growth arrest-specific 2 (GAS2) gene expression. Based on cell models, the overexpressed GAS2 was associated with cellular growth rate, cell cycle regulation, and with chemotherapeutic sensitivity. Cell division was impaired by treating cells with 2-[4-(7-chloro-2-quinoxalinyloxy)phenoxy]-propionic acid (XK469), even when the cells were overexpressing GAS2. Thus, downregulation of GAS2 expression might control CRC relapse after curative resection. GAS2 could serve as a noninvasive marker from the feces of patients with prediagnosed CRC. Our findings suggest that GAS2 could have potential clinical applications for predicting early CRC relapse after radical resection, and that XK469 might impair tumor cell division by reducing GAS2 expression or blocking its cellular translocation. This will help in selecting the best therapeutic option, 5-fluorouracil in combination with XK469, for patients overexpressing GAS2 in CRC cells. Thus, GAS2 might act as a prognostic biomolecule and potential therapeutic target in patients with CRC relapse.

Adaptation of the targeted capture Methyl-Seq platform for the mouse genome identifies novel tissue-specific DNA methylation patterns of genes involved in neurodevelopment

Methyl-Seq was recently developed as a targeted approach to assess DNA methylation (DNAm) at a genome-wide level in human. We adapted it for mouse and sought to examine DNAm differences across liver and 2 brain regions: cortex and hippocampus. A custom hybridization array was designed to isolate 99 Mb of CpG islands, shores, shelves, and regulatory elements in the mouse genome. This was followed by bisulfite conversion and sequencing on the Illumina HiSeq2000. The majority of differentially methylated cytosines (DMCs) were present at greater than expected frequency in introns, intergenic regions, near CpG islands, and transcriptional enhancers. Liver-specific enhancers were observed to be methylated in cortex, while cortex specific enhancers were methylated in the liver. Interestingly, commonly shared enhancers were differentially methylated between the liver and cortex. Gene ontology and pathway analysis showed that genes that were hypomethylated in the cortex and hippocampus were enriched for neuronal components and neuronal function. In contrast, genes that were hypomethylated in the liver were enriched for cellular components important for liver function. Bisulfite-pyrosequencing validation of 75 DMCs from 19 different loci showed a correlation of r = 0.87 with Methyl-Seq data. We also identified genes involved in neurodevelopment that were not previously reported to be differentially methylated across brain regions. This platform constitutes a valuable tool for future genome-wide studies involving mouse models of disease.

Integrated transcriptional profiling and genomic analyses reveal RPN2 and HMGB1 as promising biomarkers in colorectal cancer

Colorectal cancer (CRC) is a heterogeneous disease that is associated with a gradual accumulation of genetic and epigenetic alterations. Among all CRC stages, stage II tumors are highly heterogeneous with a high relapse rate in about 20–25 % of stage II CRC patients following surgery. Thus, a comprehensive analysis of gene signatures to identify aggressive and metastatic phenotypes in stage II CRC is desired for a more accurate disease classification and outcome prediction. By utilizing a Cancer Array, containing 440 oncogenes and tumor suppressors to profile mRNA expression, we identified a larger number of differentially expressed genes in poorly differentiated stage II colorectal adenocarcinoma tissues, compared to their matched normal tissues. Ontology and Ingenuity Pathway Analysis (IPA) indicated that these genes are involved in functional mechanisms associated with several transcription factors. Genomic alterations of these genes were also investigated through The Cancer Genome Atlas (TCGA) database, utilizing 195 published CRC specimens. The percentage of genomic alterations in these genes was ranked based on their mRNA expression, copy number variations and mutations. This data was further combined with published microarray studies from a large set of CRC tumors classified based on prognostic features. This led to the identification of eight candidate genes including RPN2, HMGB1, AARS, IGFBP3, STAT1, HYOU1, NQO1 and PEA15 that were associated with the progressive phenotype. In particular, RPN2 and HMGB1 displayed a higher genomic alteration frequency in CRC, compared to eight other major solid cancers. Immunohistochemistry was performed on additional 78 stage I–IV CRC samples, where RPN2 protein immunostaining exhibited a significant association with stage III/IV tumors, distant metastasis, and poor differentiation, indicating that RPN2 expression is associated with poor prognosis. Further, our study revealed significant transcriptional regulatory mechanisms, networks and gene signatures, underlying CRC malignant progression and phenotype warranting future clinical investigations.

Selective paired ion contrast analysis: a novel algorithm for analyzing postprocessed LC-MS metabolomics data possessing high experimental noise

One of the consequences in analyzing biological data from noisy sources, such as human subjects, is the sheer variability of experimentally irrelevant factors that cannot be controlled for. This holds true especially in metabolomics, the global study of small molecules in a particular system. While metabolomics can offer deep quantitative insight into the metabolome via easy-to-acquire biofluid samples such as urine and blood, the aforementioned confounding factors can easily overwhelm attempts to extract relevant information. This can mar potentially crucial applications such as biomarker discovery. As such, a new algorithm, called Selective Paired Ion Contrast (SPICA), has been developed with the intent of extracting potentially biologically relevant information from the noisiest of metabolomic data sets. The basic idea of SPICA is built upon redefining the fundamental unit of statistical analysis. Whereas the vast majority of algorithms analyze metabolomics data on a single-ion basis, SPICA relies on analyzing ion-pairs. A standard metabolomic data set is reinterpreted by exhaustively considering all possible ion-pair combinations. Statistical comparisons between sample groups are made only by analyzing the differences in these pairs, which may be crucial in situations where no single metabolite can be used for normalization. With SPICA, human urine data sets from patients undergoing total body irradiation (TBI) and from a colorectal cancer (CRC) relapse study were analyzed in a statistically rigorous manner not possible with conventional methods. In the TBI study, 3530 statistically significant ion-pairs were identified, from which numerous putative radiation specific metabolite-pair biomarkers that mapped to potentially perturbed metabolic pathways were elucidated. In the CRC study, SPICA identified 6461 statistically significant ion-pairs, several of which putatively mapped to folic acid biosynthesis, a key pathway in colorectal cancer. Utilizing support vector machines (SVMs), SPICA was also able to unequivocally outperform binary classifiers built from classical single-ion feature based SVMs.

The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge

The Cancer Genome Atlas (TCGA) is a public funded project that aims to catalogue and discover major cancer-causing genomic alterations to create a comprehensive "atlas" of cancer genomic profiles. So far, TCGA researchers have analysed large cohorts of over 30 human tumours through large-scale genome sequencing and integrated multi-dimensional analyses. Studies of individual cancer types, as well as comprehensive pan-cancer analyses have extended current knowledge of tumorigenesis. A major goal of the project was to provide publicly available datasets to help improve diagnostic methods, treatment standards, and finally to prevent cancer. This review discusses the current status of TCGA Research Network structure, purpose, and achievements.

Effects of negative stressors on DNA methylation in the brain: implications for mood and anxiety disorders

Stress is a major contributor to anxiety and mood disorders. The recent discovery of epigenetic changes in the brain resulting from stress has enhanced our understanding of the mechanism by which stress is able to promote these disorders. Although epigenetics encompasses chemical modifications that occur at both DNA and histones, much attention has been focused on stress-induced DNA methylation changes on behavior. Here, we review the effect of stress-induced DNA methylation changes on physiological mechanisms that govern behavior and cognition, dysregulation of which can be harmful to mental health. A literature review was performed in the areas of DNA methylation, stress, and their impact on the brain and psychiatric illness. Key findings center on genes involved in the hypothalamic-pituitary-adrenal axis, neurotransmission and neuroplasticity. Using animal models of different stress paradigms and clinical studies, we detail how DNA methylation changes to these genes can alter physiological mechanisms that influence behavior. Appropriate levels of gene expression in the brain play an important role in mental health. This dynamic control can be disrupted by stress-induced changes to DNA methylation patterns. Advancement in other areas of epigenetics, such as histone modifications and the discovery of the novel DNA epigenetic mark, 5-hydroxymethylcytosine, could provide additional avenues to consider when determining the epigenetic effects of stress on the brain.