Biomarkers in the age of omics: time for a systems biology approach

Soluble CD40 Ligand as a Promising Biomarker in Cancer Diagnosis

Cancer remains a significant challenge in medicine due to its complexity and heterogeneity. Biomarkers have emerged as vital tools for cancer research and clinical practice, facilitating early detection, prognosis assessment, and treatment monitoring. Among these, CD40 ligand (CD40L) has gained attention for its role in immune response modulation. Soluble CD40 ligand (sCD40L) has shown promise as a potential biomarker in cancer diagnosis and progression, reflecting interactions between immune cells and the tumor microenvironment. This review explores the intricate relationship between sCD40L and cancer, highlighting its diagnostic and prognostic potential. It discusses biomarker discovery, emphasizing the need for reliable markers in oncology, and elucidates the roles of CD40L in inflammatory responses and interactions with tumor cells. Additionally, it examines sCD40L as a biomarker, detailing its significance across various cancer types and clinical applications. Moreover, the review focuses on therapeutic interventions targeting CD40L in malignancies, providing insights into cellular and gene therapy approaches and recombinant protein-based strategies. The clinical effectiveness of CD40L-targeted therapy is evaluated, underscoring the need for further research to unlock the full potential of this signaling pathway in cancer management.

Climate change and cetacean health: impacts and future directions

Climate change directly impacts the foraging opportunities of cetaceans (e.g. lower prey availability), leads to habitat loss, and forces cetaceans to move to other feeding grounds. The rise in ocean temperature, low prey availability and loss of habitat can have severe consequences for cetacean survival, particularly those species that are already threatened or those with a limited habitat range. In addition, it is predicted that the concentration of contaminants in aquatic environments will increase owing to Arctic meltwater and increased rainfall events leading to higher rates of land-based runoff in downstream coastal areas. These persistent and mobile contaminants can bioaccumulate in the ecosystem, and lead to ecotoxicity with potentially severe consequences on the reproductive organs, immune system and metabolism of marine mammals. There is a need to measure and assess the cumulative impact of multiple stressors, given that climate change, habitat alteration, low prey availability and contaminants do not act in isolation. Human-caused perturbations to cetacean foraging abilities are becoming a pervasive and prevalent threat to many cetacean species on top of climate change-associated stressors. We need to move to a greater understanding of how multiple stressors impact the metabolism of cetaceans and ultimately their population trajectory. This article is part of the theme issue 'Nurturing resilient marine ecosystems'.

Serum metabolomics study of anxiety disorder patients based on LC-MS

BACKGROUND

In the current environment of increasing social pressure, anxiety disorder has become a kind of health problem that needs to be solved urgently. However, the pathological mechanism of anxiety is still unclear, the classification of clinical diagnosis and symptoms is complex, and there is still a lack of biomarkers that can be identified and judged.

METHODS

This study used LC-MS and non-targeted metabolomics to analyze the clinically collected plasma of 18 samples from anxiety disorder patients and 31 samples from healthy people to screen differential metabolites and perform subsequent metabolic pathway analysis. Binary Logistic regression was used to construct the anxiety disorder diagnosis prediction model and evaluate the prediction efficacy.

RESULTS

The results showed that 22 metabolites were disturbed in the plasma of anxiety patients compared with healthy people. These metabolites mainly participate in 6 metabolic pathways. The combined diagnostic factors 4-Acetamidobutanoate, 3-Hydroxysebacic acid, and Cytosine were used to construct the diagnosis prediction model. The prediction probability of the model is 91.8%, the Youden index is 0.889, the sensitivity is 0.889, and the specificity is 1.000, so the prediction effect is good.

CONCLUSIONS

This study preliminarily analyzed and explored the differences between plasma samples from patients with anxiety disorder and healthy individuals, increased the types of potential biomarkers for anxiety disorder, and provided a valuable reference for subsequent research related to anxiety disorder.

Precision medicine: Concept and tools

Precision medicine is the new age medicine and refers to tailoring treatments to a subpopulation who have a common susceptibility to a particular disease or similar response to a particular drug. Although the concept existed even during the times of Sir William Osler, it was given a shot in the arm with the Precision Medicine Initiative launched by Barack Obama in 2015. The main tools of precision medicine are Big data, artificial intelligence, the various omics, pharmaco-omics, environmental and social factors and the integration of these with preventive and population medicine. Big data can be acquired from electronic health records of patients and includes various biomarkers (clinical and omics based), laboratory and radiological investigations and these can be analysed through machine learning by various complex flowcharts setting up an algorithm for the management of specific subpopulations. So, there is a move away from the traditional "one size fits all" treatment to precision-based medicine. Research in "omics" has increased in leaps and bounds and advancements have included the fields of genomics, epigenomics, proteomics, transcriptomics, metabolomics and microbiomics. Pharmaco-omics has also come to the forefront with development of new drugs and suiting a particular drug to a particular subpopulation, thus avoiding their prescription to non-responders, preventing unwanted adverse effects and proving economical in the long run. Environmental, social and behavioural factors are as important or in fact more important than genetic factors in most complex diseases and managing these factors form an important part of precision medicine. Finally integrating precision with preventive and public health makes "precision medicine" a complete final product which will change the way medicine will be practised in future.

Omics-Driven Biomarkers of Psoriasis: Recent Insights, Current Challenges, and Future Prospects

Advances in omics technologies have made it possible to unravel biomarkers from different biological levels. Intensive studies have been carried out to uncover the dysregulations in psoriasis and to identify molecular signatures associated with the pathogenesis of psoriasis. In this review, we presented an overview of the current status of the omics-driven biomarker research and emphasized the transcriptomic, epigenomic, proteomic, metabolomic, and glycomic signatures proposed as psoriasis biomarkers. Furthermore, insights on the limitations and future directions of the current biomarker discovery strategies were discussed, which will continue to comprehend broader visions of psoriasis research, diagnosis, and therapy especially in the context of personalized medicine.

Metabolomic Biomarkers in Anxiety Disorders

Anxiety disorders range among the most prevalent psychiatric disorders and belong to the leading disorders in the study of the total global burden of disease. Anxiety disorders are complex conditions, with not fully understood etiological mechanisms. Numerous factors, including psychological, genetic, biological, and chemical factors, are thought to be involved in their etiology. Although the diagnosis of anxiety disorders is constantly evolving, diagnostic manuals rely on symptom lists, not on objective biomarkers and treatment effects are small to moderate. The underlying biological factors that drive anxiety disorders may be better suited to serve as biomarkers for guiding personalized medicine, as they are objective and can be measured externally. Therefore, the incorporation of novel biomarkers into current clinical methods might help to generate a classification system for anxiety disorders that can be linked to the underlying dysfunctional pathways. The study of metabolites (metabolomics) in a large-scale manner shows potential for disease diagnosis, for stratification of patients in a heterogeneous patient population, for monitoring therapeutic efficacy and disease progression, and for defining therapeutic targets. All of these are important properties for anxiety disorders, which is a multifactorial condition not involving a single-gene mutation. This review summarizes recent investigations on metabolomics studies in anxiety disorders.

Distinct pattern of one-carbon metabolism, a nutrient-sensitive pathway, in invasive breast cancer: A metabolomic study

Altered cell metabolism is a hallmark of cancer and critical for its development. Particularly, activation of one-carbon metabolism in tumor cells can sustain oncogenesis while contributing to epigenetic changes and metabolic adaptation during tumor progression. We assessed whether increased one-carbon metabolism activity is a metabolic feature of invasive ductal carcinoma (IDC). Differences in the metabolic profile between biopsies from IDC (n = 47) and its adjacent tissue (n = 43) and between biopsies from different breast cancer subtypes were assessed by gas spectrometry in targeted (Biocrates Life Science^®) and untargeted approaches, respectively. The metabolomics data were statistically treated using MetaboAnalyst 4.0, SIMCA P+ (version 12.01), Statistica 10 software and t test with p < 0.05. The Cancer Genome Atlas breast cancer dataset was also assessed to validate the metabolomic profile of IDC. Our targeted metabolomics analysis showed distinct metabolomics profiles between IDC and adjacent tissue, where IDC displayed a comparative enrichment of metabolites involved in one-carbon metabolism (serine, glycine, threonine, and methionine) and a predicted increase in the activity of pathways that receive and donate carbon units (i.e., folate, methionine, and homocysteine). In addition, the targeted and untargeted metabolomics analyses showed similar metabolomics profiles between breast cancer subtypes. The gene set enrichment analysis identified different transcription-related functions between IDC and non-tumor tissues that involved one-carbon metabolism. Our data suggest that one-carbon metabolism may be a central pathway in IDC and even in general breast tumors, representing a potential target for its treatment and prevention.

Metabolomics analysis of the hippocampus in a rat model of traumatic brain injury during the acute phase

BACKGROUND

Traumatic brain injury (TBI) has increased in rank among traumatic injuries worldwide. Traumatic brain injury is a serious obstacle given that its complex pathology represents a long-term process. Recently, systems biology strategies such as metabolomics to investigate the multifactorial nature of TBI have facilitated attempts to find biomarkers and probe molecular pathways for its diagnosis and therapy.

METHODS

This study included a group of 20 rats with controlled cortical impact and a group of 20 sham rats. We utilized mNSS tests to investigate neurological metabolic impairments on day 1 and day 3. Furthermore, we applied metabolomics and bioinformatics to determine the metabolic perturbation caused by TBI during the acute period in the hippocampus tissue of controlled cortical impact (CCI) rats. Notably, TBI-protein-metabolite subnetworks identified from a database were assessed for associations between metabolites and TBI by the dysregulation of related enzymes and transporters.

RESULTS

Our results identified 7 and 8 biomarkers on day 1 and day 3, respectively. Additionally, related pathway disorders showed effects on arginine and proline metabolism as well as taurine and hypotaurine metabolism on day 3 in acute TBI. Furthermore, according to metabolite-protein database searches, 25 metabolite-protein pairs were established as causally associated with TBI. Further, bioinformation indicated that these TBI-associated proteins mainly take part in 5'-nucleotidase activity and carboxylic acid transmembrane transport. In addition, interweaved networks were constructed to show that the development of TBI might be affected by metabolite-related proteins and their protein pathways.

CONCLUSION

The overall results show that acute TBI is susceptible to metabolic disorders, and the joint metabolite-protein network analysis provides a favorable prediction of TBI pathogenesis mechanisms in the brain. The signatures in the hippocampus might be promising for the development of biomarkers and pathways relevant to acute TBI and could further guide testable predictions of the underlying mechanism of TBI.

Differentiating Psychosomatic, Somatopsychic, Multisystem Illnesses, and Medical Uncertainty

There is often difficulty differentiating between psychosomatic, somatopsychic, multisystem illness, and different degrees of medical uncertainty. Uncommon, complex, and multisystem diseases are commonly misdiagnosed. Two case histories are described, and relevant terms differentiating psychosomatic, somatopsychic, and multisystem illnesses are identified, reviewed, and discussed. Adequate differentiation requires an understanding of the mind/body connection, which includes knowledge of general medicine, psychiatry, and the systems linking the body and the brain. A psychiatric diagnosis cannot be given solely based upon the absence of physical, laboratory, or pathological findings. Medically unexplained symptoms, somatoform disorder, and compensation neurosis are outdated and/or inaccurate terms. The terms subjective, nonspecific, and vague can be used inaccurately. Conversion disorders, functional disorders, psychogenic illness, factitious disorder imposed upon another (Munchausen's syndrome by proxy), somatic symptom disorder, psychogenic seizures, psychogenic pain, psychogenic fatigue, and delusional parasitosis can be over-diagnosed. Bodily distress disorder and bodily distress syndrome are scientifically unsupported and inaccurate. Many "all in your head" conditions may be related to the microbiome and the immune system. Better education concerning the interface between medicine and psychiatry and the associated diagnostic nomenclature as well as utilizing clinical judgment and thorough assessment, exercising humility, and maintaining our roots in traditional medicine will help to improve diagnostic accuracy and patient trust.

Changing how we think about infectious diseases

As we learn more and more about the classes of organisms that infect humans, we are discovering that many organisms, including pathogenic organisms, may have a complex relationship with humans in which infection seldom results in the production disease. In some cases, infection may be just one biological event that occurs during a multievent process that develops sequentially, over time, and involves genetic and environmental factors that may vary among individuals. Consequently, the role of infectious organisms in the development of human disease may not meet all of the criteria normally required to determine when an organism can be called the cause of a disease. This chapter reviews the expanding role of infections in the development of human disease. We discuss prion diseases of humans, a fascinating example of an infectious disease-causing agent that is not a living organism. We also discuss the diseases of unknown etiology for which infectious organisms may play a role. In addition, this chapter reviews some of the misconceptions and recurring errors associated with the classification of infectious diseases that have led to misdiagnoses and have impeded our understanding of the role of organisms in the development of human diseases.

Mechanism-Based Disease Progression Model Describing Host-Pathogen Interactions During the Pathogenesis of Acinetobacter baumannii Pneumonia

The emergence of highly resistant bacteria is a serious threat to global public health. The host immune response is vital for clearing bacteria from the infected host; however, the current drug development paradigm does not take host‐pathogen interactions into consideration. Here, we used a systems‐based approach to develop a quantitative, mechanism‐based disease progression model to describe bacterial dynamics, host immune response, and lung injury in an immunocompetent rat pneumonia model. Previously, Long‐Evans rats were infected with Acinetobacter baumannii (A. baumannii) strain 307‐0294 at five different inocula and total lung bacteria, interleukin‐1beta (IL‐1β), tumor necrosis factor‐α (TNF‐α), cytokine‐induced neutrophil chemoattractant 1 (CINC‐1), neutrophil counts, and albumin were quantified. Model development was conducted in ADAPT5 version 5.0.54 using a pooled approach with maximum likelihood estimation; all data were co‐modeled. The final model characterized host‐pathogen interactions during the natural time course of bacterial pneumonia. Parameters were estimated with good precision. Our expandable model will integrate drug effects to aid in the design of optimized antibiotic regimens.

A multi-omic analysis reveals the regulatory role of CD180 during the response of macrophages to Borrelia burgdorferi

Macrophages are cells of the innate immune system with the ability to phagocytose and induce a global pattern of responses that depend on several signaling pathways. We have determined the biosignature of murine bone marrow-derived macrophages and human blood monocytes using transcriptomic and proteomic approaches. We identified a common pattern of genes that are transcriptionally regulated and overall indicate that the response to B. burgdorferi involves the interaction of spirochetal antigens with several inflammatory pathways corresponding to primary (triggered by pattern-recognition receptors) and secondary (induced by proinflammatory cytokines) responses. We also show that the Toll-like receptor family member CD180 is downregulated by the stimulation of macrophages, but not monocytes, with the spirochete. Silencing Cd180 results in increased phagocytosis while tempering the production of the proinflammatory cytokine TNF. Cd180-silenced cells produce increased levels of Itgam and surface CD11b, suggesting that the regulation of CD180 by the spirochete initiates a cascade that increases CR3-mediated phagocytosis of the bacterium while repressing the consequent inflammatory response.

Metabolomic alterations in invasive ductal carcinoma of breast: A comprehensive metabolomic study using tissue and serum samples

Invasive ductal carcinoma (IDC) is the most common type of breast cancer and the leading cause of breast cancer related mortality. In the present study, metabolomic profiles of 72 tissue samples and 146 serum samples were analysed using targeted liquid chromatography multiple reaction monitoring mass spectrometry (LC-MRM/MS) and untargeted gas chromatography mass spectrometry (GC-MS) approaches. Combination of univariate and multivariate statistical treatment identified significant alterations of 42 and 32 metabolites in tissue and serum samples of IDC, respectively when compared to control. Some of the metabolite changes from tissue were also reflected in serum, indicating a bi-directional interaction of metabolites in IDC. Additionally, 8 tissue metabolites and 9 serum metabolites showed progressive change from control to benign to IDC suggesting their possible role in malignant transformation. We have identified a panel of three metabolites viz. tryptophan, tyrosine, and creatine in tissue and serum, which could be useful in screening of IDC subjects from both control and benign. The metabolomic alterations in IDC showed perturbations in purine and pyrimidine metabolism, amino sugar metabolism, amino acid metabolism, fatty acid biosynthesis etc. Comprehensively, this study provides valuable insights into metabolic adaptations of IDC, which can help to identify diagnostic markers as well as potential therapeutic targets.

PhyloOncology: Understanding cancer through phylogenetic analysis

Despite decades of research and an enormity of resultant data, cancer remains a significant public health problem. New tools and fresh perspectives are needed to obtain fundamental insights, to develop better prognostic and predictive tools, and to identify improved therapeutic interventions. With increasingly common genome-scale data, one suite of algorithms and concepts with potential to shed light on cancer biology is phylogenetics, a scientific discipline used in diverse fields. From grouping subsets of cancer samples to tracing subclonal evolution during cancer progression and metastasis, the use of phylogenetics is a powerful systems biology approach. Well-developed phylogenetic applications provide fast, robust approaches to analyze high-dimensional, heterogeneous cancer data sets. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Structural and functional alterations in the colonic microbiome of the rat in a model of stress induced irritable bowel syndrome

Stress is known to perturb the microbiome and exacerbate irritable bowel syndrome (IBS) associated symptoms. Characterizing structural and functional changes in the microbiome is necessary to understand how alterations affect the biomolecular environment of the gut in IBS. Repeated water avoidance (WA) stress was used to induce IBS-like symptoms in rats. The colon-mucosa associated microbiome was characterized in 13 stressed and control animals by 16S sequencing. In silico analysis of the functional domains of microbial communities was done by inferring metagenomic profiles from 16S data. Microbial communities and functional profiles were compared between conditions. WA animals exhibited higher α-diversity and moderate divergence in community structure (β-diversity) compared with controls. Specific clades and taxa were consistently and significantly modified in the WA animals. The WA microbiome was particularly enriched in Proteobacteria and depleted in several beneficial taxa. A decreased capacity in metabolic domains, including energy- and lipid-metabolism, and an increased capacity for fatty acid and sulfur metabolism was inferred for the WA microbiome. The stressed condition favored the proliferation of a greater diversity of microbes that appear to be functionally similar, resulting in a functionally poorer microbiome with implications for epithelial health. Taxa, with known beneficial effects, were found to be depleted, which supports their relevance as therapeutic agents to restore microbial health. Microbial sulfur metabolism may form a key component of visceral nerve sensitization pathways and is therefore of interest as a target metabolic domain in microbial ecological restoration.

Genomics of Colorectal Cancer in African Americans

Genome-wide studies are increasingly becoming a must, especially for complex diseases such as cancer where multiple genes and diverse molecular mechanisms are known to be involved in genes' function alteration. In this review, we report our latest genomic and epigenomic findings in African-American colorectal cancer patients. This population suffers a higher burden of the disease and most investigators in this field are looking for the underlying genetic and epigenetic targets that might be responsible for this disparity. We here report genome-wide copy number variations, single nucleotide mutations and DNA methylation findings that might be specific to this population.

A Systems Biology Interpretation of Array Comparative Genomic Hybridization (aCGH) Data through Phylogenetics

Array Comparative Genomic Hybridization (aCGH) is a rapid screening technique to detect gene deletions and duplications, providing an overview of chromosomal aberrations throughout the entire genome of a tumor, without the need for cell culturing. However, the heterogeneity of aCGH data obfuscates existing methods of data analysis. Analysis of aCGH data from a systems biology perspective or in the context of total aberrations is largely absent in the published literature. We present here a novel alternative to the functional analysis of aCGH data using the phylogenetic paradigm that is well-suited to high dimensional datasets of heterogeneous nature, but has not been widely adapted to aCGH data. Maximum parsimony phylogenetic analysis sorts out genetic data through the simplest presentation of the data on a cladogram, a graphical evolutionary tree, thus providing a powerful and efficient method for aCGH data analysis. For example, the cladogram models the multiphasic changes in the cancer genome and identifies shared early mutations in the disease progression, providing a simple yet powerful means of aCGH data interpretation. As such, applying maximum parsimony phylogenetic analysis to aCGH results allows for the differentiation between drivers and passenger genes aberrations in cancer specimens. In addition to offering a novel methodology to analyze aCGH results, we present here a crucial software suite that we wrote to carry out the analysis. In a broader context, we wish to underscore that phylogenetic analysis of aCGH data is a non-parametric method that circumvents the pitfalls and frustrations of standard analytical techniques that rely on parametric statistics. Organizing the data in a cladogram as explained in this research article provides insights into the disease common aberrations, as well as the disease subtypes and their shared aberrations (the synapomorphies) of each subtype. Hence, we report the method and make the software suite publicly and freely available at http://software.phylomcs.com so that researchers can test alternative and innovative approaches to the analysis of aCGH data.

BcCluster: A Bladder Cancer Database at the Molecular Level

BACKGROUND

Bladder Cancer (BC) has two clearly distinct phenotypes. Non-muscle invasive BC has good prognosis and is treated with tumor resection and intravesical therapy whereas muscle invasive BC has poor prognosis and requires usually systemic cisplatin based chemotherapy either prior to or after radical cystectomy. Neoadjuvant chemotherapy is not often used for patients undergoing cystectomy. High-throughput analytical omics techniques are now available that allow the identification of individual molecular signatures to characterize the invasive phenotype. However, a large amount of data produced by omics experiments is not easily accessible since it is often scattered over many publications or stored in supplementary files.

OBJECTIVE

To develop a novel open-source database, BcCluster (http://www.bccluster.org/), dedicated to the comprehensive molecular characterization of muscle invasive bladder carcinoma.

MATERIALS

A database was created containing all reported molecular features significant in invasive BC. The query interface was developed in Ruby programming language (version 1.9.3) using the web-framework Rails (version 4.1.5) (http://rubyonrails.org/).

RESULTS

BcCluster contains the data from 112 published references, providing 1,559 statistically significant features relative to BC invasion. The database also holds 435 protein-protein interaction data and 92 molecular pathways significant in BC invasion. The database can be used to retrieve binding partners and pathways for any protein of interest. We illustrate this possibility using survivin, a known BC biomarker.

CONCLUSIONS

BcCluster is an online database for retrieving molecular signatures relative to BC invasion. This application offers a comprehensive view of BC invasiveness at the molecular level and allows formulation of research hypotheses relevant to this phenotype.

Alzheimer's as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks

Alzheimer's disease (AD), and many neurodegenerative disorders, are multifactorial in nature. They involve a combination of genomic, epigenomic, interactomic and environmental factors. Progress is being made, and these complex diseases are beginning to be understood as having their origin in altered states of biological networks at the cellular level. In the case of AD, genomic susceptibility and mechanisms leading to (or accompanying) the impairment of the central Amyloid Precursor Protein (APP) processing and tau networks are widely accepted as major contributors to the diseased state. The derangement of these networks may result in both the gain and loss of functions, increased generation of toxic species (e.g., toxic soluble oligomers and aggregates) and imbalances, whose effects can propagate to supra-cellular levels. Although well sustained by empirical data and widely accepted, this global perspective often overlooks the essential roles played by the main counteracting homeostatic networks (e.g., protein quality control/proteostasis, unfolded protein response, protein folding chaperone networks, disaggregases, ER-associated degradation/ubiquitin proteasome system, endolysosomal network, autophagy, and other stress-protective and clearance networks), whose relevance to AD is just beginning to be fully realized. In this chapter, an integrative perspective is presented. Alzheimer's disease is characterized to be a result of: (a) intrinsic genomic/epigenomic susceptibility and, (b) a continued dynamic interplay between the deranged networks and the central homeostatic networks of nerve cells. This interplay of networks will underlie both the onset and rate of progression of the disease in each individual. Integrative Systems Biology approaches are required to effect its elucidation. Comprehensive Systems Biology experiments at different 'omics levels in simple model organisms, engineered to recapitulate the basic features of AD may illuminate the onset and sequence of events underlying AD. Indeed, studies of models of AD in simple organisms, differentiated cells in culture and rodents are beginning to offer hope that the onset and progression of AD, if detected at an early stage, may be stopped, delayed, or even reversed, by activating or modulating networks involved in proteostasis and the clearance of toxic species. In practice, the incorporation of next-generation neuroimaging, high-throughput and computational approaches are opening the way towards early diagnosis well before irreversible cell death. Thus, the presence or co-occurrence of: (a) accumulation of toxic Aβ oligomers and tau species; (b) altered splicing and transcriptome patterns; (c) impaired redox, proteostatic, and metabolic networks together with, (d) compromised homeostatic capacities may constitute relevant 'AD hallmarks at the cellular level' towards reliable and early diagnosis. From here, preventive lifestyle changes and tailored therapies may be investigated, such as combined strategies aimed at both lowering the production of toxic species and potentiating homeostatic responses, in order to prevent or delay the onset, and arrest, alleviate, or even reverse the progression of the disease.

Bone Marrow Stromal Antigen 2 Is a Novel Plasma Biomarker and Prognosticator for Colorectal Carcinoma: A Secretome-Based Verification Study

BACKGROUND

The cancer cell secretome has been recognized as a valuable reservoir for identifying novel serum/plasma biomarkers for different cancers, including colorectal cancer (CRC). This study aimed to verify four CRC cell-secreted proteins (tumor-associated calcium signal transducer 2/trophoblast cell surface antigen 2 (TACSTD2/TROP2), tetraspanin-6 (TSPAN6), bone marrow stromal antigen 2 (BST2), and tumor necrosis factor receptor superfamily member 16 (NGFR)) as potential plasma CRC biomarkers.

METHODS

The study population comprises 152 CRC patients and 152 controls. Target protein levels in plasma and tissue samples were assessed by ELISA and immunohistochemistry, respectively.

RESULTS

Among the four candidate proteins examined by ELISA in a small sample set, only BST2 showed significantly elevated plasma levels in CRC patients versus controls. Immunohistochemical analysis revealed the overexpression of BST2 in CRC tissues, and higher BST2 expression levels correlated with poorer 5-year survival (46.47% versus 65.57%; p = 0.044). Further verification confirmed the elevated plasma BST2 levels in CRC patients (2.35 ± 0.13 ng/mL) versus controls (1.04 ± 0.03 ng/mL) (p < 0.01), with an area under the ROC curve (AUC) being 0.858 comparable to that of CEA (0.867).

CONCLUSION

BST2, a membrane protein selectively detected in CRC cell secretome, may be a novel plasma biomarker and prognosticator for CRC.

Computational Tools for Parsimony Phylogenetic Analysis of Omics Data

High-throughput assays from genomics, proteomics, metabolomics, and next generation sequencing produce massive omics datasets that are challenging to analyze in biological or clinical contexts. Thus far, there is no publicly available program for converting quantitative omics data into input formats to be used in off-the-shelf robust phylogenetic programs. To the best of our knowledge, this is the first report on creation of two Windows-based programs, OmicsTract and SynpExtractor, to address this gap. We note, as a way of introduction and development of these programs, that one particularly useful bioinformatics inferential modeling is the phylogenetic cladogram. Cladograms are multidimensional tools that show the relatedness between subgroups of healthy and diseased individuals and the latter's shared aberrations; they also reveal some characteristics of a disease that would not otherwise be apparent by other analytical methods. The OmicsTract and SynpExtractor were written for the respective tasks of (1) accommodating advanced phylogenetic parsimony analysis (through standard programs of MIX [from PHYLIP] and TNT), and (2) extracting shared aberrations at the cladogram nodes. OmicsTract converts comma-delimited data tables through assigning each data point into a binary value ("0" for normal states and "1" for abnormal states) then outputs the converted data tables into the proper input file formats for MIX or with embedded commands for TNT. SynapExtractor uses outfiles from MIX and TNT to extract the shared aberrations of each node of the cladogram, matching them with identifying labels from the dataset and exporting them into a comma-delimited file. Labels may be gene identifiers in gene-expression datasets or m/z values in mass spectrometry datasets. By automating these steps, OmicsTract and SynpExtractor offer a veritable opportunity for rapid and standardized phylogenetic analyses of omics data; their model can also be extended to next generation sequencing (NGS) data. We make OmicsTract and SynpExtractor publicly and freely available for non-commercial use in order to strengthen and build capacity for the phylogenetic paradigm of omics analysis.

Biomarker approaches in major depressive disorder evaluated in the context of current hypotheses

Major depressive disorder is a heterogeneous disorder, mostly diagnosed on the basis of symptomatic criteria alone. It would be of great help when specific biomarkers for various subtypes and symptom clusters of depression become available to assist in diagnosis and subtyping of depression, and to enable monitoring and prognosis of treatment response. However, currently known biomarkers do not reach sufficient sensitivity and specificity, and often the relation to underlying pathophysiology is unclear. In this review, we evaluate various biomarker approaches in terms of scientific merit and clinical applicability. Finally, we discuss how combined biomarker approaches in both preclinical and clinical studies can help to make the connection between the clinical manifestations of depression and the underlying pathophysiology.

Protein interactome of muscle invasive bladder cancer

Muscle invasive bladder carcinoma is a complex, multifactorial disease caused by disruptions and alterations of several molecular pathways that result in heterogeneous phenotypes and variable disease outcome. Combining this disparate knowledge may offer insights for deciphering relevant molecular processes regarding targeted therapeutic approaches guided by molecular signatures allowing improved phenotype profiling. The aim of the study is to characterize muscle invasive bladder carcinoma on a molecular level by incorporating scientific literature screening and signatures from omics profiling. Public domain omics signatures together with molecular features associated with muscle invasive bladder cancer were derived from literature mining to provide 286 unique protein-coding genes. These were integrated in a protein-interaction network to obtain a molecular functional map of the phenotype. This feature map educated on three novel disease-associated pathways with plausible involvement in bladder cancer, namely Regulation of actin cytoskeleton, Neurotrophin signalling pathway and Endocytosis. Systematic integration approaches allow to study the molecular context of individual features reported as associated with a clinical phenotype and could potentially help to improve the molecular mechanistic description of the disorder.

Neuroendocrine hormones as prognostic biomarkers in the setting of acute stroke: overcoming the major hurdles

Stroke represents one of the major causes of disability and mortality worldwide and prediction of outcome represents a challenge for both clinicians and researchers. In the past years, many blood markers have been associated with stroke outcome but despite this evidence, no biomarker is routinely used in stroke management. In this review, we focus on markers of the neuroendocrine system, which represent potential candidates to be implemented in clinical practice. Moreover, we present a systematic review and literature-based meta-analysis for copeptin, a new biomarker of the hypothalamo-pituitary-adrenal axis that has shown additional predictive value over clinical information in a large prospective study. The meta-analysis of the included 7 studies, with more than 2000 patients, reinforced its association with poor outcome (pooled odds ratio: 2.474 [1.678-3.268]) and mortality (pooled OR: 2.569 [1.642-3.495]). We further review the current situation of the topic and next steps to implement these tools by clinicians.

Nonlinear effects of nanoparticles: biological variability from hormetic doses, small particle sizes, and dynamic adaptive interactions

Researchers are increasingly focused on the nanoscale level of organization where biological processes take place in living systems. Nanoparticles (NPs, e.g., 1-100 nm diameter) are small forms of natural or manufactured source material whose properties differ markedly from those of the respective bulk forms of the "same" material. Certain NPs have diagnostic and therapeutic uses; some NPs exhibit low-dose toxicity; other NPs show ability to stimulate low-dose adaptive responses (hormesis). Beyond dose, size, shape, and surface charge variations of NPs evoke nonlinear responses in complex adaptive systems. NPs acquire unique size-dependent biological, chemical, thermal, optical, electromagnetic, and atom-like quantum properties. Nanoparticles exhibit high surface adsorptive capacity for other substances, enhanced bioavailability, and ability to cross otherwise impermeable cell membranes including the blood-brain barrier. With super-potent effects, nano-forms can evoke cellular stress responses or therapeutic effects not only at lower doses than their bulk forms, but also for longer periods of time. Interactions of initial effects and compensatory systemic responses can alter the impact of NPs over time. Taken together, the data suggest the need to downshift the dose-response curve of NPs from that for bulk forms in order to identify the necessarily decreased no-observed-adverse-effect-level and hormetic dose range for nanoparticles.

Stress, genomic adaptation, and the evolutionary trade-off

Cells are constantly exposed to various internal and external stresses. The importance of cellular stress and its implication to disease conditions have become popular research topics. Many ongoing investigations focus on the sources of stress, their specific molecular mechanisms and interactions, especially regarding their contributions to many common and complex diseases through defined molecular pathways. Numerous molecular mechanisms have been linked to endoplasmic reticulum stress along with many unexpected findings, drastically increasing the complexity of our molecular understanding and challenging how to apply individual mechanism-based knowledge in the clinic. A newly emergent genome theory searches for the synthesis of a general evolutionary mechanism that unifies different types of stress and functional relationships from a genome-defined system point of view. Herein, we discuss the evolutionary relationship between stress and somatic cell adaptation under physiological, pathological, and somatic cell survival conditions, the multiple meanings to achieve adaptation and its potential trade-off. In particular, we purposely defocus from specific stresses and mechanisms by redirecting attention toward studying underlying general mechanisms.

Circulating microRNA biomarkers for glioma and predicting response to therapy

The need for glioma biomarkers with improved sensitivity and specificity has sparked research into short non-coding RNA known as microRNA (miRNA). Altered miRNA biogenesis and expression in glioma plays a vital role in important signaling pathways associated with a range of tumor characteristics including gliomagenesis, invasion, and malignancy. This review will discuss current research into the role of miRNA in glioma and altered miRNA expression in biofluids as candidate biomarkers with a particular focus on glioblastoma, the most malignant form of glioma. The isolation and characterization of miRNA using cellular and molecular biology techniques from the circulation of glioma patients could potentially be used for improved diagnosis, prognosis, and treatment decisions. We aim to highlight the links between research into miRNA function, their use as biomarkers, and how these biomarkers can be used to predict response to therapy. Furthermore, increased understanding of miRNA in glioma biology through biomarker research has led to the development of miRNA therapeutics which could restore normal miRNA expression and function and improve the prognosis of glioma patients. A panel of important miRNA biomarkers for glioma in various biofluids discovered to date has been summarized here. There is still a need, however, to standardize techniques for biomarker characterization to bring us closer to clinically relevant miRNA-based diagnostic and therapeutic signatures. A clinically validated biomarker panel has potential to improve time to diagnosis, predicting response to treatment and ultimately the prognosis of glioma patients.

Mass spectrometry strategies for clinical metabolomics and lipidomics in psychiatry, neurology, and neuro-oncology

Metabolomics research has the potential to provide biomarkers for the detection of disease, for subtyping complex disease populations, for monitoring disease progression and therapy, and for defining new molecular targets for therapeutic intervention. These potentials are far from being realized because of a number of technical, conceptual, financial, and bioinformatics issues. Mass spectrometry provides analytical platforms that address the technical barriers to success in metabolomics research; however, the limited commercial availability of analytical and stable isotope standards has created a bottleneck for the absolute quantitation of a number of metabolites. Conceptual and financial factors contribute to the generation of statistically under-powered clinical studies, whereas bioinformatics issues result in the publication of a large number of unidentified metabolites. The path forward in this field involves targeted metabolomics analyses of large control and patient populations to define both the normal range of a defined metabolite and the potential heterogeneity (eg, bimodal) in complex patient populations. This approach requires that metabolomics research groups, in addition to developing a number of analytical platforms, build sufficient chemistry resources to supply the analytical standards required for absolute metabolite quantitation. Examples of metabolomics evaluations of sulfur amino-acid metabolism in psychiatry, neurology, and neuro-oncology and of lipidomics in neurology will be reviewed.

The environment ontology: contextualising biological and biomedical entities

As biological and biomedical research increasingly reference the environmental context of the biological entities under study, the need for formalisation and standardisation of environment descriptors is growing. The Environment Ontology (ENVO; http://www.environmentontology.org) is a community-led, open project which seeks to provide an ontology for specifying a wide range of environments relevant to multiple life science disciplines and, through an open participation model, to accommodate the terminological requirements of all those needing to annotate data using ontology classes. This paper summarises ENVO’s motivation, content, structure, adoption, and governance approach. The ontology is available from http://purl.obolibrary.org/obo/envo.owl - an OBO format version is also available by switching the file suffix to “obo”.

Systems Biology Profiling of AMD on the Basis of Gene Expression

Genetic pathways underlying the initiation and progression of age-related macular degeneration (AMD) have not been yet sufficiently revealed, and the correlations of AMD's genotypes, phenotypes, and disease spectrum are still awaiting resolution. We are tackling both problems with systems biology phylogenetic parsimony analysis. Gene expression data (GSE29801: NCBI, Geo) of macular and extramacular specimens of the retinas and retinal pigment epithelium (RPE) choroid complexes representing dry AMD without geographic atrophy (GA), choroidal neovascularization (CNV), GA, as well as pre-AMD and subclinical pre-AMD were polarized against their respective normal specimens and then processed through the parsimony program MIX to produce phylogenetic cladograms. Gene lists from cladograms' nodes were processed in Genomatix GePS to reveal the affected signaling pathway networks. Cladograms exposed a highly heterogeneous transcriptomic profiles within all the conventional phenotypes. Moreover, clades and nodal synapomorphies did not support the classical AMD phenotypes as valid transcriptomal genotypes. Gene lists defined by cladogram nodes showed that the AMD-related deregulations occurring in the neural retina were different from those in RPE-choroidal tissue. Our analysis suggests a more complex transcriptional profile of the phenotypes than expected. Evaluation of the disease in much earlier stages is needed to elucidate the initial events of AMD.

Perspectives on zebrafish models of hallucinogenic drugs and related psychotropic compounds

Among different classes of psychotropic drugs, hallucinogenic agents exert one of the most prominent effects on human and animal behaviors, markedly altering sensory, motor, affective, and cognitive responses. The growing clinical and preclinical interest in psychedelic, dissociative, and deliriant hallucinogens necessitates novel translational, sensitive, and high-throughput in vivo models and screens. Primate and rodent models have been traditionally used to study cellular mechanisms and neural circuits of hallucinogenic drugs' action. The utility of zebrafish ( Danio rerio ) in neuroscience research is rapidly growing due to their high physiological and genetic homology to humans, ease of genetic manipulation, robust behaviors, and cost effectiveness. Possessing a fully characterized genome, both adult and larval zebrafish are currently widely used for in vivo screening of various psychotropic compounds, including hallucinogens and related drugs. Recognizing the growing importance of hallucinogens in biological psychiatry, here we discuss hallucinogenic-induced phenotypes in zebrafish and evaluate their potential as efficient preclinical models of drug-induced states in humans.

Markers for nutrition studies: review of criteria for the evaluation of markers

INTRODUCTION

Markers are important tools to assess the nutrition status and effects of nutrition interventions. There is currently insufficient consensus in nutrition sciences on how to evaluate markers, despite the need for properly evaluating them.

OBJECTIVES

To identify the criteria for the evaluation of markers related to nutrition, health and disease and to propose generic criteria for evaluation.

METHOD

The report on "Evaluation of Biomarker and Surrogate Endpoints in Chronic Disease" from the Institute of Medicine was the starting point for the literature search. Additionally, specific search strategies were developed for Pubmed.

RESULTS

In nutrition, no set of criteria or systematic approach to evaluate markers is currently available. There is a reliance on the medical area where statistical methods have been developed to quantify the evaluation of markers. Even here, a systematic approach is lacking-markers are still evaluated on a case-by-case basis. The review of publications from the literature search resulted in a database with definitions, criteria for validity and the rationale behind the criteria. It was recognized that, in nutrition, a number of methodological aspects differ from medical research.

CONCLUSIONS

The following criteria were identified as essential elements in the evaluation of markers: (1) the marker has a causal biological link with the endpoint, (2) there is a significant association between marker and endpoint in the target population, (3) marker changes consistently with the endpoint, e.g., in response to an intervention, and (4) change in the marker explains a substantial proportion of the change in the endpoint in response to the intervention.

Developing 'integrative' zebrafish models of behavioral and metabolic disorders

Recently, the pathophysiological overlap between metabolic and mental disorders has received increased recognition. Zebrafish (Danio rerio) are rapidly becoming a popular model organism for translational biomedical research due to their genetic tractability, low cost, quick reproductive cycle, and ease of behavioral, pharmacological or genetic manipulation. High homology to mammalian physiology and the availability of well-developed assays also make the zebrafish an attractive organism for studying human disorders. Zebrafish neurobehavioral and endocrine phenotypes show promise for the use of zebrafish in studies of stress, obesity and related behavioral and metabolic disorders. Here, we discuss the parallels between zebrafish and other model species in stress and obesity physiology, as well as outline the available zebrafish models of weight gain, metabolic deficits, feeding, stress, anxiety and related behavioral disorders. Overall, zebrafish demonstrate a strong potential for modeling human behavioral and metabolic disorders, and their comorbidity.

GelClust: a software tool for gel electrophoresis images analysis and dendrogram generation

This paper presents GelClust, a new software that is designed for processing gel electrophoresis images and generating the corresponding phylogenetic trees. Unlike the most of commercial and non-commercial related softwares, we found that GelClust is very user-friendly and guides the user from image toward dendrogram through seven simple steps. Furthermore, the software, which is implemented in C# programming language under Windows operating system, is more accurate than similar software regarding image processing and is the only software able to detect and correct gel 'smile' effects completely automatically. These claims are supported with experiments.

Traditional Chinese medicine ZHENG and Omics convergence: a systems approach to post-genomics medicine in a global world

Traditional Chinese medicine (TCM) is a comprehensive system of medical practice that has been used to diagnose, treat, and prevent illnesses for more than 3000 years. ZHENG (also known as "syndrome") differentiation remains the essence of TCM. In China, TCM shares equal status, and integrated with Western medicine in the healthcare system to treat many types of diseases. Yet, compared to biomolecular science and Western medicine, the ZHENG/TCM approach to diagnostics might appear unobjective, but offers at the same time long-standing clinical and phenotypic-rich insights. With the current globalization of life sciences and the arrival of "Big Data" research and development, these two silos of medical lore are rapidly coalescing. The applications of multi-omics strategies to TCM have begun to provide novel insights into the essence and molecular basis of TCM ZHENG. We searched the Chinese electronic databases and PubMed for published articles related to "Omics" and "TCM ZHENG" and observed a dramatic increase in studies over the past few years. In this article, we provide a timely synthesis of the lessons learned, and the emerging applications of omics science in TCM ZHENG research. We suggest that the global health scholarship and the field of "developing world Omics" can usefully draw from TCM, and vice versa.

Testing the nanoparticle-allostatic cross-adaptation-sensitization model for homeopathic remedy effects

Key concepts of the Nanoparticle-Allostatic Cross-Adaptation-Sensitization (NPCAS) Model for the action of homeopathic remedies in living systems include source nanoparticles as low level environmental stressors, heterotypic hormesis, cross-adaptation, allostasis (stress response network), time-dependent sensitization with endogenous amplification and bidirectional change, and self-organizing complex adaptive systems. The model accommodates the requirement for measurable physical agents in the remedy (source nanoparticles and/or source adsorbed to silica nanoparticles). Hormetic adaptive responses in the organism, triggered by nanoparticles; bipolar, metaplastic change, dependent on the history of the organism. Clinical matching of the patient's symptom picture, including modalities, to the symptom pattern that the source material can cause (cross-adaptation and cross-sensitization). Evidence for nanoparticle-related quantum macro-entanglement in homeopathic pathogenetic trials. This paper examines research implications of the model, discussing the following hypotheses: Variability in nanoparticle size, morphology, and aggregation affects remedy properties and reproducibility of findings. Homeopathic remedies modulate adaptive allostatic responses, with multiple dynamic short- and long-term effects. Simillimum remedy nanoparticles, as novel mild stressors corresponding to the organism's dysfunction initiate time-dependent cross-sensitization, reversing the direction of dysfunctional reactivity to environmental stressors. The NPCAS model suggests a way forward for systematic research on homeopathy. The central proposition is that homeopathic treatment is a form of nanomedicine acting by modulation of endogenous adaptation and metaplastic amplification processes in the organism to enhance long-term systemic resilience and health.

Identifying early events of gene expression in breast cancer with systems biology phylogenetics

Advanced omics technologies such as deep sequencing and spectral karyotyping are revealing more of cancer heterogeneity at the genetic, genomic, gene expression, epigenetic, proteomic, and metabolomic levels. With this increasing body of emerging data, the task of data analysis becomes critical for mining and modeling to better understand the relevant underlying biological processes. However, the multiple levels of heterogeneity evident within and among populations, healthy and diseased, complicate the mining and interpretation of biological data, especially when dealing with hundreds to tens of thousands of variables. Heterogeneity occurs in many diseases, such as cancers, autism, macular degeneration, and others. In cancer, heterogeneity has hampered the search for validated biomarkers for early detection, and it has complicated the task of finding clonal (driver) and nonclonal (nonexpanded or passenger) aberrations. We show that subtyping of cancer (classification of specimens) should be an a priori step to the identification of early events of cancers. Studying early events in oncogenesis can be done on histologically normal tissues from diseased individuals (HNTDI), since they most likely have been exposed to the same mutagenic insults that caused the cancer in their neighboring tissues. Polarity assessment of HNTDI data variables by using healthy specimens as outgroup(s), followed by the application of parsimony phylogenetic analysis, produces a hierarchical classification of specimens that reveals the early events of the disease ontogeny within its subtypes as shared derived changes (abnormal changes) or synapomorphies in phylogenetic terminology.

Proteomics analysis of hepatoprotective effects for scoparone using MALDI-TOF/TOF mass spectrometry with bioinformatics

Abstract Scoparone is an active ingredient of Yinchenhao (Artemisia annua L.), a well-known Chinese medicinal plant, and has been utilized in prevention and therapy of liver damage. However, the molecular drug targets associated with the pharmacological effects of scoparone are largely unknown. In the present article, we extend the previous research on Yinchenhao through a study of its active ingredient and thus the putative targets of scoparone. We employed two-dimensional gel electrophoresis, and all proteins expressed were identified by MALDI-TOF/TOF MS and database research. Protein-interacting networks and pathways were also mapped and evaluated. The possible protein network associated with scoparone was constructed, and contribution of these proteins to the protective effect of scoparone against the carbon tetrachloride-induced acute liver injury in rats are discussed herein. Hepatoprotective effects of scoparone on liver injury in rats were associated with regulated expression of six proteins which were closely related in our protein-protein interaction network, and appear to be involved in antioxidation and signal transduction, energy production, immunity, metabolism, and chaperoning. These observations collectively provide new insights on the molecular mechanisms of scoparone action against hepatic damage in rats.

Allostasis in nonalcoholic fatty liver disease: implications for risk assessment

Allostasis, a concept of anticipatory physiological regulation in response to external and internal challenges, was originally developed in the context of neuroendocrinology and behavioral medicine. Allostasis preserves function under changing conditions by abandoning physiological set points and developing new ones. Allostatic load refers to the aggregate effect of adaptation throughout life, and corresponds to the wear and tear associated with this process. In response to chronic stress, allostatic load may accumulate faster than expected if sustained activation of regulatory systems exceeds optimum operating ranges; this results in increased risk of disease. Used in a broader sense, the allostatic model of adaptive responses, trade-offs, feed-forward cycles, and collateral damage provides a framework for assessing the involvement of environmental-genetic interactions and co-morbidities in the course of chronic disease and developing a comprehensive score for personalized risk prediction. The utility of this approach is illustrated for nonalcoholic fatty liver disease, a prevalent condition with common and less common outcomes.

Processing of Mass Spectrometry Data in Clinical Applications

Mass spectrometry-based proteomics has become the leading approach for analyzing complex biological samples at a large-scale level. Its importance for clinical applications is more and more increasing, thanks to the development of high-performing instruments which allow the discovery of disease-specific biomarkers and an automated and rapid protein profiling of the analyzed samples. In this scenario, the large-scale production of proteomic data has driven the development of specific bioinformatic tools to assist researchers during the discovery processes. Here, we discuss the main methods, algorithms, and procedures to identify and use biomarkers for clinical and research purposes. In particular, we have been focused on quantitative approaches, the identification of proteotypic peptides, and the classification of samples, using proteomic data. Finally, this chapter is concluded by reporting the integration of experimental data with network datasets, as valuable instrument for identifying alterations that underline the emergence of specific phenotypes. Based on our experience, we show some examples taking into consideration experimental data obtained by multidimensional protein identification technology (MudPIT) approach.

Differential proteomic analysis of pathway biomarkers in human breast cancer by integrated bioinformatics

The aim of this study was to better understand the altered functional modules in breast cancer at pathway and network levels. An integrated bioinformatics analysis of differentially expressed proteins in human breast cancer was performed. Breast cancer protein profiles were constructed by data mining proteins in literature and public databases, including 1031 proteins with 153 secretory and 69 cell surface proteins. An experimental investigation was performed by two-dimensional electrophoresis, and 4 proteins were further validated by western blotting. Enriched bioinformatics functions were clustered. This study may be used as a reference in further studies to help identify the underlying biological interactions associated with breast cancer and discover potential cancer targets.

Genomic aberrations in an African American colorectal cancer cohort reveals a MSI-specific profile and chromosome X amplification in male patients

Objective

DNA aberrations that cause colorectal cancer (CRC) occur in multiple steps that involve microsatellite instability (MSI) and chromosomal instability (CIN). Herein, we studied CRCs from AA patients for their CIN and MSI status.

Experimental Design

Array CGH was performed on 30 AA colon tumors. The MSI status was established. The CGH data from AA were compared to published lists of 41 TSG and oncogenes in Caucasians and 68 cancer genes, proposed via systematic sequencing for somatic mutations in colon and breast tumors. The patient-by-patient CGH profiles were organized into a maximum parsimony cladogram to give insights into the tumors' aberrations lineage.

Results

The CGH analysis revealed that CIN was independent of age, gender, stage or location. However, both the number and nature of aberrations seem to depend on the MSI status. MSI-H tumors clustered together in the cladogram. The chromosomes with the highest rates of CGH aberrations were 3, 5, 7, 8, 20 and X. Chromosome X was primarily amplified in male patients. A comparison with Caucasians revealed an overall similar aberration profile with few exceptions for the following genes; THRB, RAF1, LPL, DCC, XIST, PCNT, STS and genes on the 20q12-q13 cytoband. Among the 68 CAN genes, all showed some level of alteration in our cohort.

Conclusion

Chromosome X amplification in male patients with CRC merits follow-up. The observed CIN may play a distinctive role in CRC in AAs. The clustering of MSI-H tumors in global CGH data analysis suggests that chromosomal aberrations are not random.