Cancer is a widespread worldwide chronic disease. In most cases, the high mortality rate from cancer correlates with a lack of clear symptoms, which results in late diagnosis for patients, and consequently, advanced tumor disease with poor probabilities for cure, since many patients will show chemo- and radio-resistance. Several mechanisms have been studied to explain chemo- and radio-resistance to anti-tumor therapies, including cell signaling pathways, anti-apoptotic mechanisms, stemness, metabolism, and cellular phenotypes. Interestingly, the presence of cancer stem cells (CSCs), which are a subset of cells within the tumors, has been related to therapy resistance. In this review, we focus on evaluating the presence of CSCs in different tumors such as breast cancer, gastric cancer, lung cancer, and hematological neoplasias, highlighting studies where CSCs were identified in patient samples. It is evident that there has been a great drive to identify the cell surface phenotypes of CSCs so that they can be used as a tool for anti-tumor therapy treatment design. We also review the potential effect of nanoparticles, drugs, natural compounds, aldehyde dehydrogenase inhibitors, cell signaling inhibitors, and antibodies to treat CSCs from specific tumors. Taken together, we present an overview of the role of CSCs in tumorigenesis and how research is advancing to target these highly tumorigenic cells to improve oncology patient outcomes.

BackgroundTo enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory.ResultsTo address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data.ConclusionsWe generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

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Neuroblastoma (NB) is an embryonal tumour of neuroectodermal cells, and its prognosis is based on patient age at diagnosis, tumour stage and MYCN amplification, but it can also be classified according to their degree of methylation. Considering that epigenetic aberrations could influence patient survival, we studied the methylation status of a series of 17 genes functionally involved in different cellular pathways in patients with NB and their impact on survival. We studied 82 primary NB tumours and we used methylation-specific-PCR to perform the epigenetic analysis. We evaluated the putative association among the evidence of hypermethylation with the most important NB prognostic factors, as well as to determine the relationship among methylation, clinical classification and survival. CASP8 hypermethylation showed association with relapse susceptibility and, TMS1 and APAF1 hypermethylation are associated with bad prognosis and showed high influence on NB overall survival. Hypermethylation of apoptotic genes has been identified as a good candidate of prognostic factor. We propose the simultaneous analysis of hypermethylation of APAF1, TMS1 and CASP8 apoptotic genes on primary NB tumour as a good prognostic factor of disease progression.

We reported that microRNA-155 (miR-155) deficiency in ApoE-/- mice yields a novel metabolically healthy obese (MHO) model, which exhibits improved atherosclerosis but results in obesity, non-alcoholic fatty liver disease (NAFLD) without insulin resistance. Using experimental data mining approaches combined with experiments, we found that, among 109 miRNAs, miR-155, and miR-221 are significantly modulated in all four hyperlipidemia-related diseases (HRDs), namely atherosclerosis, NAFLD, obesity and type II diabetes (T2DM). MiR-155 is significantly upregulated in atherosclerosis and decreased in other HRDs. MiR-221 is increased in three HRDs but reduced in obesity. These findings led to our new classification of types I and II MHOs, which are regulated by miR-221 and miR-155, respectively. Western blots showed that the proinflammatory adipokine, resistin, is significantly increased in white adipose tissues (WAT) of the MHO mice, revealing our newly proposed, miR-155-suppressed “secondary wave inflammatory state (SWIS),” characteristic of MHO transition to classical obesity (CO). Taken together, we are first to show that MHO may have heterogeneity in comorbidities, and is therefore classified into type I, and type II MHOs; and that increased expression of resistin in miR-155-/- white adipose tissues may be a driver for SWIS in MHO transition to CO. Our findings provide novel insights into the pathogenesis of MHO, MHO transition to CO, hyperlipidemic pathways related to cancer, and new therapeutic targets.