Deregulation of embryonic transcription factors in human epithelial cancers: new perspectives in breast and liver tumors

Carcinogenesis is commonly referred to as a multi-step process in which normal cells develop progressively into hyperplasia, carcinoma in situ, invasive cancer and metastasis. Several evidences indicate that transcription factors, which act as master regulators of embryonic development, may play a central role in this pathologic process. Indeed, growing evidence suggests that cancer cells often reactivate latent developmental programs in order to efficiently execute the multi-step process of tumorigenesis. Reminiscent of their function during development, embryonic transcription factors regulate changes in gene expression that promote tumor cell growth, cell survival and motility, as well as a morphogenetic process called epithelial-mesenchymal transition (EMT), which is implicated in both metastasis and tumor recurrence. Because of their pivotal roles in tumor progression, these factors represent valuable new biomarkers for cancer detection as well as promising new targets for alternative anti-cancer therapies. The present doctoral work explores the role of embryonic transcription factors deregulation in epithelial cancers and their therapeutic implications in the frontiers of precision oncology. More specifically, the first project identified MDM2 as a specific synthetic lethal partner of GATA3, an embryonic master regulator of the mammary gland often mutated in estrogen receptor-positive breast cancers. The second project identified the homeobox transcription factor HOXA13 as a novel oncogene, whose overexpression results in hepatocarcinogenesis in mice through the induction of chromosomal instability

Cofactor of BRCA1 as a modulator of hepatocellular carcinoma growth and migration

Cofactor of BRCA1 (COBRA1) is one of the four subunits that make up the Negative Elongation Factor Complex (NELF) which is involved in the stalling of RNA polymerase II early during transcription elongation. As such, COBRA1 is able to regulate a substantial number of genes involved in a number of pathways, including cell cycle control, metabolism, cell proliferation and DNA repair. In the field of cancer, the role of COBRA1 is not yet fully understood. The aim of our study was to investigate the functional role of COBRA1 in the tumorigenesis of hepatocellular carcinoma (HCC). We investigated the gene expression pattern of COBRA1 in HCC tumors using the publicly available Oncomine Cancer Microarray Database. Results from three different microarray datasets reveal the frequent overexpression of COBRA1 in HCC tumors versus their normal counterparts. To elucidate the biological significance for this overexpression in HCC, RNA interference was used to silence the expression of COBRA1 in the well differentiated HCC cell line, HepG2. The silencing efficiency was confirmed by both reverse transcription-polymerase chain reaction (RT-PCR) and Western blot analysis. Interestingly, knockdown of COBRA1 resulted in a significant decrease in cell proliferation, accompanied by a concomitant decrease in the expression of the proliferation marker, Ki-67. A scratch wound healing assay revealed a significant decrease in the migratory potential of the HepG2 cell line in culture upon COBRA1 knockdown. In addition, silencing of COBRA1 was associated with a significant decrease in the expression of survivin, suggesting that survivin might be one of the mechanisms by which COBRA1 mediates its role in the tumorigenicity of HCC. Collectively, data findings presented here highlight an oncogenic role for COBRA1 in hepatocellular carcinoma. To the best of our knowledge, our study provides evidence for the first time to support a positive role for COBRA1 in the growth and migration of HCC

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

MicroRNA in Solid Tumor and Hematological Diseases

MicroRNAs (miRNAs), which are a type of short non-coding RNA, are involved in number of processes, such as differentiation, development, inflammation, immune response, and cancer. miRNAs, which act as oncogenes or tumor suppressor genes, can control and regulate the translation and stability of target messenger RNA, contributing to cancer pathogenesis. Despite the progress that has been made in discovering the mechanisms of how miRNAs function in tumors, many questions and aspects of miRNA biology and processing still remain to be determined. This Special Issue, titled “MicroRNA in Solid Tumor and Hematological Diseases”, provides a panorama of the existing knowledge gaps and potential uses of microRNAs to predict clinical outcome or response to therapies and provides evidence to explain their role as biomarkers to modulate the biological pathways that are critical for cancer development and progression. It includes eleven peer-reviewed papers that cover the role of microRNAs in different tumor types and their potential applications in diagnosis and clinical approaches

Inflammatory Diseases

"Inflammatory Diseases - A Modern Perspective" represents an extended and thoroughly revised collection of papers on inflammation. This book explores a wide range of topics relevant to inflammation and inflammatory diseases while its main objective is to help in understanding the molecular mechanism and a concrete review of inflammation. One of the interesting things about this book is its diversity in topics which include pharmacology, medicine, rational drug design, microbiology and biochemistry. Each topic focuses on inflammation and its related disease thus giving a unique platform which integrates all the useful information regarding inflammation

Computational Proteomics Using Network-Based Strategies

This thesis examines the productive application of networks towards proteomics, with a specific biological focus on liver cancer. Contempory proteomics (shot- gun) is plagued by coverage and consistency issues. These can be resolved via network-based approaches. The application of 3 classes of network-based approaches are examined: A traditional cluster based approach termed Proteomics Expansion Pipeline), a generalization of PEP termed Maxlink and a feature-based approach termed Proteomics Signature Profiling. PEP is an improvement on prevailing cluster-based approaches. It uses a state- of-the-art cluster identification algorithm as well as network-cleaning approaches to identify the critical network regions indicated by the liver cancer data set. The top PARP1 associated-cluster was identified and independently validated. Maxlink allows identification of undetected proteins based on the number of links to identified differential proteins. It is more sensitive than PEP due to more relaxed requirements. Here, the novel roles of ARRB1/2 and ACTB are identified and discussed in the context of liver cancer. Both PEP and Maxlink are unable to deal with consistency issues, PSP is the first method able to deal with both, and is termed feature-based since the network- based clusters it uses are predicted independently of the data. It is also capable of using real complexes or predicted pathway subnets. By combining pathways and complexes, a novel basis of liver cancer progression implicating nucleotide pool imbalance aggravated by mutations of key DNA repair complexes was identified. Finally, comparative evaluations suggested that pure network-based methods are vastly outperformed by feature-based network methods utilizing real complexes. This is indicative that the quality of current networks are insufficient to provide strong biological rigor for data analysis, and should be carefully evaluated before further validations.Open Acces

New Prognostic and Predictive Markers in Cancer Progression

Biomarkers are of critical medical importance for oncologists, allowing them to predict and detect disease and to determine the best course of action for cancer patient care. Prognostic markers are used to evaluate a patient’s outcome and cancer recurrence probability after initial interventions such as surgery or drug treatments and, hence, to select follow-up and further treatment strategies. On the other hand, predictive markers are increasingly being used to evaluate the probability of benefit from clinical intervention(s), driving personalized medicine. Evolving technologies and the increasing availability of “multiomics” data are leading to the selection of numerous potential biomarkers, based on DNA, RNA, miRNA, protein, and metabolic alterations within cancer cells or tumor microenvironment, that may be combined with clinical and pathological data to greatly improve the prediction of both cancer progression and therapeutic treatment responses. However, in recent years, few biomarkers have progressed from discovery to become validated tools to be used in clinical practice. This Special Issue comprises eight review articles and five original studies on novel potential prognostic and predictive markers for different cancer types