MYCN Amplifications and Metabolic Rewiring in Neuroblastoma

Cancer is a disease caused by (epi)genomic and gene expression abnormalities and characterized by metabolic phenotypes that are substantially different from the normal phenotypes of the tissues of origin. Metabolic reprogramming is one of the key features of tumors, including those established in the human nervous system. In this work, we emphasize a well-known cancerous genomic alteration: the amplification of MYCN and its downstream effects in neuroblastoma phenotype evolution. Herein, we extend our previous computational biology investigations by conducting an integrative workflow applied to published genomics datasets and comprehensively assess the impact of MYCN amplification in the upregulation of metabolism-related transcription factor (TF)-encoding genes in neuroblastoma cells. The results obtained first emphasized overexpressed TFs, and subsequently those committed in metabolic cellular processes, as validated by gene ontology analyses (GOs) and literature curation. Several genes encoding for those TFs were investigated at the mechanistic and regulatory levels by conducting further omics-based computational biology assessments applied on published ChIP-seq datasets retrieved from MYCN-amplified- and MYCN-enforced-overexpression within in vivo systems of study. Hence, we approached the mechanistic interrelationship between amplified MYCN and overexpression of metabolism-related TFs in neuroblastoma and showed that many are direct targets of MYCN in an amplification-inducible fashion. These results illuminate how MYCN executes its regulatory underpinnings on metabolic processes in neuroblastoma.

HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens

Genes with similar expression patterns in a set of diverse samples may be considered coexpressed. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes. The website is based on the hierarchical clustering of 55,431 Homo sapiens genes based on a large-scale coexpression analysis of 3500 GTEx bulk RNA-Seq samples of healthy individuals, which were selected as the best representative samples of each tissue type. HGCA2.0 presents subclades of coexpressed genes to a gene of interest, and performs various built-in gene term enrichment analyses on the coexpressed genes, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes. Benchmarking showed that HGCA2.0 belongs to the top performing coexpression webtools, as shown by STRING analysis. HGCA2.0 creates working hypotheses for the discovery of gene partners or common biological processes that can be experimentally validated. It offers a simple and intuitive website design and user interface, as well as an API endpoint.