Hemangioma

Hemangioma is a common benign tumor in the childhood. The body area more frequently affected by hemangioma is the head and neck, mainly the face, with an association with the embryological development of the face. The identification of genetic and epigenetic alterations in proliferating and involuting hemangioma lesions will likely contribute to better understand the underlying molecular mechanisms of development and progression of this disease.

Data Sources

• PubMed (http://www.ncbi.nlm.nih.gov/pubmed)
• Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/)
• Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/)
• Drug-Gene Interaction Database (DGIdb, https://www.dgidb.org/)
• DrugBank (https://go.drugbank.com/)
• Therapeutic Target Database (TTD, http://db.idrblab.net/)
• Pharmacogenomics Knowledge Base (PharmGKB, https://www.pharmgkb.org/)

Computational Tools

• mirDIP, a computational tool that integrates several predicted and validated miRNA databases.
• Biological Networks Gene Ontology (BiNGO) tool, application available in Cytoscape.

miRNAs and genes may play important roles in the development and progression of hemangioma. MicroRNAs (miRNAs) have been shown as gene expression regulators with an important role in disease pathogenesis. Additionally, these molecules show potential to be targets for drugs that may be clinically useful in the development of new therapies for patients affected by this tumor. We are dedicated to identifying miRNA-mRNA expression networks associated with hemangioma.

• Meta-analysis of gene expression data.
• Identify deregulated genes in the meta-analysis by gene enrichment analysis.
• Identify differentially expressed genes.
• Gene ontology analysis.
• Construction of protein-protein interaction (PPI) networks.
• Generate visualization and annotation data of PPI and miRNA-gene interaction networks.
• Validation analysis in a large representative cohort.
• Establish robust biomarkers for prediction.

CD ComputaBio utilizes microarray, deep sequencing platforms, advanced biostatistical and computational analyses methods to detect biological signals in highly dimensional and often noisy genomic data. We are also interested in how the machine learning-based integration of multi-omic datasets can aid in the discovery of new cancer subgroups and biomarkers.

Moreover, CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.

Reference:

1. Bertoni N, Pereira LM, Severino FE, Moura R, Yoshida WB, Reis PP. Integrative meta-analysis identifies microRNA-regulated networks in infantile hemangioma. BMC Med Genet. 2016 Jan 15;17:4.