Analysis of sebaceous gland carcinoma associated genes using network analysis to identify potentially actionable genes

Abstract

Eyelid sebaceous gland carcinoma (SGC) is a rare but life-threatening condi-tion. However, there is limited computational research associated with un-derlying protein interactions specific to eyelid sebaceous gland carcinoma. The aim of our study is to identify and analyse the genes associated with eyelid sebaceous gland carcinoma using text mining and to develop a protein-protein interaction network to predict significant biological pathways using bioinformatics tool. Genes associated with eyelid sebaceous gland carcinoma were retrieved from the PubMed database using text mining with key terms ‘eyelid’, ‘sebaceous gland carcinoma’ and excluding the genes for ‘Muir-Torre Syndrome’. The interaction partners were identified using STRING. Cytoscape was used for visualization and analysis of the PPI network. Molec-ular complexes in the network were predicted using MCODE plug-in and ana-lyzed for gene ontology terms using DAVID. PubMed retrieval process identi-fied 79 genes related to eyelid sebaceous gland carcinoma. The PPI network associated with eyelid sebaceous gland carcinoma produced 79 nodes, 1768 edges. Network analysis using Cytoscape identified nine key genes and two molecular complexes to be enriched in the protein-protein interaction net-work. GO enrichment analysis identified biological processes cell fate com-mitment, Wnt signalling pathway, retinoic acid signalling and response to cytokines to be enriched in our network. Genes identified in the study might play a pivotal role in understanding the underlying molecular pathways in-volved in the development and progression of eyelid sebaceous gland carci-noma. Furthermore, it may aid in the identification of candidate biomarkers and therapeutic targets in the treatment of eyelid sebaceous gland carcino-ma.