Decoding Retinoblastoma: Unveiling Gene Networks and Potential Targets through In Silico Analysis

Retinoblastoma is an aggressive cancer whose majority of patients are infants and children below the age of five. Approximately 80% of the total patients of retinoblastoma reside in low-to-middle-income countries like India. Lack of public and medical awareness and the absence of significant and regular clinical trials to test and authenticate new potential treatments impede the process of treating retinoblastoma. Attempts have been made to establish an effective way to diagnose retinoblastoma early so that it can be controlled in time, but so far, no significant success has been documented on that front. Moreover, recent strategies include computational and informatics solutions to identify potential targets at a genetic level to alter the expression of defective proteins in human subjects.

The main aim of the current study is to unravel the potential targets of Retinoblastoma, an aggressive pediatric cancer, utilizing an in silico network biology approach.

In the present study, we have utilized the gene network analysis approach to identify hub genes that affect the expression in the human system. We developed the Protein – Protein Interaction network utilizing 158 genes extracted from the NCBI OMIM database and identified 15 key genes, which were then subjected to metascape analysis to identify pathways and processes that affect and prioritize genes based on their significance scores. We were able to identify the following target genes: RBBP4, TFDP1, and RBBP7.

RBBP4, TFDP1, and RBBP7 were identified as the most novel target genes against retinoblastoma after gene network and enrichment analysis.

Our in-silico network analysis unveiled the intricate mechanisms behind the progression of retinoblastoma by dissecting 158 associated genes in humans. Thus, this work not only illuminates the underlying dynamics of the disease but also offers a promising avenue for intervention.