Systemic Bioinformatics computational analysis of Hazard Ratio (HR) level of RNA-binding proteins in human Breast, Colon and Lung cancer

Abstract

Abstract Breast, colon and lung carcinomas are classified as aggressive tumors that have poor relapse-free survival (RFS) or progression-free survival (PF) and poor hazard ratios (HRs) despite of extensive therapy. Therefore, it is essential to identify a gene expression signature correlating with RFS/PF and HR status to predict the efficiency of treatment. RNA Binding Proteins (RBPs) play a critical role in RNA metabolic activities including RNA transcription, maturation and posttranslational regulation. However, their particular involvement in cancers is not yet understood. In this study, we used computational bioinformatics to classify the function and the correlation of RBPs among solid cancers. We aimed to identify the molecular biomarker that would help in disease prognosis prediction or improve therapeutic efficiency in treated patients. The intersection analysis summarized more than 1659 RBPs across three recently updated RNA databases. The bioinformatics analysis showed that 58 RBPs were common in breast, colon and lung cancers with HR values < 1 and > 1 and a significant Q-value < 0.0001. RBP gene clusters were identified based on RFS/PF, HR, P-value and fold of induction. In order to define union RBPs, the common genes were subjected to hierarchical clustering and classified into two groups. Poor survival with high-risk HR genes included CDKN2A, MEX3A, RPL39L and VARS (valine cytoplasmic-localized aminoacyl-tRNA synthetase) and poor survival with low-risk HR genes included GSPT1, SNRPE, SSR1 and TIA1, PPARGC1B, EIF4E3 and SMAD9. This study may highlight the significant contribution of the 11 RBP genes as prognostic predictors in breast, colon and lung cancer patient and their potential application in personalized therapy.