Genomics and molecular analysis of RPL9 and LIAS in lung cancer: Emerging implications in carcinogenesis

Abstract

Worldwide, lung cancer is a leading cause of cancer-related deaths and is the most commonly diagnosed form of cancer. A major characteristic of lung cancer is its profound clinical, histological and molecular heterogeneity. This heterogeneity is not only spatial but also temporal thus stressing the need for personalized patient-tailored treatment planning. The current optimal treatment planning is currently based on real-time monitoring of the evolving molecular profiling of the tumour throughout the course of the disease and treatment. In the current work, we will investigate the emerging role that that RPL9 and LIAS could have in carcinogenesis. While the aberrant expression of RPL9 has already been shown to occur in colorectal cancer its role in lung cancer is not yet known. In a similar manner, the role of LIAS, as a metabolism-linked gene, in cancer biology and especially in lung cancer is still unknown. Emerging research reveals both RPL9 and LIAS as interacting partners and apoptosis resistance genes. The aim of this study is to determine the differential expression of the rpl9 and lias genes in both normal lung tissue and lung cancer samples. This was achieved by using in situ hybridization (ISH) and quantitative Real-time PCR (qPCR). Further data on the role played by RPL9 in lung cancer was established through the use of in silico bioinformatic analysis. This was done in order to map biological pathways enriched by the expression of these genes. Both the KEGG pathway and Reactome analysis confirmed the role of these genes in RNA metabolic pathways. Furthermore, RPL9 was shown to play a role in signal transduction, autophagy, and cellular response to stress pathways. The function of these two proteins overlapped with regard to protein metabolism. STRING analysis also demonstrated an interaction between RPL9 and LIAS. Here we propose that the aberrant expression of RPL9 and LIAS may contribute to lung carcinogenesis and can be targeted for molecular therapy.

Keywords

• Lung cancer
• RPL9
• LIAS
• HSPA9
• In silico bioinformatics analysis
• qPCR