Regulatory networks in non-small cell lung cancer: Connecting differentially expressed genes, miRNAs, and lncRNAs
First and foremost, I would like to thank my thesis advisor, Dr. Hassan Azzazy for all of his support. I would like to thank him as he helped me grow not only as a student but also as a scientist and a teacher. He continues to inspire me with his devotion to students and immense scientific knowledge. He is a role model for hard work and scientific innovations. Next, I would like to thank all my professors in the AUC Biotechnology program for contributing to the honing of my scientific knowledge. Also, I would like to thank Ahmed Elhosseiny for his support, bioinformatics help, and late hours. He is an icon of hard work and resilience. Also, I would like to extend many thanks to Marwa Zahra as she is supportive and provided her time, guidance, and intelligence. Last but not least, I would like to extend my gratitude to my mother and my friends for believing in me and for their continuous support.
Abstract
Non-small cell lung cancer (NSCLC) is the most prevalent class of lung cancer and the most common cancer worldwide. NSCLC accounts for 85% of total lung cancer cases and leads to the most cancer-related deaths worldwide. Micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs) are gene regulatory elements that play crucial roles in cancer biology such as cancer cell proliferation, apoptosis, and metastasis. Understanding the gene regulatory elements that influence cancer biology is critical for diagnostic and therapeutic purposes. A systems approach can help simulate interactions between these elements. In this study 110 microarray samples from NSCLC patients were analyzed by computational methods to identify differentially expressed genes in two tissue types: NSCLC and normal lung tissue. Identified differentially expressed genes were functionally clustered and annotated with their miRNA and lncRNA targets using miRTarBase and starBase, respectively. Regulatory networks were created to suggest an interplay between these miRNAs, lncRNAs, and differentially expressed genes. This approach led to the identification of 108 differentially expressed genes. Innumerable miRNAs target the differentially expressed genes but 66 miRNAs were identified by literature mining and strong evidence validation methods to identify miRNA and differentially expressed gene targets. The filtered miRNAs were also paired with seven of the most common NSCLC-associated lncRNAs. Based on the findings of this study and other computational studies in literature, connections of differentially expressed genes, miRNAs, and lncRNAs were suggested. TGFBR3 and HHIP, tumor suppressor genes, and CAV1, an oncogene, were functionally related to carcinogenesis and cancer cell metastasis, respectively and were related to cell signaling and extracellular matrix genes. This study suggests that MALAT1, PVT1, and GAS5 are lncRNAs that regulate gene expression via miRNA targeting. Since miRNAs, and lncRNAs are instrumental gene regulatory factors in determining NSCLC diagnosis and prognosis, these regulatory pathways can lead to novel approaches in cancer therapy. Therefore, these networks propose mechanisms of actions to further study miRNAs and lncRNAs suggesting a crosstalk between miRNAs, lncRNAs, and differentially expressed genes.