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.
MS in Biotechnology
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(2016).Regulatory networks in non-small cell lung cancer: Connecting differentially expressed genes, miRNAs, and lncRNAs [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
Omran, Jasmine. Regulatory networks in non-small cell lung cancer: Connecting differentially expressed genes, miRNAs, and lncRNAs. 2016. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.