Tobacco smoking induced lung cancer prediction by lc-micrornas secondary structure prediction and target comparison

摘要

Cancer is deadly, a genetic disorder in which invoke uncontrolled growth of living cells in a particular region. Cancer is usually invoked by many natural causes like exposure to UV rays, smoking tobacco, oil fogs, and exposure to radioactive frequencies. In this work, we concentrate on tobacco-induced lung cancer. There are many existing systems to detect lung cancer. But none are efficient enough to prevent it before it spreads. In this work, we propose an efficient tool to analyze the possibility of getting affected by Non-Small Cell Lung Cancer (NSCLC) by comparing Lung Cancer microRNAs (LC-miRNAs) structures. Here we use global optimal alignment and TargetScan for target comparison and binding location detection. A previous research showed that major lung cancer genes are targeted by 8 type miRNAs. These 8 LC-miRNAs (let-7a-1, miR-7-1, miR-17, miR-21, miR-96, miR-125a-5p, miR-128b, and miR-145) were used for this analysis for accuracy in research. From this work, we concluded that use of computational techniques in miRNA and sequence analysis can reduce the cost of research and increase the accuracy.