The Recognition Algorithm And Systematic Analysis Of RNA Editing In Lung Cancer RNA-seq Data

Nowadays lung cancer has become one of the fastest-growing malignant tumors in terms of incidence and mortality. The pathogenesis of the lung cancer has not been fully explored yet. Analysis based on molecular biology will facilitate the early diagnosis of lung cancer.RNA editing events play an important role in the occurrence and development of tumor, cell invasion and metastasis, resistance to chemotherapy and prognosis. However, there is almost no complete study to incorporate RNA editing mechanisms when investigating the pathogenesis of lung cancer at present. In this paper, we would data mining RNA editing sites in lung cancer based on the large RNA-Seq dataset and make a systematic analysis about the biological function of specific RNA editing sites.In this study, we developed a set of RNA editing recognition algorithms to pinpoint RNA editing events in lung cancer precisely. This recognition algorithm consists of cluster analysis、binomial distribution、Student’s-test and Benjamini & Hochberg multiple correction. What’s more, to reduce the false positives in the candidate RNA editing sites dataset,this algorithm filtered SNP、cancer mutation、sequencing error、simple sequence repeats and paralogs from the dataset. Based on the difference of editing level in cancer and adjacent normal tissues, we identified 149 specific sites and 11 of them located in the CDS region while 97 in 3’UTR region. In the systematic analysis about the connection between the specific sites and lung cancer pathology, there were 9 RNA editing sites in the CDS region that altered the amino acid sequence, among which editing events in IGFBP7 and AZIN1 would take part in the biological process of lung cancer potentially. Meanwhile, 78 specific editing sites located in the3’UTRwouldcreate、interrupt、decrease or enhance the target interaction between m RNA and mi RNA. In addition, we also recognized a plenty of mi RNAs associated with the pathogenesis of lung cancer.In this study, using RNA editing mechanism to elucidate pathological mechanisms of lung cancer would provide a theoretical basis for identifying potential drug targets and finding better prognostic indicators, diagnostic criteria and treatments.