STUDYING ABERRANT METHYLATION AND miRNA PROFILING FOR THE DETECTION OF LUNG CANCER BIOMARKERS

Lung cancer is a major chronic disease responsible for the highest mortality rate, among other types of cancer, and represents 29% of all deaths in Canada. The clinical diagnosis of lung carcinoma still requires a standard diagnostic approach, as there are no symptoms in its early stage. Therefore, it is usually diagnosed at a later stage, when the survival rate is low. With the recent advancement in molecular biology and biotechnology, a molecular biomarker approach for the diagnosis of early lung cancer seems to be a potential option. In this study, we aimed to investigate and standardize a promising Lung ,Cancer Biomarker by studying the aberrant methylation of two tumour suppressor genes, namely RASSFIA and RAR-B, and the miRNA profiling of four . commonly deregulated miRNA (miR-199a-3p, miR-182, miR-lOO and miR-221). Four lung cancer cell lines were used (two SCLC and two NSCLC), with comparisons being made with normal lung cell lines. Our results, we found that none of these genes were methylated. We then evaluated TP53, and found the promoter of this gene to be methylated in the cancer cell lines, as compared to the normal cell lines, indicating gene inactivation. We carried out miRNA profiling of the cancer cell lines and reported that 80 miRNAs are deregulated in lung cancer cell lines as compared to the normal cell lines. Our study was the first of its kind to indicate that hsa-mir-4301, hsa-mir-4707-5p and hsa-mir-4497 (newly discovered miRNAs) are deregulated in lung cancer cell lines. We also investigated miR-199a-3p, mir-lOO and miR-182, and found that miR-199a -3p and mir-l00 were down-regulated in cancer lines, whereas miR-182 was up-regulated in the cancer cell lines. In the final part of the study we observed that mir-221 could be a putative biomarker to distinguish between the two types of lung cancer because it was down-regulated in SCLC, and up-regulated in the NSCLC cell lines. In conclusion, we found four miRNA molecular biomarkers that possibly could be used in the early diagnosis of the lung cancer. More studies are still required with larger numbers of samples to effectively establish these as molecular biomarkers for the diagnosis of lung cancer

Predicting the Risk of Lung Cancer in Never-smokers

Despite being considered a disease of smokers, approximately 10-15% of lung cancer cases occur in never-smokers. Lung cancer risk prediction models have demonstrated excellent ability to discriminate cases from non-cases, and have been shown to be more efficient at selecting individuals for future screening than current criteria. Existing models have primarily been developed in populations of smokers, thus there was a need to develop an accurate model in never-smokers. This study focused on developing and validating a model using never-smokers from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Cox regression analysis, with six-year follow-up, was used for model building. Predictors included: age, body mass index, education level, personal history of cancer, family history of lung cancer, previous chest X-ray, and secondhand smoke exposure. This model achieved fair discrimination (optimism corrected c-statistic = 0.6645) and good calibration. This represents an improvement on existing neversmoker models, but is not suitable for individual-level risk prediction.