Analysis of Novel Variants Associated with Three Human Ovarian Cancer Cell Lines - Mekala, Venugopala R.; Chang, Jan-Gowth; Ng, Ka-Lok (Current Bioinformatics, 2022)
Background: Identification of mutations is of great significance in cancer research, as it can contribute to the development of therapeutic strategies and prevention of cancer formation. Ovarian cancer is one of the leading cancer-related causes of death in Taiwan. Furthermore, it has been observed that the accumulation of genetic mutations can lead to cancer. Objective: We utilized whole-exome sequencing to explore cancer-associated missense variants in three human ovarian cancer cell lines derived from Taiwanese patients. Methods: We utilized cell line whole-exome sequencing data, 188 patients’ whole-exome sequencing data, and in vitro experiments to verify predicted variant results. We established an effective analysis workflow for the discovery of novel ovarian cancer variants, comprising three steps: (i) use of public databases and in-house hospital data to select novel variants, (ii) investigation of protein structural stability caused by genetic mutations, and (iii) use of in vitro experiments to verify predictions. Results: Our study enumerated 296 novel variants by imposing specific criteria and using sophisticated bioinformatics tools for further analysis. Eleven and 54 missense novel variants associated with cancerous and non-cancerous genes, respectively, were identified. A total of 13 missense mutations were found to affect the stability of protein 3D structure, while 11 disease-causing novel variants were confirmed by PCR sequencing. Among these, ten variants were predicted to be pathogenic, while the pathogenicity of one variant was uncertain. Conclusion: It was confirmed that novel variant genes play a crucial role in ovarian cancer patients, with 11 novel variants that may promote the progression and development of ovarian cancer.
Keywords: structural variants