SWIM TOOL FOR STUDYING HUMAN PAPILLARY THYROID CARCINOMA

Thyroid cancer is the most frequent endocrine malignancy and accounts for 1% of all tumors. Papillary thyroid carcinoma (PTC) and follicular thyroid carcinoma are the most frequent. PTC has a strong genetic component since it displays the highest relative risks in first degree relatives. PTC has a social as well as economic impact that motivates investigation of novel methods and tools that can exploit the wealth of whole-genome expression data made available in recent years to improve cancer clinical practice. Such "big data" provide at the same time an opportunity and a challenge since devising algorithms and tools effectively harvesting the knowledge buried inside them is a major computational endeavor. Here, we study the PTC dataset from TCGA by running a recently developed software called SWIM, in order to extract a small pool of genes, called switch genes, crucially for the transition from physiological to pathological phenotype of the disease understudy. In particular, SWIM unveiled 131 switch genes out of 1718 differential expressed genes whose up-regulation was found strongly associated with p53 signaling pathway. Among the switch genes, we selected some promising candidate to be disease genes for thyroid carcinoma.