Papillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer, making up about 85% of the cases. Although in recent years several genes (e.g. BRAF, RAS, RET/PTC, TERT) have been demonstrated to play crucial roles in PTC, the molecular basis of carcinogenesis is not well understood. Using a bioinformatics tool, we investigated, through a network unbiased approach, the genes and pathways involved in this complex landscape. We exploited SWItchMiner (SWIM) software to analyze gene expression profiles available on The Cancer Genome Atlas. SWIM is able to identify a small pool of regulatory genes (switch genes), which are likely to be critically associated with drastic changes in cell phenotypes. We built networks based on switch genes (nodes) and their Pearson correlation value > |0.7| (connection). Finally, we selected those genes that were present only in the tumor or in the normal network. Comparing RNA-sequencing data between thyroid cancers and thyroid normal tissue samples, we identified 131 switch genes out of 1718 differentially expressed genes. We selected forty of them: four were present only in normal network while the others 36 in cancer network. Most of the selected genes are involved in cAMP-dependent pathway and phospholipase C signaling, and suggest a potential inhibition of the former and activation of the latter, with a release of Ca(2+) from the endoplasmic reticulum into the cytoplasm. Moreover, there was an over-expression of genes promoting cellular cycle, NFkB and Wnt/beta catenin pathways and a down-regulation of genes involved in apoptosis and in the non-homologous end joining. Genes involved in metabolism suggest an increase in glycine and lipid synthesis, with an increase in the storage of the latter in lipid drops. Network analysis may provide an additional approach to explore the molecular basis of cancer and to select the main mechanisms involved in carcinogenesis. Although the data obtained need to be validated by vitro experiments, they may result in significant progress in diagnosis, prognosis and therapy of PTC.
COMPUTATIONAL ANALYSIS OF EXPRESSION PROFILING DATA IN PAPILLARY THYROID CANCER
G Fiscon;F Conte
2018
Abstract
Papillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer, making up about 85% of the cases. Although in recent years several genes (e.g. BRAF, RAS, RET/PTC, TERT) have been demonstrated to play crucial roles in PTC, the molecular basis of carcinogenesis is not well understood. Using a bioinformatics tool, we investigated, through a network unbiased approach, the genes and pathways involved in this complex landscape. We exploited SWItchMiner (SWIM) software to analyze gene expression profiles available on The Cancer Genome Atlas. SWIM is able to identify a small pool of regulatory genes (switch genes), which are likely to be critically associated with drastic changes in cell phenotypes. We built networks based on switch genes (nodes) and their Pearson correlation value > |0.7| (connection). Finally, we selected those genes that were present only in the tumor or in the normal network. Comparing RNA-sequencing data between thyroid cancers and thyroid normal tissue samples, we identified 131 switch genes out of 1718 differentially expressed genes. We selected forty of them: four were present only in normal network while the others 36 in cancer network. Most of the selected genes are involved in cAMP-dependent pathway and phospholipase C signaling, and suggest a potential inhibition of the former and activation of the latter, with a release of Ca(2+) from the endoplasmic reticulum into the cytoplasm. Moreover, there was an over-expression of genes promoting cellular cycle, NFkB and Wnt/beta catenin pathways and a down-regulation of genes involved in apoptosis and in the non-homologous end joining. Genes involved in metabolism suggest an increase in glycine and lipid synthesis, with an increase in the storage of the latter in lipid drops. Network analysis may provide an additional approach to explore the molecular basis of cancer and to select the main mechanisms involved in carcinogenesis. Although the data obtained need to be validated by vitro experiments, they may result in significant progress in diagnosis, prognosis and therapy of PTC.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
