Identifying Diagnostic and Prognostic Targets for Papillary Thyroid Carcinoma Through Mining Gene Expression BIG Datasets Using Adaptive Filtering and Advanced Bioinformatics Algorithms

Abstract

Thyroid Cancer is the most common endocrine malignancy. Although the mortality rate of thyroid cancer is considered to be low, however the reoccurrence and persistence of the disease is still considered high. The most common type of thyroid cancer is papillary thyroid carcinoma consisting of >70% of all types of thyroid cancer. Thyroid cancer is heterogeneous and complex. BIG data in the form of publicly available gene expression (transcriptomics) datasets can provide valuable source to gain deeper understanding of complex diseases such as papillary thyroid carcinoma (PTC). In this study, we used a novel bioinformatics method based on adaptive filtering to reduce the number of genes expressed eliminating genes that are invariant across the various disease stages. In order to shed light on some of the mechanisms involved in PTC, the filtered genes were used in systematic pathway analysis searches across 20,500 annotated cellular pathways using modified Kolmogorov-Smirnov algorithm to identify the relevant differentially activated cellular pathways across the various stages of the disease. Our analysis from 95 PTC patient biopsies consisting of 41 normal, 28 nonaggressive and 26 metastatic papillary thyroid carcinoma revealed 2193 differential activated cellular pathways among non-aggressive samples and 1969 among metastatic samples compared to normal tissue. The key pathways for non-aggressive PTC includes calcium and potassium ion transport, hormone signaling pathways, protein tyrosine phosphatase activity and protein tyrosine kinase activity. The key pathways for metastatic PTC include growth, apoptosis, activation of MAPK activity and regulation of serine threonine kinase activity. The most frequent genes across the enriched pathways were KCNQ1, CACNA1D, KCNN4, BCL2, and PTK2B for non-aggressive PTC, and EGFR, PTK2B, KCNN4 and BCL2 for metastatic PTC. Survival analysis results showed that PTK2B, CACNA1D and BCL2 contributed to poor survival of PTC patients. The study identified insights into mechanisms of PTC.