Assessment of risk based on variant pathways and establishment of an artificial neural network model of thyroid cancer.

ABSTRACT:

Background

This study aimed to establish an artificial neural network (ANN) model based on variant pathways to predict the risk of thyroid cancer.

Methods

The RNASeq data of 482 thyroid cancer samples were downloaded from the TCGA database. The samples were divided into low-risk and high-risk groups, followed by identification of differentially expressed genes (DEGs). Co-expression analysis and pathway enrichment analysis were then performed. The variant pathways were screened according to the functional deviation score of each pathway, and an ANN model was established. Finally, the efficiency of the ANN model for risk assessment was validated by survival analysis and analysis of an independent microarray dataset (GSE34289) for thyroid cancer.

Results

In total, 190 DE

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC6537382 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature