Development of A Three-Gene Signature Prediction Model for Lymph Node Metastasis in Papillary Thyroid Cancer

Abstract

Background: Thyroid cancer is one of the most prevalent endocrine cancers with a rising incidence rate over the past years. Papillary thyroid cancer (PTC) is the dominant historical type of thyroid cancer. Early lymph node metastasis happens frequently in PTC. However, some of the lymph node metastasis may be troublesome for detecting because of limited methods.Methods: Robust rank aggregation afforded us the shared differential expression genes among multiple datasets. Gene ontology analysis was performed to identify potential functions. Weighted gene co-expression network analysis was used to research the correlations between gene expression patterns with clinical characteristic. Protein-protein interaction network was performed to identify the hub genes. The least absolute shrinkage and selection operator and Logistic regression were performed to construct a prediction model.Results: We developed a three-gene signature prediction model for lymph node metastasis in PTC through transcriptomic analysis. After quality control, we collected 8 microarray datasets from GEO database and an RNA sequencing dataset from TCGA database. We found the transcriptome profiles were correlated with lymph node metastasis and 3 genes were verified to be independent prediction factors towards those statistic approach. Afterwards, we designed a predicable risk score system and effectively confirmed the model in two independent papillary thyroid cancer cohorts.Conclusions: We recommended a successful predicable model of lymph node metastasis in papillary thyroid cancer patients with moderate accuracy.