The construction and analysis of gene co-expression network of differentially expressed genes identifies potential biomarkers in thyroid cancer

Li Chai, Dongyan Han, Jin Li, Zhongwei Lv


Background: The incidence and mortality of thyroid cancer has been increasing steadily in the United States. However, the molecular pathogenesis of thyroid cancer is not fully characterized.
Methods: The R package of DESeq2 was applied to detect differentially expressed genes (DEGs) between 57 paired thyroid cancer and noncancerous tissues using RNA sequencing data from The Cancer Genome Atlas database. Weighted gene co-expression network analysis was used to construct co-expression modules and study the relationship between co-expression modules and clinical traits. Gene ontology (GO) enrichment analysis was performed on these genes from interested co-expression modules.
Results: A cohort of 750 up-regulated and 296 down-regulated genes were identified in thyroid cancer. The weighted gene co-expression network analysis identified 5 gene co-expression modules. Two modules were significantly associated with patients’ age, cancer stage and multifocality. SERPINA1 and MRO were their hub genes respectively.
Conclusions: The bioinformatics study for those co-expression modules and hub genes paves the way for the development of molecular biomarkers in thyroid cancer.