Integrated bioinformatics analysis to screen hub genes in the lymph node metastasis of thyroid cancer

Abstract

Thyroid cancer (TC) is one of the most common types of malignancy of the endocrine-system. At present, there is a lack of effective methods to predict neck lymph node metastasis (LNM) in TC. The present study compared the expression profiles from The Cancer Genome Atlas between N1M0 and N0M0 subgroups in each T1-4 stages TC in order to identify the four groups of TC LNM-associated differentially expressed genes (DEGs). Subsequently, DEGs were combined to obtain a total of 493 integrated DEGs by using the method of Robust Rank Aggregation. Furthermore, the underlying mechanisms of LNM were investigated. The results from Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses demonstrated that the identified DEGs may promote LNM via numerous pathways, including extracellular matrix-receptor interaction, PI3K-AKT signaling pathway and focal adhesion. Following construction of a protein-protein interaction network, the significance score for each gene was calculated and seven hub genes were screened, including interleukin 6, actinin α2, collagen type I α 1 chain, actin α1, calbindin 2, thrombospondin 1 and parathyroid hormone. These genes were predicted to serve crucial roles in TC with LNM. The results from the present study could therefore improve the understanding of LNM in TC. In addition, the seven DEGs identified may be considered as potential novel targets for the development of biomarkers that could be used in the diagnosis and therapy of TC.

Elevated mRNA expression levels of DLGAP5 are associated with poor prognosis in breast cancer

“…Integration of the microarray data. To identify significant and reliable DEGs, the Robust Rank Aggregation (RRA) package (19) was used. The RRA package assumes that each gene is randomly ordered within each dataset (20).…”

Molecular and Metabolic Reprogramming: Pulling the Strings Toward Tumor Metastasis

“…The stiffness and resistance of stromal ECM are dependent on the relative composition of ECM protein types (3,13,14). Our team and others have published that certain ECM proteins, such as fibronectin and different types of collagen and laminin, can favor the cancer metastatic process (15)(16)(17)(18).…”