In this study, we examined the molecular mechanism of thyroid carcinoma (THCA) using bioinformatics. RNA-sequencing data of THCA (N = 498) and normal thyroid tissue (N = 59) were downloaded from The Cancer Genome Atlas. Next, gene expression levels were calculated using the TCC package and differentially expressed genes (DEGs) were identified using the edgeR package. A co-expression network was constructed using the EBcoexpress package and visualized by Cytoscape, and functional and pathway enrichment of DEGs in the co-expression network was analyzed with DAVID and KOBAS 2.0.
Microvesicles (MVs) are submicrometric membrane fragments that can “engulf” cytoplasmic contents such as microRNAs (miRNAs) from their cellular origin. The study of miRNAs carried within MVs might provide insights into the roles that miRNAs play in the underlying pathophysiologic processes of acute lymphoblastic leukemia (ALL). We identified numerous dysregulated MV miRNAs in patients with B- and T-cell ALL by using Agilent microarray analysis.
Head and neck cancer (HNC) is one of the most prevalent cancers; it is often diagnosed at its advanced stage and has a low 5-year survival rate. Evidence suggests that noninvasive biomarker microRNAs (miRNAs) are valuable for early diagnosis of HNC. This meta-analysis assessed the diagnostic value of miRNAs in HNC detection. A systematic literature search for relevant studies up to August 4, 2014 was conducted in databases and other sources. Statistical analysis was conducted using STATA 12.0.
The aim of this meta-analysis was to systematically evaluate the diagnostic accuracy of microRNAs (miRNAs) in distinguishing malignant thyroid lesions from benign ones and to determine the potential of miRNAs as diagnostic biomarkers for thyroid cancer. The random-effect model was used to summarize the pooled estimates of diagnostic accuracy, including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR).