In silico evaluation of the role of the long non-coding RNA LINC00092 in thyroid cancer progression ; regulation of the miR-34a-5p/RCAN1 axis - Morovat, Saman; Morovat, Pejman; Kamali, Mohammad Javad; Teimourian, Shahram (bioRxiv, 2022)
As the most prevalent endocrine cancer, thyroid cancer (TC) accounts for 1.7% of all cancer cases. A significant increase in TC morbidity has been observed over the past three decades. TC diagnosis has been reported to be problematic based on the current approach. As a result, it is imperative to develop molecular biomarkers to improve the accuracy of the diagnosis. An analysis of bioinformatics data was conducted in this study to analyze lncRNAs and their roles as ceRNAs associated with the development and progression of TC. The first step in this study was to collect RNA-seq data from the GDC database. Then, DESeq2 was used to analyze differentially expressed lncRNAs (DElncRNAs), miRNAs (DEMIs), and mRNAs (DEGs) between TC patients and healthy subjects. Our study identified DElnc-related miRNAs and miRNA-related genes to develop a lncRNA/miRNA/mRNA axis using online tools and screening. A co-expression analysis was performed to investigate correlations between DElncs and their associated mRNAs. Next, a protein-protein interaction (PPI) network was constructed. Functional enrichment and pathway enrichment were conducted on genes in the PPI network to discover additional biological activities among these molecules. Lastly, a correlation between the expression levels and the infiltration abundance of immune cells was assessed through immune infiltration analysis. There were 58 DElncs, 34 DEMIs, and 864 DEGs in thyroid tumor tissue and non-tumor tissue samples. Following validation of our lncRNA results with the intersection of differentially expressed lncRNAs in TCGA and GEPIA2, we selected two downregulated DElncs, including AC007743.1 and LINC00092, as the final research elements. We then performed an interaction analysis to predict lncRNAs-miRNAs and miRNAs-mRNAs interactions, which led to identifying the LINC00092/miR-34a-5p and miR-34a-5p/RCAN1 axis, respectively. There was a correlation between LINC00092 and RCAN1 according to Pearson correlation analysis. To improve our understanding of RCAN1, we developed a PPI network. According to the Immune Infiltration Analysis, RCAN1 expression was positively correlated with CD8+ T cells, macrophages, and neutrophils. The results of this study suggest that LINC00092/miR-34a-5p/RCAN1 axis may have a functional role in the progression of TC. LINC00092 may be used as a promising biomarker for TC prognosis and may be a better diagnostic and therapeutic target.