Initial evaluation of LncRNA A2M-AS1 gene expression in multiple sclerosis patients

Abstract

Introduction: Multiple sclerosis (MS) is one of the three leading neurodegenerative diseases worldwide. Genes expression profiles studies play important role in recognition and prevention of disease. Considering the inherent ability of biomarkers to diagnose and prognosis the occurrence of a disease, with the aim of gene therapy and changing gene expression, it can be helped to treat it. In this study, by examining the gene interaction and expression of non-coding gene in patients with multiple sclerosis, using bioinformatics analyzes and laboratory research, to find the gene expression pattern and the interaction of potential biomarkers of this disease for It was tried to find suitable treatment targets. Materials and methods: First, by using microarray data analysis of GEO database, the expression status of Lnc RNA A2M-AS1 gene was investigated in patients with MS. Then lncRNA-mRNA interaction analysis was performed in the lncRRisearch and ENCORI database. After sample collection, the total RNA extracted using the RNA extraction kit from 20 patient samples and 20 healthy samples was synthesized into cDNA with the synthesis kit. In the following, two pairs of forward and reverse primers were designed for A2M-AS1 gene, and finally, the expression level was measured by Real Time-PCR technique. Result: Based on bioinformatic and laboratory analysis, the expression of A2M-AS1 gene in MS samples showed a significant decrease in expression compared to healthy samples. Also, based on the ROC analysis, lncRNA A2M-AS1 can be introduced as an acceptable diagnostic biomarker to distinguish MS samples from healthy samples. Conclusion: lncRNA A2M-AS1 by reducing its expression as an acceptable diagnostic biomarker can increases the risk of developing MS.

LINC01132 and LINC02542 modulate the PIK3/ACT signaling pathway in colorectal cancer patients through regulation of IGF2: a systems biology-based biomarker discovery

“…Previous studies demonstrated some potential non-coding biomarkers of diverse cancer types, including BC [44][45], GC [46], and also different immune-related diseases including multiple sclerosis [47].To gather new and reliable data and develop a sound experiment, we conducted a consistent bioinformatics analysis to identify a key gene involved in colorectal cancer (CRC) development and explore other biological factors that may influence gene expression and the progression of CRC. Through our initial bioinformatics analysis, we discovered two novel long non-coding RNAs (lncRNAs), LINC01132 and LINC02542, which showed significant downregulation in CRC samples.…”