bioRxivCancer BiologyPreprint
The Expression Pattern of miR-17, -24, -124 and -145 as Diagnostic Factor for Metastatic Gastric Cancer; a Lesson from Gastric Cancer Stem cells
H. Yasavoli-Sharahi, S. Jahangiri-Tazehkand, Z. Iranmehr, C. Eslahchi, A. Emami Razavi, S. Moradi, N. Shayan Asl, F. Memari, M. Ebrahimi
Abstract
BackgroundDistant metastasis of Gastric Cancer (GC) causes more than 700 000 deaths worldwide. Cancer Stem Cells (CSCs) are a subpopulation of cancer cells responsible for aggressiveness and chemoresistance in clinical settings. MicroRNAs (miRNAs) emerge as important players in regulating self-renewal and metastasis in CSCs. Understanding the role of miRNAs in CSCs offer a potential diagnostic tool for GC patients. This study is aimed to identify miRNAs that target both stemness and metastasis in gastric cancer stem cells (GCSCs) and differentially expressed in metastatic GC patients as diagnostic biomarkers for GC metastasis. MethodsWe investigate the gene expression profile of patients using the GEO database and Rstudio software. To obtain the regulatory networks and miRNAs, the STRING and miRwalk database used. The gastric cancer tissues were obtained from Iranian National Tumor Bank (INTB) to validate the results. ResultsOur results indicated three important regulatory cores affecting the immune systems regulation, tumor progress, and metastasis. Based on the bioinformatics results, four miRNAs miR-17-5p, miR-24-3p, miR-124-3p, and miR-145-5p, were selected, and their expression pattern was evaluated in 10 patients metastatic tumors compared to 10 nonmetastatic tumors by real-time PCR. The expression level of mir-17, -24, and -124 was upregulated about 8, 10, 60 folds, respectively, and miR-145 was down-regulated 4.5 folds in metastatic tumors compared to nonmetastatic tumors. Conclusionthe high expression level of miR-17, -24, -124, and low level of miR-145 in GC patients samples could be a potential biomarker for the presence of GCSCs and the diagnosis of metastasis.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
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