An integrated bioinformatics analysis to investigate the targets of miR-133a-1 in breast cancer.

Abstract

BACKGROUND:

The microRNA (miRNA) miR-133a-1 has been identified as a tumor suppressor in breast cancer. However, the underlying mechanisms of miR-133a-1 in breast cancer have not been fully elucidated. This study aimed to explore the targets of miR-133a-1 in breast cancer using an integrated bioinformatics approach.

METHODS:

Human SKBR3 breast cancer cells were transfected with miR-133a-1 or a miRNA negative control (miRNA-NC) for 48 hours. The RNA-seq sequencing technique was performed to identify the differential expression of genes induced by miR-133a-1 overexpression. Functional enrichment analysis was conducted to determine the target genes and pathways involved in breast cancer.

RESULTS:

Breast cancer patients with high levels of miR-133a-1 expression commonly showed longer overall survival compared to patients with a low level of miR-133a-1 expression. Using Cuffdiff, we identified 1,216 differentially expressed genes induced by miR-133a-1 overexpression, including 653 upregulated and 563 downregulated genes. MOCS3 was the most upregulated gene and KRT14 was the most downregulated gene. The top 10 pathways related to the differentially expressed genes were identified through Gene Ontology (GO) enrichment analysis. Sex-determining region Y-box 9 (SOX9) demonstrated the highest semantic similarities among the differentially expressed genes. Since SOX9 and CD44 were hub nodes in the protein-protein interaction network, the SOX9 gene may be a target of miR-133a-1 in breast cancer.

CONCLUSIONS:

This report provides useful insights for understanding the underlying mechanisms in the pathogenesis of breast cancer.

• Authors: Yanchun Meng, Hao Tang, Zhiguo Luo, Wenlong Tan, Lin Chen, Yiqun Du, Zhonghua Tao, Mingzhu Huang, Wenhua Li, Jun Cao, Leiping Wang, Ting Li, Xin Liu, Fangfang Lv, Xiaojian Liu, Jian Zhang, Lei Zheng, Xichun Hu
• Journal: Translational cancer research (Transl Cancer Res) Vol. 10 Issue 3 Pg. 1238-1248 (Mar 2021) ISSN: 2219-6803 [Electronic] China
• PMID: 35116451 (Publication Type: Journal Article)
• Copyright: 2021 Translational Cancer Research. All rights reserved.