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. 2023 Nov 29;9(12):e22874.
doi: 10.1016/j.heliyon.2023.e22874. eCollection 2023 Dec.

WASF3 overexpression affects the expression of circular RNA hsa-circ-0100153, which promotes breast cancer progression by sponging hsa-miR-31, hsa-miR-767-3p, and hsa-miR-935

Majid Mokhtari  1 , Samane Khoshbakht  1 , Mohammad Esmaeil Akbari  2 , Moravveji Sayyed Sajjad  1
Affiliations

WASF3 overexpression affects the expression of circular RNA hsa-circ-0100153, which promotes breast cancer progression by sponging hsa-miR-31, hsa-miR-767-3p, and hsa-miR-935

Majid Mokhtari et al. Heliyon. .

Abstract

Background: The WASF3 gene has been linked to promoting metastasis in breast cancer (BC) cells, and low expression reduces invasion potential. Circular RNAs (circRNAs) function as microRNA (miRNA) modulators and are involved in cancer progression, but the relationship between these factors remains unclear.

Methods: This study used bioinformatics methods and a computational approach to investigate the role of circRNAs and miRNAs in the context of WASF3 overexpression. Differentially expressed mRNAs, circRNAs, and miRNAs were identified using Gene Expression Omnibus (GEO) datasets. A competing endogenous RNA (ceRNA) network was constructed based on circRNA-miRNA pairs and miRNA-mRNA pairs. Functional and pathway enrichment analyses were predicted using a circRNA-miRNA-mRNA network.

Results: RNA expression patterns were significantly different between normal and tumor samples. A total of 190 circRNAs, 76 miRNAs, and 678 mRNAs were differentially expressed. The analysis of the circRNA-miRNA-mRNA regulatory network revealed interactions between hsa-circ-0100153, hsa-miR-31, hsa-miR-767-3p, and hsa-miR-935 with WASF3 in cancer. These interactions primarily function in DNA replication and the cell cycle.

Conclusions: This study reveals a mechanism by which WASF3 overexpression affects the expression of circRNAs hsa-circ-0100153, promoting BC progression by sponging hsa-miR-31/hsa-miR-767-3p /hsa-miR-935. This mechanism may increase the invasive potential of cancers, in addition to other reported molecular mechanisms involving the WASF3 gene.

Keywords: Bioinformatics; Cancer; Gene expression; Oncology; circRNA; miRNA.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Image 1
Graphical abstract
Fig. 1
Fig. 1
Graphical Abstract. The flowchart illustrates the study procedure.
Fig. 2
Fig. 2
The distribution and regulation of different RNAs. (A) The pie chart shows the distribution of circular RNAs (circRNAs) based on their location within the genome. The chart indicates that 79 % of circRNAs are composed of exons, 10 % map to intronic regions, and 7 % are located in intergenic regions. (B) Chart shows the size distribution of circular RNAs, with 80 % having a predicted spliced length of less than 800 nucleotides. The volcano plots in C, D, and E demonstrate that circRNAs, miRNAs, and mRNAs were differentially regulated between breast cancer and normal samples.
Fig. 3
Fig. 3
Differential Expression Analysis of circRNA, miRNA, and mRNA. A-C shows the hierarchical clustering analysis of differentially expressed circRNA, miRNA, and mRNA. Dysregulated miRNAs were mostly upregulated. The analysis clearly distinguishes breast cancer and normal groups. D and E depict Venn diagrams indicating 45 joint mRNAs between DE mRNAs and miRNA targets, and 51 joint mRNAs between miRNA targets and parental genes of DE circRNAs.
Fig. 4
Fig. 4
Network Analysis of Circular RNAs, miRNAs, and mRNAs (A) shows the CircRNA-miRNA network analysis results, revealing 16 miRNAs with potential interactions with eight circRNAs. (B) Depicts a miRNA-mRNA interaction network created by predicting 320 targets for each of the 16 DE miRNAs. (C) represents a ceRNA network established by integrating circRNA-miRNA and miRNA-mRNA interactions, identifying important nodes in breast cancer.
Fig. 5
Fig. 5
Pathway Analysis of Circular RNAs, miRNAs, and mRNAs. (A) The circus plot shows cross-dataset pathway results of differentially expressed mRNAs, circRNAs, and miRNAs, indicating metabolic pathways' significant role in breast cancer. (B) Gene ontology analysis of extracted mRNAs, parental genes of circRNAs, and target genes of miRNAs from the ceRNA network. Enrichment in cancer-related biological processes such as “extracellular matrix organization” and “growth factor activity”. (C) Kyoto Encyclopedia of Genes and Genomes pathway analysis of extracted mRNAs, parental genes of circRNAs, and target genes of miRNAs from the ceRNA network. Enrichment in cancer-related pathways such as “p53 signaling pathway” and “ECM-receptor interaction."
Fig. 6
Fig. 6
Correlation and Binding Site Analysis of Circular RNAs, miRNAs, and mRNAs. (A) plot shows a positive correlation between the expression of WASF3 and the circular RNA hsa-circ-0100153, as well as a negative correlation between these two and three miRNAs (hsa-miR-31, hsa-miR-767-3p, and hsa-miR-935). (B) Shows the parental gene of hsa-circ-0100153, WASF3, identified on chromosome 13 using the UCSC Genome Browser. (C) Shows predicted miRNA binding sites for hsa-miR-31, hsa-miR-767-3p, and hsa-miR-935 on the circular RNA hsa-circ-0100153 using web tools, Circular RNA Interactome (circBase), CSCD, and Circbank.
Fig. 7
Fig. 7
RNA Expression and Survival Analysis in Breast Cancer (A) shows the expression differences between cancer and normal tissues of different RNAs and WASF3 in breast cancer. (B) Shows the Kaplan-Meier survival curves that demonstrate the relationship between the expression.
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