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: Cancer, Oncology, Gene expression, circRNA, miRNA,
   Bioinformatics

Graphical abstract

   Image 1
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1. Introduction

   Cancer is a devastating disease that remains one of the leading causes
   of death globally, with an estimated 10 million deaths each year
   [[30]1]. Among the various types of cancer, breast cancer (BC) is the
   most commonly diagnosed form in women. Unfortunately, the primary cause
   of death in BC patients is often due to metastasis, which is the spread
   of cancer cells from the primary tumor to other parts of the body
   [[31]2]. An essential gene identified for its pivotal role in promoting
   invasion and metastasis is WASF3, also known as WAVE3. This gene is a
   member of the Wiskott-Aldridge Syndrome (WAS) family of genes, which
   code for proteins that contain several highly conserved motifs [[32]3].
   These motifs include a WASP-homology-2 domain (WHD/V), a
   cofilin-homology domain (C), and an acidic (A) domain, all located at
   the C-terminal end of the protein. The verprolin central acidic (VCA)
   domain is particularly important because it coordinates the recruitment
   of monomeric actin and the ARP2/3 complex of proteins, which facilitate
   actin polymerization [[33]4,[34]5]. Actin polymerization is essential
   for cell movement and invasion, which are key processes in the
   development of metastatic cancer [[35]6,[36]7]. Research has shown that
   WASF3 forms a complex with several other proteins, including the p85
   component of PI3K and ABL kinase [[37]8,[38]9]. Additionally, other
   members of the WASF family, such as Abi1/2, CYFIP1/2, NCKAP1, and
   HSPC300, keep the protein in an inactive state. When activated, the
   protein complex is released, and the VCA domain becomes exposed,
   allowing the ARP2/3 complexes to bind and initiate actin
   polymerization, facilitating cell movement and metastasis [[39][10],
   [40][11], [41][12], [42][13]]. Thanks to previous research
   [[43]3,[44]11], our understanding of the function and role of WASF3 in
   promoting metastasis has greatly improved. By identifying the complex
   interplay between various biological molecules involved in this
   process, we can develop more effective strategies to target this
   pathway and ultimately improve outcomes for cancer patients.

   Recent studies have identified a new class of non-coding RNAs, called
   covalently closed circular RNAs (circRNAs), which can evade
   exonuclease-mediated degradation and are more stable in blood or plasma
   than linear RNAs [[45]14,[46]15]. As a result, circRNAs are considered
   ideal candidates for developing new diagnostic or prognostic biomarkers
   for cancers like BC [[47]16,[48]17]. CircRNAs have been shown to play a
   role in various BC hallmarks, including proliferation, apoptosis, and
   activating invasion and metastasis [[49]18,[50]19].

   miRNAs are another type of non-coding RNA that regulates gene
   expression at the post-transcriptional level. CircRNAs have recently
   been discovered to regulate miRNA function by sponging miRNAs, which
   can either increase or decrease the expression levels of miRNAs
   [[51]20,[52]21]. For example, the oncogenic circRNA has-circ-0052112
   enhances tumor cell invasion and migration by sponging miR-125a-5p, a
   tumor suppressor that inhibits the BAP1 oncogene. MiRNAs play essential
   roles in tumorigenesis, cancer invasion, metastasis, relapse, and drug
   resistance, making them attractive targets for cancer research
   [[53]22].

   In this study, we used a bioinformatics approach to identify additional
   functional pathways of the WASF3 gene and highlight its role in the
   regulation of circRNAs and miRNAs in cancer progression. We collected
   expression profiles of circRNAs, miRNAs, and mRNAs in BC and normal
   breast tissues from the Gene Expression Omnibus (GEO) datasets and The
   Cancer Genome Atlas (TCGA) database. We identified differentially
   expressed mRNAs, circRNAs, and miRNAs using R software and
   reconstructed a circRNA-miRNA-mRNA regulatory network. We then
   predicted miRNA sponging by circRNA and miRNA target genes and assessed
   the competitive endogenous RNA (ceRNA) network using gene ontology (GO)
   annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
   analyses to determine the main functional pathways of BC. Graphical
   abstract ([54]Fig. 1) shows the flowchart of the study procedure.

Fig. 1.

   [55]Fig. 1
   [56]Open in a new tab

   Graphical Abstract.

   The flowchart illustrates the study procedure.

   The results of this study provide insight into the functional pathways
   regulated by the WASF3 gene and its role in BC progression.
   Additionally, the identification of circRNA-miRNA-mRNA regulatory
   networks and the ceRNA network can facilitate the development of new
   diagnostic and therapeutic strategies for BC treatment. This study
   contributes to our understanding of the genetic and epigenetic changes
   that drive BC progression and provides a foundation for further
   research in this field.

2. Materials and Methods

2.1. Raw data

   In this research, to concurrently investigate the regulatory
   interactions between circRNA, miRNA, and mRNA, we chose and utilized
   three datasets from the GEO database ([57]GSE113230, [58]GSE56614, and
   [59]GSE173661). These datasets comprised 100 samples of breast cancer
   and normal breast tissue, all processed through Illumina HiSeq 2000,
   3000, 4000, and 5000 systems. To ensure data consistency and data
   uniformity, 47 samples from the GSEs were excluded due to factors such
   as drug interference or sequencing system incompatibility.
   Consequently, 49 breast cancer tissue samples and four normal breast
   tissue samples were retained for the study, resulting in a total of 53
   samples for analysis. By leveraging these valuable resources in the GEO
   database, researchers can gain insights into the molecular
   underpinnings of breast cancer and ultimately improve diagnosis and
   treatment options for patients.

2.2. Data pre-processing

   Trimmomatic-0.4 was employed to filter the raw data obtained from RNA
   sequencing by removing adapter sequences, low-quality reads, and
   invalid reads [[60]23,[61]24]. The resulting clean reads were further
   processed to eliminate any rRNA residues by comparing against known
   rRNA information in the RNA central database. Subsequently, these clean
   reads were aligned to the human reference genome-HG38 available in the
   UCSC genome database. External factors such as sample preparation or
   sequencing process that are not biologically relevant may affect the
   expression of individual samples, and as such, it is necessary to
   ensure that all samples have a similar range and distribution of
   expression values. Normalization is required to achieve this, and the R
   software's “edgeR” package (version 3.50.3) was used for normalization
   of raw data and subsequent data processing. The package ensures that
   the expression distributions of each sample are consistent throughout
   the experiment. The ability to accurately normalize RNA sequencing data
   is crucial for reliable downstream analysis and ultimately leads to a
   better understanding of gene expression in various biological contexts
   [[62]25].

2.3. Analysis of mRNAs differential expression (DE)

   To preprocess the RNA sequencing data, Trimmomatic-0.36 was utilized to
   filter out low-quality reads, adapter sequences, and undetermined
   bases. Next, rRNA residues were removed to ensure high-quality clean
   reads. The clean reads were aligned to the reference human genome-HG38
   using TopHat 2.1.1 version, with annotation references selected from