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Int J Cancer Manag

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A Prospective to Regulatory Role of miRNAs on Wnt/β-catenin Signaling and Its Crosstalk to the Other Cellular Pathways in Tumorigenesis of Glioblastoma by a Systems Biology Approach

Author(s):
Morteza SaeidiMorteza SaeidiMorteza Saeidi ORCID1, Alireza PasdarAlireza PasdarAlireza Pasdar ORCID2, Farzad RahmaniFarzad Rahmani3, Abozar GhorbaniAbozar GhorbaniAbozar Ghorbani ORCID4, Negar MottaghiNegar Mottaghi5, Forouzan AmerizadehForouzan AmerizadehForouzan Amerizadeh ORCID1, 6,*
1Department of Neurology, Mashhad University of Medical Sciences, Mashhad, Iran
2Department of Medical Genetics and Molecular Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
3Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
4Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute (NSTRI), Karaj, Iran
5Department of Pharmacognosy and Pharmaceutical Biotechnology, School of Pharmacy, Iran University of Medical Sciences, Tehran, Iran
6Department of Internal Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

International Journal of Cancer Management:Vol. 18, issue 1; e156834
Published online:Jun 29, 2025
Article type:Research Article
Received:Nov 04, 2024
Accepted:May 19, 2025
How to Cite:Saeidi M, Pasdar A, Rahmani F, Ghorbani A, Mottaghi N, et al. A Prospective to Regulatory Role of miRNAs on Wnt/β-catenin Signaling and Its Crosstalk to the Other Cellular Pathways in Tumorigenesis of Glioblastoma by a Systems Biology Approach.Int J Cancer Manag.2025;18(1):e156834.https://doi.org/10.5812/ijcm-156834.

Abstract

Background:

The Wnt plays a crucial role in the initiation, progression, and spread of glioblastoma (GBM). Recently, microRNAs (miRNAs) have been demonstrated to be key players in controlling cell growth and tumor formation.

Objectives:

The present study offers the latest insight into miRNAs that influence the Wnt pathway and their interaction with protein-coding genes.

Methods:

Previous studies on the regulatory function of miRNAs targeting the Wnt/catenin pathway were reviewed, and all miRNA-targeted genes were found in the miRDB database. Protein-protein interactions (PPIs) of miRNA-targeted genes were investigated using String and Cytoscape software, and hub proteins were examined. Gene-subnetwork Gene Ontology (GO) analysis was performed.

Results:

At first, 13 downregulated and 25 upregulated miRNAs targeting the Wnt pathway were obtained, each targeting 1,685 and 1,313 genes, respectively; 12 and 15 hub proteins were found in dysregulated miRNA-targeted genes, which interacted with most genes. The PPI network analysis and subnetwork GO analysis showed these proteins cross-talk with many other proteins that have key roles in the pathways that cause proliferation and malignancy in cells.

Conclusions:

Hub proteins are oncogenic proteins that increase gene replication and suppress apoptotic pathways, or tumor suppressors that prevent cancer. By focusing on hub proteins alone or as part of a multi-target approach, it is possible to treat GBM tumors successfully.

1. Background

Glioblastoma (GBM) represents one of the most common cancers affecting the central nervous system. Despite recent improvements in therapeutic methods, there is an immediate need to discover new and efficient treatment options for managing GBM, since the average survival duration ranges from 12 to 15 months (1). The progression of GBM is a complex process marked by numerous genetic and epigenetic alterations, including deletions and/or amplifications of chromosomal regions, loss of heterozygosity (LOH), single-nucleotide polymorphisms (SNPs), and uncontrolled promoter methylation, resulting in the downregulation of tumor-inhibiting genes or activation of oncogenic genes (1).

Recent molecular research suggests that microRNAs (miRNAs) act as either oncogenes or tumor suppressors. They are capable of regulating various cellular processes, including growth, migration, angiogenesis, cell death (apoptosis), and metastasis by regulating the expression of their associated genes (2, 3). The miRNAs are naturally occurring small RNA molecules (18 - 23 nucleotides) that control the expression of specific mRNAs by directly binding to their target sequences and regulating their transcription and translation processes. The miRNAs are emerging as novel prognostic biomarkers for GBM, which are associated with drug resistance, tumor metastasis, and recurrence. The miRNAs may function as potential oncogenic or tumor-suppressive molecules by regulating multiple oncogenic cellular signaling routes, including the PI3K/AKT and Wnt/β-catenin signaling pathways, as supported by accumulating evidence. It has been shown that the upregulation of canonical Wnt signaling has an essential function in the proliferation and advancement of tumor cells across different human cancers, including GBM, breast, colorectal, and liver cancers (4, 5).

The miRNAs regulate Wnt/-catenin signaling by downregulating tumor suppressor proteins such as GSK-3 and APC or inhibiting Wnt signaling downstream targets such as cyclin D1 and -catenin proteins (Table 1). Some of them, like miRNAs such as miR-144-3p, miR-138-2-3p, miR-146b-5p, miR-370, miR-181c, and miR-150-5p, have all been shown to inhibit GBM initiation and invasion by targeting catenin proteins (6-11). Recent findings indicate that the regulatory mechanism of miRNAs on canonical Wnt signaling may occur through the regulation of specific transcription factors, such as T-cell factor (TCF) (12). Further studies demonstrated that miR-24, miR-27a, and miR-92b have inhibitory effects on GBM cell growth and metastasis by suppressing TFs like TCF4, SOX7, or FBXW7 (12-15). However, additional research is needed to explore the regulatory roles of miRNAs, transcription factors, and mRNAs in GBM tumorigenesis. Microarray analysis shows the changes in all genes that are expressed at a given time point, and the analysis of these data has important results about gene interactions.

Table 1.List of the Dysregulated MicroRNAs Inhibiting Glioblastoma Tumorigenesis, Their Molecular Alterations, and Targets in the Wnt/β-catenin Signaling Pathway
Molecular AlterationTarget
Upregulation
miR-19β-catenin/TCF4
miR-21β-catenin and Sox2
miR-22-3-pβ-catenin
miR-22-5-p
miR-24β-catenin/TCF4
miR-27aβ-catenin/TCF4 and SFRP1
miR-92bβ-catenin/TCF4 and NLK
miR-106a-5pAPC
miR-135bGSK-3 β
miR-296-3pβ-catenin
miR-603β-catenin, WIF1, and CTNNBIP1
miR-1249APC2
miR-4476APC
Downregulation
miR-34aGSK-3β
miR-101GSK-3β
miR-124aIQGAP1 and β-catenin
miR-126-3pβ-catenin and Sox2
miR-137EZH2 and β-catenin
miR-138AKT and MMP2
miR-138-2-3pβ-catenin
miR-139-5pFlt1 and β-catenin
miR-142-5pWnt3a and β-catenin
miR-144-3pβ-catenin
miR-146b-5pβ-catenin
miR-150-5pβ-catenin
miR-181cβ-catenin
miR-188β-catenin
miR-206Frizzled 7
miR-211β-catenin
miR-320aβ-catenin, cyclin D1, and MMPs (2, 7)
miR-370-3pβ-catenin
miR-370β-catenin
miR-449b-5pWnt2b
miR-505-3pAKT
miR-577LRP6 and β-catenin
miR-708β-catenin
miR-769-3pZEB2
miR-1825CDK-14

List of the Dysregulated MicroRNAs Inhibiting Glioblastoma Tumorigenesis, Their Molecular Alterations, and Targets in the Wnt/β-catenin Signaling Pathway

2. Objectives

In this study, we identified Wnt-related differentially expressed genes (DEGs) controlled by miRNAs and transcription factors in GBM. This research helps in a more precise identification of gene interactions in GBM tumorigenesis, offering valuable information for future studies.

3. Methods

3.1. Literature and Database Mining

The initial selection of miRNAs associated with the Wnt/β-catenin signaling pathway was based on a previously published review article titled 'Regulatory role of miRNAs on Wnt/β-catenin signaling in tumorigenesis of glioblastoma' by Rahmani et al. (16). miRNAs were divided into 2 groups: Upregulated and downregulated miRNAs. The miRNA-targeted genes were identified using the miRDB database, an accessible resource containing annotated and published miRNA sequences, and miRNA-gene interactions were selected with a score of > 90.

3.2. Protein Network Analysis

In order to predict protein-protein interactions (PPIs), the STRING database version 11.5 was employed. This database compiles both direct and indirect interaction data. These interactions are sourced from computational methods, cross-species knowledge transfer, and curated information from primary literature. For further analysis and visualization of these complex networks, Cytoscape software (version 3.9.1), an open-source tool for visualizing biological networks, was utilized. Cytoscape provides a flexible framework for integrating various attribute data, making it a crucial tool for network analysis. To identify key hub proteins within the network, the Cytohubba plugin (version 0.1) was used, which includes multiple topological algorithms. These methods provide a comprehensive approach for identifying significant hub proteins in the network. The ranking of hub proteins, as shown in Tables 2 and 3, was determined based on their scores calculated using 3 topological algorithms: Maximal Clique Centrality (MCC), Degree, and Maximum Neighborhood Component (MNC) (within the CytoHubba plugin in Cytoscape. Higher-ranked proteins demonstrate greater centrality and potential regulatory significance within the PPI network.

Table 2.Key Hub Proteins in Genes Modulated by Downregulated MicroRNAs
Hob ProteinsMethodRank
FN1MCC/MNC/Degree2, 4, 4
JUNMCC/MNC/Degree3, 2, 1
RHOAMCC/MNC/Degree5, 3, 3
PTENMNC/Degree1, 2
SIRT1MNC/Degree5, 5
CD44MCC1
IGF1MCC4
AGKDMNC1
BCLAF1DMNC2
TRAK1DMNC3
SGCEDMNC4
AEBP2DMNC4

Key Hub Proteins in Genes Modulated by Downregulated MicroRNAs

Table 3.Key Hub Proteins in Genes Modulated by Upregulated MicroRNAs
Hob ProteinsMethodRank
EGFRMNC/Degree1, 1
KRASMNC/Degree2, 2
STAT3MNC/Degree3, 3
SIRT1MNC/Degree4, 5
GRIA2MNC/Degree5, 4
SNAP25MCC1
SYPMCC2
SLC17A7MCC3
CPLX2MCC4
SLC17A6MCC5
PANK1DMNC1
NCALDDMNC1
SV2BDMNC3
CCNJLDMNC4
GNSDMNC5

Key Hub Proteins in Genes Modulated by Upregulated MicroRNAs

3.3. Functional and Pathway Enrichment Analysis

To better understand the roles and interactions of the identified genes, enrichment analyses were performed. Gene Ontology (GO) analysis was carried out using STRING version 11.5. The GO provides a comprehensive framework for annotating genes or gene products by examining 3 main domains: Biological processes (BPs), molecular functions (MFs), and cellular components (CC), giving insights into their functional roles and cellular locations. The Pathway analysis was conducted through KEGG enrichment using the STRING platform.

3.4. Analysis of the Network's Clusters

The network's nodes were grouped using CytoCluster (version 2.1.0) to facilitate the identification of significant clusters. For cluster analysis within the subnetwork, the identification of protein complexes was conducted using the integrated protein complex analysis (IPCA) technique. A threshold value of 2 was applied to define the clusters. STRING (version 11.5) was, then, employed to perform a detailed analysis of each cluster's genes, focusing on identifying the KEGG pathways associated with them (17, 18).

3.5. Promoter Motif Analysis of Hub Genes

To examine the promoter regions of hub genes, upstream flanking regions (UFRs) covering 1 kilobase pair (1 kbp) were obtained from the Ensembl database. These sequences were analyzed for motif identification using MEME Suite (version 5.5.2). The default settings were used, with specific adjustments to the P and E values, which were set to 0.01 for enhanced accuracy (19). To remove redundant motifs and detect known cis-regulatory elements (CREs), Tomtom (version 5.5.2) was applied, utilizing the JASPAR CORE 2022 database for reference (20). Additionally, the GoMo tool was employed to predict the potential biological functions of the identified motifs. This analysis provided deeper insights into the regulatory elements within the promoter regions of the hub genes (21).

4. Results

4.1. Protein-Protein Interaction Networks and Hub Analysis of Dysregulated MicroRNA-Targeted Genes

In this study, we analyzed the role of dysregulated miRNAs in regulating the Wnt/β-catenin signaling pathway in GBM. A total of 1 685 and 1 313 target genes were identified for downregulated and upregulated miRNAs, respectively. The gene interaction networks are illustrated in Figures 1 and 2. A variety of miRNAs show altered expression patterns in GBM, playing significant roles in the proliferation and spread of cancer cells by directly influencing specific oncogenes or tumor-suppressing genes in glioma (22, 23). These miRNAs contribute to GBM development by modulating key oncogenes and tumor suppressors. To better understand their impact, we conducted a network analysis using proteins with interaction scores above 90. Hub proteins were identified using the CytoHubba plugin in Cytoscape (Tables 2 and 3).

Network of proteins targeted by the downregulated microRNA (miRNA) presented by cytoscape software
Figure 1.

Network of proteins targeted by the downregulated microRNA (miRNA) presented by cytoscape software

Network of proteins targeted by the upregulated microRNA (miRNA) presented by cytoscape software
Figure 2.

Network of proteins targeted by the upregulated microRNA (miRNA) presented by cytoscape software

For downregulated miRNAs, key hub proteins included FN1, JUN, RHOA, PTEN, and SIRT1, identified by multiple topological algorithms (MCC, MNC, degree). Additional hubs like CD44, IGF1, AGK, and TRAK1 were detected by single algorithms. In the upregulated group, hub proteins such as EGFR, KRAS, STAT3, SIRT1, and GRIA2 were prominent, with other hubs including SNAP25, SYP, and PANK1.

Overall, 12 unique hub proteins were consistently identified, several of which (e.g., SIRT1, STAT3) appeared in both regulatory groups, suggesting central roles in the Wnt/β-catenin network and its crosstalk with other oncogenic pathways (Figures 3 and 4).

Subnetwork of hob proteins targeted by downregulated microRNA (miRNA) shown in <a href="#A156834TBL3">Table 3</a>.
Figure 3.

Subnetwork of hob proteins targeted by downregulated microRNA (miRNA) shown in Table 3.

Subnetwork of hob proteins targeted by upregulated microRNAs (miRNAs) shown in <a href="#A156834TBL4">Table 4</a>.
Figure 4.

Subnetwork of hob proteins targeted by upregulated microRNAs (miRNAs) shown in Table 4.

Table 4.Leading 5 Subnetwork Clusters from CytoCluster Analysis for Downregulated MicroRNA Targets
ClustersRanksNodesEdgesFunctions
1128249
Metabolism of inositol phosphate
EGFR tyrosine kinase inhibitor resistance
Endocrine resistance
MAPK pathway
ErbB pathway
2226216
Bacterial attack of epithelial cells
Renal cell carcinoma
EGFR tyrosine kinase inhibitor resistance
Aldosterone-regulated sodium reabsorption
T cell receptor pathway
3326199
EGFR tyrosine kinase inhibitor resistance
Colorectal cancer
FoxO pathway
AGE-RAGE pathway in diabetic complications
Apoptosis - multiple species
4424195
Adherens junction
TGF-beta pathway
AGE-RAGE pathway in diabetic complications
Colorectal cancer
Bacterial invasion of epithelial cells
5524204
EGFR tyrosine kinase inhibitor resistance
Bacterial invasion of epithelial cells
Renal cell carcinoma
Aldosterone-regulated sodium reabsorption
Central carbon metabolism in cancer

Leading 5 Subnetwork Clusters from CytoCluster Analysis for Downregulated MicroRNA Targets

4.2. Functional and Pathway Enrichment Analysis

Subnetwork analysis is used to predict key pathways and significant processes within hub protein connections. In order to elucidate crucial pathways and processes in miRNA-targeted genes, hub protein interactions were subjected to subnetwork analysis. The GO database is one of the most comprehensive global resources for information on gene function, offering a basis for computational studies in large-scale molecular biology and genetic research (24). The GO analysis was conducted by examining BP, MF, and CC of the hub protein subnetwork (Figure 5). The Go analysis recognized 1,708 BPs, including positive regulation of BPs, negative regulation of cellular processes, regulation of developmental processes, positive cellular regulation, and regulation of multicellular organismal processes. In addition, 132 CCs were identified, including intracellular, nucleoplasm, nuclear lumen, intracellular organelle lumen, and protein-containing complex. Furthermore, 134 MFs were found, including protein binding, enzyme binding, MF regulator, and signaling receptor binding.

A, Biological process; B, Gene Ontology (GO): Cellular component (CC); C, GO: Molecular function (MF); D, KEGG pathway enrichment for subnetwork genes modulated by downregulated microRNAs (miRNAs).
Figure 5.

A, Biological process; B, Gene Ontology (GO): Cellular component (CC); C, GO: Molecular function (MF); D, KEGG pathway enrichment for subnetwork genes modulated by downregulated microRNAs (miRNAs).

KEGG pathway analysis identified 162 pathways, encompassing pathways related to cancer, including PI3K-Akt signaling, FoxO signaling, axon guidance, and focal adhesion, enriched between subnetwork genes in interaction with the hub proteins targeted by downregulated miRNAs. As expected, cancer pathways are enriched in the group of downregulated miRNA-targeted genes, including important proliferation pathways such as PI3K and MAPK.

The GO analysis for the upregulated miRNAs revealed 1 075 BPs, such as localization control, positive regulation of biological activities, cell-to-cell communication, signaling pathways, and cellular process regulation. A total of 166 CCs were recognized, including cell junctions, synaptic regions (presynaptic and postsynaptic), and the plasma membrane. Furthermore, 109 MFs were identified, such as protein binding, enzyme interaction, MF regulation, and protein kinase association. Also, KEGG pathway analysis revealed that miRNAs are involved in targeting several pathways, such as cancer, PI3K-Akt, focal adhesion, and neurotrophin signaling, in genes influenced by upregulated miRNAs (Figures 5 and 6).

A, Biological process; B, Gene Ontology (GO): Cellular component (CC); C, GO: Molecular function (MF); D, KEGG pathway enrichment for subnetwork genes modulated by upregulated microRNAs (miRNAs).
Figure 6.

A, Biological process; B, Gene Ontology (GO): Cellular component (CC); C, GO: Molecular function (MF); D, KEGG pathway enrichment for subnetwork genes modulated by upregulated microRNAs (miRNAs).

4.3. Analysis of the Network by Clusters

The organization of biological networks can be uncovered through cluster analysis, which is a crucial technique for detecting practical modules, forecasting protein complexes, and categorizing biomarkers within networks. In this study, subnetwork cluster analysis identified 817 clusters for downregulated miRNA-targeted genes and 706 clusters for upregulated miRNA-targeted genes, from which clusters ranked 1 to 5 were selected. As can be seen in Tables 4 and 5, the proteins that are often targeted by miRNAs with low expression are the proteins and pathways responsible for cell proliferation, angiogenesis, and carbon metabolism, such as HIF and TGF-beta. The reduction of miRNAs targeting these proteins causes an increase in these proteins, which increases the proliferation and growth of cells. On the contrary, the proteins that are targeted by miRNAs with higher expression target most of the pathways of cell connections and communication, and this disruption and reduction of cell communication can reduce the communication between cells, and the messages that prevent cell proliferation between cells are not transferred.

Table 5.Leading 5 Subnetwork Clusters from CytoCluster Analysis for Upregulated MicroRNA Targets
ClustersRanksNodesEdgesKEGG Pathway Enrichment of Nodes
1120125
Nicotine addiction
Synaptic vesicle cycle
Glutamatergic synapse
Retrograde endocannabinoid signaling
GABAergic synapse
2220124
Nicotine addiction
Synaptic vesicle cycle
Glutamatergic synapse
Retrograde endocannabinoid signaling
GABAergic synapse
3318102
Nicotine addiction
Synaptic vesicle cycle
Glutamatergic synapse
Retrograde endocannabinoid signaling
4418114
Nicotine addiction
Synaptic vesicle cycle
GABAergic synapse
Retrograde endocannabinoid signaling
5518110
Nicotine addiction
Synaptic vesicle cycle
Glutamatergic synapse
Retrograde endocannabinoid signaling

Leading 5 Subnetwork Clusters from CytoCluster Analysis for Upregulated MicroRNA Targets

4.4. Promoter Motif Analysis of Hub Genes

Promoter motif analysis of the hub genes targeted by dysregulated miRNAs revealed several conserved CREs. For downregulated miRNAs (Figure 7), the identified motifs were predominantly associated with functions such as negative regulation of signal transduction, transcription corepressor activity, ion transport, and neuron fate commitment. These motifs may contribute to the suppression of tumor-inhibitory pathways when their targeting miRNAs are downregulated. In contrast, motifs identified in the upregulated miRNA-targeted hub genes (Figure 8) were enriched in regulatory functions such as transcription inhibition from RNA polymerase II promoters, signal transduction modulation, and inner ear morphogenesis. Notably, common motifs such as SP1, SP2, and ZN467 were shared across both groups, indicating potential shared regulatory mechanisms. Overall, these findings suggest that dysregulated miRNAs modulate the transcriptional landscape of GBM by targeting key regulatory motifs in promoter regions of critical genes involved in cell signaling, apoptosis, and differentiation (Figures 7 and 8).

Promoter analysis of downregulated microRNA (miRNA)-targeted hub proteins
Figure 7.

Promoter analysis of downregulated microRNA (miRNA)-targeted hub proteins

Promoter analysis of upregulated microRNA (miRNA)-targeted hub proteins
Figure 8.

Promoter analysis of upregulated microRNA (miRNA)-targeted hub proteins

5. Discussion

In this study, we explored the regulatory role of dysregulated miRNAs on the Wnt/β-catenin signaling pathway in GBM using a systems biology approach. Our analysis revealed that both upregulated and downregulated miRNAs target a wide array of genes within the Wnt signaling network, many of which are critically involved in tumorigenesis, cellular proliferation, and therapy resistance in GBM. Among the downregulated miRNAs, we identified key targets such as PTEN, RHOA, and SIRT1, which are known tumor suppressors. The reduced expression of these miRNAs may lead to overactivation of oncogenic pathways, thereby promoting glioma cell proliferation and invasion. In contrast, upregulated miRNAs were found to target genes like epidermal growth factor receptor (EGFR), KRAS, and STAT3, which are pivotal drivers in GBM progression. These findings highlight the dual and context-dependent roles of miRNAs as either oncogenes or tumor suppressors, depending on their expression levels and target genes.

The identification of hub proteins through PPI network analysis further emphasized the centrality of certain genes in the regulation of tumor-promoting signaling pathways. Proteins such as FN1, JUN, and STAT3 emerged as major nodes in the network, indicating that they may serve as effective therapeutic targets or biomarkers for GBM. In a recent study, Song et al. demonstrated that the upregulation of FN1 reduced the levels of protein tyrosine phosphatase receptor type M (PTPRM) through enhanced methylation, which subsequently led to increased STAT3 phosphorylation and the stimulation of GBM cell proliferation (25). Thompson illustrated how the transcription factor JUN collaborates with YAP-TEAD to promote tumor growth in GBM and also works alongside MRTF-SRF to intensify the activation of cancer-associated fibroblasts, matrix stiffening, and metastasis (26). Cui et al. explored the proliferation of glioma cells, finding that RhoA and COX-2 levels were elevated in brain glioma tissues (27). An animal study performed by Li et al. showed that RhoA protein has a tumor suppressor role in glioma cancer. They demonstrated that Pard3 controls the levels, localization within the cell, and transcriptional activity of RhoA. Experiments using mouse models demonstrated that elevated RhoA expression suppresses glioma cell proliferation in living organisms (28).

PTEN, a well-known tumor suppressor present in nearly all body tissues, has been shown to carry mutations in multiple cancer types, such as glioma, breast, and colorectal cancers. In addition to the role of this protein in causing or promoting the onset of cancers, Ma et al. showed that phosphorylation of PTEN at Y240, facilitated by FGFR, is key to radiation resistance and may serve as a promising target to improve radiotherapy outcomes (29).

In glioma tissues and cell lines, SIRT1 expression was significantly reduced, with elevated levels being linked to better prognosis in glioma patients. Therefore, this protein can be considered a tumor suppressor (30). Increased mRNA levels of EGFR, a type of receptor tyrosine kinase, have been detected in various cancer types and are thought to stimulate the growth of solid tumors (31).

KRAS, a key hub protein, functions as a central node for cellular signaling pathways that drive cell growth and proliferation. Mutations in this protein have been found in various cancer types, including colorectal, breast, prostate, and lung cancers (32). Around 90% of GBM tissues and cell lines showed STAT3 phosphorylation at Tyr-705 and Ser-727, which was positively associated with higher histopathological grades and decreased patient survival (33, 34).

Cluster analysis revealed distinct functional patterns for genes targeted by upregulated and downregulated miRNAs. In the case of downregulated miRNAs, enriched clusters were mainly associated with cancer-related pathways such as MAPK signaling, TGF-β signaling, and central carbon metabolism, suggesting that reduced miRNA expression may lead to the activation of oncogenic processes and enhanced cell proliferation.

In contrast, clusters of upregulated miRNA targets were enriched in synaptic signaling and neuronal communication pathways like glutamatergic synapse, GABAergic synapse, and endocannabinoid signaling. These findings imply that upregulated miRNAs may suppress genes involved in neural-like signaling, potentially affecting tumor–neuron interactions and microenvironmental dynamics.

Overall, the distinct clustering patterns highlight the dual role of miRNAs in GBM, influencing both intrinsic tumor behavior and its interaction with the neural microenvironment.

Promoter analysis demonstrated that certain regulatory elements are commonly found in both downregulated and upregulated miRNA-targeted hub gene groups (Figures 7 and 8). In downregulated miRNA targets, regulatory motifs were associated with anterior/posterior pattern formation, transcription corepression, and estradiol response. Conversely, motifs in upregulated miRNA targets were linked to inhibition of RNA polymerase II-driven transcription and signal transduction. More clearly, the reduction of miRNAs targeting corepressors reduces transcription inhibition and ultimately facilitates transcription and protein synthesis. Anterior-posterior patterning involves the regionalization process that forms distinct regions of cell differentiation along the anterior-posterior axis, leading to cellular polarity. The loss of cellular polarity has been documented in multiple types of cancer (35). Inhibiting transcription and signal transduction pathways can lead to enhanced protein synthesis and increased cell growth (36).

5.1. Conclusions

This study provides a systems-level understanding of how dysregulated miRNAs influence the Wnt/β-catenin signaling pathway in GBM. By integrating bioinformatics tools, we identified key hub genes such as PTEN, STAT3, KRAS, SIRT1, and FN1, which play central roles in tumor progression and resistance mechanisms. Our cluster and promoter motif analyses revealed distinct regulatory patterns for upregulated and downregulated miRNAs, linking them to critical pathways including MAPK, TGF-β, and synaptic signaling. These findings suggest that specific miRNAs and their target genes may serve as potential diagnostic biomarkers or therapeutic targets in GBM. The results of this study pave the way for future experimental validation and the development of miRNA-based precision therapies for GBM.

5.2. Study Limitations

One of the primary limitations of this study is the lack of laboratory validation of the findings. While our research provides valuable insights into the interactions between miRNAs and the Wnt/β-catenin signaling pathway, the conclusions drawn are largely based on computational analyses and existing literature. This approach has several implications; the findings are reliant on previously published data, which may have inherent biases or limitations. Without direct experimental validation, the accuracy and applicability of these results to clinical settings remain uncertain. Biological systems are complex and can exhibit variability that is not captured in computational models. Laboratory experiments can account for this variability and provide a more nuanced understanding of the biological mechanisms involved. To address this limitation, we recommend that future studies include laboratory experiments that validate the computational findings. This could involve in vitro and in vivo studies to confirm the roles of specific miRNAs and their target genes in the Wnt pathway.

Acknowledgments

Footnotes

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