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INTERNATIONAL CONGRESS OF

CancerGenetics

• Exploring the Role of TRIB3 in Temozolomide Resistance of Glioblastoma through a ceRNA Regulatory Network Analysis

• Sara Nafei Milani,^1,* Habib MOtieghader,²
  1. Department of Biology, Tabriz Branch, Islamic Azad University, Tabriz, Iran
  2. Department of Biology, Tabriz Branch, Islamic Azad University, Tabriz, Iran
  
  
  
• Introduction: Glioblastoma multiforme (GBM) is a highly aggressive brain tumor with limited therapeutic options and frequent resistance to temozolomide (TMZ), the current standard chemotherapy. In this study, we integrated disease-associated genes from the DisGeNET database with transcriptomic profiles to identify candidate genes and regulatory elements contributing to TMZ resistance. A protein–protein interaction (PPI) network was constructed using STRING, revealing 18 functionally connected genes. Differential expression analysis of the GSE193957 dataset in U87 cells identified six genes with significant alterations between TMZ-resistant and sensitive samples. Among them, TRIB3 was upregulated in resistant cells, while five genes (RPS6KA3, PRKCA, MAPKAPK3, HSPB8, and AKT3) were downregulated and associated with TMZ sensitivity. To explore post-transcriptional regulation, we predicted miRNAs targeting these genes using four databases (miRNet, miRDB, miRTarBase, and TargetScan), followed by construction of a bipartite miRNA–mRNA network. Since TRIB3 expression remained elevated despite predicted miRNA regulation, we further identified lncRNAs that may act as miRNA sponges, reconstructing a lncRNA-miRNA-mRNA ceRNA network. Finally, drug–gene interaction analysis using DGIdb highlighted several candidate compounds, including Indapamide and Perindopril, as potential TRIB3-targeting drugs. Collectively, our results suggest that TRIB3 and its regulatory ceRNA network may serve as promising biomarkers and therapeutic targets for overcoming TMZ resistance in GBM.
• Methods: Glioblastoma multiforme (GBM)-associated genes were obtained from the DisGeNET database, and their functional relationships were explored through a protein–protein interaction (PPI) network constructed in STRING. To ensure accuracy, all 18 functionally connected genes from the network were included in subsequent analyses. To investigate drug response, the GSE193957 microarray dataset was analyzed, which contains gene expression profiles of U87 glioma cells and TMZ-resistant U87 cells. GEO2R was used for differential expression analysis, and genes with adjusted p-values < 0.001 were considered significant. Expression fold change values were used to classify genes as associated with TMZ resistance (upregulated) or sensitivity (downregulated). For post-transcriptional regulation analysis, potential miRNAs targeting the six significant genes were identified using four databases: miRNet, miRDB, miRTarBase, and TargetScan. Only conserved and brain-relevant miRNAs were prioritized. A bipartite miRNA–mRNA network was then constructed to visualize regulatory relationships. As TRIB3 was the only gene consistently upregulated in resistant samples, its regulation was further investigated. Relevant lncRNAs acting as competing endogenous RNAs were identified using miRNet, ENCORI, and dbdemc, leading to the construction of an lncRNA–miRNA–mRNA network for TRIB3. Finally, the DGIdb database was used to identify drug–gene interactions. Candidate drugs targeting TRIB3 were recorded as potential therapeutic alternatives for overcoming TMZ resistance in GBM.
• Results: A total of thirty-two protein-coding genes associated with glioblastoma multiforme (GBM) were retrieved from the DisGeNET database. To investigate functional associations, a protein–protein interaction (PPI) network was constructed using the STRING database. Among the retrieved genes, eighteen were found to be functionally connected through experimentally validated and predicted interactions. Expression analysis using the UALCAN platform indicated that some of these genes were upregulated, suggesting potential oncogenic activity, while others were downregulated, indicating possible tumor-suppressive roles. To further explore their role in drug response, microarray expression data from the GSE193957 dataset were analyzed. Three TMZ-resistant U87 glioma samples and three TMZ-sensitive samples were compared. After normalization and statistical analysis, six of the eighteen genes exhibited significant expression differences between resistant and sensitive groups. TRIB3 was identified as upregulated in resistant cells, whereas RPS6KA3, PRKCA, MAPKAPK3, HSPB8, and AKT3 were downregulated and thus associated with drug sensitivity. Given the central role of post-transcriptional regulation, miRNAs targeting these six genes were predicted using four databases: miRNet, miRDB, miRTarBase, and TargetScan. Cross-comparison of the predicted and validated results identified biologically relevant candidate miRNAs, which were integrated into a bipartite regulatory network linking miRNAs to the resistant and sensitive genes. Particularly, TRIB3 remained highly expressed despite the presence of several miRNAs predicted to suppress its expression, indicating the involvement of additional regulatory mechanisms. To address this, lncRNAs acting as competing endogenous RNAs (ceRNAs) were identified using the miRNet, ENCORI, and dbdemc databases. These lncRNAs are proposed to function as sponges, binding to TRIB3-targeting miRNAs and preventing their regulatory activity. The resulting lncRNA-miRNA-mRNA network highlights a possible mechanism sustaining TRIB3 overexpression and contributing to TMZ resistance in GBM. Finally, drug-gene interaction analysis was performed to identify therapeutic candidates targeting TRIB3. Three drugs, namely Indapamide, Methylphenidate Hydrochloride, and Perindopril, were predicted to interact with TRIB3, with varying interaction scores. These results suggest that TRIB3, together with its regulatory network, may serve as a promising biomarker and therapeutic target for overcoming drug resistance in glioblastoma.
• Conclusion: This study provides a systematic bioinformatics framework to uncover molecular mechanisms underlying temozolomide resistance in glioblastoma. By integrating disease-associated genes, transcriptomic profiles, and regulatory interactions, we identified TRIB3 as a key resistance-associated gene and reconstructed its regulatory network involving miRNAs and lncRNAs. The persistence of TRIB3 overexpression despite predicted miRNA regulation suggests that ceRNA interactions may play a critical role in sustaining drug resistance. Furthermore, the identification of candidate TRIB3-targeting drugs offers new opportunities for therapeutic intervention. These findings deepen our understanding of GBM chemoresistance and highlight the potential of combining ceRNA network analysis with drug-gene interaction data to guide the development of personalized treatment strategies.
• Keywords: Glioblastoma multiform, drug resistance, biomarker, bioinformatics, non- coding RNA