Targeting a miRNA–mRNA Regulatory Network to Overcome Radioresistance in Head and Neck Cancer: Identification of I-OMe-AG-538 via Transcriptome-Guided Drug Repurposing

Targeting a miRNA–mRNA Regulatory Network to Overcome Radioresistance in Head and Neck Cancer: Identification of I-OMe-AG-538 via Transcriptome-Guided Drug Repurposing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Targeting a miRNA–mRNA Regulatory Network to Overcome Radioresistance in Head and Neck Cancer: Identification of I-OMe-AG-538 via Transcriptome-Guided Drug Repurposing Ann-Joy Cheng, Guo-Rung You, Joseph T. Chang, Hung-Han Huang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8888021/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Radioresistance is a major obstacle to successful radiotherapy in head and neck cancer (HNC), leading to treatment failure, recurrence, and poor patient outcomes. MicroRNAs (miRNAs) are key post-transcriptional regulators implicated in radiosensitivity, but comprehensive miRNA–mRNA networks driving radioresistance in HNC remain poorly defined. Here, we established isogenic radioresistant (RR) sublines from OECM1 and Detroit HNC cells through long-term fractionated irradiation and performed global miRNA profiling to identify a consistent 25-miRNA signature (12 upregulated oncogenic miRNAs [OncomiRs] and 13 downregulated tumor-suppressive miRNAs [TSmiRs]) associated with radioresistance. Integrative target prediction, pathway enrichment, and network construction revealed that these miRNAs converge on oncogenic modules including receptor tyrosine kinase (RTK) signaling, cell motility, and stress/cancer stemness pathways, with central hubs such as EGFR, IGF1R, and MYC. The refined miRNA–mRNA network (68 interaction pairs) highlighted key regulatory miRNAs (e.g., miR-199b-5p, miR-522-3p), whose ectopic overexpression significantly enhanced radiosensitivity in HNC cells. Transcriptome-guided drug repurposing via the Connectivity Map platform prioritized I-OMe-AG-538, an IGF1R inhibitor (τ = –87), as the top candidate radiosensitizer. Validation showed that I-OMe-AG-538 dose-dependently suppressed IGF1R and Erk phosphorylation, reprogrammed the RR miRNA profile by downregulating OncomiRs and upregulating TSmiRs, elevated intracellular ROS levels, and synergistically increased radiosensitivity in clonogenic assays. TCGA-HNSC analysis confirmed that high IGF1R expression correlates with poor prognosis and upregulation of ROS-scavenging genes. These findings delineate a coordinated miRNA–mRNA regulatory network underlying radioresistance in HNC and identify I-OMe-AG-538 as a promising radiosensitizer that disrupts oncogenic signaling and redox homeostasis through inhibiting IGF1R and miRNAs, offering a potential strategy to enhance radiotherapy efficacy in refractory HNC. Biological sciences/Cancer/Head and neck cancer Biological sciences/Cancer/Cancer therapy/Radiotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Head and neck cancer (HNC) represents a heterogeneous group of malignancies, predominantly squamous cell carcinomas arising from the oral cavity and oropharynx. Globally, HNC remains among the most prevalent cancers, with a significant incidence in middle-aged men, underscoring its substantial impact on patients, families, and society [ 1 , 2 ]. Current therapeutic approaches for HNC include surgery, radiotherapy, chemotherapy, or multimodal combinations. Despite considerable advancements in treatment modalities and supportive care over recent decades, the overall 5-year survival rate for HNC patients has shown only limited improvement [ 2 – 4 ]. Radiotherapy remains a cornerstone of curative-intent treatment for HNC; however, local and regional recurrence following radiation therapy continues to pose a major clinical challenge [ 3 , 4 ]. Intrinsic and acquired tumor radioresistance are key contributors to this recurrence. Therefore, identifying reliable biomarkers associated with both radioresistance and radiosensitivity is essential for optimizing treatment strategies and improving therapeutic outcomes. MicroRNAs (miRNAs) are small non-coding RNAs, typically 18–22 nucleotides in length, that function as critical post-transcriptional regulators of gene expression. miRNAs exert their effects primarily by binding to the 3’-untranslated region (3’-UTR) of target messenger RNAs (mRNAs), leading to mRNA degradation or translational inhibition [ 5 ]. These molecules play fundamental roles in diverse biological processes, including cell differentiation, proliferation, stress responses, and apoptosis, and their dysregulation has been widely implicated in human diseases, particularly cancer [ 5 , 6 ]. Notably, accumulating evidence indicates that miRNAs are pivotal modulators of radiosensitivity and radioresistance across multiple cancer types. For example, the oncogenic miRNA miR-21 has been extensively reported to promote radioresistance by suppressing pro-apoptotic pathways [ 7 , 8 ]. In HNC specifically, several miRNAs have been shown to confer radioresistance through distinct molecular mechanisms. Oncogenic miRNAs (OncomiRs) such as miR-630, miR-96-5p, and miR-196a promote radioresistance by modulating the Nrf2–GPX2 axis, targeting PTEN, or inhibiting annexin-A1, respectively [ 9 – 11 ]. Conversely, tumor-suppressive miRNAs (TSmiRs), including miR-520b, miR-494-3p, and miR-526b-3p, have been demonstrated to enhance radiosensitivity by suppressing cancer stemness, inducing cellular senescence, or inhibiting autophagy [ 12 – 14 ]. While these studies have provided important mechanistic insights, most investigations have focused on individual miRNAs, thereby limiting a comprehensive understanding of the broader miRNA-mediated regulatory networks that govern radiosensitivity. To achieve a more holistic understanding of radioresistance, recent studies have employed global miRNA profiling strategies. One approach involves direct comparisons of clinical samples exhibiting different levels of radiosensitivity [ 15 – 17 ]. Although this method reflects authentic tumor biology, genetic heterogeneity among patients may confound the interpretation of radioresistance mechanisms. An alternative strategy compares miRNA expression profiles in HNC cell lines before and after irradiation [ 18 , 19 ], which reduces inter-individual variability. However, these studies often rely on acute, short-term irradiation, which may primarily capture transient radiation-induced responses rather than stable, intrinsic radioresistance. To better model the intrinsic radioresistance observed in patients, our group previously developed isogenic HNC radioresistant (RR) sublines through long-term, low-dose serial irradiation [ 20 , 21 ]. These sublines are genetically identical to their parental cells yet exhibit a markedly enhanced radioresistant phenotype, ensuring that observed molecular alterations are predominantly associated with resistance rather than genetic background differences. Furthermore, we previously identified a radioresistance-associated gene set using cDNA microarray analysis of these RR sublines [ 21 ], providing a valuable transcriptomic foundation for further investigation. However, a systematic, network-based understanding of how coordinated miRNA dysregulation shapes radioresistance in HNC remains largely unexplored. To address this gap, we developed a streamlined strategy to construct a refined miRNA–mRNA regulatory network associated with radiosensitivity. Additionally, using an in silico drug repurposing approach, we identified candidate compounds targeting this network and validated a potent compound that reverses radioresistance in HNC cells. This strategy is particularly attractive because it accelerates clinical translation by leveraging compounds with existing pharmacological profiles. Collectively, this study provides comprehensive insights into miRNA-mediated mechanisms of radioresistance in HNC and highlights drug repurposing as a promising strategy for developing novel radiosensitizers, potentially enhancing therapeutic outcomes for patients with refractory disease. RESULTS Profiling miRNAs Associated with Radioresistance in HNC To investigate the molecular basis of radioresistance in HNC, we established radioresistant (RR) sublines, OECM1-RR and Detroit-RR, through repeated fractionated irradiation until the cells exhibited a highly radioresistant phenotype. The radiosensitivity of these sublines was validated using a clonogenic survival assay, which showed significantly greater radiation resistance in OECM1-RR and Detroit-RR cells compared to their parental counterparts, OECM1-Pt and Detroit-Pt, respectively (Fig. 1 A). To elucidate the role of miRNAs in radioresistance, we performed comprehensive miRNA expression profiling using the Agilent miRNA microarray platform, which encompasses 463 human miRNAs. This analysis compared miRNA expression profiles between parental and RR sublines in both OECM1 and Detroit cell models. Differentially expressed miRNAs (DEMs) in RR sublines displayed distinct expression patterns relative to their parental cells (Fig. 1 B). Applying a threshold of |fold change (FC)| ≥ 2, we identified 48 upregulated and 76 downregulated miRNAs in OECM1-RR cells and 37 upregulated and 133 downregulated miRNAs in Detroit-RR cells, indicating extensive and coordinated miRNA dysregulation associated with the acquisition of radioresistance (Supplementary Tables S1 and S2). To pinpoint miRNAs consistently associated with radioresistance across both cell models, we analyzed overlapping DEMs between OECM1-RR and Detroit-RR sublines. This approach revealed 12 miRNAs that were consistently upregulated and 30 consistently downregulated in both RR sublines (Fig. 1 C, Table 1 ). To prioritize key regulatory miRNAs, we applied stringent criteria for downregulated miRNAs, requiring an average |FC| ≥ 5.0 and a standard deviation (SD) < 0.02 across both RR sublines. This refined analysis identified 12 upregulated OncomiRs, including miR-513a-5p and miR-654-5p (Fig. 1 D), and 13 downregulated TSmiRs, such as miR-302b-3p and miR-146a-5p (Fig. 1 E), exhibiting significant dysregulation in RR sublines. Collectively, this study defines a miRNA signature associated with radioresistance in HNC, offering insights into the molecular mechanisms underlying this trait. Table 1 Summary of the 42 candidate miRNAs-associated with RR in HNC cells. OECM1 Detroit miRNA Fold (RR/Pt) Fold (RR/Pt) Fold (mean) SD Up regulation hsa-miR-513a-5p 82.600 6.835 44.718 53.574 hsa-miR-654-5p 22.932 51.473 37.202 20.182 hsa-miR-662 7.624 11.77 9.697 2.932 hsa-miR-198 3.463 10.509 6.986 4.983 hsa-miR-564 5.849 4.022 4.935 1.292 hsa-miR-622 4.076 5.593 4.835 1.073 hsa-miR-638 2.875 6.733 4.804 2.728 hsa-miR-129-5p 5.172 3.071 4.121 1.486 hsa-miR-214-3p 3.338 2.723 3.031 0.435 hsa-miR-572 3.06 2.784 2.922 0.195 hsa-miR-630 2.431 3.257 2.844 0.584 hsa-miR-370-3p 2.699 2.881 2.790 0.129 Down regulation hsa-miR-146a-5p 0.230 0.015 0.122 0.152 hsa-miR-34c-5p 0.236 0.036 0.136 0.142 hsa-miR-302b-3p 0.156 0.152 0.154 0.003 hsa-miR-451a 0.147 0.165 0.156 0.013 hsa-miR-299-3p 0.166 0.223 0.194 0.04 hsa-miR-409-5p 0.326 0.069 0.197 0.182 hsa-miR-380-3p 0.305 0.094 0.200 0.149 hsa-miR-499a-5p 0.176 0.227 0.201 0.036 hsa-miR-517a-3p 0.199 0.209 0.204 0.007 hsa-miR-522-3p 0.199 0.218 0.208 0.013 hsa-miR-520a-3p 0.121 0.328 0.224 0.146 hsa-miR-548c-3p 0.322 0.145 0.234 0.125 hsa-miR-520f-3p 0.249 0.238 0.244 0.008 hsa-miR-571 0.281 0.214 0.247 0.048 hsa-miR-519c-3p 0.222 0.273 0.247 0.036 hsa-miR-302a-3p 0.142 0.356 0.249 0.151 hsa-miR-561-3p 0.121 0.424 0.272 0.214 hsa-miR-649 0.468 0.081 0.274 0.274 hsa-miR-382-5p 0.407 0.183 0.295 0.159 hsa-miR-495-3p 0.134 0.485 0.309 0.248 hsa-miR-548b-3p 0.344 0.289 0.316 0.039 hsa-miR-493-5p 0.491 0.148 0.319 0.242 hsa-miR-34b-5p 0.359 0.324 0.342 0.025 hsa-miR-520g-3p 0.498 0.227 0.363 0.191 hsa-miR-122-5p 0.427 0.316 0.371 0.078 hsa-miR-519b-3p 0.397 0.354 0.375 0.031 hsa-miR-520c-3p 0.499 0.343 0.421 0.110 hsa-miR-126-3p 0.474 0.373 0.423 0.072 hsa-miR-193a-3p 0.440 0.497 0.469 0.040 hsa-miR-199b-5p 0.484 0.457 0.471 0.019 RR, radioresistance; Pt, parental; SD, standard deviation miRNA-Mediated Pathways Underlying Radioresistance in HNC To elucidate the mechanisms by which miRNAs regulate radioresistance in HNC, we identified target genes of the previously defined radioresistance-associated miRNAs and performed integrative pathway analyses. Target genes were predicted using three algorithms, TargetScan, miRTarBase, and miRDB, with genes predicted by at least two algorithms considered as potential targets. For the 12 OncomiRs, the number of predicted target transcripts per miRNA ranged from 68 to 628. UpSetR plot analysis revealed significant overlap, identifying 2,363 unique genes co-regulated by these OncomiRs (Fig. 2 A). Similarly, across the 13 TSmiRs, predicted targets per miRNA ranged from 47 to 824 transcripts, and 2,827 unique genes were commonly regulated by TSmiRs (Fig. 2 B). These predicted targets provide a comprehensive set of candidate genes that may mediate miRNA-driven radioresistance. To pinpoint gene sets critical to radioresistance, we integrated the miRNA target predictions with a previously established radioresistance-associated gene set derived from cDNA microarray analysis of HNC RR sublines [ 21 ]. This gene set comprised 1,045 upregulated oncogenes and 616 downregulated tumor suppressor genes as differentially expressed genes (DEGs). To explore oncogenic mechanisms, we intersected the 1,045 upregulated DEGs with the 2,827 TSmiR target genes, yielding 252 overlapping genes. This oncogene panel reflects genes upregulated due to TSmiR downregulation, likely promoting radioresistance. Conversely, to investigate tumor-suppressive mechanisms, we intersected the 616 downregulated DEGs with the 2,363 OncomiR target genes, identifying 99 overlapping genes indicating miRNA-mediated silencing of tumor suppressors (Fig. 2 C). To further characterize the molecular pathways underlying radioresistance, we conducted KEGG pathway enrichment analysis using the DAVID platform. Functional annotation of the 99 tumor suppressor genes highlighted pathways involved in endocrine signaling, inflammatory response, and transcriptional dysregulation, suggesting that loss of cellular homeostasis is a key driver of radioresistance (Fig. 2 D). In contrast, the 252 oncogene targets were enriched in 17 oncogenic pathways and categorized into three functional modules (Fig. 2 E). The cell motility module included four pathways related to cell junction dynamics: adherens junction, focal adhesion, ECM-receptor interaction, and actin cytoskeleton regulation. The receptor tyrosine kinase (RTK) signaling module encompasses seven critical oncogenic pathways: ErbB, EGFR, TGF-β, MAPK, PI3K-Akt, Ras, and Rap1. The stress and cancer stemness module comprised six pathways associated with cellular stress responses and cancer stemness, including p53, Notch, HIF-1, and Wnt signaling. These findings indicate that miRNA-mediated pathways not only drive radioresistance but also contribute to broader malignant phenotypes in HNC. Collectively, these results highlight the complexity of miRNA-mediated regulation in HNC radioresistance, characterized by the oncogenic activation of specific miRNAs and the silencing of tumor suppressor genes. The identified pathways provide critical insights into the molecular mechanisms underlying radioresistance and highlight potential therapeutic targets for overcoming treatment resistance in HNC. miRNA–mRNA Regulatory Network Driving Radioresistance in HNC To elucidate the regulatory mechanisms underpinning radioresistance in HNC, we constructed an integrated miRNA–mRNA interaction network, focusing on molecules within the three previously identified pathway modules (cell motility, receptor tyrosine kinase signaling, and stress/cancer stemness). These molecules, derived from experimentally validated microarray data, provide robust targets for analysis [ 21 ]. Supplementary Table S3 summarizes the 13 TSmiRs and their enriched target oncogenes across the pathway modules, and Fig. 3 A presents an alluvial plot visualizing the associations between individual miRNAs and their target oncogenes within these modules. Collectively, the 13 TSmiRs co-target 54 oncogenes across all three pathway modules. Notably, eight hub genes (EGFR, GSK3B, IGF1R, JUN, NRAS, RRAS, THBS1, and VEGFA) participate in all three modules, and 12 genes, including CDKN1A, MYC, ITGB4, and LAMC1, are involved in two modules, suggesting the presence of central molecular hubs orchestrating multiple oncogenic pathways to modulate radioresistance (Supplementary Table S3 ). Taken together, the presence of shared hub genes across multiple pathway modules suggests that radioresistance is orchestrated by a limited number of central signaling nodes rather than isolated molecular events. To explore the functional connectivity of these 54 oncogenes, we constructed a protein-protein interaction (PPI) network using the STRING database. The resulting network (Fig. 3 B) identified EGFR, IGF1R, and MYC as core nodes with high connectivity, underscoring their pivotal roles in mediating oncogenic signaling associated with radioresistance. A comprehensive miRNA–mRNA regulatory network was generated in Cytoscape, incorporating 13 TSmiRs and 54 oncogenes, yielding 68 miRNA–mRNA interaction pairs (Fig. 3 C). This network revealed intricate cross-regulatory interactions, with multiple miRNAs converging on shared oncogenic targets. Key hub miRNAs, including miR-199b-5p, miR-34c-5p, miR-520f-3p, miR-522-3p, and miR-380-3p, each of which regulates more than seven oncogenes. Reciprocal regulation was also evident; for instance, miR-199b-5p targeted VEGFA, LAMC1, GSK3B, MYH9, and ITGA3, while VEGFA was additionally modulated by miR-299-3p. Similarly, miR-522-3p regulated IGF1R, FZD1, and MYH10, which were concurrently targeted by miR-380-3p, miR-146a-5p, and miR-499a-5p, respectively. These findings highlight collaborative regulatory interactions within the miRNA–mRNA network that drive oncogenic mechanisms underlying radioresistance. To validate the functional role of hub miRNAs in radiosensitivity, we ectopically overexpressed two TSmiRs, miR-199b-5p and miR-522-3p, in three HNC cell lines and assessed their impact on radiosensitivity. Both miRNAs consistently reduced survival fractions following irradiation across all tested cell lines, with statistically significant effects (Fig. 3 D), confirming their roles as radiosensitizers in HNC cells. Collectively, these results delineate a comprehensive miRNA–mRNA regulatory network underlying radioresistance in HNC, identifying hub miRNAs and oncogenes as critical regulators of radiosensitivity. This network provides valuable insights into the molecular mechanisms underlying radioresistance and identifies potential therapeutic targets to improve treatment efficacy in HNC. Drug Repurposing to Identify Radiosensitizing Compounds for HNC Radioresistance remains a major cause of treatment failure in a clinically significant subset of HNC patients, underscoring the need for compounds that enhance tumor radiosensitivity. To address this, we employed a drug repurposing strategy using the Connectivity Map (CMap) platform at the Broad Institute [ 22 – 23 ] to identify candidate therapeutic compounds efficiently. CMap compares gene expression signatures, where highly negative connectivity scores (τ values) indicate drugs that may reverse disease-associated signatures (e.g., radioresistance). In contrast, positive scores indicate compounds with mechanisms similar to those of the disease state (Fig. 4 A). To identify compounds capable of reversing radioresistance, we analyzed the gene expression signatures of a previously defined radioresistance-associated gene panel (Fig. 2 C) comprising 150 oncogenes and 99 tumor suppressor genes co-regulated by miRNAs and mRNAs. Using the CMap platform, we filtered for compounds with median τ values < -60. This threshold was selected to prioritize compounds with strong inverse transcriptional signatures relative to the RR phenotype. As shown in Fig. 4 B, a total of 27 compounds were identified, including I-OMe-AG-538, oligomycin-A, and etomoxir, which are the most promising. Among these, I-OMe-AG-538 exhibited the strongest inverse correlation with the radioresistance gene signature (τ = -87), highlighting its potential as a leading candidate for mitigating radioresistance in HNC. I-OMe-AG-538 Modulates miRNA-Mediated Molecular Pathways in HNC Following in silico analysis, which identified I-OMe-AG-538 as a leading candidate for reversing the RR gene signature, we selected this compound for experimental validation. I-OMe-AG-538, a small-molecule inhibitor of the insulin-like growth factor 1 receptor (IGF1R), has been reported to modulate chemotherapeutic resistance in breast cancer cells [ 24 , 25 ] (Fig. 5 A). In our study, IGF1R was identified within the RR gene panel, exhibiting cross-talk with multiple oncoproteins (Fig. 3 B) and regulation by several miRNAs across various signaling pathways (Fig. 3 A, Supplementary Table S3 ). To confirm the clinical relevance of IGF1R, survival analysis using the TCGA-HNSC dataset and the KM Plotter tool demonstrated that high IGF1R expression was significantly associated with a poor prognosis in HNC patients (Fig. 5 A). To investigate the molecular effects of I-OMe-AG-538, we assessed its impact on IGF1R and downstream signaling molecules (Erk and Akt) via Western blot analysis in two HNC cell lines. I-OMe-AG-538 significantly reduced IGF1R expression in a dose-dependent manner and markedly inhibited Erk phosphorylation (p-Erk/Erk), while having a minimal effect on Akt phosphorylation (p-Akt/Akt) (Fig. 5 B). These findings suggest that I-OMe-AG-538 predominantly modulates the IGF1R–MAPK/Erk signaling axis. Given IGF1R’s regulation by multiple miRNAs (Fig. 3 A, Supplementary Table S3 ), we evaluated the effect of I-OMe-AG-538 on RR-associated miRNA expression in three HNC cell lines. Six OncomiRs (miR-198, miR-370-3p, miR-513a-5p, miR-622, miR-630, and miR-654-5p) consistently showed reduced expression following I-OMe-AG-538 treatment, despite varying levels across cell lines (Fig. 5 C). Conversely, six TSmiRs (miR-199b-5p, miR-302b-3p, miR-451a, miR-520f-3p, miR-517a-3p, and miR-522-3p) exhibited increased expression in all tested cell lines, although various levels were noted (Fig. 5 D). These results indicate that I-OMe-AG-538 broadly modulates the RR-associated miRNA network, likely influencing multiple downstream molecular pathways. I-OMe-AG-538 Mediates ROS levels to Enhance Radiosensitivity in HNC I-OMe-AG-538 has been reported as a ROS-enhancing regulatory compound, and elevated intracellular ROS levels are strongly associated with oxidative DNA damage and radiation-induced cytotoxicity [ 26 , 27 ]. To evaluate the impact of I-OMe-AG-538 on cellular redox status, intracellular ROS production was quantified using the fluorescent probe H 2 DCFDA followed by flow cytometric analysis. As shown in Fig. 6 A, treatment with I-OMe-AG-538 significantly increased intracellular ROS levels in HNC cells compared with untreated controls, indicating that this compound effectively shifts the cellular redox balance toward a more oxidative state. To further explore the potential molecular basis of this ROS modulation, we examined the relationship between IGF1R expression and key ROS scavenger genes using TCGA-HNSC patient data via the GEPIA2 platform. Correlation analysis revealed that IGF1R expression was positively associated with eight major ROS detoxification genes, including GCLM, GCLC, NFE2L2, CAT, SOD2, SOD3, GPX2, and HMOX1 (Fig. 6 B), suggesting that IGF1R signaling is closely linked to the cellular antioxidant defense system. Moreover, the combined expression signature of these eight ROS scavengers exhibited a significant positive correlation with IGF1R levels (R = 0.39) (Fig. 6 C), supporting the notion that IGF1R exerts a broad regulatory influence over intracellular redox homeostasis. Together, these findings suggest that I-OMe-AG-538 increases intracellular ROS levels at least in part through disruption of an IGF1R–ROS scavenger regulatory axis in HNC. To determine whether this ROS elevation translates into enhanced radiosensitivity, we performed clonogenic survival assays in three HNC cell lines. While either I-OMe-AG-538 treatment or irradiation alone reduced cell survival, the combination of I-OMe-AG-538 and irradiation produced a significantly greater reduction in clonogenic survival compared with either treatment alone (Fig. 6 D). Collectively, these results demonstrate that I-OMe-AG-538 enhances radiosensitivity in HNC cells by increasing intracellular ROS levels and modulating IGF1R-associated antioxidant signaling, supporting its potential as a promising radiosensitizer for improving radiotherapy efficacy in refractory HNC. DISCUSSION Head and neck cancer (HNC) frequently exhibits intrinsic and acquired radioresistance, which substantially limits the efficacy of radiotherapy and contributes to local recurrence and poor clinical outcomes. In this study, we provide a comprehensive, network-based characterization of the miRNA-mediated regulatory landscape underlying radioresistance in HNC. Using isogenic radioresistant (RR) sublines derived from OECM1 and Detroit cells, we identified a robust miRNA signature comprising 12 upregulated oncogenic miRNAs (OncomiRs) and 13 downregulated tumor-suppressive miRNAs (TSmiRs) associated with acquired radioresistance. Integrative bioinformatic analyses revealed that these miRNAs collectively regulate a gene network enriched in receptor tyrosine kinase (RTK) signaling, cell motility, and cellular stress/cancer stemness pathways—biological processes fundamentally linked to radiation response. By constructing a refined miRNA–mRNA regulatory network, we identified central hub molecules, including EGFR, IGF1R, and MYC, as well as key regulatory miRNAs such as miR-199b-5p and miR-522-3p. Furthermore, transcriptome-guided drug repurposing using the Connectivity Map (CMap) platform nominated I-OMe-AG-538, an IGF1R inhibitor, as the top candidate radiosensitizer. Functional validation demonstrated that I-OMe-AG-538 reprogrammed the RR-associated miRNA landscape, increased intracellular ROS, and significantly enhanced radiosensitivity in HNC cells (Fig. 7 ). Together, these findings establish a mechanistic framework linking coordinated miRNA dysregulation, oncogenic signaling, redox homeostasis, and radioresistance in HNC. A major strength of this study lies in the use of isogenic RR sublines generated through long-term, low-dose serial irradiation. Both OECM1-RR and Detroit-RR cells exhibited stable and significantly enhanced radioresistance compared with their parental counterparts (Fig. 1 A). Unlike analyses based on heterogeneous clinical samples or acutely irradiated cells, this model minimizes genetic variability and captures clinically relevant acquired radioresistance rather than transient radiation responses. Consequently, the molecular alterations identified are more likely to represent true drivers of resistance rather than secondary or confounding effects. Global miRNA profiling revealed extensive and coordinated miRNA dysregulation in RR sublines (Fig. 1 B). We defined a consistent 25-miRNA signature (12 OncomiRs and 13 TSmiRs) shared across both cell models (Fig. 1 C–E, Table 1 ), underscoring its robustness and biological relevance. Several of these miRNAs have been previously implicated in radiation or stress responses. For example, miR-630 promotes radioresistance by modulating oxidative stress pathways [ 9 , 28 ], miR-622 suppresses Rb tumor suppressor activity [ 29 ], and miR-654-5p has been associated with chemoresistance in HNC, supporting a broader role in therapy resistance [ 30 ]. Conversely, multiple TSmiRs identified here—including miR-199b-5p, miR-499a-5p, and miR-146a-5p—have been shown to enhance radiosensitivity through regulation of autophagy, DNA damage repair, and apoptotic signaling [ 31 – 34 ]. Beyond confirming known regulators, our study highlights novel candidates such as OncomiRs miR-370-3p and miR-654-5p, and TSmiRs miR-522-3p and miR-380-3p, thereby expanding the repertoire of miRNAs linked to HNC radioresistance. This signature offers potential as a molecular tool for stratifying patients according to radiosensitivity and guiding personalized radiotherapy strategies. To move beyond single-miRNA effects, we integrated miRNA target prediction with our previously established RR-associated transcriptomic dataset [ 21 ]. This approach identified 252 oncogenes upregulated in association with TSmiR loss and 99 tumor suppressor genes suppressed by OncomiRs (Fig. 2 C). Pathway enrichment analysis revealed that these targets converge on three major functional modules: (i) cell motility and adhesion, (ii) RTK signaling, and (iii) stress response and cancer stemness (Fig. 2 D–E). These pathways are well-recognized mediators of tumor progression, epithelial–mesenchymal transition (EMT), stemness, and survival under genotoxic stress—processes intimately linked to radioresistance [ 35 – 40 ]. The convergence of miRNA dysregulation on these interconnected pathways suggests that radioresistance arises from coordinated rewiring of oncogenic signaling networks rather than isolated molecular events. Our network analysis further identified core nodes, including EGFR, IGF1R, and MYC (Fig. 3 B), which are central to multiple oncogenic signaling pathways and frequently dysregulated in cancer [ 41 – 44 ]. The comprehensive miRNA–mRNA regulatory network, comprising 68 miRNA–mRNA interaction pairs (Fig. 3 C), provided a systems-level view of intricate cross-regulatory interactions. Key hub miRNAs such as miR-199b-5p, miR-34c-5p, miR-522-3p, and miR-380-3p, each regulating more than seven oncogenes, highlight the coordinated nature of miRNA control over radioresistance. The presence of shared hub genes across multiple pathway modules suggests that radioresistance is orchestrated by a limited number of central signaling nodes. Experimental validation—ectopic overexpression of miR-199b-5p and miR-522-3p significantly reduced survival fractions following irradiation in three HNC cell lines (Fig. 3 D)—provides direct functional evidence for their roles as crucial radiosensitizers and validates the therapeutic potential suggested by the network analysis. Recognizing the need for actionable radiosensitizers, we employed a transcriptome-guided drug repurposing strategy using the CMap platform to identify compounds capable of reversing the radioresistance-associated gene signature. Among 27 prioritized candidates, I-OMe-AG-538 exhibited the strongest inverse correlation (τ = − 87) (Fig. 4 B). As a small-molecule inhibitor of insulin-like growth factor 1 receptor (IGF1R) [ 24 , 25 ], it targets a clinically relevant hub: high IGF1R expression is significantly associated with poor prognosis in HNC patients (Fig. 5 A), and IGF1R emerged as a central node regulated by multiple miRNAs across signaling pathways (Fig. 3 A, Supplementary Table S3 ). Mechanistically, I-OMe-AG-538 dose-dependently suppressed IGF1R expression and selectively inhibited Erk phosphorylation, with minimal impact on Akt signaling (Fig. 5 B), indicating predominant modulation of the IGF1R–MAPK/Erk axis. We also demonstrate that I-OMe-AG-538 broadly reshapes the RR-associated miRNA landscape—downregulating multiple OncomiRs (e.g., miR-513a-5p, miR-654-5p, miR-622) while restoring several TSmiRs (e.g., miR-199b-5p, miR-522-3p, miR-520f-3p) across tested cell lines (Fig. 5 C–D). This dual action on protein signaling and miRNA regulation suggests that I-OMe-AG-538 functions not only as a direct kinase inhibitor but also as an upstream modulator of the radioresistance network. A key mechanistic insight is the connection between IGF1R signaling, redox homeostasis, and radiosensitivity. Treatment with I-OMe-AG-538 significantly increased intracellular ROS levels (Fig. 6 A), a well-established mediator of radiation-induced DNA damage [ 26 , 27 ]. Correlation analysis of TCGA-HNSC data revealed that IGF1R expression is positively associated with eight ROS scavenger-associated genes (GCLM, GCLC, NFE2L2, CAT, SOD2, SOD3, GPX2, HMOX1; Fig. 6 B–C), suggesting that IGF1R contributes to antioxidant defense and ROS buffering in HNC. Consistent with this, combining I-OMe-AG-538 with irradiation produced synergistic reductions in clonogenic survival compared with either treatment alone (Fig. 6 D). These results support a model in which IGF1R inhibition by I-OMe-AG-538 disrupts redox homeostasis, elevates oxidative stress, and thereby sensitizes tumor cells to radiation. Despite these strengths, several limitations should be considered. First, although our isogenic RR model provides mechanistic clarity, it does not fully recapitulate the complexity of the tumor microenvironment, such as immune and stromal interactions. Second, while bioinformatic predictions and network construction are robust, direct experimental validation of all identified miRNA–mRNA interactions would further strengthen mechanistic claims. Third, although the in vitro radiosensitizing effect of I-OMe-AG-538 is compelling, preclinical in vivo efficacy and toxicity studies in relevant HNC animal models are essential before clinical translation. Finally, while TCGA-based analyses support clinical relevance, prospective clinical trials are necessary to validate the miRNA signature as a predictive biomarker for radioresistance and to evaluate the therapeutic benefit of I-OMe-AG-538 in HNC patients. CONCLUSION This study delineates a coordinated miRNA–mRNA regulatory network that drives radioresistance in HNC and demonstrates how transcriptome-guided drug repurposing can translate systems biology insights into actionable therapeutics. By integrating miRNA profiling, network modeling, and functional validation, we identified I-OMe-AG-538 as a promising radiosensitizer that reprograms oncogenic signaling and redox balance. More broadly, our multi-omics framework provides a generalizable strategy for uncovering resistance mechanisms and discovering therapeutic vulnerabilities in other radiation-refractory cancers. We anticipate that these findings will contribute to the development of more precise, mechanism-based radiotherapy strategies for patients with HNC. MATERIALS AND METHODS Cell Lines, Culture Conditions, and Radioresistant Sublines Three HNC cell lines—OECM1, Detroit, and FaDu—were used in this study. OECM1 cells were cultured in RPMI-1640 medium (Gibco; Thermo Fisher Scientific, Waltham, MA, USA), while Detroit and FaDu cells were maintained in Minimum Essential Medium (MEM; Gibco). All media were supplemented with 10% fetal bovine serum (FBS; Gibco) and 1% antibiotic-antimycotic solution (Gibco). Cells were incubated at 37°C in a humidified atmosphere with 5% CO₂. Radioresistant (RR) sublines were generated through serial fractionated irradiation, as previously described [ 21 ]. Briefly, cells were exposed to 2 Gy per fraction until a cumulative dose of 60 Gy was reached. This protocol yielded stable, isogenic RR sublines that model acquired rather than acute radioresistance, thereby better reflecting clinically relevant treatment failure. Clonogenic Survival Assay for Ionizing Radiation The clonogenic survival of cells following ionizing radiation was assessed using a standard colony formation assay, as previously reported [ 9 ]. Cells were seeded at a density of 800 cells per 3 cm² culture dish and incubated overnight to allow adherence. The following day, cells were exposed to gamma irradiation at doses of 0, 2, 4, or 6 Gy using a Gammacell® 3000 Elan irradiator (Best Theratronics, Ottawa, ON, Canada) with a Cesium-137 (¹³⁷Cs) source. After irradiation, cells were cultured under standard conditions (37°C, 5% CO₂) for 7–14 days to facilitate colony formation. Colonies were fixed with formaldehyde (Sigma-Aldrich, St. Louis, MO, USA), stained with 0.5% crystal violet (Sigma-Aldrich) for 30 minutes at room temperature, and rinsed with water to remove excess stain. Plates were air-dried, and colonies containing ≥ 50 cells were quantified using ImageJ software (NIH, Bethesda, MD, USA). The surviving fraction was calculated by normalizing the number of colonies at each radiation dose to the non-irradiated control (0 Gy). RNA Extraction and miRNA Profiling Total RNA was extracted from parental and RR subline cells using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions and established protocols [ 9 ]. RNA concentration and purity were determined using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and RNA integrity was verified by agarose gel electrophoresis. Global miRNA expression profiling was conducted using the Agilent Human miRNA Microarray V1 platform (G4470A, Agilent Technologies, Santa Clara, CA, USA), which includes 463 human miRNA probes. Seven probes from an earlier version (470 miRNAs) were excluded due to obsolescence based on miRBase version 22. Microarray hybridization was performed according to Agilent’s standard protocols. Arrays were scanned using the Agilent G2565A Microarray Scanner with high-dynamic-range settings. Raw image data were processed with Feature Extraction software (Agilent Technologies), and expression data were analyzed using GeneSpring GX software (version 7.3.1, Agilent Technologies). Differentially expressed miRNAs (DEMs) were identified using a |fold change| ≥ 2 threshold. Agilent’s present/absent detection flags were applied, and low-intensity features were filtered to remove background noise. For the validation of individual miRNA expression, total RNA was extracted using TRIzol reagent (Invitrogen) according to the manufacturer’s instructions. Reverse transcription was performed with miRNA-specific stem-loop RT primers and M-MLV reverse transcriptase (Invitrogen). Quantitative PCR was performed using TaqMan MicroRNA Assays, which included sequence-specific TaqMan probes and primers. Amplification was performed with iQ™ Supermix reagent (Bio-Rad Laboratories, Hercules, CA, USA) on the CFX96 Real-Time PCR Detection System (Bio-Rad). U6 small nuclear RNA served as the endogenous control for normalization. Relative expression was calculated using the 2^(-ΔΔCt) method. Primers and probes used for the qRT-PCR experiments were obtained from the Genomics Core Laboratory of the Molecular Medicine Research Center, Chang Gung University, Taiwan. Protein Extraction and Western Blot Analysis Protein extraction and Western blot analysis were performed as previously described [ 20 ]. Cells were lysed in CHAPS lysis buffer and incubated on ice for 10 minutes. To ensure complete cellular disruption and enhance protein solubilization, lysates were sonicated using a Qsonica sonicator (Qsonica, LLC, Newtown, CT, USA) with cup horns. Sonication was performed on cold water with three 10-second pulses at 50% amplitude, with 30-second intervals between pulses. Lysates were centrifuged at 12,000 × g for 30 minutes at 4°C to isolate the soluble protein fraction. Protein concentrations were quantified using the Bradford assay (Bio-Rad). Equal amounts of total protein (30 µg per sample) were separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to nitrocellulose membranes (Pall Corporation, Port Washington, NY, USA). Membranes were blocked with 5% bovine serum albumin (BSA) and incubated overnight at 4°C with primary antibodies, followed by incubation with horseradish peroxidase (HRP)-conjugated secondary antibodies. Protein bands were visualized using enhanced chemiluminescence (ECL) detection reagent (Merck Millipore, Burlington, MA, USA) and imaged with the Amersham Imager 600 system (GE Healthcare, Chicago, IL, USA). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) served as the loading control. Details of the antibodies used are listed in Supplementary Table S4 . Determination of Intracellular Reactive Oxygen Species (ROS) Levels Intracellular reactive oxygen species (ROS) levels were measured using the cell-permeable fluorescent probe 2’,7’-dichlorofluorescein diacetate (H2DCFDA; Invitrogen, Carlsbad, CA, USA), which is widely used to assess overall oxidative stress in living cells. Briefly, HNC cells were seeded and allowed to adhere overnight, followed by treatment with I-OMe-AG-538 or vehicle control as indicated. Cells were then incubated in culture medium containing 10 µM H2DCFDA for 30 min at 37°C in the dark to prevent photobleaching. After staining, cells were washed once with phosphate-buffered saline (PBS) to remove excess dye and immediately subjected to flow cytometric analysis using a Guava easyCyte™ flow cytometer (Merck Millipore, Burlington, MA, USA). Fluorescence intensity was recorded in the FITC channel, and mean fluorescence intensity (MFI) was quantified as a surrogate measure of intracellular ROS levels. Data were analyzed using the instrument’s standard software, and results were presented as relative ROS levels normalized to untreated controls. miRNA–mRNA Target Prediction and Network Construction To predict miRNA–mRNA interactions, three bioinformatic algorithms—TargetScan, miRDB, and miRTarBase—were used. Predicted miRNA–mRNA pairs supported by at least two of these databases were retained to ensure reliability for downstream analysis. An UpSetR plot was generated to quantify unique and shared predicted targets for each miRNA. Pathway enrichment analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database through the DAVID bioinformatics web tool to identify regulatory modules associated with radioresistance. Molecules enriched in key oncogenic pathways linked to the RR phenotype were extracted, and their roles in RR-related regulatory circuits were annotated. Interaction networks between miRNAs and their target genes were constructed and visualized in Cytoscape to elucidate the core regulatory relationships that may drive radioresistance. In Silico Drug Repurposing for Radioresistance Reversal To identify therapeutic compounds capable of reversing the radioresistance-associated gene expression signature, a drug repurposing analysis was performed using the Connectivity Map (CMap) platform (Broad Institute, Cambridge, MA, USA) [ 22 , 23 ]. Differentially expressed genes (DEGs) were derived from transcriptomic comparisons between RR and parental HNC cell lines. A signature comprising 150 significantly upregulated and 99 downregulated genes was selected based on a fold-change threshold of |FC| ≥ 1.3. This signature also included predicted targets of miRNAs dysregulated in the RR phenotype. This gene signature was submitted to the CLUE (CMap LINCS Unified Environment) platform for analysis within the L1000 dataset, which includes over one million gene expression profiles induced by small-molecule perturbagens across various human cell lines. The platform calculates a connectivity score (τ value) for each compound, reflecting the degree of similarity (positive τ) or inverse correlation (negative τ) between the input signature and the drug-induced transcriptional profile. Compounds with strongly negative connectivity scores (median τ ≤ − 60) were prioritized as candidates with high potential to counteract the radioresistance-associated gene expression pattern for further investigation as radiosensitizing agents in HNC. Declarations CONFLICT OF INTEREST The authors declare no conflict of interest. FUNDING STATEMENT This work was financially supported by the National Science and Technology Council of Taiwan, with the grant number NSTC-112-2314-B-182A-128 and NSTC-113-2314-B-182A-075. AUTHOR CONTRIBUTIONS Guo-Rung You : Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. Joseph T. Chang : Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing. Hung-Han Huang and Yen-Liang Li : Conceptualization, Methodology, Data curation, Visualization, Software, Writing – review & editing. Eric Yi-Liang Shen and Yin-Ju Chen : Conceptualization, Writing – review & editing. Ann-Joy Cheng : Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft., Writing – review & editing. ACKNOWLEDGEMENTS The qRT-PCR experiments for microRNA expression analysis were supported by the Genomics Core Laboratory of the Molecular Medicine Research Center, Chang Gung University, Taiwan. We also thank Dr. Kun-Yi Chien from the Proteomics Core Laboratory, Molecular Medicine Research Center, Chang Gung University, for providing antibodies used in the Western blot experiments. DATA AVAILABILITY Research data are stored in an institutional repository and will be shared upon request to the corresponding author. References Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 74, 229–263 (2024). Johnson, D. E., Burtness, B., Leemans, C. R., Lui, V. W. Y., Bauman, J. E. & Grandis, J. R. Head and neck squamous cell carcinoma. Nat Rev Dis Primers 6, 92 (2020). Higgins, G. S., O'Cathail, S. M., Muschel, R. 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The insulin-like growth factor-I receptor (IGF-IR) in breast cancer: biology and treatment strategies. Tumour Biol 37, 11711–11721 (2016). Dhanasekaran, R., Deutzmann, A., Mahauad-Fernandez, W. D., Hansen, A. S., Gouw, A. M. & Felsher, D. W. The MYC oncogene - the grand orchestrator of cancer growth and immune evasion. Nat Rev Clin Oncol 19, 23–36 (2022). Casacuberta-Serra, S., Gonzalez-Larreategui, I., Capitan-Leo, D. & Soucek, L. MYC and KRAS cooperation: from historical challenges to therapeutic opportunities in cancer. Signal Transduct Target Ther 9, 205 (2024). Table Table 1 is available in the Supplementary Files section. Additional Declarations There is no conflict of interest Supplementary Files Table1.docx Table 1. Summary of the 42 candidate miRNAs-associated with RR in HNC cells. SupplementaryFigureS1.pdf Supplementary Figure S1. Raw immunoblots corresponding to the cropped blots presented in Figure 5B. SupplementaryTableS1.docx ADDITIONAL INFORMATION Supplementary Table S1. List of differentially expressed miRNAs associated with the radioresistant phenotype in OECM1 cells. SupplementaryTableS2.docx Supplementary Table S2. List of differentially expressed miRNAs associated with the radioresistant phenotype in Detroit cells. SupplementaryTableS3.docx Supplementary Table S3. List of enriched genes and miRNA in respective molecular pathways associated with radioresistance in HNC cells. SupplementaryTableS4.docx Supplementary Table S4. List of antibodies used for Western blot analysis. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: revise 08 May, 2026 Review # 3 received at journal 05 May, 2026 Reviewer # 3 agreed at journal 03 May, 2026 Reviewer # 2 agreed at journal 19 Mar, 2026 Reviewer # 1 agreed at journal 15 Mar, 2026 Reviewers invited by journal 13 Mar, 2026 Submission checks completed at journal 16 Feb, 2026 Editor assigned by journal 15 Feb, 2026 First submitted to journal 15 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8888021","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":605471793,"identity":"88d1a2a4-0f60-4d72-8399-2a5529fc6487","order_by":0,"name":"Ann-Joy Cheng","email":"data:image/png;base64,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","orcid":"","institution":"Chang Gung University-College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ann-Joy","middleName":"","lastName":"Cheng","suffix":""},{"id":605471794,"identity":"b5de0973-7de1-4130-aae2-d8177698bdae","order_by":1,"name":"Guo-Rung You","email":"","orcid":"","institution":"Chang Gung University-College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Guo-Rung","middleName":"","lastName":"You","suffix":""},{"id":605471795,"identity":"4ae78f66-b46f-46e5-b929-acab06f73510","order_by":2,"name":"Joseph T. 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(A)\u003c/strong\u003e Clonogenic survival assay comparing radiosensitivity of parental (OECM1-Pt, Detroit-Pt) and radioresistant (OECM1-RR, Detroit-RR) HNC cell lines exposed to increasing doses of ionizing radiation (0–6 Gy). Data represent mean surviving fractions ± SD from three independent experiments. Statistical significance was determined by two-way ANOVA (***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). \u003cstrong\u003e(B)\u003c/strong\u003e Molecular profiling of differentially expressed miRNAs (DEMs) in OECM1-RR and Detroit-RR cells versus parental cells, derived from Agilent Human miRNA Microarray analysis (|fold change| ≥ 2). Red indicates upregulation; blue indicates downregulation. \u003cstrong\u003e(C)\u003c/strong\u003e Scatter plot illustration of overlapping DEMs between OECM1-RR and Detroit-RR sublines, identifying commonly upregulated (red dots) and downregulated (blue dots) miRNAs.\u003cstrong\u003e (D, E)\u003c/strong\u003e Top upregulated OncomiRs \u003cstrong\u003e(D)\u003c/strong\u003e and downregulated TSmiRs \u003cstrong\u003e(E) \u003c/strong\u003ewith stringent criteria (|FC| ≥ 5.0, SD \u0026lt; 0.02) in RR sublines. Scatter plot illustration of the average fold-change expression (X-axis) and the standard derivation (SD) (Y-axis) of the 12 common upregulated OncomiRs \u003cstrong\u003e(D)\u003c/strong\u003eand 30 downregulated TSmiRs \u003cstrong\u003e(E)\u003c/strong\u003e in OECM1-RR and Detroit-RR sublines.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/2a65710b5963b66f52083804.png"},{"id":104874620,"identity":"72d6a425-362d-498b-be2f-ea071ff9db38","added_by":"auto","created_at":"2026-03-18 08:32:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1093702,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003emiRNA-Mediated Pathways Underlying Radioresistance in HNC. (A, B)\u003c/strong\u003e Computational workflow and UpSetR plots showing overlap of predicted target genes for 12 OncomiRs \u003cstrong\u003e(A)\u003c/strong\u003e (2,363 genes) and 13 TSmiRs\u003cstrong\u003e (B)\u003c/strong\u003e (2,827 genes). Each miRNA targets were derived from TargetScan, miRDB, and miRTarBase algorithms (predicted by ≥2 databases), and the unique target genes commonly regulated by various miRNA combinations were shown. \u003cstrong\u003e(C) \u003c/strong\u003eStrategy for identifying miRNA-mediated radioresistance gene sets.Flowchart illustrating the intersection of predicted miRNA target genes with RR-associated differentially expressed genes (DEGs; 1,045 upregulated, 616 downregulated) from cDNA microarray [21], yielding 252 oncogenes (TSmiR targets) and 99 tumor suppressor genes (OncomiR targets). \u003cstrong\u003e(D)\u003c/strong\u003eKEGG pathway enrichment of tumor suppressive mechanism. Bubble plot showing top enriched KEGG pathways for the 99 tumor suppressor genes, highlighting pathways related to endocrine signaling, inflammatory response, and transcriptional dysregulation. The size of each bubble corresponds to the number of genes enriched in that pathway, while the color reflects the statistical significance of the enrichment, indicated by the \u003cem\u003eP\u003c/em\u003e-value calculated using the DAVID platform. (E) KEGG pathway enrichment of oncogenic mechanism. Bubble plot showing the 17 enriched oncogenic KEGG pathways for the 252 oncogenes, categorized into three functional modules: cell motility (four pathways), receptor tyrosine kinase signaling (seven pathways), and stress/cancer stemness (six pathways). The size of each bubble corresponds to the number of genes enriched in that pathway, while the color reflects the statistical significance of the enrichment, indicated by the \u003cem\u003eP\u003c/em\u003e-value calculated using the DAVID platform.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/15cf8c6641c4eb97d9b272ba.png"},{"id":105034248,"identity":"638f1f09-5dd4-48a3-ac98-7ce455da813b","added_by":"auto","created_at":"2026-03-20 07:22:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2039683,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003emiRNA–mRNA Regulatory Network Driving Radioresistance and Functional Validation of Hub MiRNAs.\u003c/strong\u003e (A) Alluvial plot of TSmiRs and their target oncogenes across pathway modules. Visualization showing the connections between the 13 TSmiRs and their 54 target oncogenes distributed across three pathway modules (cell motility, receptor tyrosine kinase signaling, stress/cancer stemness). (B) Protein-protein interaction (PPI) network of 54 oncogenes. Network constructed using the STRING database, highlighting key hub genes (e.g., EGFR, IGF1R, MYC) with high connectivity. (C) Integrated miRNA-mRNA regulatory network. Cytoscape visualization of 13 TSmiRs, 54 oncogenes, and 68 miRNA–mRNA pairs, highlighting hub miRNAs and reciprocal regulation. Blue hexagon represents TSmiRs; yellow/orange circles represent oncogenes. Lines indicate predicted interactions. Color scale represents the relative gene expression level in RR cells compared to Pt cells. (D) Radiosensitizing effect of hub TSmiRs. Clonogenic survival assays demonstrating the effect of ectopic overexpression of miR-199b-5p and miR-522-3p on radiosensitivity in three HNC cell lines. Data are presented as mean ± SD of three independent experiments. Statistical significance was determined by two-way ANOVA (*, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; **,\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/04cdd209b9d58398ee583172.png"},{"id":104874612,"identity":"fead099c-17cd-430a-b630-df0cf7f0b026","added_by":"auto","created_at":"2026-03-18 08:32:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":486219,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn Silico Drug Repurposing to Identify Radiosensitizing Compounds.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Principle of Connectivity Map (CMap) analysis for drug repurposing. Schematic of Connectivity Map (CMap) analysis, where negative connectivity scores (τ) indicate compounds that reverse the radioresistance gene signature, and positive scores indicate similar mechanisms. \u003cstrong\u003e(B)\u003c/strong\u003eHeatmap illustrating the top candidate compounds identified by CMap. Bar plot of 27 candidate compounds with median τ ≤ –60, identified using the CMap L1000 dataset, with I-OMe-AG-538 showing the strongest inverse correlation (τ = -87). Color scale indicates the median τ scores.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/85da6faab4a3bf84fc98e9d6.png"},{"id":105034106,"identity":"f80fea61-f434-4144-8807-e3c7b3d40413","added_by":"auto","created_at":"2026-03-20 07:22:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":626352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eI-OMe-AG-538 Modulates miRNA-Mediated Molecular Pathways. (A)\u003c/strong\u003e Chemical structure of I-OMe-AG-538 and Kaplan-Meier survival curve from TCGA-HNSC dataset showing high IGF1R expression associated with poor prognosis in HNC patients (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). \u003cstrong\u003e(B)\u003c/strong\u003e Effect of I-OMe-AG-538 on IGF1R signaling. Western blot analysis of IGF1R, p-Erk/Erk, and p-Akt/Akt in two HNC cell lines treated with increasing doses of I-OMe-AG-538, with GAPDH as loading control. Densitometry quantification for each blot is presented in the right panel. Data are presented as mean ± SD of three independent experiments. Statistical significance was determined by t-test (*, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01).\u003cstrong\u003e (C)\u003c/strong\u003e I-OMe-AG-538 modulates RR-associated OncomiRs. Quantitative PCR analysis of six OncomiRs in three HNC cell lines (OECM1, FaDu, Detroit) after I-OMe-AG-538 treatment (10 µM for OECM1 and FaDu 15 µM for Detroit; 48h). \u003cstrong\u003e(D)\u003c/strong\u003e I-OMe-AG-538 modulates RR-associated TSmiRs. Quantitative PCR analysis of six TSmiRs in three HNC cell lines (OECM1, FaDu, Detroit) after I-OMe-AG-538 treatment (10 µM for OECM1 and FaDu 15 µM for Detroit; for 48h).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/4279056e80bab4b91bfff2be.png"},{"id":104874619,"identity":"81f23cf9-24b5-4acb-8c4d-7f48360c94ac","added_by":"auto","created_at":"2026-03-18 08:32:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":818435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eI-OMe-AG-538 Mediates ROS levels to Enhance Radiosensitivity. (A)\u003c/strong\u003e Upper panel: Reactive oxygen species (ROS) were measured by H₂DCFDA staining and analyzed by flow cytometry in OECM1, FaDu, and Detroit cell lines after 48 h treatment with I-OMe-AG-538 (10 µM for OECM1 and FaDu; 15 µM for Detroit). Lower panel: Quantification of relative ROS levels (fold change) is shown. Data are presented as mean ± SD of three independent experiments. Statistical significance was assessed by Student’s t-test (*\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05; **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01). \u003cstrong\u003e(B)\u003c/strong\u003e Correlation between IGF1R expression and individual ROS scavenger–associated genes (GCLM, GCLC, NFE2L2, CAT, SOD1, SOD2, SOD3, GPX2, GPX4, and HMOX1) in tumor tissues from TCGA-HNSC patients. Correlation expression data were obtained from the GEPIA2 database. The x-axis represents the Spearman correlation coefficient (R), and the y-axis indicates the P value (log\u003csub\u003e10\u003c/sub\u003e). \u003cstrong\u003e(C)\u003c/strong\u003e IGF1R expression shows a strong positive correlation with the eight positive ROS scavenger–related gene signature in TCGA-HNSC tumor samples. The x-axis represents IGF1R expression (TPM, log\u003csub\u003e2\u003c/sub\u003e), and the y-axis represents the ROS scavenger–related gene signature score (TPM, log\u003csub\u003e2\u003c/sub\u003e). Statistical analysis was performed using the Spearman correlation coefficient (R) based on data retrieved from the GEPIA2 database. \u003cstrong\u003e(D) \u003c/strong\u003eI-OMe-AG-538 enhances radiosensitivity in HNC cells. Clonogenic survival curves of three HNC cell lines treated with I-OMe-AG-538 alone (10 µM for OECM1 and FaDu; 15 µM for Detroit; 48 h), irradiation alone (2 Gy), or their combination. Data are presented as mean ± SD of three independent experiments. Statistical significance was determined by t-test (**, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/f9ad815bc9ffc69479116e00.png"},{"id":104874621,"identity":"e66e0843-d1a3-4a4f-8c33-80c4c8722dfd","added_by":"auto","created_at":"2026-03-18 08:32:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":360315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic overview of miRNA-mediated regulatory networks and drug repurposing, identifying I-OMe-AG-538 as a potential compound to overcome radioresistance in HNC.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/5f302f7201f64c3986b73624.png"},{"id":106092922,"identity":"b843a226-47b1-474d-9d40-86a60b6f4d3c","added_by":"auto","created_at":"2026-04-03 11:30:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7365353,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/f16d4347-7f9a-4d4d-ad9f-75ad9c50a822.pdf"},{"id":105034188,"identity":"69a0ce7b-b20f-4e07-a577-2f099490303b","added_by":"auto","created_at":"2026-03-20 07:22:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15736,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Summary of the 42 candidate miRNAs-associated with RR in HNC cells.\u003c/p\u003e","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/9738b724a40a2142df521d56.docx"},{"id":105034025,"identity":"01e9642d-a14e-4849-aedf-190c6aa89e0d","added_by":"auto","created_at":"2026-03-20 07:22:28","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":445845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1. \u003c/strong\u003eRaw immunoblots corresponding to the cropped blots presented in Figure 5B.\u003c/p\u003e","description":"","filename":"SupplementaryFigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/875ef11bc2fce18550018b3f.pdf"},{"id":105033993,"identity":"00b86c96-7dbe-4db8-83d6-313a68ce2ad9","added_by":"auto","created_at":"2026-03-20 07:22:23","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18647,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eADDITIONAL INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S1.\u003c/strong\u003e List of differentially expressed miRNAs associated with the radioresistant phenotype in OECM1 cells.\u003c/p\u003e","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/ef635269f9e2f991202ef2f9.docx"},{"id":104874616,"identity":"5edd5849-bb84-43db-ae0a-7e328550766e","added_by":"auto","created_at":"2026-03-18 08:32:10","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S2.\u003c/strong\u003e List of differentially expressed miRNAs associated with the radioresistant phenotype in Detroit cells.\u003c/p\u003e","description":"","filename":"SupplementaryTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/5a2717ff2f7c813378d10a3f.docx"},{"id":104874622,"identity":"a4c28f3e-cdb8-4136-92c5-40ff87f4da5c","added_by":"auto","created_at":"2026-03-18 08:32:10","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":374400,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S3. \u003c/strong\u003eList of enriched genes and miRNA in respective molecular pathways associated with radioresistance in HNC cells.\u003c/p\u003e","description":"","filename":"SupplementaryTableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/060be26d7caf9b7388d26cc3.docx"},{"id":104874623,"identity":"309506fb-846e-4318-be49-5ee172589a8d","added_by":"auto","created_at":"2026-03-18 08:32:10","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":14500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S4.\u003c/strong\u003e List of antibodies used for Western blot analysis.\u003c/p\u003e","description":"","filename":"SupplementaryTableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8888021/v1/37da0db15456d9a42e0e27f5.docx"}],"financialInterests":"There is no conflict of interest","formattedTitle":"Targeting a miRNA–mRNA Regulatory Network to Overcome Radioresistance in Head and Neck Cancer: Identification of I-OMe-AG-538 via Transcriptome-Guided Drug Repurposing","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eHead and neck cancer (HNC) represents a heterogeneous group of malignancies, predominantly squamous cell carcinomas arising from the oral cavity and oropharynx. Globally, HNC remains among the most prevalent cancers, with a significant incidence in middle-aged men, underscoring its substantial impact on patients, families, and society [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Current therapeutic approaches for HNC include surgery, radiotherapy, chemotherapy, or multimodal combinations. Despite considerable advancements in treatment modalities and supportive care over recent decades, the overall 5-year survival rate for HNC patients has shown only limited improvement [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Radiotherapy remains a cornerstone of curative-intent treatment for HNC; however, local and regional recurrence following radiation therapy continues to pose a major clinical challenge [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Intrinsic and acquired tumor radioresistance are key contributors to this recurrence. Therefore, identifying reliable biomarkers associated with both radioresistance and radiosensitivity is essential for optimizing treatment strategies and improving therapeutic outcomes.\u003c/p\u003e \u003cp\u003eMicroRNAs (miRNAs) are small non-coding RNAs, typically 18\u0026ndash;22 nucleotides in length, that function as critical post-transcriptional regulators of gene expression. miRNAs exert their effects primarily by binding to the 3\u0026rsquo;-untranslated region (3\u0026rsquo;-UTR) of target messenger RNAs (mRNAs), leading to mRNA degradation or translational inhibition [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These molecules play fundamental roles in diverse biological processes, including cell differentiation, proliferation, stress responses, and apoptosis, and their dysregulation has been widely implicated in human diseases, particularly cancer [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Notably, accumulating evidence indicates that miRNAs are pivotal modulators of radiosensitivity and radioresistance across multiple cancer types. For example, the oncogenic miRNA miR-21 has been extensively reported to promote radioresistance by suppressing pro-apoptotic pathways [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In HNC specifically, several miRNAs have been shown to confer radioresistance through distinct molecular mechanisms. Oncogenic miRNAs (OncomiRs) such as miR-630, miR-96-5p, and miR-196a promote radioresistance by modulating the Nrf2\u0026ndash;GPX2 axis, targeting PTEN, or inhibiting annexin-A1, respectively [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Conversely, tumor-suppressive miRNAs (TSmiRs), including miR-520b, miR-494-3p, and miR-526b-3p, have been demonstrated to enhance radiosensitivity by suppressing cancer stemness, inducing cellular senescence, or inhibiting autophagy [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While these studies have provided important mechanistic insights, most investigations have focused on individual miRNAs, thereby limiting a comprehensive understanding of the broader miRNA-mediated regulatory networks that govern radiosensitivity.\u003c/p\u003e \u003cp\u003eTo achieve a more holistic understanding of radioresistance, recent studies have employed global miRNA profiling strategies. One approach involves direct comparisons of clinical samples exhibiting different levels of radiosensitivity [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although this method reflects authentic tumor biology, genetic heterogeneity among patients may confound the interpretation of radioresistance mechanisms. An alternative strategy compares miRNA expression profiles in HNC cell lines before and after irradiation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which reduces inter-individual variability. However, these studies often rely on acute, short-term irradiation, which may primarily capture transient radiation-induced responses rather than stable, intrinsic radioresistance.\u003c/p\u003e \u003cp\u003eTo better model the intrinsic radioresistance observed in patients, our group previously developed isogenic HNC radioresistant (RR) sublines through long-term, low-dose serial irradiation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These sublines are genetically identical to their parental cells yet exhibit a markedly enhanced radioresistant phenotype, ensuring that observed molecular alterations are predominantly associated with resistance rather than genetic background differences. Furthermore, we previously identified a radioresistance-associated gene set using cDNA microarray analysis of these RR sublines [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], providing a valuable transcriptomic foundation for further investigation.\u003c/p\u003e \u003cp\u003eHowever, a systematic, network-based understanding of how coordinated miRNA dysregulation shapes radioresistance in HNC remains largely unexplored. To address this gap, we developed a streamlined strategy to construct a refined miRNA\u0026ndash;mRNA regulatory network associated with radiosensitivity. Additionally, using an in silico drug repurposing approach, we identified candidate compounds targeting this network and validated a potent compound that reverses radioresistance in HNC cells. This strategy is particularly attractive because it accelerates clinical translation by leveraging compounds with existing pharmacological profiles. Collectively, this study provides comprehensive insights into miRNA-mediated mechanisms of radioresistance in HNC and highlights drug repurposing as a promising strategy for developing novel radiosensitizers, potentially enhancing therapeutic outcomes for patients with refractory disease.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProfiling miRNAs Associated with Radioresistance in HNC\u003c/h2\u003e \u003cp\u003eTo investigate the molecular basis of radioresistance in HNC, we established radioresistant (RR) sublines, OECM1-RR and Detroit-RR, through repeated fractionated irradiation until the cells exhibited a highly radioresistant phenotype. The radiosensitivity of these sublines was validated using a clonogenic survival assay, which showed significantly greater radiation resistance in OECM1-RR and Detroit-RR cells compared to their parental counterparts, OECM1-Pt and Detroit-Pt, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo elucidate the role of miRNAs in radioresistance, we performed comprehensive miRNA expression profiling using the Agilent miRNA microarray platform, which encompasses 463 human miRNAs. This analysis compared miRNA expression profiles between parental and RR sublines in both OECM1 and Detroit cell models. Differentially expressed miRNAs (DEMs) in RR sublines displayed distinct expression patterns relative to their parental cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Applying a threshold of |fold change (FC)| \u0026ge; 2, we identified 48 upregulated and 76 downregulated miRNAs in OECM1-RR cells and 37 upregulated and 133 downregulated miRNAs in Detroit-RR cells, indicating extensive and coordinated miRNA dysregulation associated with the acquisition of radioresistance (Supplementary Tables S1 and S2).\u003c/p\u003e \u003cp\u003eTo pinpoint miRNAs consistently associated with radioresistance across both cell models, we analyzed overlapping DEMs between OECM1-RR and Detroit-RR sublines. This approach revealed 12 miRNAs that were consistently upregulated and 30 consistently downregulated in both RR sublines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To prioritize key regulatory miRNAs, we applied stringent criteria for downregulated miRNAs, requiring an average |FC| \u0026ge; 5.0 and a standard deviation (SD)\u0026thinsp;\u0026lt;\u0026thinsp;0.02 across both RR sublines. This refined analysis identified 12 upregulated OncomiRs, including miR-513a-5p and miR-654-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), and 13 downregulated TSmiRs, such as miR-302b-3p and miR-146a-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), exhibiting significant dysregulation in RR sublines. Collectively, this study defines a miRNA signature associated with radioresistance in HNC, offering insights into the molecular mechanisms underlying this trait.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the 42 candidate miRNAs-associated with RR in HNC cells.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOECM1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetroit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFold (RR/Pt)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFold (RR/Pt)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFold (mean)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp regulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-513a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-654-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-129-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-214-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-370-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDown regulation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-146a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-34c-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-302b-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-451a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-299-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-409-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-380-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-499a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-517a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-522-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-520a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-548c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-520f-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-519c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-302a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-561-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-382-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-495-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-548b-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-493-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-34b-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-520g-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-122-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-519b-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-520c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-126-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-193a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-199b-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eRR, radioresistance; Pt, parental; SD, standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003emiRNA-Mediated Pathways Underlying Radioresistance in HNC\u003c/h3\u003e\n\u003cp\u003eTo elucidate the mechanisms by which miRNAs regulate radioresistance in HNC, we identified target genes of the previously defined radioresistance-associated miRNAs and performed integrative pathway analyses. Target genes were predicted using three algorithms, TargetScan, miRTarBase, and miRDB, with genes predicted by at least two algorithms considered as potential targets. For the 12 OncomiRs, the number of predicted target transcripts per miRNA ranged from 68 to 628. UpSetR plot analysis revealed significant overlap, identifying 2,363 unique genes co-regulated by these OncomiRs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Similarly, across the 13 TSmiRs, predicted targets per miRNA ranged from 47 to 824 transcripts, and 2,827 unique genes were commonly regulated by TSmiRs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These predicted targets provide a comprehensive set of candidate genes that may mediate miRNA-driven radioresistance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo pinpoint gene sets critical to radioresistance, we integrated the miRNA target predictions with a previously established radioresistance-associated gene set derived from cDNA microarray analysis of HNC RR sublines [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This gene set comprised 1,045 upregulated oncogenes and 616 downregulated tumor suppressor genes as differentially expressed genes (DEGs). To explore oncogenic mechanisms, we intersected the 1,045 upregulated DEGs with the 2,827 TSmiR target genes, yielding 252 overlapping genes. This oncogene panel reflects genes upregulated due to TSmiR downregulation, likely promoting radioresistance. Conversely, to investigate tumor-suppressive mechanisms, we intersected the 616 downregulated DEGs with the 2,363 OncomiR target genes, identifying 99 overlapping genes indicating miRNA-mediated silencing of tumor suppressors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTo further characterize the molecular pathways underlying radioresistance, we conducted KEGG pathway enrichment analysis using the DAVID platform. Functional annotation of the 99 tumor suppressor genes highlighted pathways involved in endocrine signaling, inflammatory response, and transcriptional dysregulation, suggesting that loss of cellular homeostasis is a key driver of radioresistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). In contrast, the 252 oncogene targets were enriched in 17 oncogenic pathways and categorized into three functional modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The cell motility module included four pathways related to cell junction dynamics: adherens junction, focal adhesion, ECM-receptor interaction, and actin cytoskeleton regulation. The receptor tyrosine kinase (RTK) signaling module encompasses seven critical oncogenic pathways: ErbB, EGFR, TGF-β, MAPK, PI3K-Akt, Ras, and Rap1. The stress and cancer stemness module comprised six pathways associated with cellular stress responses and cancer stemness, including p53, Notch, HIF-1, and Wnt signaling. These findings indicate that miRNA-mediated pathways not only drive radioresistance but also contribute to broader malignant phenotypes in HNC.\u003c/p\u003e \u003cp\u003eCollectively, these results highlight the complexity of miRNA-mediated regulation in HNC radioresistance, characterized by the oncogenic activation of specific miRNAs and the silencing of tumor suppressor genes. The identified pathways provide critical insights into the molecular mechanisms underlying radioresistance and highlight potential therapeutic targets for overcoming treatment resistance in HNC.\u003c/p\u003e\n\u003ch3\u003emiRNA–mRNA Regulatory Network Driving Radioresistance in HNC\u003c/h3\u003e\n\u003cp\u003eTo elucidate the regulatory mechanisms underpinning radioresistance in HNC, we constructed an integrated miRNA\u0026ndash;mRNA interaction network, focusing on molecules within the three previously identified pathway modules (cell motility, receptor tyrosine kinase signaling, and stress/cancer stemness). These molecules, derived from experimentally validated microarray data, provide robust targets for analysis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e summarizes the 13 TSmiRs and their enriched target oncogenes across the pathway modules, and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA presents an alluvial plot visualizing the associations between individual miRNAs and their target oncogenes within these modules. Collectively, the 13 TSmiRs co-target 54 oncogenes across all three pathway modules. Notably, eight hub genes (EGFR, GSK3B, IGF1R, JUN, NRAS, RRAS, THBS1, and VEGFA) participate in all three modules, and 12 genes, including CDKN1A, MYC, ITGB4, and LAMC1, are involved in two modules, suggesting the presence of central molecular hubs orchestrating multiple oncogenic pathways to modulate radioresistance (Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Taken together, the presence of shared hub genes across multiple pathway modules suggests that radioresistance is orchestrated by a limited number of central signaling nodes rather than isolated molecular events.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo explore the functional connectivity of these 54 oncogenes, we constructed a protein-protein interaction (PPI) network using the STRING database. The resulting network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) identified EGFR, IGF1R, and MYC as core nodes with high connectivity, underscoring their pivotal roles in mediating oncogenic signaling associated with radioresistance.\u003c/p\u003e \u003cp\u003eA comprehensive miRNA\u0026ndash;mRNA regulatory network was generated in Cytoscape, incorporating 13 TSmiRs and 54 oncogenes, yielding 68 miRNA\u0026ndash;mRNA interaction pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). This network revealed intricate cross-regulatory interactions, with multiple miRNAs converging on shared oncogenic targets. Key hub miRNAs, including miR-199b-5p, miR-34c-5p, miR-520f-3p, miR-522-3p, and miR-380-3p, each of which regulates more than seven oncogenes. Reciprocal regulation was also evident; for instance, miR-199b-5p targeted VEGFA, LAMC1, GSK3B, MYH9, and ITGA3, while VEGFA was additionally modulated by miR-299-3p. Similarly, miR-522-3p regulated IGF1R, FZD1, and MYH10, which were concurrently targeted by miR-380-3p, miR-146a-5p, and miR-499a-5p, respectively. These findings highlight collaborative regulatory interactions within the miRNA\u0026ndash;mRNA network that drive oncogenic mechanisms underlying radioresistance.\u003c/p\u003e \u003cp\u003eTo validate the functional role of hub miRNAs in radiosensitivity, we ectopically overexpressed two TSmiRs, miR-199b-5p and miR-522-3p, in three HNC cell lines and assessed their impact on radiosensitivity. Both miRNAs consistently reduced survival fractions following irradiation across all tested cell lines, with statistically significant effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), confirming their roles as radiosensitizers in HNC cells. Collectively, these results delineate a comprehensive miRNA\u0026ndash;mRNA regulatory network underlying radioresistance in HNC, identifying hub miRNAs and oncogenes as critical regulators of radiosensitivity. This network provides valuable insights into the molecular mechanisms underlying radioresistance and identifies potential therapeutic targets to improve treatment efficacy in HNC.\u003c/p\u003e\n\u003ch3\u003eDrug Repurposing to Identify Radiosensitizing Compounds for HNC\u003c/h3\u003e\n\u003cp\u003eRadioresistance remains a major cause of treatment failure in a clinically significant subset of HNC patients, underscoring the need for compounds that enhance tumor radiosensitivity. To address this, we employed a drug repurposing strategy using the Connectivity Map (CMap) platform at the Broad Institute [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] to identify candidate therapeutic compounds efficiently. CMap compares gene expression signatures, where highly negative connectivity scores (τ values) indicate drugs that may reverse disease-associated signatures (e.g., radioresistance). In contrast, positive scores indicate compounds with mechanisms similar to those of the disease state (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo identify compounds capable of reversing radioresistance, we analyzed the gene expression signatures of a previously defined radioresistance-associated gene panel (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) comprising 150 oncogenes and 99 tumor suppressor genes co-regulated by miRNAs and mRNAs. Using the CMap platform, we filtered for compounds with median τ values \u0026lt; -60. This threshold was selected to prioritize compounds with strong inverse transcriptional signatures relative to the RR phenotype. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, a total of 27 compounds were identified, including I-OMe-AG-538, oligomycin-A, and etomoxir, which are the most promising. Among these, I-OMe-AG-538 exhibited the strongest inverse correlation with the radioresistance gene signature (τ = -87), highlighting its potential as a leading candidate for mitigating radioresistance in HNC.\u003c/p\u003e\n\u003ch3\u003eI-OMe-AG-538 Modulates miRNA-Mediated Molecular Pathways in HNC\u003c/h3\u003e\n\u003cp\u003eFollowing in silico analysis, which identified I-OMe-AG-538 as a leading candidate for reversing the RR gene signature, we selected this compound for experimental validation. I-OMe-AG-538, a small-molecule inhibitor of the insulin-like growth factor 1 receptor (IGF1R), has been reported to modulate chemotherapeutic resistance in breast cancer cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In our study, IGF1R was identified within the RR gene panel, exhibiting cross-talk with multiple oncoproteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and regulation by several miRNAs across various signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). To confirm the clinical relevance of IGF1R, survival analysis using the TCGA-HNSC dataset and the KM Plotter tool demonstrated that high IGF1R expression was significantly associated with a poor prognosis in HNC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo investigate the molecular effects of I-OMe-AG-538, we assessed its impact on IGF1R and downstream signaling molecules (Erk and Akt) via Western blot analysis in two HNC cell lines. I-OMe-AG-538 significantly reduced IGF1R expression in a dose-dependent manner and markedly inhibited Erk phosphorylation (p-Erk/Erk), while having a minimal effect on Akt phosphorylation (p-Akt/Akt) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). These findings suggest that I-OMe-AG-538 predominantly modulates the IGF1R\u0026ndash;MAPK/Erk signaling axis.\u003c/p\u003e \u003cp\u003eGiven IGF1R\u0026rsquo;s regulation by multiple miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), we evaluated the effect of I-OMe-AG-538 on RR-associated miRNA expression in three HNC cell lines. Six OncomiRs (miR-198, miR-370-3p, miR-513a-5p, miR-622, miR-630, and miR-654-5p) consistently showed reduced expression following I-OMe-AG-538 treatment, despite varying levels across cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Conversely, six TSmiRs (miR-199b-5p, miR-302b-3p, miR-451a, miR-520f-3p, miR-517a-3p, and miR-522-3p) exhibited increased expression in all tested cell lines, although various levels were noted (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). These results indicate that I-OMe-AG-538 broadly modulates the RR-associated miRNA network, likely influencing multiple downstream molecular pathways.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eI-OMe-AG-538 Mediates ROS levels to Enhance Radiosensitivity in HNC\u003c/h2\u003e \u003cp\u003eI-OMe-AG-538 has been reported as a ROS-enhancing regulatory compound, and elevated intracellular ROS levels are strongly associated with oxidative DNA damage and radiation-induced cytotoxicity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To evaluate the impact of I-OMe-AG-538 on cellular redox status, intracellular ROS production was quantified using the fluorescent probe H\u003csub\u003e2\u003c/sub\u003eDCFDA followed by flow cytometric analysis. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, treatment with I-OMe-AG-538 significantly increased intracellular ROS levels in HNC cells compared with untreated controls, indicating that this compound effectively shifts the cellular redox balance toward a more oxidative state.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore the potential molecular basis of this ROS modulation, we examined the relationship between IGF1R expression and key ROS scavenger genes using TCGA-HNSC patient data via the GEPIA2 platform. Correlation analysis revealed that IGF1R expression was positively associated with eight major ROS detoxification genes, including GCLM, GCLC, NFE2L2, CAT, SOD2, SOD3, GPX2, and HMOX1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), suggesting that IGF1R signaling is closely linked to the cellular antioxidant defense system. Moreover, the combined expression signature of these eight ROS scavengers exhibited a significant positive correlation with IGF1R levels (R\u0026thinsp;=\u0026thinsp;0.39) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), supporting the notion that IGF1R exerts a broad regulatory influence over intracellular redox homeostasis. Together, these findings suggest that I-OMe-AG-538 increases intracellular ROS levels at least in part through disruption of an IGF1R\u0026ndash;ROS scavenger regulatory axis in HNC.\u003c/p\u003e \u003cp\u003eTo determine whether this ROS elevation translates into enhanced radiosensitivity, we performed clonogenic survival assays in three HNC cell lines. While either I-OMe-AG-538 treatment or irradiation alone reduced cell survival, the combination of I-OMe-AG-538 and irradiation produced a significantly greater reduction in clonogenic survival compared with either treatment alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Collectively, these results demonstrate that I-OMe-AG-538 enhances radiosensitivity in HNC cells by increasing intracellular ROS levels and modulating IGF1R-associated antioxidant signaling, supporting its potential as a promising radiosensitizer for improving radiotherapy efficacy in refractory HNC.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eHead and neck cancer (HNC) frequently exhibits intrinsic and acquired radioresistance, which substantially limits the efficacy of radiotherapy and contributes to local recurrence and poor clinical outcomes. In this study, we provide a comprehensive, network-based characterization of the miRNA-mediated regulatory landscape underlying radioresistance in HNC. Using isogenic radioresistant (RR) sublines derived from OECM1 and Detroit cells, we identified a robust miRNA signature comprising 12 upregulated oncogenic miRNAs (OncomiRs) and 13 downregulated tumor-suppressive miRNAs (TSmiRs) associated with acquired radioresistance. Integrative bioinformatic analyses revealed that these miRNAs collectively regulate a gene network enriched in receptor tyrosine kinase (RTK) signaling, cell motility, and cellular stress/cancer stemness pathways\u0026mdash;biological processes fundamentally linked to radiation response. By constructing a refined miRNA\u0026ndash;mRNA regulatory network, we identified central hub molecules, including EGFR, IGF1R, and MYC, as well as key regulatory miRNAs such as miR-199b-5p and miR-522-3p. Furthermore, transcriptome-guided drug repurposing using the Connectivity Map (CMap) platform nominated I-OMe-AG-538, an IGF1R inhibitor, as the top candidate radiosensitizer. Functional validation demonstrated that I-OMe-AG-538 reprogrammed the RR-associated miRNA landscape, increased intracellular ROS, and significantly enhanced radiosensitivity in HNC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Together, these findings establish a mechanistic framework linking coordinated miRNA dysregulation, oncogenic signaling, redox homeostasis, and radioresistance in HNC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA major strength of this study lies in the use of isogenic RR sublines generated through long-term, low-dose serial irradiation. Both OECM1-RR and Detroit-RR cells exhibited stable and significantly enhanced radioresistance compared with their parental counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Unlike analyses based on heterogeneous clinical samples or acutely irradiated cells, this model minimizes genetic variability and captures clinically relevant acquired radioresistance rather than transient radiation responses. Consequently, the molecular alterations identified are more likely to represent true drivers of resistance rather than secondary or confounding effects.\u003c/p\u003e \u003cp\u003eGlobal miRNA profiling revealed extensive and coordinated miRNA dysregulation in RR sublines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). We defined a consistent 25-miRNA signature (12 OncomiRs and 13 TSmiRs) shared across both cell models (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u0026ndash;E, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), underscoring its robustness and biological relevance. Several of these miRNAs have been previously implicated in radiation or stress responses. For example, miR-630 promotes radioresistance by modulating oxidative stress pathways [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], miR-622 suppresses Rb tumor suppressor activity [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and miR-654-5p has been associated with chemoresistance in HNC, supporting a broader role in therapy resistance [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Conversely, multiple TSmiRs identified here\u0026mdash;including miR-199b-5p, miR-499a-5p, and miR-146a-5p\u0026mdash;have been shown to enhance radiosensitivity through regulation of autophagy, DNA damage repair, and apoptotic signaling [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Beyond confirming known regulators, our study highlights novel candidates such as OncomiRs miR-370-3p and miR-654-5p, and TSmiRs miR-522-3p and miR-380-3p, thereby expanding the repertoire of miRNAs linked to HNC radioresistance. This signature offers potential as a molecular tool for stratifying patients according to radiosensitivity and guiding personalized radiotherapy strategies.\u003c/p\u003e \u003cp\u003eTo move beyond single-miRNA effects, we integrated miRNA target prediction with our previously established RR-associated transcriptomic dataset [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This approach identified 252 oncogenes upregulated in association with TSmiR loss and 99 tumor suppressor genes suppressed by OncomiRs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Pathway enrichment analysis revealed that these targets converge on three major functional modules: (i) cell motility and adhesion, (ii) RTK signaling, and (iii) stress response and cancer stemness (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u0026ndash;E). These pathways are well-recognized mediators of tumor progression, epithelial\u0026ndash;mesenchymal transition (EMT), stemness, and survival under genotoxic stress\u0026mdash;processes intimately linked to radioresistance [\u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The convergence of miRNA dysregulation on these interconnected pathways suggests that radioresistance arises from coordinated rewiring of oncogenic signaling networks rather than isolated molecular events.\u003c/p\u003e \u003cp\u003eOur network analysis further identified core nodes, including EGFR, IGF1R, and MYC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), which are central to multiple oncogenic signaling pathways and frequently dysregulated in cancer [\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The comprehensive miRNA\u0026ndash;mRNA regulatory network, comprising 68 miRNA\u0026ndash;mRNA interaction pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), provided a systems-level view of intricate cross-regulatory interactions. Key hub miRNAs such as miR-199b-5p, miR-34c-5p, miR-522-3p, and miR-380-3p, each regulating more than seven oncogenes, highlight the coordinated nature of miRNA control over radioresistance. The presence of shared hub genes across multiple pathway modules suggests that radioresistance is orchestrated by a limited number of central signaling nodes. Experimental validation\u0026mdash;ectopic overexpression of miR-199b-5p and miR-522-3p significantly reduced survival fractions following irradiation in three HNC cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD)\u0026mdash;provides direct functional evidence for their roles as crucial radiosensitizers and validates the therapeutic potential suggested by the network analysis.\u003c/p\u003e \u003cp\u003eRecognizing the need for actionable radiosensitizers, we employed a transcriptome-guided drug repurposing strategy using the CMap platform to identify compounds capable of reversing the radioresistance-associated gene signature. Among 27 prioritized candidates, I-OMe-AG-538 exhibited the strongest inverse correlation (τ = \u0026minus;\u0026thinsp;87) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). As a small-molecule inhibitor of insulin-like growth factor 1 receptor (IGF1R) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], it targets a clinically relevant hub: high IGF1R expression is significantly associated with poor prognosis in HNC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), and IGF1R emerged as a central node regulated by multiple miRNAs across signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMechanistically, I-OMe-AG-538 dose-dependently suppressed IGF1R expression and selectively inhibited Erk phosphorylation, with minimal impact on Akt signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), indicating predominant modulation of the IGF1R\u0026ndash;MAPK/Erk axis. We also demonstrate that I-OMe-AG-538 broadly reshapes the RR-associated miRNA landscape\u0026mdash;downregulating multiple OncomiRs (e.g., miR-513a-5p, miR-654-5p, miR-622) while restoring several TSmiRs (e.g., miR-199b-5p, miR-522-3p, miR-520f-3p) across tested cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u0026ndash;D). This dual action on protein signaling and miRNA regulation suggests that I-OMe-AG-538 functions not only as a direct kinase inhibitor but also as an upstream modulator of the radioresistance network.\u003c/p\u003e \u003cp\u003eA key mechanistic insight is the connection between IGF1R signaling, redox homeostasis, and radiosensitivity. Treatment with I-OMe-AG-538 significantly increased intracellular ROS levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), a well-established mediator of radiation-induced DNA damage [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Correlation analysis of TCGA-HNSC data revealed that IGF1R expression is positively associated with eight ROS scavenger-associated genes (GCLM, GCLC, NFE2L2, CAT, SOD2, SOD3, GPX2, HMOX1; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u0026ndash;C), suggesting that IGF1R contributes to antioxidant defense and ROS buffering in HNC. Consistent with this, combining I-OMe-AG-538 with irradiation produced synergistic reductions in clonogenic survival compared with either treatment alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). These results support a model in which IGF1R inhibition by I-OMe-AG-538 disrupts redox homeostasis, elevates oxidative stress, and thereby sensitizes tumor cells to radiation.\u003c/p\u003e \u003cp\u003eDespite these strengths, several limitations should be considered. First, although our isogenic RR model provides mechanistic clarity, it does not fully recapitulate the complexity of the tumor microenvironment, such as immune and stromal interactions. Second, while bioinformatic predictions and network construction are robust, direct experimental validation of all identified miRNA\u0026ndash;mRNA interactions would further strengthen mechanistic claims. Third, although the in vitro radiosensitizing effect of I-OMe-AG-538 is compelling, preclinical in vivo efficacy and toxicity studies in relevant HNC animal models are essential before clinical translation. Finally, while TCGA-based analyses support clinical relevance, prospective clinical trials are necessary to validate the miRNA signature as a predictive biomarker for radioresistance and to evaluate the therapeutic benefit of I-OMe-AG-538 in HNC patients.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study delineates a coordinated miRNA\u0026ndash;mRNA regulatory network that drives radioresistance in HNC and demonstrates how transcriptome-guided drug repurposing can translate systems biology insights into actionable therapeutics. By integrating miRNA profiling, network modeling, and functional validation, we identified I-OMe-AG-538 as a promising radiosensitizer that reprograms oncogenic signaling and redox balance. More broadly, our multi-omics framework provides a generalizable strategy for uncovering resistance mechanisms and discovering therapeutic vulnerabilities in other radiation-refractory cancers. We anticipate that these findings will contribute to the development of more precise, mechanism-based radiotherapy strategies for patients with HNC.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS ","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell Lines, Culture Conditions, and Radioresistant Sublines\u003c/h2\u003e \u003cp\u003eThree HNC cell lines\u0026mdash;OECM1, Detroit, and FaDu\u0026mdash;were used in this study. OECM1 cells were cultured in RPMI-1640 medium (Gibco; Thermo Fisher Scientific, Waltham, MA, USA), while Detroit and FaDu cells were maintained in Minimum Essential Medium (MEM; Gibco). All media were supplemented with 10% fetal bovine serum (FBS; Gibco) and 1% antibiotic-antimycotic solution (Gibco). Cells were incubated at 37\u0026deg;C in a humidified atmosphere with 5% CO₂.\u003c/p\u003e \u003cp\u003eRadioresistant (RR) sublines were generated through serial fractionated irradiation, as previously described [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Briefly, cells were exposed to 2 Gy per fraction until a cumulative dose of 60 Gy was reached. This protocol yielded stable, isogenic RR sublines that model acquired rather than acute radioresistance, thereby better reflecting clinically relevant treatment failure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eClonogenic Survival Assay for Ionizing Radiation\u003c/h2\u003e \u003cp\u003eThe clonogenic survival of cells following ionizing radiation was assessed using a standard colony formation assay, as previously reported [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Cells were seeded at a density of 800 cells per 3 cm\u0026sup2; culture dish and incubated overnight to allow adherence. The following day, cells were exposed to gamma irradiation at doses of 0, 2, 4, or 6 Gy using a Gammacell\u0026reg; 3000 Elan irradiator (Best Theratronics, Ottawa, ON, Canada) with a Cesium-137 (\u0026sup1;\u0026sup3;⁷Cs) source.\u003c/p\u003e \u003cp\u003eAfter irradiation, cells were cultured under standard conditions (37\u0026deg;C, 5% CO₂) for 7\u0026ndash;14 days to facilitate colony formation. Colonies were fixed with formaldehyde (Sigma-Aldrich, St. Louis, MO, USA), stained with 0.5% crystal violet (Sigma-Aldrich) for 30 minutes at room temperature, and rinsed with water to remove excess stain. Plates were air-dried, and colonies containing\u0026thinsp;\u0026ge;\u0026thinsp;50 cells were quantified using ImageJ software (NIH, Bethesda, MD, USA). The surviving fraction was calculated by normalizing the number of colonies at each radiation dose to the non-irradiated control (0 Gy).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRNA Extraction and miRNA Profiling\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from parental and RR subline cells using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer\u0026rsquo;s instructions and established protocols [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. RNA concentration and purity were determined using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and RNA integrity was verified by agarose gel electrophoresis.\u003c/p\u003e \u003cp\u003eGlobal miRNA expression profiling was conducted using the Agilent Human miRNA Microarray V1 platform (G4470A, Agilent Technologies, Santa Clara, CA, USA), which includes 463 human miRNA probes. Seven probes from an earlier version (470 miRNAs) were excluded due to obsolescence based on miRBase version 22. Microarray hybridization was performed according to Agilent\u0026rsquo;s standard protocols. Arrays were scanned using the Agilent G2565A Microarray Scanner with high-dynamic-range settings. Raw image data were processed with Feature Extraction software (Agilent Technologies), and expression data were analyzed using GeneSpring GX software (version 7.3.1, Agilent Technologies). Differentially expressed miRNAs (DEMs) were identified using a |fold change| \u0026ge; 2 threshold. Agilent\u0026rsquo;s present/absent detection flags were applied, and low-intensity features were filtered to remove background noise.\u003c/p\u003e \u003cp\u003eFor the validation of individual miRNA expression, total RNA was extracted using TRIzol reagent (Invitrogen) according to the manufacturer\u0026rsquo;s instructions. Reverse transcription was performed with miRNA-specific stem-loop RT primers and M-MLV reverse transcriptase (Invitrogen). Quantitative PCR was performed using TaqMan MicroRNA Assays, which included sequence-specific TaqMan probes and primers. Amplification was performed with iQ\u0026trade; Supermix reagent (Bio-Rad Laboratories, Hercules, CA, USA) on the CFX96 Real-Time PCR Detection System (Bio-Rad). U6 small nuclear RNA served as the endogenous control for normalization. Relative expression was calculated using the 2^(-ΔΔCt) method. Primers and probes used for the qRT-PCR experiments were obtained from the Genomics Core Laboratory of the Molecular Medicine Research Center, Chang Gung University, Taiwan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eProtein Extraction and Western Blot Analysis\u003c/h2\u003e \u003cp\u003eProtein extraction and Western blot analysis were performed as previously described [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Cells were lysed in CHAPS lysis buffer and incubated on ice for 10 minutes. To ensure complete cellular disruption and enhance protein solubilization, lysates were sonicated using a Qsonica sonicator (Qsonica, LLC, Newtown, CT, USA) with cup horns. Sonication was performed on cold water with three 10-second pulses at 50% amplitude, with 30-second intervals between pulses. Lysates were centrifuged at 12,000 \u0026times; g for 30 minutes at 4\u0026deg;C to isolate the soluble protein fraction. Protein concentrations were quantified using the Bradford assay (Bio-Rad).\u003c/p\u003e \u003cp\u003eEqual amounts of total protein (30 \u0026micro;g per sample) were separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to nitrocellulose membranes (Pall Corporation, Port Washington, NY, USA). Membranes were blocked with 5% bovine serum albumin (BSA) and incubated overnight at 4\u0026deg;C with primary antibodies, followed by incubation with horseradish peroxidase (HRP)-conjugated secondary antibodies. Protein bands were visualized using enhanced chemiluminescence (ECL) detection reagent (Merck Millipore, Burlington, MA, USA) and imaged with the Amersham Imager 600 system (GE Healthcare, Chicago, IL, USA). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) served as the loading control. Details of the antibodies used are listed in Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of Intracellular Reactive Oxygen Species (ROS) Levels\u003c/h2\u003e \u003cp\u003eIntracellular reactive oxygen species (ROS) levels were measured using the cell-permeable fluorescent probe 2\u0026rsquo;,7\u0026rsquo;-dichlorofluorescein diacetate (H2DCFDA; Invitrogen, Carlsbad, CA, USA), which is widely used to assess overall oxidative stress in living cells. Briefly, HNC cells were seeded and allowed to adhere overnight, followed by treatment with I-OMe-AG-538 or vehicle control as indicated. Cells were then incubated in culture medium containing 10 \u0026micro;M H2DCFDA for 30 min at 37\u0026deg;C in the dark to prevent photobleaching.\u003c/p\u003e \u003cp\u003eAfter staining, cells were washed once with phosphate-buffered saline (PBS) to remove excess dye and immediately subjected to flow cytometric analysis using a Guava easyCyte\u0026trade; flow cytometer (Merck Millipore, Burlington, MA, USA). Fluorescence intensity was recorded in the FITC channel, and mean fluorescence intensity (MFI) was quantified as a surrogate measure of intracellular ROS levels. Data were analyzed using the instrument\u0026rsquo;s standard software, and results were presented as relative ROS levels normalized to untreated controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003emiRNA\u0026ndash;mRNA Target Prediction and Network Construction\u003c/h2\u003e \u003cp\u003eTo predict miRNA\u0026ndash;mRNA interactions, three bioinformatic algorithms\u0026mdash;TargetScan, miRDB, and miRTarBase\u0026mdash;were used. Predicted miRNA\u0026ndash;mRNA pairs supported by at least two of these databases were retained to ensure reliability for downstream analysis. An UpSetR plot was generated to quantify unique and shared predicted targets for each miRNA.\u003c/p\u003e \u003cp\u003ePathway enrichment analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database through the DAVID bioinformatics web tool to identify regulatory modules associated with radioresistance. Molecules enriched in key oncogenic pathways linked to the RR phenotype were extracted, and their roles in RR-related regulatory circuits were annotated. Interaction networks between miRNAs and their target genes were constructed and visualized in Cytoscape to elucidate the core regulatory relationships that may drive radioresistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eIn Silico Drug Repurposing for Radioresistance Reversal\u003c/h2\u003e \u003cp\u003eTo identify therapeutic compounds capable of reversing the radioresistance-associated gene expression signature, a drug repurposing analysis was performed using the Connectivity Map (CMap) platform (Broad Institute, Cambridge, MA, USA) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Differentially expressed genes (DEGs) were derived from transcriptomic comparisons between RR and parental HNC cell lines. A signature comprising 150 significantly upregulated and 99 downregulated genes was selected based on a fold-change threshold of |FC| \u0026ge; 1.3. This signature also included predicted targets of miRNAs dysregulated in the RR phenotype.\u003c/p\u003e \u003cp\u003eThis gene signature was submitted to the CLUE (CMap LINCS Unified Environment) platform for analysis within the L1000 dataset, which includes over one million gene expression profiles induced by small-molecule perturbagens across various human cell lines. The platform calculates a connectivity score (τ value) for each compound, reflecting the degree of similarity (positive τ) or inverse correlation (negative τ) between the input signature and the drug-induced transcriptional profile. Compounds with strongly negative connectivity scores (median τ \u0026le; \u0026minus;\u0026thinsp;60) were prioritized as candidates with high potential to counteract the radioresistance-associated gene expression pattern for further investigation as radiosensitizing agents in HNC.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCONFLICT OF INTEREST\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFUNDING STATEMENT\u003c/h2\u003e\n\u003cp\u003eThis work was financially supported by the National Science and Technology Council of Taiwan, with the grant number NSTC-112-2314-B-182A-128 and NSTC-113-2314-B-182A-075.\u003c/p\u003e\n\u003ch2\u003eAUTHOR CONTRIBUTIONS\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eGuo-Rung You\u003c/strong\u003e: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. \u003cstrong\u003eJoseph T. Chang\u003c/strong\u003e: Funding acquisition, Project administration, Resources, Supervision, Writing – review \u0026amp; editing. \u003cstrong\u003eHung-Han Huang and Yen-Liang Li\u003c/strong\u003e: Conceptualization, Methodology, Data curation, Visualization, Software, Writing – review \u0026amp; editing. \u003cstrong\u003eEric Yi-Liang Shen and Yin-Ju Chen\u003c/strong\u003e: Conceptualization, Writing – review \u0026amp; editing. \u003cstrong\u003eAnn-Joy Cheng\u003c/strong\u003e: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft., Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e\n\u003cp\u003eThe qRT-PCR experiments for microRNA expression analysis were supported by the Genomics Core Laboratory of the Molecular Medicine Research Center, Chang Gung University, Taiwan. We also thank Dr. Kun-Yi Chien from the Proteomics Core Laboratory, Molecular Medicine Research Center, Chang Gung University, for providing antibodies used in the Western blot experiments.\u003c/p\u003e\u003ch2\u003e\u003cstrong\u003eDATA AVAILABILITY\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eResearch data are stored in an institutional repository and will be shared upon request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I. \u003cem\u003eet al.\u003c/em\u003e Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e 74, 229\u0026ndash;263 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson, D. E., Burtness, B., Leemans, C. R., Lui, V. W. Y., Bauman, J. E. \u0026amp; Grandis, J. R. 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MYC and KRAS cooperation: from historical challenges to therapeutic opportunities in cancer. \u003cem\u003eSignal Transduct Target Ther\u003c/em\u003e 9, 205 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cell-death-discovery","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddiscovery","sideBox":"Learn more about [Cell Death Discovery](http://www.nature.com/cddiscovery/)","snPcode":"41420","submissionUrl":"https://mts-cddiscovery.nature.com/","title":"Cell Death Discovery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8888021/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8888021/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRadioresistance is a major obstacle to successful radiotherapy in head and neck cancer (HNC), leading to treatment failure, recurrence, and poor patient outcomes. MicroRNAs (miRNAs) are key post-transcriptional regulators implicated in radiosensitivity, but comprehensive miRNA–mRNA networks driving radioresistance in HNC remain poorly defined. Here, we established isogenic radioresistant (RR) sublines from OECM1 and Detroit HNC cells through long-term fractionated irradiation and performed global miRNA profiling to identify a consistent 25-miRNA signature (12 upregulated oncogenic miRNAs [OncomiRs] and 13 downregulated tumor-suppressive miRNAs [TSmiRs]) associated with radioresistance. Integrative target prediction, pathway enrichment, and network construction revealed that these miRNAs converge on oncogenic modules including receptor tyrosine kinase (RTK) signaling, cell motility, and stress/cancer stemness pathways, with central hubs such as EGFR, IGF1R, and MYC. The refined miRNA–mRNA network (68 interaction pairs) highlighted key regulatory miRNAs (e.g., miR-199b-5p, miR-522-3p), whose ectopic overexpression significantly enhanced radiosensitivity in HNC cells. Transcriptome-guided drug repurposing via the Connectivity Map platform prioritized I-OMe-AG-538, an IGF1R inhibitor (τ = –87), as the top candidate radiosensitizer. Validation showed that I-OMe-AG-538 dose-dependently suppressed IGF1R and Erk phosphorylation, reprogrammed the RR miRNA profile by downregulating OncomiRs and upregulating TSmiRs, elevated intracellular ROS levels, and synergistically increased radiosensitivity in clonogenic assays. TCGA-HNSC analysis confirmed that high IGF1R expression correlates with poor prognosis and upregulation of ROS-scavenging genes. These findings delineate a coordinated miRNA–mRNA regulatory network underlying radioresistance in HNC and identify I-OMe-AG-538 as a promising radiosensitizer that disrupts oncogenic signaling and redox homeostasis through inhibiting IGF1R and miRNAs, offering a potential strategy to enhance radiotherapy efficacy in refractory HNC.\u003c/p\u003e","manuscriptTitle":"Targeting a miRNA–mRNA Regulatory Network to Overcome Radioresistance in Head and Neck Cancer: Identification of I-OMe-AG-538 via Transcriptome-Guided Drug Repurposing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 08:32:05","doi":"10.21203/rs.3.rs-8888021/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-05-08T09:18:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-05-05T12:39:08+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-03T07:34:53+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-03-19T07:11:14+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-03-15T22:03:38+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-03-13T06:58:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T09:59:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-15T19:14:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cell Death Discovery","date":"2026-02-15T19:14:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cell-death-discovery","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddiscovery","sideBox":"Learn more about [Cell Death Discovery](http://www.nature.com/cddiscovery/)","snPcode":"41420","submissionUrl":"https://mts-cddiscovery.nature.com/","title":"Cell Death Discovery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f24cc4d-647b-4f71-9f16-51472b3d4f1a","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"revise","date":"2026-05-08T09:18:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-05-05T12:39:08+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-03T07:34:53+00:00","index":3,"fulltext":"This content is not available."}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":64437324,"name":"Biological sciences/Cancer/Head and neck cancer"},{"id":64437325,"name":"Biological sciences/Cancer/Cancer therapy/Radiotherapy"}],"tags":[],"updatedAt":"2026-05-08T09:22:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 08:32:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8888021","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8888021","identity":"rs-8888021","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}