{"paper_id":"2bf260db-a2f3-4069-b76e-fa93c3ae8757","body_text":"Revealing the Protective Role of the TSC1-BRD2-Ferroptosis Axis in Papillary Thyroid Carcinoma: A Multi-Stage Genetic and Functional Integrative Analysis Study | 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 Research Article Revealing the Protective Role of the TSC1-BRD2-Ferroptosis Axis in Papillary Thyroid Carcinoma: A Multi-Stage Genetic and Functional Integrative Analysis Study XIAMEI CHEN, CHEN GAO This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6458091/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction : Papillary thyroid carcinoma (PTC) represents the most prevalent thyroid malignancy, with some cases exhibiting aggressive features and treatment resistance. Ferroptosis, an iron-dependent form of programmed cell death, remains poorly understood in PTC. The aim of this study was to explore ferroptosis-related mechanisms and identify key regulatory networks and potential therapeutic targets in PTC. Methods : A multi-stage integrative analysis strategy was employed, combining Mendelian randomization (MR), mediation analysis, and bioinformatics validation. Ferroptosis-related proteins associated with PTC risk were screened using protein quantitative trait loci (pQTL) data from the deCODE database and PTC genome-wide association study (GWAS) data from FinnGen. Upstream regulatory molecules were identified using pQTL data from the UK Biobank, and a molecular regulatory axis was constructed through mediation analysis. Differential expression and co-expression analyses were conducted using TCGA and GTEx databases to validate functional relevance. Results : The study revealed the TSC1-BRD2-ferroptosis axis, where TSC1 regulated BRD2 expression to suppress the ferroptosis pathway. This regulatory axis demonstrated significant differential expression and co-expression relationships in PTC tissues, supporting its functional relevance. Conclusion : This study established, for the first time, the protective role of the TSC1-BRD2-ferroptosis axis in PTC, providing new insights into the pathogenesis of the disease. Papillary thyroid carcinoma Ferroptosis TSC1 BRD2 Mendelian randomization Bioinformatics analysis Figures Figure 1 Figure 2 Figure 3 Introduction Papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid malignancy, accounting for 80–85% of all thyroid cancer cases. Although the majority of PTC patients exhibit favorable outcomes, a subset of cases demonstrates aggressive characteristics, such as local invasion, distant metastasis, and resistance to radioactive iodine therapy. Current standard treatments, including surgical resection and thyroid-stimulating hormone (TSH) suppression therapy, have limited efficacy in advanced cases, underscoring the urgent need to elucidate the molecular mechanisms underlying PTC pathogenesis. While high-frequency driver gene alterations have been extensively studied, significant gaps remain in our understanding of tumor heterogeneity, immune evasion, and therapeutic resistance in PTC [ ] . In recent years, dysregulation of cell death pathways, particularly ferroptosis—an iron-dependent form of regulated cell death—has been implicated in PTC progression [ [ ] . However, the precise regulatory networks and mechanisms of ferroptosis in PTC remain incompletely understood. Ferroptosis is a regulated cell death pathway driven by iron-dependent lipid peroxidation, characterized by the accumulation of lipid peroxides due to the loss of glutathione peroxidase 4 (GPX4) function. Distinct from apoptosis and necrosis, ferroptosis exhibits unique morphological and biochemical features. In thyroid cancer, ferroptosis plays a dual role in tumor progression: on one hand, aberrant expression of ferroptosis-related genes (FRGs) is closely associated with PTC prognosis [ ] ; on the other hand, dynamic changes in ferroptosis within the tumor microenvironment may influence tumor progression [ ] . Nevertheless, how genetic variations regulate ferroptosis to impact thyroid cancer development remains an unresolved question. Mendelian randomization (MR) is a genetic-based causal inference method. By using genetic variants as instrumental variables, MR can elucidate causal relationships between exposures and disease outcomes [ ] . By simulating randomized controlled trials, MR effectively mitigates confounding bias and reverse causation, making it a powerful tool for dissecting the mechanisms of complex diseases [ ] .In recent years, the establishment of large-scale protein quantitative trait locus (pQTL) databases has enabled the widespread application of MR to explore protein-disease causal networks. When combined with mediation analysis strategies, researchers can further dissect \"gene-protein-phenotype\" regulatory axes [ ] . These methodological advances provide a new paradigm for unraveling the genetic regulatory mechanisms of ferroptosis in PTC. This study employs a multi-stage integrative analysis to systematically dissect the genetic regulatory network of ferroptosis in PTC, revealing the protective role of the TSC1-BRD2-ferroptosis axis. First, based on pQTL data from the deCODE database and PTC genome-wide association study (GWAS) data from the Finnish FinnGen database, ferroptosis-related proteins associated with PTC risk were identified. Second, using the UK Biobank pQTL data and FinnGen GWAS, upstream regulatory factors of ferroptosis-related proteins were screened. Subsequently, a two-step mediation analysis confirmed a key TSC1-BRD2-ferroptosis axis and demonstrated that the protective effect of TSC1 on PTC is primarily mediated through the BRD2-dependent ferroptosis inhibition pathway. Finally, differential expression and correlation analyses from the TCGA and GTEx databases further validated this regulatory relationship. This multi-dimensional \"genetic-protein-function\" evidence chain not only reveals the protective role of the TSC1-BRD2-ferroptosis axis in PTC but also provides a theoretical foundation for precision therapies targeting ferroptosis. Methods 2.1 Study Design The flowchart of the study design is illustrated in Fig. 1 . First, we extracted pQTL data related to ferroptosis as exposure variables and utilized PTC GWAS data as the outcome variables to conduct a two-sample Mendelian randomization (MR) analysis to evaluate the causal relationship between ferroptosis-related molecules and PTC. Subsequently, we screened upstream regulatory molecules of ferroptosis-related proteins and further identified molecular regulatory axes through mediation analysis. Finally, the molecular regulatory axis was validated using bioinformatics and correlation analyses. Throughout the analysis, we selected appropriate single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) based on strict inclusion and exclusion criteria and performed multiple sensitivity analyses to ensure the quality of the MR analysis. This study adhered to rigorous ethical standards, and all data used were approved by ethics committees and obtained with informed consent from participants in their original studies. 2.2 Data Sources pQTL data related to ferroptosis for the Icelandic population from the deCODE database were obtained from the FerrDb website ( http://www.zhounan.org/ferrdb/current/ ), including ferroptosis drivers and suppressors (Table S1 ). Proteomics data were derived from pQTL data of the Icelandic population in the deCODE database [ ] ( https://www.decode.com/summarydata/ ) and the plasma proteomics pQTL data from the UK Biobank Pharma Proteomics Project (UKB-PPP) [ ] ( https://www.synapse.org/Synapse:syn51364943/wiki/622119 ). GWAS summary statistics for PTC were obtained from the Finnish FinnGen database, comprising 1,472 cases and 314,193 controls. 2.3 Instrumental Variable (IV) Selection To ensure the reliability and accuracy of the MR analysis, IVs must satisfy the following three key assumptions: (1) IVs are strongly associated with the exposure variable (ferroptosis-related proteins); (2) IVs are independent of any confounding factors; and (3) IVs exhibit no horizontal pleiotropy, meaning they influence the outcome variable only through the exposure variable, rather than through other potential pathways [ ] . Based on these assumptions, we implemented a strict IV selection process for each gene. First, SNPs were selected using a genome-wide significance threshold (P < 5.0 × 10^-8). Then, linkage disequilibrium (LD) clumping was performed based on the 1,000 Genomes Project European population data, with a threshold of r² < 0.1 and a window size of 10,000 kb [ ] , to ensure SNP independence. SNPs with incompatible alleles between exposure and outcome were excluded. For palindromic SNPs, if the positive strand allele could not be inferred, they were directly excluded. Finally, weak IVs with an F-statistic < 10 were excluded to avoid weak instrument bias [ ] . 2.4 Mendelian Randomization Analysis and Sensitivity Analysis In the MR analysis, the inverse-variance weighted (IVW) method was used as the primary analysis method, assuming no horizontal pleiotropy, to obtain more accurate causal effect estimates [ ] . Additionally, MR-Egger, weighted median, simple mode, and weighted mode methods were used as supplementary analyses to enhance the robustness of the results. Compared to other methods, the IVW method is more conservative but more stable, and thus was prioritized regardless of heterogeneity, with other methods used as complements. To ensure reliability, multiple sensitivity analyses were performed, including Cochran’s Q test to assess heterogeneity [ ] , calculation of the MR-Egger intercept to detect directional pleiotropy and bias caused by invalid IVs [ ] , MR-PRESSO to detect and adjust for heterogeneous SNPs to reduce potential confounding effects [ ] , and leave-one-out (LOO) analysis to evaluate the influence of each SNP on the overall results. All MR analyses were performed using R version 4.3.1, with the “MendelianRandomization,” “TwoSampleMR,” and “MR-PRESSO” packages. Furthermore, Steiger’s test was conducted to assess potential bias and minimize the impact of reverse causality. 2.5 Mediation Analysis To explore the regulatory axis of ferroptosis pathways, we employed a two-step mediation analysis [ ][ ] . First, the causal effect of ferroptosis-related proteins on PTC (β2) was evaluated using two-sample MR. Then, upstream regulatory molecules associated with ferroptosis-related proteins were identified, and their effects on ferroptosis-related proteins (β1) and PTC (α) were assessed separately. Finally, the mediation effect (β1 × β2 / α) was calculated,(Fig. 2 ). and the regulatory axis with the highest mediation proportion was selected for further analysis. 2.6 Bioinformatics Analysis To further validate the functional relevance of the molecular regulatory axis, we performed bioinformatics analyses using public databases. The GEPIA2 database ( http://gepia2.cancer-pku.cn/#index ) was used to compare differential gene expression between TCGA-THCA tumor tissues and GTEx normal thyroid tissues, and significantly differentially expressed genes were filtered (threshold: |log2FC| > 0.585, FDR < 0.05). Additionally, the correlation analysis module of GEPIA2 was used to calculate the co-expression levels (Pearson correlation coefficient) of key molecules to verify their functional associations in PTC. Results 3.1 Identification and Extraction of Ferroptosis-Related pQTL Data We retrieved a comprehensive list of ferroptosis-related molecules, including ferroptosis drivers and suppressors, from the FerrDb database and integrated them with pQTL data from the deCODE cohort. Through intersection analysis, we successfully identified and extracted 159 ferroptosis-related pQTL molecules (Figure S1 ). 3.2 Identification of Differentially Expressed Genes To enhance the robustness of our findings, we conducted differential expression analysis using the GEPIA2 database, comparing TCGA-THCA tumor tissues with GTEx normal thyroid tissues. Applying stringent thresholds (|log2FC| > 0.585, corresponding to a 1.5-fold change, and adjusted p-value < 0.05), we identified 8,419 significantly differentially expressed genes (Table S2 ). 3.3 Identification of Ferroptosis-Related Proteins Associated with PTC Using a two-sample Mendelian Randomization (MR) approach, with the inverse-variance weighted (IVW) method as the primary analytical tool, we identified 13 ferroptosis-related molecules potentially exerting causal effects on PTC (Table S3 ). To ensure the reliability of these findings, we performed multiple sensitivity analyses. The Cochran Q test (p > 0.05) indicated no significant heterogeneity, the MR-Egger intercept test (p > 0.05) ruled out horizontal pleiotropy, and the Steiger test confirmed the correct direction of causality. Integrating differential expression data with MR results, we identified two ferroptosis-related proteins, ADAM23 and BRD2, as significantly associated with PTC (Tables 1 and 2 ). 3.4 Screening of Upstream Regulators of Ferroptosis-Related Proteins To investigate upstream regulatory mechanisms, we utilized pQTL data from the UK Biobank Pharma Proteomics Project (UKB-PPP) and performed MR analysis to identify PTC-associated upstream molecules. Combined with differential expression analysis, we identified potential upstream regulators (Table S4 ,S5). Further MR analysis revealed 14 upstream regulators significantly associated with ferroptosis-related proteins that may modulate PTC pathogenesis (Tables 1 and 2 ). Table 1 Causal Relationships of Ferroptosis-Related Proteins and Upstream Regulators with PTC Identified by Mendelian Randomization Analysis Exposure Outcome Mendelian randomization analysis method p beta OR OR(95%CI) deCODE FinnGen ADAM23 PTC IVW 0.0104 -0.07 0.93 (0.88,0.98) BRD2 IVW 0.0420 -0.530 0.59 (0.35,0.98) UKB-PPP FinnGen ERBB4 PTC IVW 0.0259 -0.150 0.86 (0.75,0.98) DPP6 IVW 0.0072 -0.213 0.81 (0.69,0.94) TSC1 IVW 0.0301 -0.536 0.59 (0.36,0.95) FLT3LG IVW 0.0436 -0.161 0.85 (0.73,0.99) KDM3A IVW 0.0191 -0.482 0.62 (0.41,0.92) ITIH4 IVW 0.0281 -0.162 0.85 (0.74,0.98) LTB IVW 0.0051 0.204 1.23 (1.06,1.41) ADAM23 IVW 0.0221 -0.067 0.94 (0.88,0.99) PKD1 IVW 0.0172 -0.161 0.85 (0.75,0.97) CASP4 IVW 0.0156 -0.496 0.61 (0.41,0.91) CRELD2 IVW 0.0091 -0.154 0.86 (0.76,0.96) ENTPD6 IVW 0.0001 -0.148 0.86 (0.79,0.93) UKB-PPP deCODE ERBB4 ADAM23 IVW 0.0301 0.109 1.12 (1.01,1.23) STAB2 IVW 0.0177 0.059 1.06 (1.01,1.11) DPP6 IVW 0.0118 0.066 1.06 (1.01,1.13) TSC1 IVW 0.0025 0.120 1.13 (1.04,1.22) FLT3LG IVW 0.0386 0.027 1.03 (1.00,1.05) TSC1 BRD2 IVW 2.58e-06 0.311 1.36 (1.20,1.55) KDM3A IVW 1.45e-06 0.224 1.25 (1.14,1.37) ITIH4 IVW 0.0002 0.069 1.07 (1.03,1.11) LTB IVW 2.09e-06 -0.082 0.92 (0.89,0.95) ADAM23 IVW 0.0046 0.019 1.02 (1.00,1.03) PKD1 IVW 0.004 0.045 1.05 (1.01,1.08) CASP4 IVW 0.034 0.088 1.09 (1.00,1.18) CRELD2 IVW 0.025 0.026 1.03 (1.00,1.05) ENTPD6 IVW 0.022 0.017 1.02 (1.00,1.03) Table 2 Sensitivity Analysis and Directionality Test of Causal Relationships in Mendelian Randomization for Ferroptosis-Related Proteins and Upstream Regulators Exposure Outcome SNP Steiger direction Steiger P value Heterogeneity Pleiotropy MR-PRESSO deCODE FinnGen ADAM23 PTC rs117203002 TRUE 1.26e-192 0.466 0.056 0.435 BRD2 rs10922098 TRUE 5.37e-30 0.902 0.686 0.889 UKB-PPP FinnGen ERBB4 PTC rs6735267 TRUE 1.40e-132 0.286 0.448 0.29 DPP6 rs3734960 TRUE 4.89e-198 0.520 0.531 0.595 TSC1 rs28672722 TRUE 1.33e-13 0.541 0.832 0.686 FLT3LG rs76428106 TRUE 0 0.0001 0.588 < 0.001 KDM3A rs28672722 TRUE 1.86e-20 0.109 0.857 0.166 ITIH4 rs2710331 TRUE 1.99e-200 0.44 0.959 0.515 LTB rs3094191 TRUE 1.68e-56 0.13 0.705 0.133 ADAM23 rs117203002 TRUE 3.43e-292 0.79 0.092 0.721 PKD1 rs148501094 TRUE 4.67e-150 0.65 0.10 0.573 CASP4 rs117001788 TRUE 5.50e-43 0.359 0.442 0.473 CRELD2 rs28562884 TRUE 1.93e-275 0.39 0.705 0.403 ENTPD6 rs1108812 TRUE 1.96e-286 0.271 0.150 0.259 UKB-PPP deCODE ERBB4 ADAM23 rs6735267 TRUE 1.35e-66 3.99e-167 0.820 < 0.001 STAB2 rs117130272 TRUE 2.04e-205 2.63e-126 0.182 < 0.001 DPP6 rs3734960 TRUE 4.52e-114 4.27e-06 0.119 < 0.001 TSC1 rs4548048 TRUE 7.68e-09 0.679 0.321 0.681 FLT3LG rs76428106 TRUE 1.24e-177 0.392 0.278 0.378 TSC1 BRD2 rs4548048 TRUE 2.54e-06 0.215 0.217 0.237 KDM3A rs28672722 TRUE 1.21e-05 0.011 0.024 0.001 ITIH4 rs2710331 TRUE 6.72e-115 0.150 0.033 0.066 LTB rs2395470 TRUE 1.34e-46 1.47e-05 0.707 < 0.001 ADAM23 rs117203002 TRUE 1.65e-169 0.008 0.476 0.008 PKD1 rs148501094 TRUE 1.34e-81 0.529 0.278 0.567 CASP4 rs117001788 TRUE 6.99e-25 0.689 0.162 0.439 CRELD2 rs28562884 TRUE 5.6e-159 0.198 0.678 0.235 ENTPD6 rs1108812 TRUE 2.41e-159 0.758 0.965 0.78 3.5 Mediation Analysis and Validation of Molecular Regulatory Axes To elucidate the regulatory mechanisms of ferroptosis-related molecules in PTC, we conducted mediation analysis to quantify the mediation effects and proportions between upstream regulators and ferroptosis-related proteins (Table S6 ). The TSC1-BRD2 regulatory axis exhibited the most significant mediation effect (Table 3 ). To validate the functional relevance of this axis, we performed differential expression and correlation analyses using GEPIA2, confirming the differential expression and co-expression patterns of TSC1 and BRD2 in PTC tissues (Fig. 3 ). These findings support the hypothesis that TSC1 exerts a protective effect in PTC by modulating the BRD2-dependent ferroptosis pathway. Table 3 Mediation Effect of the TSC1-BRD2 Regulatory Axis in PTC. Exposure Mediator Proportion mediated α β1 β2 β1*β2/α TSC1 BRD2 -0.536 0.311 -0.530 31% Discussion This study systematically dissected the genetic regulatory network of ferroptosis in papillary thyroid carcinoma (PTC) through a multi-stage integrative analysis. First, based on protein quantitative trait locus (pQTL) data from the deCODE database, we extracted ferroptosis-related proteins as exposure variables and integrated them with PTC genome-wide association study (GWAS) data from the Finnish FinnGen database. Using a two-sample Mendelian randomization (MR) approach, we evaluated the causal relationship between ferroptosis-related proteins and PTC risk. Next, by screening upstream regulatory molecules and performing mediation analysis, we revealed the protective role of the TSC1-BRD2-ferroptosis axis in PTC. Finally, through bioinformatics analysis, we further validated the functional relevance of this regulatory axis. This multi-dimensional evidence chain of \"genetics-protein-function\" not only provides new insights into the pathogenesis of PTC but also lays a theoretical foundation for ferroptosis-targeted precision therapies. Ferroptosis, an iron-dependent form of cell death driven by lipid peroxidation, has been shown to play significant roles in various cancers [ ][ ][ ] . Notably, the traditional view suggests that ferroptosis exerts a dual function in cancer, either promoting or suppressing tumorigenesis, depending on the cancer type and its microenvironment. However, our study found that both the ferroptosis driver ADAM23 and the suppressor BRD2 were negatively associated with PTC risk, indicating that ferroptosis may primarily function to inhibit tumor progression in PTC. Specifically, as a driver, ADAM23 may directly suppress tumor cell survival by promoting ferroptosis, while BRD2, as a suppressor, may protect normal cells from ferroptosis by regulating lipid peroxidation or other pathways, thereby maintaining tissue homeostasis and suppressing tumorigenesis. This finding suggests that the role of ferroptosis in PTC is not the traditional dual function but rather a synergistic effect of drivers and suppressors collectively inhibiting tumor progression. This unique mechanism provides a new direction and challenge for developing ferroptosis-targeted therapies and underscores the importance of further studying its specific regulatory mechanisms. Although BRD2 is widely regarded as a tumor-promoting epigenetic regulator in various cancers, it may exert a unique inhibitory effect in PTC by regulating ferroptosis-related pathways [ ] . As a member of the bromodomain and extraterminal domain (BET) protein family, BRD2 can regulate gene transcription by binding to acetylated lysine residues on histones [ ] . In PTC, BRD2 may upregulate the expression of ferroptosis suppressor genes, reduce intracellular iron levels and lipid peroxidation, and thereby inhibit abnormal proliferation and survival of tumor cells. Furthermore, BRD2 may enhance its anti-tumor effects by modulating immune cell infiltration and function in the tumor microenvironment [ ] . For example, BRD2 may promote anti-tumor immune responses by regulating immune-related gene expression, thereby inhibiting PTC progression. Additionally, BRD2 may form a complex regulatory network in PTC through interactions with signaling pathways such as mTORC1. For instance, BRD2 may reduce metabolic reprogramming and energy supply in tumor cells by inhibiting mTORC1 activity, thereby indirectly suppressing tumor growth [ ] . These multiple mechanisms make BRD2 a potential therapeutic target. Based on its tumor-suppressive role, future studies should focus on developing BRD2 activators to further enhance its protective function in PTC. TSC1 (tuberous sclerosis complex 1), a tumor suppressor, is closely associated with the initiation and progression of various cancers [ [ ] . In PTC, TSC1 may exert significant inhibitory effects by regulating the expression and function of BRD2. The TSC1-TSC2 complex inhibits the mTORC1 (mechanistic target of rapamycin complex 1) signaling pathway, regulating cell growth and metabolism [ ] . In PTC, TSC1 may reduce abnormal proliferation and metabolic reprogramming of tumor cells by inhibiting mTORC1 activity, thereby suppressing tumor progression. Moreover, TSC1 may indirectly affect the ferroptosis pathway by regulating BRD2 expression, further enhancing its anti-tumor effects. For instance, TSC1 may upregulate ferroptosis suppressor genes by activating BRD2 expression, thereby reducing tumor cell susceptibility to ferroptosis and inhibiting their malignant behavior. Given the protective role of TSC1 in PTC, future studies should focus on developing TSC1 activators to enhance its function and suppress tumor progression. Additionally, a combined activation strategy targeting the TSC1-BRD2 axis may provide novel therapeutic opportunities for PTC. Our study revealed the protective role of the TSC1-BRD2-ferroptosis axis in PTC. TSC1, as a key regulator of the mTOR pathway [ ] , inhibits cell proliferation and metabolism by suppressing mTORC1 activity, while BRD2, as an epigenetic regulator, is involved in cell cycle and inflammatory response regulation [ [ ] . We propose that TSC1 may regulate BRD2 expression to influence the ferroptosis pathway, thereby suppressing PTC progression. Although existing literature does not directly describe the specific interaction between TSC1 and BRD2, integrating mTORC1 signaling and transcriptional regulatory networks, we speculate that TSC1 may indirectly regulate BRD2 function by inhibiting mTORC1 activity, forming a dynamic balance. TSC1 limits cell proliferation by suppressing mTORC1 signaling, while BRD2 supports normal cell function by promoting transcriptional activation, and their interaction may finely regulate the balance between cell growth and ferroptosis, ensuring tissue homeostasis and inhibiting tumorigenesis [ ] . The discovery of this mechanism provides a potential therapeutic target for PTC, and future studies should further elucidate the specific molecular mechanisms of this regulatory axis and its complex role in the tumor microenvironment. Based on the tumor-suppressive roles of TSC1 and BRD2, developing their activators may become an effective strategy for treating PTC. The strength of this study lies in its multi-level, multi-dimensional analysis strategy. First, we employed a multi-stage integrative approach combining MR, mediation analysis, and bioinformatics analysis, constructing a comprehensive \"gene-protein-function\" evidence chain, which enhances the robustness and reliability of the results. Second, this study utilized data from multiple large-scale databases, including the deCODE database, UKB-PPP database, and FinnGen database, ensuring broad applicability and representativeness of the findings. Additionally, we conducted rigorous sensitivity analyses, such as MR-Egger, MR-PRESSO, and Steiger tests, to minimize potential biases and strengthen the reliability of the results. Finally, this study is the first to reveal the protective role of the TSC1-BRD2-ferroptosis axis in PTC, an innovative discovery that provides a new theoretical basis for ferroptosis-targeted precision therapies and has significant clinical implications. Despite the important advancements made in this study, several limitations should be acknowledged. First, the data used in this study were primarily derived from European populations. While this ensures the reliability and representativeness of the results within this specific population, it may limit their generalizability to other ethnic and geographic groups. Future studies should validate these findings in other populations to ensure their broad applicability. Second, our study primarily inferred causality through bioinformatics analysis and MR methods. Although we implemented rigorous sensitivity analyses to reduce bias, further validation through in vitro and in vivo experiments is needed to elucidate the specific molecular mechanisms. Additionally, while MR analysis provides evidence for causal inference, the results may be influenced by potential confounding factors. Although we implemented strict controls during the analysis, the potential impact of these factors cannot be completely ruled out. Finally, this study focused primarily on the TSC1-BRD2-ferroptosis axis, but the mechanisms of ferroptosis in PTC may be more complex, involving interactions with other related molecules and pathways. Future studies should further explore these potential mechanisms to comprehensively understand the regulatory network of ferroptosis in PTC. Conclusion This study is the first to reveal the protective role of the TSC1-BRD2-ferroptosis axis in papillary thyroid carcinoma (PTC) through a multi-stage integrative analysis. By combining pQTL data, GWAS data, Mendelian randomization (MR) analysis, and bioinformatics analysis, we constructed a comprehensive \"gene-protein-function\" multi-dimensional evidence chain, providing novel insights into the pathogenesis of PTC. This discovery not only enriches the theoretical understanding of the role of ferroptosis in cancer but also lays a critical theoretical foundation for precision therapies targeting ferroptosis. Future research should further validate the specific molecular mechanisms of this regulatory axis and explore its potential roles in other cancer types, offering additional possibilities for cancer treatment. Additionally, the development of activators targeting TSC1 and BRD2 may open new avenues for PTC therapy, holding significant value for clinical translation. Declarations Data availability statement The analytical approach was ethically sound, as all data utilized in this study had received prior approval and consent in their original studies. 1.Proteome data were sourced from the deCODE database (https://www.decode.com/summarydata/) for the Icelandic population[[[] Ferkingstad E,Sulem P,Atlason BA, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53 (12):1712-1721. doi:10.1038/s41588-021-00978-w]], as well as from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP) (https://www.synapse.org/Synapse:syn51364943/wiki/622119) 2.papillary thyroid carcinoma GWAS statistics were obtained from the Finnish Database Consortium 3.Differential expression data for papillary thyroid carcinoma (PTC) were obtained from the TCGA and GETx databases using the GEPIA2 tool (http://gepia2.cancer-pku.cn/#index). Funding Declaration This work was supported by the Startup Fund for Scientific Research, Fujian Medical University (Grant No. 2020QH1224).The funder had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to submit the work for publication. Ethics and Consent Statements The analysis in this study utilized summary statistics from a genome-wide association study. The original studies had obtained ethical approval and informed consent from participants, as confirmed by the institutional review boards. As this analysis did not involve any new data collection or require additional ethical clearance, there was no need for further ethical approval or informed consent for this study specifically. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Clinical Trial Registration Statement This study is not a clinical trial. No clinical trial registration is required as the research exclusively analyzed pre-existing genetic and proteomic datasets. Competing Interests The authors declare no competing financial or non-financial interests directly or indirectly related to this work. The corresponding author (CHEN GAO) affirms full responsibility for the integrity of this declaration. Author Contributions XIAMEI CHEN: Conceptualization, Methodology, Data Curation, Formal Analysis, Writing – Original Draft. CHEN GAO: Supervision, Funding Acquisition, Resources, Validation, Writing – Review & Editing. All authors critically reviewed and approved the final manuscript, and agree to be accountable for all aspects of the work. References Boucai L,Zafereo M,Cabanillas ME. Thyroid Cancer: A Review. JAMA. 2024;331 (5):425-435. doi:10.1001/jama.2023.26348 Chen X,Chen X,Xie W, et al. BRAF-activated ARSI suppressed EREG-mediated ferroptosis to promote BRAF V600E (mutant) papillary thyroid carcinoma progression and sorafenib resistance. Int J Biol Sci. 2025;21 (1):128-142. doi:10.7150/ijbs.99423 Ji FH,Fu XH,Li GQ, et al. FTO Prevents Thyroid Cancer Progression by SLC7A11 m6A Methylation in a Ferroptosis-Dependent Manner. Front Endocrinol (Lausanne). 2022;13:857765. doi:10.3389/fendo.2022.857765 Chen H,Peng F,Xu J, et al. 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Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int J Epidemiol. 2015;44 (2):484-95. doi:10.1093/ije/dyu176 Ferkingstad E,Sulem P,Atlason BA, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53 (12):1712-1721. doi:10.1038/s41588-021-00978-w Sun BB,Chiou J,Traylor M, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 2023;622 (7982):329-338. doi:10.1038/s41586-023-06592-6 Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. doi:10.1136/bmj.k601 Gkatzionis A, Burgess S, Newcombe PJ. Statistical methods for cis-Mendelian randomization with two-sample summary-level data. Genet Epidemiol. 2023 Feb;47(1):3-25. doi: 10.1002/gepi.22506. Chen Z,Wang X,Teng Z, et al. Modifiable lifestyle factors influencing psychiatric disorders mediated by plasma proteins: A systemic Mendelian randomization study. J Affect Disord. 2024;350:582-589. doi:10.1016/j.jad.2024.01.169 Pierce BL,Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013;178 (7):1177-84. doi:10.1093/aje/kwt084 Cohen JF,Chalumeau M,Cohen R, et al. Cochran's Q test was useful to assess heterogeneity in likelihood ratios in studies of diagnostic accuracy. J Clin Epidemiol. 2015;68 (3):299-306. doi:10.1016/j.jclinepi.2014.09.005 Hemani G,Bowden J,Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27 (R2):R195-R208. doi:10.1093/hmg/ddy163 Verbanck M,Chen CY,Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50 (5):693-698. doi:10.1038/s41588-018-0099-7 Sanderson E. Multivariable Mendelian Randomization and Mediation. Cold Spring Harb Perspect Med. 2021;11 (2):. doi:10.1101/cshperspect.a038984 Carter AR,Sanderson E,Hammerton G, et al. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol. 2021;36 (5):465-478. doi:10.1007/s10654-021-00757-1 Zheng Y,Qin C,Li F, et al. Self-assembled thioether-bridged paclitaxel-dihydroartemisinin prodrug for amplified antitumor efficacy-based cancer ferroptotic-chemotherapy. Biomater Sci. 2023;11 (9):3321-3334. doi:10.1039/d2bm02032g Li Y,Li M,Liu L, et al. Cell-Specific Metabolic Reprogramming of Tumors for Bioactivatable Ferroptosis Therapy. ACS Nano. 2022;16 (3):3965-3984. doi:10.1021/acsnano.1c09480 Chen W,Xie L,Lv C, et al. Transferrin-Targeted Cascade Nanoplatform for Inhibiting Transcription Factor Nuclear Factor Erythroid 2-Related Factor 2 and Enhancing Ferroptosis Anticancer Therapy. ACS Appl Mater Interfaces. 2023;15 (24):28879-28890. doi:10.1021/acsami.3c01499 Yang M,Liu K,Chen P, et al. Bromodomain-containing protein 4 (BRD4) as an epigenetic regulator of fatty acid metabolism genes and ferroptosis. Cell Death Dis. 2022;13 (10):912. doi:10.1038/s41419-022-05344-0 French CA. Small-Molecule Targeting of BET Proteins in Cancer. Adv Cancer Res. 2016;131:21-58. doi:10.1016/bs.acr.2016.04.001 Milner JJ,Toma C,Quon S, et al. Bromodomain protein BRD4 directs and sustains CD8 T cell differentiation during infection. J Exp Med. 2021;218 (8):. doi:10.1084/jem.20202512 Srivastava RK,Guroji P,Jin L, et al. Combined inhibition of BET bromodomain and mTORC1/2 provides therapeutic advantage for rhabdomyosarcoma by switching cell death mechanism. Mol Carcinog. 2022;61 (8):737-751. doi:10.1002/mc.23414 Tyburczy ME,Jozwiak S,Malinowska IA, et al. A shower of second hit events as the cause of multifocal renal cell carcinoma in tuberous sclerosis complex. Hum Mol Genet. 2015;24 (7):1836-42. doi:10.1093/hmg/ddu597 Huang Q,Li F,Hu H, et al. Loss of TSC1/TSC2 sensitizes immune checkpoint blockade in non-small cell lung cancer. Sci Adv. 2022;8 (5):eabi9533. doi:10.1126/sciadv.abi9533 Inoki K,Li Y,Xu T, et al. Rheb GTPase is a direct target of TSC2 GAP activity and regulates mTOR signaling. Genes Dev. 2003;17 (15):1829-34. doi:10.1101/gad.1110003 Qin J,Wang Z,Hoogeveen-Westerveld M, et al. Structural Basis of the Interaction between Tuberous Sclerosis Complex 1 (TSC1) and Tre2-Bub2-Cdc16 Domain Family Member 7 (TBC1D7). J Biol Chem. 2016;291 (16):8591-601. doi:10.1074/jbc.M115.701870 Greenwald RJ,Tumang JR,Sinha A, et al. E mu-BRD2 transgenic mice develop B-cell lymphoma and leukemia. Blood. 2004;103 (4):1475-84. doi:10.1182/blood-2003-06-2116 Lenburg ME,Sinha A,Faller DV, et al. Tumor-specific and proliferation-specific gene expression typifies murine transgenic B cell lymphomagenesis. J Biol Chem. 2007;282 (7):4803-4811. doi:10.1074/jbc.M605870200 Mak BC,Kenerson HL,Aicher LD, et al. Aberrant beta-catenin signaling in tuberous sclerosis. Am J Pathol. 2005;167 (1):107-16. doi:10.1016/s0002-9440(10)62958-6 Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx FigureS1.jpg Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-6458091\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":454875859,\"identity\":\"34ef6e3d-ca9b-4c22-91db-d2995115dff4\",\"order_by\":0,\"name\":\"XIAMEI CHEN\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Authors' affiliations：Department of Operation, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"XIAMEI\",\"middleName\":\"\",\"lastName\":\"CHEN\",\"suffix\":\"\"},{\"id\":454875860,\"identity\":\"7a60cd2b-feff-4e38-a7b5-f62b773f8566\",\"order_by\":1,\"name\":\"CHEN GAO\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"CHEN\",\"middleName\":\"\",\"lastName\":\"GAO\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-04-15 22:23:13\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6458091/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6458091/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":82622110,\"identity\":\"c48bed3d-a4d7-4035-af03-fb041b5c44c0\",\"added_by\":\"auto\",\"created_at\":\"2025-05-13 12:24:13\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":997530,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eStudy workflow. MR, mendelian randomization; pQTL, protein quantitative trait locus.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6458091/v1/026011f760f5da5cc0520f7b.jpg\"},{\"id\":82620692,\"identity\":\"cce637c7-32b4-48c1-b478-a3bf8d7b2e5f\",\"added_by\":\"auto\",\"created_at\":\"2025-05-13 12:16:13\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1157245,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eWorkflow of mediation analysis for regulatory axis screening.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6458091/v1/fe073eaca1d5999db16ddf71.jpg\"},{\"id\":82620694,\"identity\":\"92cba9a7-1028-4553-95f5-791f2965951d\",\"added_by\":\"auto\",\"created_at\":\"2025-05-13 12:16:13\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 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12:16:13\",\"extension\":\"xlsx\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":30521,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"TableS5.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6458091/v1/6e66f5f76641074ab42e08b0.xlsx\"},{\"id\":82622117,\"identity\":\"e0a59295-0c7f-4c52-a2ef-7935f1bc0b69\",\"added_by\":\"auto\",\"created_at\":\"2025-05-13 12:24:13\",\"extension\":\"xlsx\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":15764,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"TableS6.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6458091/v1/26a59689ac36d5fdb97f8eca.xlsx\"},{\"id\":82620709,\"identity\":\"95d1b783-a8f5-442c-a20a-5df1b2e3e625\",\"added_by\":\"auto\",\"created_at\":\"2025-05-13 12:16:13\",\"extension\":\"jpg\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":614461,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"FigureS1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6458091/v1/dcf66c8edb3ca2eec95f5c18.jpg\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Revealing the Protective Role of the TSC1-BRD2-Ferroptosis Axis in Papillary Thyroid Carcinoma: A Multi-Stage Genetic and Functional Integrative Analysis Study\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003ePapillary thyroid carcinoma (PTC) is the most prevalent type of thyroid malignancy, accounting for 80\\u0026ndash;85% of all thyroid cancer cases. Although the majority of PTC patients exhibit favorable outcomes, a subset of cases demonstrates aggressive characteristics, such as local invasion, distant metastasis, and resistance to radioactive iodine therapy. Current standard treatments, including surgical resection and thyroid-stimulating hormone (TSH) suppression therapy, have limited efficacy in advanced cases, underscoring the urgent need to elucidate the molecular mechanisms underlying PTC pathogenesis. While high-frequency driver gene alterations have been extensively studied, significant gaps remain in our understanding of tumor heterogeneity, immune evasion, and therapeutic resistance in PTC\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn1\\\" id=\\\"#FNLinkFn1\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. In recent years, dysregulation of cell death pathways, particularly ferroptosis\\u0026mdash;an iron-dependent form of regulated cell death\\u0026mdash;has been implicated in PTC progression\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn2\\\" id=\\\"#FNLinkFn2\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn3\\\" id=\\\"#FNLinkFn3\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. However, the precise regulatory networks and mechanisms of ferroptosis in PTC remain incompletely understood.\\u003c/p\\u003e \\u003cp\\u003eFerroptosis is a regulated cell death pathway driven by iron-dependent lipid peroxidation, characterized by the accumulation of lipid peroxides due to the loss of glutathione peroxidase 4 (GPX4) function. Distinct from apoptosis and necrosis, ferroptosis exhibits unique morphological and biochemical features. In thyroid cancer, ferroptosis plays a dual role in tumor progression: on one hand, aberrant expression of ferroptosis-related genes (FRGs) is closely associated with PTC prognosis\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn4\\\" id=\\\"#FNLinkFn4\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e; on the other hand, dynamic changes in ferroptosis within the tumor microenvironment may influence tumor progression\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn5\\\" id=\\\"#FNLinkFn5\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. Nevertheless, how genetic variations regulate ferroptosis to impact thyroid cancer development remains an unresolved question.\\u003c/p\\u003e \\u003cp\\u003eMendelian randomization (MR) is a genetic-based causal inference method. By using genetic variants as instrumental variables, MR can elucidate causal relationships between exposures and disease outcomes\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn6\\\" id=\\\"#FNLinkFn6\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. By simulating randomized controlled trials, MR effectively mitigates confounding bias and reverse causation, making it a powerful tool for dissecting the mechanisms of complex diseases\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn7\\\" id=\\\"#FNLinkFn7\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e.In recent years, the establishment of large-scale protein quantitative trait locus (pQTL) databases has enabled the widespread application of MR to explore protein-disease causal networks. When combined with mediation analysis strategies, researchers can further dissect \\\"gene-protein-phenotype\\\" regulatory axes\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn8\\\" id=\\\"#FNLinkFn8\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. These methodological advances provide a new paradigm for unraveling the genetic regulatory mechanisms of ferroptosis in PTC.\\u003c/p\\u003e \\u003cp\\u003eThis study employs a multi-stage integrative analysis to systematically dissect the genetic regulatory network of ferroptosis in PTC, revealing the protective role of the TSC1-BRD2-ferroptosis axis. First, based on pQTL data from the deCODE database and PTC genome-wide association study (GWAS) data from the Finnish FinnGen database, ferroptosis-related proteins associated with PTC risk were identified. Second, using the UK Biobank pQTL data and FinnGen GWAS, upstream regulatory factors of ferroptosis-related proteins were screened. Subsequently, a two-step mediation analysis confirmed a key TSC1-BRD2-ferroptosis axis and demonstrated that the protective effect of TSC1 on PTC is primarily mediated through the BRD2-dependent ferroptosis inhibition pathway. Finally, differential expression and correlation analyses from the TCGA and GTEx databases further validated this regulatory relationship. This multi-dimensional \\\"genetic-protein-function\\\" evidence chain not only reveals the protective role of the TSC1-BRD2-ferroptosis axis in PTC but also provides a theoretical foundation for precision therapies targeting ferroptosis.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Study Design\\u003c/h2\\u003e \\u003cp\\u003eThe flowchart of the study design is illustrated in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. First, we extracted pQTL data related to ferroptosis as exposure variables and utilized PTC GWAS data as the outcome variables to conduct a two-sample Mendelian randomization (MR) analysis to evaluate the causal relationship between ferroptosis-related molecules and PTC. Subsequently, we screened upstream regulatory molecules of ferroptosis-related proteins and further identified molecular regulatory axes through mediation analysis. Finally, the molecular regulatory axis was validated using bioinformatics and correlation analyses. Throughout the analysis, we selected appropriate single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) based on strict inclusion and exclusion criteria and performed multiple sensitivity analyses to ensure the quality of the MR analysis. This study adhered to rigorous ethical standards, and all data used were approved by ethics committees and obtained with informed consent from participants in their original studies.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003e2.2 Data Sources\\u003c/h3\\u003e\\n\\u003cp\\u003epQTL data related to ferroptosis for the Icelandic population from the deCODE database were obtained from the FerrDb website (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.zhounan.org/ferrdb/current/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.zhounan.org/ferrdb/current/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), including ferroptosis drivers and suppressors (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Proteomics data were derived from pQTL data of the Icelandic population in the deCODE database\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn9\\\" id=\\\"#FNLinkFn9\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.decode.com/summarydata/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.decode.com/summarydata/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) and the plasma proteomics pQTL data from the UK Biobank Pharma Proteomics Project (UKB-PPP)\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn10\\\" id=\\\"#FNLinkFn10\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.synapse.org/Synapse:syn51364943/wiki/622119\\u003c/span\\u003e\\u003cspan address=\\\"https://www.synapse.org/Synapse:syn51364943/wiki/622119\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). GWAS summary statistics for PTC were obtained from the Finnish FinnGen database, comprising 1,472 cases and 314,193 controls.\\u003c/p\\u003e\\n\\u003ch3\\u003e2.3 Instrumental Variable (IV) Selection\\u003c/h3\\u003e\\n\\u003cp\\u003eTo ensure the reliability and accuracy of the MR analysis, IVs must satisfy the following three key assumptions: (1) IVs are strongly associated with the exposure variable (ferroptosis-related proteins); (2) IVs are independent of any confounding factors; and (3) IVs exhibit no horizontal pleiotropy, meaning they influence the outcome variable only through the exposure variable, rather than through other potential pathways\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn11\\\" id=\\\"#FNLinkFn11\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. Based on these assumptions, we implemented a strict IV selection process for each gene. First, SNPs were selected using a genome-wide significance threshold (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;5.0 \\u0026times; 10^-8). Then, linkage disequilibrium (LD) clumping was performed based on the 1,000 Genomes Project European population data, with a threshold of r\\u0026sup2; \\u0026lt; 0.1 and a window size of 10,000 kb\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn12\\\" id=\\\"#FNLinkFn12\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e, to ensure SNP independence. SNPs with incompatible alleles between exposure and outcome were excluded. For palindromic SNPs, if the positive strand allele could not be inferred, they were directly excluded. Finally, weak IVs with an F-statistic\\u0026thinsp;\\u0026lt;\\u0026thinsp;10 were excluded to avoid weak instrument bias\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn13\\\" id=\\\"#FNLinkFn13\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003ch3\\u003e2.4 Mendelian Randomization Analysis and Sensitivity Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eIn the MR analysis, the inverse-variance weighted (IVW) method was used as the primary analysis method, assuming no horizontal pleiotropy, to obtain more accurate causal effect estimates\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn14\\\" id=\\\"#FNLinkFn14\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. Additionally, MR-Egger, weighted median, simple mode, and weighted mode methods were used as supplementary analyses to enhance the robustness of the results. Compared to other methods, the IVW method is more conservative but more stable, and thus was prioritized regardless of heterogeneity, with other methods used as complements. To ensure reliability, multiple sensitivity analyses were performed, including Cochran\\u0026rsquo;s Q test to assess heterogeneity\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn15\\\" id=\\\"#FNLinkFn15\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e, calculation of the MR-Egger intercept to detect directional pleiotropy and bias caused by invalid IVs\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn16\\\" id=\\\"#FNLinkFn16\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e, MR-PRESSO to detect and adjust for heterogeneous SNPs to reduce potential confounding effects\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn17\\\" id=\\\"#FNLinkFn17\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e, and leave-one-out (LOO) analysis to evaluate the influence of each SNP on the overall results. All MR analyses were performed using R version 4.3.1, with the \\u0026ldquo;MendelianRandomization,\\u0026rdquo; \\u0026ldquo;TwoSampleMR,\\u0026rdquo; and \\u0026ldquo;MR-PRESSO\\u0026rdquo; packages. Furthermore, Steiger\\u0026rsquo;s test was conducted to assess potential bias and minimize the impact of reverse causality.\\u003c/p\\u003e\\n\\u003ch3\\u003e2.5 Mediation Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eTo explore the regulatory axis of ferroptosis pathways, we employed a two-step mediation analysis\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn18\\\" id=\\\"#FNLinkFn18\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e][\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn19\\\" id=\\\"#FNLinkFn19\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. First, the causal effect of ferroptosis-related proteins on PTC (β2) was evaluated using two-sample MR. Then, upstream regulatory molecules associated with ferroptosis-related proteins were identified, and their effects on ferroptosis-related proteins (β1) and PTC (α) were assessed separately. Finally, the mediation effect (β1\\u0026thinsp;\\u0026times;\\u0026thinsp;β2 / α) was calculated,(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). and the regulatory axis with the highest mediation proportion was selected for further analysis.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6 Bioinformatics Analysis\\u003c/h2\\u003e \\u003cp\\u003eTo further validate the functional relevance of the molecular regulatory axis, we performed bioinformatics analyses using public databases. The GEPIA2 database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://gepia2.cancer-pku.cn/#index\\u003c/span\\u003e\\u003cspan address=\\\"http://gepia2.cancer-pku.cn/#index\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) was used to compare differential gene expression between TCGA-THCA tumor tissues and GTEx normal thyroid tissues, and significantly differentially expressed genes were filtered (threshold: |log2FC| \\u0026gt; 0.585, FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Additionally, the correlation analysis module of GEPIA2 was used to calculate the co-expression levels (Pearson correlation coefficient) of key molecules to verify their functional associations in PTC.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Identification and Extraction of Ferroptosis-Related pQTL Data\\u003c/h2\\u003e \\u003cp\\u003eWe retrieved a comprehensive list of ferroptosis-related molecules, including ferroptosis drivers and suppressors, from the FerrDb database and integrated them with pQTL data from the deCODE cohort. Through intersection analysis, we successfully identified and extracted 159 ferroptosis-related pQTL molecules (Figure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Identification of Differentially Expressed Genes\\u003c/h2\\u003e \\u003cp\\u003eTo enhance the robustness of our findings, we conducted differential expression analysis using the GEPIA2 database, comparing TCGA-THCA tumor tissues with GTEx normal thyroid tissues. Applying stringent thresholds (|log2FC| \\u0026gt; 0.585, corresponding to a 1.5-fold change, and adjusted p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), we identified 8,419 significantly differentially expressed genes (Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Identification of Ferroptosis-Related Proteins Associated with PTC\\u003c/h2\\u003e \\u003cp\\u003eUsing a two-sample Mendelian Randomization (MR) approach, with the inverse-variance weighted (IVW) method as the primary analytical tool, we identified 13 ferroptosis-related molecules potentially exerting causal effects on PTC (Table \\u003cspan refid=\\\"MOESM3\\\" class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e). To ensure the reliability of these findings, we performed multiple sensitivity analyses. The Cochran Q test (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05) indicated no significant heterogeneity, the MR-Egger intercept test (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05) ruled out horizontal pleiotropy, and the Steiger test confirmed the correct direction of causality. Integrating differential expression data with MR results, we identified two ferroptosis-related proteins, ADAM23 and BRD2, as significantly associated with PTC (Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Screening of Upstream Regulators of Ferroptosis-Related Proteins\\u003c/h2\\u003e \\u003cp\\u003eTo investigate upstream regulatory mechanisms, we utilized pQTL data from the UK Biobank Pharma Proteomics Project (UKB-PPP) and performed MR analysis to identify PTC-associated upstream molecules. Combined with differential expression analysis, we identified potential upstream regulators (Table \\u003cspan refid=\\\"MOESM4\\\" class=\\\"InternalRef\\\"\\u003eS4\\u003c/span\\u003e,S5). Further MR analysis revealed 14 upstream regulators significantly associated with ferroptosis-related proteins that may modulate PTC pathogenesis (Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\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\\u003eCausal Relationships of Ferroptosis-Related Proteins and Upstream Regulators with PTC Identified by Mendelian Randomization Analysis\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eExposure\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eOutcome\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c7\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eMendelian randomization analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003emethod\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ebeta\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eOR\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eOR(95%CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003edeCODE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFinnGen\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c7\\\" namest=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eADAM23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePTC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIVW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0104\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.93\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(0.88,0.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIVW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0420\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.530\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(0.35,0.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUKB-PPP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFinnGen\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c7\\\" namest=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eERBB4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"11\\\" rowspan=\\\"12\\\"\\u003e \\u003cp\\u003ePTC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIVW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0259\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.150\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(0.75,0.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDPP6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIVW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0072\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.213\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(0.69,0.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTSC1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIVW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0301\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.536\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(0.36,0.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFLT3LG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIVW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0436\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.161\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e 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align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.45e-06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.224\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(1.14,1.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eITIH4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIVW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.069\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e 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align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.466\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.056\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.435\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ers10922098\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTRUE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.37e-30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.902\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e 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colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTRUE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.40e-132\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.286\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.448\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDPP6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ers3734960\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTRUE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.89e-198\\u003c/p\\u003e 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align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTRUE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.65e-169\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.476\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePKD1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ers148501094\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTRUE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.34e-81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.529\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.278\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.567\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCASP4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ers117001788\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTRUE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.99e-25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.689\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.162\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.439\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCRELD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ers28562884\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTRUE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.6e-159\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.198\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.678\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.235\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eENTPD6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ers1108812\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTRUE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.41e-159\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.758\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.965\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.78\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 Mediation Analysis and Validation of Molecular Regulatory Axes\\u003c/h2\\u003e \\u003cp\\u003eTo elucidate the regulatory mechanisms of ferroptosis-related molecules in PTC, we conducted mediation analysis to quantify the mediation effects and proportions between upstream regulators and ferroptosis-related proteins (Table \\u003cspan refid=\\\"MOESM6\\\" class=\\\"InternalRef\\\"\\u003eS6\\u003c/span\\u003e). The TSC1-BRD2 regulatory axis exhibited the most significant mediation effect (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). To validate the functional relevance of this axis, we performed differential expression and correlation analyses using GEPIA2, confirming the differential expression and co-expression patterns of TSC1 and BRD2 in PTC tissues (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). These findings support the hypothesis that TSC1 exerts a protective effect in PTC by modulating the BRD2-dependent ferroptosis pathway.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMediation Effect of the TSC1-BRD2 Regulatory Axis in PTC.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" 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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eExposure\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMediator\\u003c/p\\u003e \\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 \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eProportion mediated\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eα\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eβ1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eβ2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eβ1*β2/α\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTSC1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBRD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.536\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.311\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.530\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e31%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study systematically dissected the genetic regulatory network of ferroptosis in papillary thyroid carcinoma (PTC) through a multi-stage integrative analysis. First, based on protein quantitative trait locus (pQTL) data from the deCODE database, we extracted ferroptosis-related proteins as exposure variables and integrated them with PTC genome-wide association study (GWAS) data from the Finnish FinnGen database. Using a two-sample Mendelian randomization (MR) approach, we evaluated the causal relationship between ferroptosis-related proteins and PTC risk. Next, by screening upstream regulatory molecules and performing mediation analysis, we revealed the protective role of the TSC1-BRD2-ferroptosis axis in PTC. Finally, through bioinformatics analysis, we further validated the functional relevance of this regulatory axis. This multi-dimensional evidence chain of \\\"genetics-protein-function\\\" not only provides new insights into the pathogenesis of PTC but also lays a theoretical foundation for ferroptosis-targeted precision therapies.\\u003c/p\\u003e \\u003cp\\u003eFerroptosis, an iron-dependent form of cell death driven by lipid peroxidation, has been shown to play significant roles in various cancers\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn20\\\" id=\\\"#FNLinkFn20\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e][\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn21\\\" id=\\\"#FNLinkFn21\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e][\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn22\\\" id=\\\"#FNLinkFn22\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. Notably, the traditional view suggests that ferroptosis exerts a dual function in cancer, either promoting or suppressing tumorigenesis, depending on the cancer type and its microenvironment. However, our study found that both the ferroptosis driver ADAM23 and the suppressor BRD2 were negatively associated with PTC risk, indicating that ferroptosis may primarily function to inhibit tumor progression in PTC. Specifically, as a driver, ADAM23 may directly suppress tumor cell survival by promoting ferroptosis, while BRD2, as a suppressor, may protect normal cells from ferroptosis by regulating lipid peroxidation or other pathways, thereby maintaining tissue homeostasis and suppressing tumorigenesis. This finding suggests that the role of ferroptosis in PTC is not the traditional dual function but rather a synergistic effect of drivers and suppressors collectively inhibiting tumor progression. This unique mechanism provides a new direction and challenge for developing ferroptosis-targeted therapies and underscores the importance of further studying its specific regulatory mechanisms.\\u003c/p\\u003e \\u003cp\\u003eAlthough BRD2 is widely regarded as a tumor-promoting epigenetic regulator in various cancers, it may exert a unique inhibitory effect in PTC by regulating ferroptosis-related pathways\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn23\\\" id=\\\"#FNLinkFn23\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. As a member of the bromodomain and extraterminal domain (BET) protein family, BRD2 can regulate gene transcription by binding to acetylated lysine residues on histones\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn24\\\" id=\\\"#FNLinkFn24\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. In PTC, BRD2 may upregulate the expression of ferroptosis suppressor genes, reduce intracellular iron levels and lipid peroxidation, and thereby inhibit abnormal proliferation and survival of tumor cells. Furthermore, BRD2 may enhance its anti-tumor effects by modulating immune cell infiltration and function in the tumor microenvironment\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn25\\\" id=\\\"#FNLinkFn25\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. For example, BRD2 may promote anti-tumor immune responses by regulating immune-related gene expression, thereby inhibiting PTC progression. Additionally, BRD2 may form a complex regulatory network in PTC through interactions with signaling pathways such as mTORC1. For instance, BRD2 may reduce metabolic reprogramming and energy supply in tumor cells by inhibiting mTORC1 activity, thereby indirectly suppressing tumor growth\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn26\\\" id=\\\"#FNLinkFn26\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. These multiple mechanisms make BRD2 a potential therapeutic target. Based on its tumor-suppressive role, future studies should focus on developing BRD2 activators to further enhance its protective function in PTC.\\u003c/p\\u003e \\u003cp\\u003eTSC1 (tuberous sclerosis complex 1), a tumor suppressor, is closely associated with the initiation and progression of various cancers\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn27\\\" id=\\\"#FNLinkFn27\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn28\\\" id=\\\"#FNLinkFn28\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. In PTC, TSC1 may exert significant inhibitory effects by regulating the expression and function of BRD2. The TSC1-TSC2 complex inhibits the mTORC1 (mechanistic target of rapamycin complex 1) signaling pathway, regulating cell growth and metabolism\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn29\\\" id=\\\"#FNLinkFn29\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. In PTC, TSC1 may reduce abnormal proliferation and metabolic reprogramming of tumor cells by inhibiting mTORC1 activity, thereby suppressing tumor progression. Moreover, TSC1 may indirectly affect the ferroptosis pathway by regulating BRD2 expression, further enhancing its anti-tumor effects. For instance, TSC1 may upregulate ferroptosis suppressor genes by activating BRD2 expression, thereby reducing tumor cell susceptibility to ferroptosis and inhibiting their malignant behavior. Given the protective role of TSC1 in PTC, future studies should focus on developing TSC1 activators to enhance its function and suppress tumor progression. Additionally, a combined activation strategy targeting the TSC1-BRD2 axis may provide novel therapeutic opportunities for PTC.\\u003c/p\\u003e \\u003cp\\u003eOur study revealed the protective role of the TSC1-BRD2-ferroptosis axis in PTC. TSC1, as a key regulator of the mTOR pathway\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn30\\\" id=\\\"#FNLinkFn30\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e, inhibits cell proliferation and metabolism by suppressing mTORC1 activity, while BRD2, as an epigenetic regulator, is involved in cell cycle and inflammatory response regulation\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn31\\\" id=\\\"#FNLinkFn31\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn32\\\" id=\\\"#FNLinkFn32\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. We propose that TSC1 may regulate BRD2 expression to influence the ferroptosis pathway, thereby suppressing PTC progression. Although existing literature does not directly describe the specific interaction between TSC1 and BRD2, integrating mTORC1 signaling and transcriptional regulatory networks, we speculate that TSC1 may indirectly regulate BRD2 function by inhibiting mTORC1 activity, forming a dynamic balance. TSC1 limits cell proliferation by suppressing mTORC1 signaling, while BRD2 supports normal cell function by promoting transcriptional activation, and their interaction may finely regulate the balance between cell growth and ferroptosis, ensuring tissue homeostasis and inhibiting tumorigenesis\\u003csup\\u003e[\\u003c/sup\\u003e\\u003ca class=\\\"FNLink\\\" href=\\\"#Fn33\\\" id=\\\"#FNLinkFn33\\\"\\u003e\\u003c/a\\u003e\\u003csup\\u003e]\\u003c/sup\\u003e. The discovery of this mechanism provides a potential therapeutic target for PTC, and future studies should further elucidate the specific molecular mechanisms of this regulatory axis and its complex role in the tumor microenvironment. Based on the tumor-suppressive roles of TSC1 and BRD2, developing their activators may become an effective strategy for treating PTC.\\u003c/p\\u003e \\u003cp\\u003eThe strength of this study lies in its multi-level, multi-dimensional analysis strategy. First, we employed a multi-stage integrative approach combining MR, mediation analysis, and bioinformatics analysis, constructing a comprehensive \\\"gene-protein-function\\\" evidence chain, which enhances the robustness and reliability of the results. Second, this study utilized data from multiple large-scale databases, including the deCODE database, UKB-PPP database, and FinnGen database, ensuring broad applicability and representativeness of the findings. Additionally, we conducted rigorous sensitivity analyses, such as MR-Egger, MR-PRESSO, and Steiger tests, to minimize potential biases and strengthen the reliability of the results. Finally, this study is the first to reveal the protective role of the TSC1-BRD2-ferroptosis axis in PTC, an innovative discovery that provides a new theoretical basis for ferroptosis-targeted precision therapies and has significant clinical implications.\\u003c/p\\u003e \\u003cp\\u003eDespite the important advancements made in this study, several limitations should be acknowledged. First, the data used in this study were primarily derived from European populations. While this ensures the reliability and representativeness of the results within this specific population, it may limit their generalizability to other ethnic and geographic groups. Future studies should validate these findings in other populations to ensure their broad applicability. Second, our study primarily inferred causality through bioinformatics analysis and MR methods. Although we implemented rigorous sensitivity analyses to reduce bias, further validation through in vitro and in vivo experiments is needed to elucidate the specific molecular mechanisms. Additionally, while MR analysis provides evidence for causal inference, the results may be influenced by potential confounding factors. Although we implemented strict controls during the analysis, the potential impact of these factors cannot be completely ruled out. Finally, this study focused primarily on the TSC1-BRD2-ferroptosis axis, but the mechanisms of ferroptosis in PTC may be more complex, involving interactions with other related molecules and pathways. Future studies should further explore these potential mechanisms to comprehensively understand the regulatory network of ferroptosis in PTC.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study is the first to reveal the protective role of the TSC1-BRD2-ferroptosis axis in papillary thyroid carcinoma (PTC) through a multi-stage integrative analysis. By combining pQTL data, GWAS data, Mendelian randomization (MR) analysis, and bioinformatics analysis, we constructed a comprehensive \\\"gene-protein-function\\\" multi-dimensional evidence chain, providing novel insights into the pathogenesis of PTC. This discovery not only enriches the theoretical understanding of the role of ferroptosis in cancer but also lays a critical theoretical foundation for precision therapies targeting ferroptosis. Future research should further validate the specific molecular mechanisms of this regulatory axis and explore its potential roles in other cancer types, offering additional possibilities for cancer treatment. Additionally, the development of activators targeting TSC1 and BRD2 may open new avenues for PTC therapy, holding significant value for clinical translation.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eData availability statement\\u003c/p\\u003e\\n\\u003cp\\u003eThe analytical approach was ethically sound, as all data utilized in this study had received prior approval and consent in their original studies.\\u003c/p\\u003e\\n\\u003cp\\u003e1.Proteome data were sourced from the deCODE database (https://www.decode.com/summarydata/) for the Icelandic population[[[] Ferkingstad E,Sulem P,Atlason BA, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53 (12):1712-1721. doi:10.1038/s41588-021-00978-w]], as well as from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP) (https://www.synapse.org/Synapse:syn51364943/wiki/622119)\\u003c/p\\u003e\\n\\u003cp\\u003e2.papillary thyroid carcinoma \\u0026nbsp;GWAS statistics were obtained from the Finnish Database Consortium\\u003c/p\\u003e\\n\\u003cp\\u003e3.Differential expression data for papillary thyroid carcinoma (PTC) were obtained from the TCGA and GETx databases using the GEPIA2 tool (http://gepia2.cancer-pku.cn/#index).\\u003c/p\\u003e\\n\\u003cp\\u003eFunding Declaration\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by the Startup Fund for Scientific Research, Fujian Medical University (Grant No. 2020QH1224).The funder had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to submit the work for publication.\\u003c/p\\u003e\\n\\u003cp\\u003eEthics and Consent Statements\\u003c/p\\u003e\\n\\u003cp\\u003eThe analysis in this study utilized summary statistics from a genome-wide association study. The original studies had obtained ethical approval and informed consent from participants, as confirmed by the institutional review boards. As this analysis did not involve any new data collection or require additional ethical clearance, there was no need for further ethical approval or informed consent for this study specifically.\\u003c/p\\u003e\\n\\u003cp\\u003eDeclaration of competing interest\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\\u003c/p\\u003e\\n\\u003cp\\u003eClinical Trial Registration Statement\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThis study is not a clinical trial. No clinical trial registration is required as the research exclusively analyzed pre-existing genetic and proteomic datasets.\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting Interests\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing financial or non-financial interests directly or indirectly related to this work. The corresponding author (CHEN GAO) affirms full responsibility for the integrity of this declaration.\\u003c/p\\u003e\\n\\u003cp\\u003eAuthor Contributions\\u003c/p\\u003e\\n\\u003cp\\u003eXIAMEI CHEN: Conceptualization, Methodology, Data Curation, Formal Analysis, Writing \\u0026ndash; Original Draft.\\u003c/p\\u003e\\n\\u003cp\\u003eCHEN GAO: Supervision, Funding Acquisition, Resources, Validation, Writing \\u0026ndash; Review \\u0026amp; Editing.\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors critically reviewed and approved the final manuscript, and agree to be accountable for all aspects of the work.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eBoucai L,Zafereo M,Cabanillas ME. Thyroid Cancer: A Review. JAMA. 2024;331 (5):425-435. doi:10.1001/jama.2023.26348\\u003c/li\\u003e\\n\\u003cli\\u003eChen X,Chen X,Xie W, et al. 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Nat Genet. 2021;53 (12):1712-1721. doi:10.1038/s41588-021-00978-w\\u003c/li\\u003e\\n\\u003cli\\u003eSun BB,Chiou J,Traylor M, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 2023;622 (7982):329-338. doi:10.1038/s41586-023-06592-6\\u003c/li\\u003e\\n\\u003cli\\u003eReading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. doi:10.1136/bmj.k601\\u003c/li\\u003e\\n\\u003cli\\u003eGkatzionis A, Burgess S, Newcombe PJ. Statistical methods for cis-Mendelian randomization with two-sample summary-level data. Genet Epidemiol. 2023 Feb;47(1):3-25. doi: 10.1002/gepi.22506.\\u003c/li\\u003e\\n\\u003cli\\u003eChen Z,Wang X,Teng Z, et al. Modifiable lifestyle factors influencing psychiatric disorders mediated by plasma proteins: A systemic Mendelian randomization study. J Affect Disord. 2024;350:582-589. doi:10.1016/j.jad.2024.01.169\\u003c/li\\u003e\\n\\u003cli\\u003ePierce BL,Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013;178 (7):1177-84. doi:10.1093/aje/kwt084\\u003c/li\\u003e\\n\\u003cli\\u003eCohen JF,Chalumeau M,Cohen R, et al. Cochran\\u0026apos;s Q test was useful to assess heterogeneity in likelihood ratios in studies of diagnostic accuracy. J Clin Epidemiol. 2015;68 (3):299-306. doi:10.1016/j.jclinepi.2014.09.005\\u003c/li\\u003e\\n\\u003cli\\u003eHemani G,Bowden J,Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27 (R2):R195-R208. doi:10.1093/hmg/ddy163\\u003c/li\\u003e\\n\\u003cli\\u003eVerbanck M,Chen CY,Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50 (5):693-698. doi:10.1038/s41588-018-0099-7\\u003c/li\\u003e\\n\\u003cli\\u003eSanderson E. Multivariable Mendelian Randomization and Mediation. Cold Spring Harb Perspect Med. 2021;11 (2):. doi:10.1101/cshperspect.a038984\\u003c/li\\u003e\\n\\u003cli\\u003eCarter AR,Sanderson E,Hammerton G, et al. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol. 2021;36 (5):465-478. doi:10.1007/s10654-021-00757-1\\u003c/li\\u003e\\n\\u003cli\\u003eZheng Y,Qin C,Li F, et al. Self-assembled thioether-bridged paclitaxel-dihydroartemisinin prodrug for amplified antitumor efficacy-based cancer ferroptotic-chemotherapy. Biomater Sci. 2023;11 (9):3321-3334. doi:10.1039/d2bm02032g\\u003c/li\\u003e\\n\\u003cli\\u003eLi Y,Li M,Liu L, et al. Cell-Specific Metabolic Reprogramming of Tumors for Bioactivatable Ferroptosis Therapy. ACS Nano. 2022;16 (3):3965-3984. doi:10.1021/acsnano.1c09480\\u003c/li\\u003e\\n\\u003cli\\u003eChen W,Xie L,Lv C, et al. Transferrin-Targeted Cascade Nanoplatform for Inhibiting Transcription Factor Nuclear Factor Erythroid 2-Related Factor 2 and Enhancing Ferroptosis Anticancer Therapy. ACS Appl Mater Interfaces. 2023;15 (24):28879-28890. doi:10.1021/acsami.3c01499\\u003c/li\\u003e\\n\\u003cli\\u003eYang M,Liu K,Chen P, et al. Bromodomain-containing protein 4 (BRD4) as an epigenetic regulator of fatty acid metabolism genes and ferroptosis. Cell Death Dis. 2022;13 (10):912. doi:10.1038/s41419-022-05344-0\\u003c/li\\u003e\\n\\u003cli\\u003eFrench CA. Small-Molecule Targeting of BET Proteins in Cancer. Adv Cancer Res. 2016;131:21-58. doi:10.1016/bs.acr.2016.04.001\\u003c/li\\u003e\\n\\u003cli\\u003eMilner JJ,Toma C,Quon S, et al. Bromodomain protein BRD4 directs and sustains CD8 T cell differentiation during infection. J Exp Med. 2021;218 (8):. doi:10.1084/jem.20202512\\u003c/li\\u003e\\n\\u003cli\\u003eSrivastava RK,Guroji P,Jin L, et al. Combined inhibition of BET bromodomain and mTORC1/2 provides therapeutic advantage for rhabdomyosarcoma by switching cell death mechanism. Mol Carcinog. 2022;61 (8):737-751. doi:10.1002/mc.23414\\u003c/li\\u003e\\n\\u003cli\\u003eTyburczy ME,Jozwiak S,Malinowska IA, et al. A shower of second hit events as the cause of multifocal renal cell carcinoma in tuberous sclerosis complex. Hum Mol Genet. 2015;24 (7):1836-42. doi:10.1093/hmg/ddu597\\u003c/li\\u003e\\n\\u003cli\\u003eHuang Q,Li F,Hu H, et al. Loss of TSC1/TSC2 sensitizes immune checkpoint blockade in non-small cell lung cancer. Sci Adv. 2022;8 (5):eabi9533. doi:10.1126/sciadv.abi9533\\u003c/li\\u003e\\n\\u003cli\\u003eInoki K,Li Y,Xu T, et al. Rheb GTPase is a direct target of TSC2 GAP activity and regulates mTOR signaling. Genes Dev. 2003;17 (15):1829-34. doi:10.1101/gad.1110003\\u003c/li\\u003e\\n\\u003cli\\u003eQin J,Wang Z,Hoogeveen-Westerveld M, et al. Structural Basis of the Interaction between Tuberous Sclerosis Complex 1 (TSC1) and Tre2-Bub2-Cdc16 Domain Family Member 7 (TBC1D7). J Biol Chem. 2016;291 (16):8591-601. doi:10.1074/jbc.M115.701870\\u003c/li\\u003e\\n\\u003cli\\u003eGreenwald RJ,Tumang JR,Sinha A, et al. E mu-BRD2 transgenic mice develop B-cell lymphoma and leukemia. Blood. 2004;103 (4):1475-84. doi:10.1182/blood-2003-06-2116\\u003c/li\\u003e\\n\\u003cli\\u003eLenburg ME,Sinha A,Faller DV, et al. Tumor-specific and proliferation-specific gene expression typifies murine transgenic B cell lymphomagenesis. J Biol Chem. 2007;282 (7):4803-4811. doi:10.1074/jbc.M605870200\\u003c/li\\u003e\\n\\u003cli\\u003eMak BC,Kenerson HL,Aicher LD, et al. Aberrant beta-catenin signaling in tuberous sclerosis. Am J Pathol. 2005;167 (1):107-16. doi:10.1016/s0002-9440(10)62958-6\\u003c/li\\u003e\\n\\u003c/ol\\u003e\\n\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Papillary thyroid carcinoma, Ferroptosis, TSC1, BRD2, Mendelian randomization, Bioinformatics analysis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6458091/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6458091/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eIntroduction\\u003c/strong\\u003e: Papillary thyroid carcinoma (PTC) represents the most prevalent thyroid malignancy, with some cases exhibiting aggressive features and treatment resistance. Ferroptosis, an iron-dependent form of programmed cell death, remains poorly understood in PTC. The aim of this study was to explore ferroptosis-related mechanisms and identify key regulatory networks and potential therapeutic targets in PTC.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e: A multi-stage integrative analysis strategy was employed, combining Mendelian randomization (MR), mediation analysis, and bioinformatics validation. Ferroptosis-related proteins associated with PTC risk were screened using protein quantitative trait loci (pQTL) data from the deCODE database and PTC genome-wide association study (GWAS) data from FinnGen. Upstream regulatory molecules were identified using pQTL data from the UK Biobank, and a molecular regulatory axis was constructed through mediation analysis. Differential expression and co-expression analyses were conducted using TCGA and GTEx databases to validate functional relevance.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e: The study revealed the TSC1-BRD2-ferroptosis axis, where TSC1 regulated BRD2 expression to suppress the ferroptosis pathway. This regulatory axis demonstrated significant differential expression and co-expression relationships in PTC tissues, supporting its functional relevance.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion\\u003c/strong\\u003e: This study established, for the first time, the protective role of the TSC1-BRD2-ferroptosis axis in PTC, providing new insights into the pathogenesis of the disease.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Revealing the Protective Role of the TSC1-BRD2-Ferroptosis Axis in Papillary Thyroid Carcinoma: A Multi-Stage Genetic and Functional Integrative Analysis Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-13 12:16:08\",\"doi\":\"10.21203/rs.3.rs-6458091/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"061cedfe-32d1-49ea-9aaa-d52880b2c347\",\"owner\":[],\"postedDate\":\"May 13th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-07-22T12:38:36+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-05-13 12:16:08\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6458091\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6458091\",\"identity\":\"rs-6458091\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}