Ferroptosis-Associated Genes GSTZ1 and MT1G Orchestrate Multidimensional Remodeling of the Nasopharyngeal Carcinoma Microenvironment via the TGF-β Signaling

Ferroptosis-Associated Genes GSTZ1 and MT1G Orchestrate Multidimensional Remodeling of the Nasopharyngeal Carcinoma Microenvironment via the TGF-β Signaling | 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 Ferroptosis-Associated Genes GSTZ1 and MT1G Orchestrate Multidimensional Remodeling of the Nasopharyngeal Carcinoma Microenvironment via the TGF-β Signaling Zheng Ma, Xinran Niu, Weijie Liu, Liping Zhang, Moran Liu, Ping Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6741407/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 Background: Nasopharyngeal carcinoma (NPC) progression involves dynamic interactions between ferroptosis and tumor microenvironment (TME) remodeling, yet the regulatory roles of ferroptosis-related genes (GSTZ1, PTGS2, MT1G) remain poorly characterized. This study aimed to dissect their multidimensional networks and therapeutic implications in NPC. Methods: Through bioinformatics analysis and machine learning algorithms (including XGBoost-driven prognostic modeling), we systematically investigated the expression patterns, pathway interactions, and immune modulation of these hub genes. KEGG enrichment, immune infiltration deconvolution, and survival analyses were performed in NPC. Results: The ferroptosis-associated genes exhibited NPC-specific dysregulation: GSTZ1 and MT1G are lowly expressed in NPC, while PTGS2 is highly expressed.The prognostic model integrating these genes achieved superior predictive accuracy (AUC >0.9). In addition A novel TGF-β‒tryptophan metabolic axis was identified, coordinating epithelial-mesenchymal transition (EMT) and immunosuppression.Immunologically, GSTZ1 showed dual regulation—positively correlating with B cells/CD4+ TRM cells but suppressing M1 macrophages, whereas PTGS2 promoted M1 polarization while inhibiting follicular helper T cells. Interestingly, Pulsatilla chinensis can target regulated Ferroptosis-Associated genens to inhibit tumor progression. Conclusion: This research found that Ferroptose-related genes GSTZ1、PTGS2、MT1G as multifunctional regulators bridging ferroptosis, metabolic reprogramming, and immune evasion in NPC.we also found thatthese demonstrated co-promote NPC TME.The TGFβ signaling pathway, as a connecting bridge, provides a deeper understanding of the important molecular mechanisms by which ferroptosis induces the progression of NPC Ferroptosis tumor microenvironment (TME) Nasopharyngeal carcinoma (NPC) GSTZ1 PTGS2 MT1G Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Nasopharyngeal carcinoma (NPC) is a malignancy of the head and neck with unique epidemiological characteristics and molecular heterogeneity, closely associated with Epstein-Barr virus (EBV) infection, genetic susceptibility, and environmental factors( 1 ). In recent years, the rapid advancement of bioinformatics technologies has provided new perspectives for precision diagnosis and treatment of NPC through molecular subtyping and biomarker discovery based on multi-omics data( 2 ). For instance, by integrating transcriptomic and methylation data analyses, researchers found that the dysregulation of ferroptosis-related genes (such as ACSL4 and GPX4) is significantly correlated with the sensitivity of NPC patients to radiotherapy and chemotherapy( 3 – 5 ). Additionally, a risk prediction model for metastasis constructed using machine learning algorithms revealed a potential association between the ferroptosis regulatory network and tumor immune evasion. In the field of biomarkers, the combination of liquid biopsy technology and bioinformatics has significantly enhanced the dynamic monitoring capabilities of NPC( 6 – 10 ). By employing targeted sequencing and deep learning methods, researchers developed a recurrence warning model based on the fragmentation patterns of plasma EBV DNA, achieving predictive efficacy that far exceeds traditional quantitative detection methods( 11 ). Furthermore, single-cell RNA sequencing (scRNA-seq) technology has elucidated the heterogeneity of cancer-associated fibroblasts (CAFs) in the TME of NPC, revealing that the FGF5/FGFR2 signaling axis inhibits ferroptosis by regulating the Nrf2 pathway—a mechanism that was validated in cisplatin-resistant patients through spatial transcriptomics analysis( 12 ). Quantification results from bioinformatics tools on immune infiltration further indicated that ferroptosis sensitivity is positively correlated with the abundance of CD8 + T cell infiltration and negatively correlated with the proportion of M2 macrophages, suggesting a synergistic effect of metabolic reprogramming and immune suppression in the microenvironment( 13 – 15 ). The interactive regulatory network between ferroptosis and the immune microenvironment has become a current research hotspot. Through weighted gene co-expression network analysis (WGCNA), researchers discovered a strong association between the cholesterol synthesis pathway and the ferroptosis-resistant phenotype in NPC( 16 – 20 ). ACSL3 as a critical node linking lipid metabolic reprogramming and ferroptosis. Additionally, bioinformatics-driven drug repositioning strategies have identified metabolic modulators such as metformin, which can enhance the anti-tumor effects of ferroptosis inducers by inhibiting the mTORC1 signaling pathway. The integration of multi-modal strategies based on bioinformatics is set to advance NPC research to a higher dimension. By constructing a dynamic network model of ferroptosis-immune regulation, real-time simulations of TME evolution under therapeutic interventions can be performed. Furthermore, the fusion of spatial multi-omics technology and digital pathology is expected to elucidate the immune-related mechanisms of microenvironment remodeling induced by ferroptosis at single-cell resolution. This interdisciplinary paradigm not only provides a precise roadmap for personalized treatment of NPC but also lays a methodological foundation for the systemic regulation of the metabolic-immune axis in solid tumor therapies. 2. Materials and methods 2.1 The discrepancy analyse of ferroptosis-related genes In this study, we obtained two samples (GSE12452). After integrating data through standardization, the R software analysis package was used for differential analysis. DEGs with∣log2FoldChange∣ >1 and P.adjust < 0.05 as screening conditions( 21 – 25 ). 2.2 Identification of ferroptosis correlation genes based on WGCNA The software package "WGCNA" was employed to build multiple gene models. Subsequently, a set of gene network connections were assessed using Tom. The dynamic cutting method was then utilized to partition different modules, with a cutting confidence value of 0.9 being chosen. Through the R package "WGCNA," various clustering algorithms were executed, and treemaps were generated to examine the correlation among modules. 2.3 Functional Enrichment Analysis Enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed using the “ClusterProfiler” package of R software. A critical value of error detection rate < 0.05 was considered statistically significant.Gene set variation analysis (GSVA) is a special type of gene set enrichment method, which converts gene expression data from the level of individual genes to the enrichment degree of gene sets by calculating the enrichment level of gene sets in each sample( 26 , 27 ). 2.4 Immune infiltration and functional analysis from ssGSEA The "GSVA" R package was used to perform ssGSEA on the samples to calculate the absolute enrichment scores of immune cells and immune-related functions in the samples, and the correlation between key genes and immune infiltration was explored by correlation analysis( 28 ). 2.5 Kaplan-Meier Survival Analysis The Kaplan-Meier survival curve serves as a non-parametric method to evaluate survival outcomes, with its primary utility lying in visualizing the temporal dynamics of survival probabilities through event occurrence data. This method quantifies survival processes using a step-function algorithm: the x-axis represents continuous observation time, while the y-axis denotes cumulative survival probability. Each horizontal step reflects survival status within specific time intervals, with vertical drops at step termini indicating event occurrences. In this study, we employed this approach to predict prognostic outcomes in NPC by analyzing the co-expression patterns of ferroptosis-associated genes (GSTZ1, MT1G, PTGS2) combined with the proliferation marker MKi67. 2.6 Statistical analysis All data processing and statistical analysis were conducted using R software version 3.6.1. Student's t-test was used to determine differences between groups and a p-value less than 0.05 was considered to indicate statistical significance. 3. Result 3.1 Identifies Tumor-Associated Gene Dysregulation and Biomarker Potential Bioinformatics analysis of the GSE12452 dataset revealed 1,351 differentially expressed genes (DEGs) using stringent threshold criteria of |logFC| > 1 and adjusted P-value < 0.05. The DEGs comprised 1,037 upregulated genes and 314 downregulated genes (Fig. 1 A). All identified DEGs were establish a foundation for subsequent investigations. The identification of these molecular signatures not only highlights critical transcriptional differences between tumor and normal tissues, but also provides critical insights for functional enrichment analyses and potential biomarker discovery. These findings offer valuable resources for understanding tumor pathogenesis and developing targeted therapeutic strategies. 3.2WGCNA Unravels Key Modules and Driver ferroptosis-related gene Linked to NPC Progression Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify critical driver genes and risk-associated modules involved in NPC progression. During this process, we established an optimal soft threshold power of 16, which minimized scale-free topology model fitting errors (Fig. 2 A-B). Through centrality analysis of these co-expression modules, three distinct modules (skyblue, salmon, green, darkred, magenta, and grey60) exhibited significant differential expression patterns in NPC tissues compared to controls (Fig. 2 C). Subsequent module-trait association analysis revealed strong correlations between these modules and clinically relevant NPC characteristics (Fig. 2 D). Notably, the skyblue and salmon modules demonstrated the highest connectivity with tumor progression markers, suggesting their potential roles as regulatory hubs in NPC pathogenesis. These findings provide a framework for prioritizing candidate genes and pathways underlying NPC development. Intersection analysis between the identified hub genes and ferroptosis-related gene sets revealed 43 conserved ferroptosis-associated genes, establishing a crucial molecular framework for subsequent mechanistic investigations (Fig. 2 E). These candidate genes will be prioritized for functional validation studies to elucidate their precise roles in NPC progression, particularly focusing on ferroptosis regulation and therapeutic vulnerability. The integrated analytical pipeline not only delineates characteristic ferroptotic signatures in NPC pathogenesis but also provides a translational roadmap for developing targeted therapies. Preliminary pathway enrichment analysis suggests these genes predominantly cluster in iron metabolism and oxidative stress response pathways, potentially serving as dual biomarkers and therapeutic targets. 3.3 Identification of ferroptosis hub marker GSTZ1/PTGS2/MT1G genes in NPC To further screen hub characteristic genes, we employed multiple machine learning algorithms including Lasso regression, support vector machine (SVM), and Boruta algorithm for comprehensive analysis. This integrated analytical strategy aimed to precisely identify critical feature genes (Fig. 3 A-D). Notably, GSTZ1, PTGS2, and MT1G were consistently identified as risk factors potentially accelerating disease progression. Subsequent residual analysis through boxplots revealed key distribution characteristics, with median values, interquartile ranges, and outlier patterns systematically examined (Fig. 3 E). The reverse cumulative distribution plot of sample residuals demonstrated that over 85% of residuals were concentrated within the 0.00-0.25 interval, indicating close alignment between model predictions and actual observations with minimal prediction errors. Model performance was further validated by ROC curve analysis, showing excellent discriminatory capacity with AUC values exceeding 0.9 across all models. Feature importance analysis from multiple algorithms consistently highlighted several genes with prominent relative importance (Fig. 3 F). These findings establish a robust theoretical foundation for subsequent biological validation and clinical translation, underscoring the potential pathogenic roles of these genes in disease progression. 3.4 Ferroptosis related GSTZ1/MT1G/PTGS2 gene to Poor Prognosis in NPC Our evaluation of key gene expression profiles revealed significant differential expression in GSTZ1 (glutathione S-transferase zeta 1), PTGS2 (prostaglandin-endoperoxide synthase 2), and MT1G (metallothionein 1G). Specifically, PTGS2 exhibited downregulation, while GSTZ1 and MT1G showed marked upregulation (Fig. 4 A). Subsequent XGBoost analysis identified MT1G as the most enriched ferroptosis-related factor, followed by GSTZ1 and PTGS2(Fig. 4 B). Mechanistic investigation revealed that glutathione S-transferases (GSTs), a family of Phase II detoxification enzymes, catalyze the conjugation of glutathione (GSH) with endogenous or exogenous electrophilic compounds. As a critical member of the GST superfamily, abnormal GSTZ1 expression may promote ferroptosis through disrupted detoxification pathways. Notably, survival analysis demonstrated that combined overexpression of GSTZ1, PTGS2, MT1G, and the proliferation marker Ki-67 significantly correlated with poor clinical prognosis. These findings suggest a synergistic effect between ferroptosis-related genes and cellular proliferation in disease progression(Fig. 4 C). 3.5 TGF-βSignaling and Tryptophan Metabolic Reprogramming Synergistically Drive NPC Progression via EMT Induction and Immunosuppression Through systematic KEGG pathway enrichment analysis of hub genes, we identified significant activation of the TGF-β signaling pathway in NPC specimens. Furthermore, metabolic pathway profiling revealed marked enrichment of tryptophan metabolism-related gene sets. These findings suggest that these critical hub genes may influence the TME through dual regulatory mechanisms: activating the TGF-β signaling axis to promote EMT. In addition immunosuppressive states via tryptophan metabolic reprogramming(Fig. 5 A). The dynamic interplay between this signaling transduction network and metabolic remodeling likely constitutes a pivotal mechanism driving malignant progression in NPC. 3.6 Ferroptosis-related genes suppress Immunization cell functionality Immunoinfiltration analysis of the GSE12452 dataset revealed marked heterogeneity in the expression levels of different immune cell subpopulations, with significant correlations observed among specific immune indicators(Fig. 6 A-B). Further investigation demonstrated that GSTZ1, PTGS2, and MT1G genes exhibit specific regulatory roles in shaping the immune microenvironment: GSTZ1 showed significant positive correlations with B cells and CD4 + tissue-resident memory T cells, while displaying negative associations with M1 macrophages and activated CD4 + memory T cells (Fig. 7 A-B). MT1G specifically promoted mast cell infiltration(Fig. 7 C-D). PTGS2 manifested bidirectional regulatory characteristics, with its high expression positively correlated with resting CD4 + memory T cells, M1 macrophages, and γδ T cells, but negatively associated with follicular helper T cells, memory B cells, and tissue-resident CD4 + T cell activity(Fig. 7 E-F). Notably, GSTZ1 and PTGS2 exhibited antagonistic regulatory patterns toward M1 macrophages, suggesting their potential involvement in inflammatory or tumor immune responses through macrophage polarization modulation. These findings provide novel insights into the regulatory networks of these genes within the immune microenvironment and their potential translational implications. 3.7 Pulsatilla chinensis Targets regulated Ferroptosis-Associated genens To investigate the therapeutic effects of Pulsatilla chinensis on NPC, we first identified common therapeutic targets by intersecting Pulsatilla chinensis targets with NPC-related targets, yielding 139 overlapping therapeutic targets visualized through a Venn diagram (Fig. 8 A). Subsequent PPI network analysis revealed a complex interaction network comprising 138 nodes and 4,475 edges, with ferroptosis-associated genes including GSTZ1, PTGS2, and MT1G (Fig. 8 B). Furthermore, a comprehensive herb-component-target-disease network was constructed with 172 nodes and 420 interaction edges. Critical bioactive components were identified through degree value ranking, with the top five constituents being: Emodin, 3beta-Hydroxyurs-12-En-28-Syre, Colchicine, β-Sitosterol, and Isorhamnetin (Fig. 8 C). These findings suggest these components may serve as primary active agents mediating Pulsatilla chinensis anti-tumor effects in nasopharyngeal carcinoma through multi-target regulatory mechanisms. 4. Discussion This study systematically revealed the regulatory networks of ferroptosis-related hub genes (GSTZ1, PTGS2, MT1G) and their mechanisms in remodeling the TME during the progression of NPC through the integration of multi-dimensional bioinformatics analyses and machine learning algorithms. These findings not only deepen the understanding of the molecular pathological features of NPC but also provide a theoretical basis for combined therapeutic strategies targeting ferroptosis and the immune microenvironment. The abnormal expression of GSTZ1, PTGS2, and MT1G as ferroptosis-related genes in NPC holds significant biological implications( 29 , 30 ). GSTZ1, a key enzyme in glutathione metabolism, may promote ferroptosis by depleting the glutathione (GSH) pool, thereby exacerbating lipid peroxidation. This finding aligns with previous studies suggesting that imbalances in GSH metabolism are core drivers of ferroptosis. Notably, the significantly downregulated expression of PTGS2 (cyclooxygenase-2) in NPC contrasts with its reported pro-inflammatory and tumor-promoting roles in most solid tumors, indicating a unique regulatory pattern of ferroptosis in NPC( 30 ). The high expression of MT1G may indirectly affect iron homeostasis-related pathways by chelating intracellular free iron ions, although its specific regulatory mechanisms require further experimental validation. In addition, the KEGG enrichment analysis indicated that the activation of the TGF-β signaling pathway and tryptophan metabolic reprogramming serve as dual engines for the regulation of NPC progression by these hub genes. The role of the TGF-β pathway in promoting tumor invasion and metastasis through epithelial-mesenchymal transition (EMT) is widely recognized( 31 ). Our study found that its synergistic effect with tryptophan metabolism may drive immune evasion by depleting tryptophan and accumulating immunosuppressive metabolites via the kynurenine pathway. This proposed "signal-metabolism axis" provides a new perspective to elucidate the dynamic relationship between EMT and immune suppression within the NPC microenvironment. On the immunoregulation front, the specific modulation of immune cell subpopulations by GSTZ1, PTGS2, and MT1G highlights their multifaceted roles. The positive correlation of GSTZ1 with B cells and CD4 + tissue-resident memory T cells suggests its potential role in inhibiting tumor progression by maintaining adaptive immune responses. However, its negative regulation on M1 macrophages might weaken innate immune killing, indicating a contradictory effect that could be linked to its dynamic expression across different stages of tumor progression. PTGS2's positive regulation of resting CD4 + memory T cells and M1 macrophages is consistent with its classic pro-inflammatory function, while its suppression of follicular helper T cells may promote the formation of an immune-tolerant microenvironment. Importantly, the antagonistic regulation of M1 macrophages by GSTZ1 and PTGS2 may reflect the interplay between ferroptosis and inflammatory signaling during the polarization of tumor-associated macrophages (TAMs), offering a potential entry point for targeted therapies aimed at TAM polarization. Recent studies have confirmed that inducing ferroptosis can enhance tumor immunogenicity and improve responses to immunotherapy, making the identified hub genes potential key targets for optimizing combination treatment regimens( 32 – 35 ). However, this study has certain limitations: The retrospective analysis based on public databases needs validation through in vitro experiments and clinical samples; The specific mechanisms of ferroptosis-related genes in metabolic/immune pathways are not fully elucidated; Immune infiltration analysis relies on computational models and requires further validation through flow cytometry or single-cell sequencing. 5. Conclusion This study has systematically illuminated the multidimensional regulatory networks of ferroptosis-related hub genes GSTZ1, PTGS2, and MT1G in nasopharyngeal carcinoma (NPC) through the integration of bioinformatics analysis, machine learning algorithms, and immune microenvironment characterization. For the first time, it proposed a mechanistic model in which the "TGF-β signaling-tryptophan metabolic axis" drives the malignant cycle of "invasion-immune evasion" in NPC, elucidating the dual roles of the TGF-β pathway in inducing EMT and expanding regulatory T cells (Tregs). The important thing is that Pulsatilla chinensis can target regulated Ferroptosis-Associated genens to inhibit tumor progression. Declarations 6.Data availability statement The datasets analyzed in this study can be found in GEO (https://www.ncbi.nlm.nih.gov/geo/), Xena (https://xena.ucsc.edu/). The datasets used and analyzed in this study are available from the corresponding authors of this study upon reasonable request. 7.Conflicts of interest The authors declare no conflict of interest. 8.Author contributions Zheng Ma and Xinran Niu conceived the study.x drafted the manuscript. Zheng Ma, Xinran Niu, Weijie Liu, Liping Zhang, Moran Liu, Ping Chen and Li Hou performed the literature search and collected the data. Ping Chen and Li Hou analyzed and visualized the data. Li Hou helped with the final revision of this manuscript. All authors reviewed and approved the final manuscript. 9.Funding Thanks to the support of Natural Science Foundation of Ningxia (2023AAC02065,2024AAC03622), Key R&D Programme of the Ningxia Autonomous Region(2022BEG03105). 10.Ethics approval and consent to participate There was no need for any special permissions. 11.Consent for publication Not applicable. 12.Competing interests The authors declare no competing interests. References Guo R, Mao YP, Tang LL, Chen L, Sun Y, Ma J. The evolution of nasopharyngeal carcinoma staging. Br J Radiol. 2019 Oct;92(1102):20190244. Guan S, Wei J, Huang L, Wu L. Chemotherapy and chemo-resistance in nasopharyngeal carcinoma. Eur J Med Chem. 2020 Dec 1;207:112758. Su ZY, Siak PY, Lwin YY, Cheah S-C. 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Wu L, Zhou Y, Fu J. KIAA1429 promotes nasopharyngeal carcinoma progression by mediating m6A modification of PTGS2. Crit Rev Immunol. 2023;43(4):15-27. Xu H, Yin Y, Li Y, Shi N, Xie W, Luo W, et al. FLOT2 promotes nasopharyngeal carcinoma progression through suppression of TGF-β pathway via facilitating CD109 expression. iScience. 2023 Nov 25;27(1):108580. Tong X, Tang R, Xiao M, Xu J, Wang W, Zhang B, et al. Targeting cell death pathways for cancer therapy: recent developments in necroptosis, pyroptosis, ferroptosis, and cuproptosis research. J Hematol Oncol. 2022 Dec 8;15(1):174. Ma X, Xiao L, Liu L, Ye L, Su P, Bi E, et al. CD36-mediated ferroptosis dampens intratumoral CD8+ T cell effector function and impairs their antitumor ability. Cell Metab. 2021 May 4;33(5):1001-12. e5. Yang P, Yang W, Wei Z, Li Y, Yang Y, Wang J. Novel targets for gastric cancer: The tumor microenvironment (TME), N6-methyladenosine (m6A), pyroptosis, autophagy, ferroptosis and cuproptosis. Biomed Pharmacother. 2023 Jul;163:114883. Kim R, Taylor D, Vonderheide RH, Gabrilovich DI. Ferroptosis of immune cells in the tumor microenvironment. Trends Pharmacol Sci. 2023 Aug;44(8):542-52. Additional Declarations No competing interests reported. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6741407","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478323808,"identity":"e68e675b-20fb-4e5c-85f2-f871f1f4508e","order_by":0,"name":"Zheng Ma","email":"","orcid":"","institution":"General Hospital of Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Ma","suffix":""},{"id":478323809,"identity":"54dd43be-346d-48ab-9deb-ad0693a4e8ad","order_by":1,"name":"Xinran Niu","email":"","orcid":"","institution":"The Second Clinical Medical College，Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinran","middleName":"","lastName":"Niu","suffix":""},{"id":478323810,"identity":"38279069-f7b1-46ce-a40b-fbbce2104b4f","order_by":2,"name":"Weijie Liu","email":"","orcid":"","institution":"Peking University First Hospital Ningxia Women and Children’ Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weijie","middleName":"","lastName":"Liu","suffix":""},{"id":478323811,"identity":"539debdd-f0b1-4039-a182-03b2b018ced0","order_by":3,"name":"Liping Zhang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liping","middleName":"","lastName":"Zhang","suffix":""},{"id":478323812,"identity":"b6890253-198b-4c02-b05e-955d9f754799","order_by":4,"name":"Moran Liu","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Moran","middleName":"","lastName":"Liu","suffix":""},{"id":478323813,"identity":"55a0f899-8988-46f0-9fec-2ae8a1c0dc1a","order_by":5,"name":"Ping Chen","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Chen","suffix":""},{"id":478323814,"identity":"95970b4a-0cb4-45b2-bc83-f5c7bd31ba44","order_by":6,"name":"Li Hou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYBACA3YGNoYHFTZyDMxEa2EGakk4k2ZMopbEtsOJDUQ7zJyZ+dkDoJb0+e28Bz8w1NhEE9Ri2cxmbpBwLj13w2G+ZAmGY2m5BK0zOMxgJpFQZp27gZnHQIKx4TAxWti/SSSwMafLN/MY/yBSCw/QljbnBAYQg1gtZRLAQDbcANRikUCUX463b5P4UGEjL99/xvjGhxobwlpQQQJpykfBKBgFo2AU4AIApo46P1PLTEIAAAAASUVORK5CYII=","orcid":"","institution":"General Hospital of Ningxia Medical University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Hou","suffix":""}],"badges":[],"createdAt":"2025-05-25 02:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6741407/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6741407/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85844977,"identity":"8b649b03-5a78-4199-b30d-d49cdc6505df","added_by":"auto","created_at":"2025-07-02 09:35:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":397319,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially Expressed Genes in NPC. (A) The volcano plot illustrates the differential gene expression in NPC, with blue representing downregulated genes and red indicating upregulated genes.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6741407/v1/fa8b9c969d766f13e542af55.png"},{"id":85844979,"identity":"1e18f9ce-cf87-4c99-a5ff-8ff72dd2f747","added_by":"auto","created_at":"2025-07-02 09:35:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3759114,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of Ferroptosis-Related Genes in NPC.(A, C) The WGCNA modules display different gene clusters, with each module containing no fewer than 50 genes.(B) The optimal soft threshold is determined to be 16, indicating minimal error.(D) Correlation of module traits is shown.(E) The Venn diagram illustrates the selection of differentially expressed ferroptosis-related genes.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6741407/v1/fc90c8cef1c5437b1e5b2483.png"},{"id":85844981,"identity":"6fcc7094-e88f-4f26-b520-44edb4daf7e4","added_by":"auto","created_at":"2025-07-02 09:35:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4554486,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of Ferroptosis Genes through Machine Learning.(A-D) Lasso, Support Vector Machine (SVM), and Boruta machine learning methods are employed to identify key genes.(E) The residual reverse cumulative distribution plot assesses the distribution of model prediction errors, while the ROC curve evaluates the sensitivity and specificity of the identified genes.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6741407/v1/27f376f2a12597930bc9cbfc.png"},{"id":85844984,"identity":"f54a9caa-cca3-4327-90bc-a62e5786a62b","added_by":"auto","created_at":"2025-07-02 09:35:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2229735,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of Key Ferroptosis Genes Accelerates Poor Prognosis in NPC.(A) GSTZ1 and MT1G are lowly expressed in NPC, while PTGS2 is highly expressed.(B) XGBoost analysis highlights GSTZ1, MT1G, and PTGS2 as high-variance factors.(C) The combined expression of GSTZ1, MT1G, and PTGS2 with MKI67 reveals prognostic implications for NPC.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6741407/v1/d480d06252b93b42e08fdeb3.png"},{"id":85847105,"identity":"5cea669d-548a-4a2e-8926-bd5c1a2da9f4","added_by":"auto","created_at":"2025-07-02 09:51:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4242657,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG Enrichment Analysis.(A) The enrichment of KEGG pathways is illustrated.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6741407/v1/98e12fa46442613e61c95421.png"},{"id":85846554,"identity":"bfd47861-ab8e-41e1-8005-59870d9e3b3f","added_by":"auto","created_at":"2025-07-02 09:43:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5470520,"visible":true,"origin":"","legend":"\u003cp\u003eImmune Infiltration in NPC.(A) Comparison of the proportions of various immune factors between normal and NPC groups.(B) Overall expression levels of immune-related genes are shown.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6741407/v1/6497ce44a684cd2e03f30fd3.png"},{"id":85844985,"identity":"41ab6ba1-d49c-46a9-9f08-2cf1578c74e0","added_by":"auto","created_at":"2025-07-02 09:35:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4422837,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristic Immune Changes of GSTZ1, PTGS2, and MT1G.(A,C,E) Bar charts display the immune changes associated with GSTZ1, PTGS2, and MT1G.(B,D,F) The correlation changes between GSTZ1, PTGS2, and MT1G and various immune genes are presented.\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6741407/v1/14892284c1eae12f58d9b3a6.png"},{"id":85844986,"identity":"772d8fe0-78ff-422e-9ecb-834f9b0933de","added_by":"auto","created_at":"2025-07-02 09:35:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":8964824,"visible":true,"origin":"","legend":"\u003cp\u003eTargeting NPC Treatment with Pulsatilla chinensis.(A) Screening of common targets between Pulsatillae Radix and NPC disease.(B) The protein-protein interaction network and its related expression patterns are shown.(C) Node analysis of the medical herb, components, targets, and disease network.\u003c/p\u003e","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6741407/v1/8a8653a420f7d7445e02d5b8.png"},{"id":90005564,"identity":"70bab260-e73c-4c19-b86e-8f1343aba37f","added_by":"auto","created_at":"2025-08-27 09:33:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":34213457,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6741407/v1/f1e1b184-3ab1-4896-853a-77f98bf6a244.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ferroptosis-Associated Genes GSTZ1 and MT1G Orchestrate Multidimensional Remodeling of the Nasopharyngeal Carcinoma Microenvironment via the TGF-β Signaling","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNasopharyngeal carcinoma (NPC) is a malignancy of the head and neck with unique epidemiological characteristics and molecular heterogeneity, closely associated with Epstein-Barr virus (EBV) infection, genetic susceptibility, and environmental factors(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In recent years, the rapid advancement of bioinformatics technologies has provided new perspectives for precision diagnosis and treatment of NPC through molecular subtyping and biomarker discovery based on multi-omics data(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). For instance, by integrating transcriptomic and methylation data analyses, researchers found that the dysregulation of ferroptosis-related genes (such as ACSL4 and GPX4) is significantly correlated with the sensitivity of NPC patients to radiotherapy and chemotherapy(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Additionally, a risk prediction model for metastasis constructed using machine learning algorithms revealed a potential association between the ferroptosis regulatory network and tumor immune evasion.\u003c/p\u003e \u003cp\u003eIn the field of biomarkers, the combination of liquid biopsy technology and bioinformatics has significantly enhanced the dynamic monitoring capabilities of NPC(\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). By employing targeted sequencing and deep learning methods, researchers developed a recurrence warning model based on the fragmentation patterns of plasma EBV DNA, achieving predictive efficacy that far exceeds traditional quantitative detection methods(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Furthermore, single-cell RNA sequencing (scRNA-seq) technology has elucidated the heterogeneity of cancer-associated fibroblasts (CAFs) in the TME of NPC, revealing that the FGF5/FGFR2 signaling axis inhibits ferroptosis by regulating the Nrf2 pathway\u0026mdash;a mechanism that was validated in cisplatin-resistant patients through spatial transcriptomics analysis(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Quantification results from bioinformatics tools on immune infiltration further indicated that ferroptosis sensitivity is positively correlated with the abundance of CD8\u0026thinsp;+\u0026thinsp;T cell infiltration and negatively correlated with the proportion of M2 macrophages, suggesting a synergistic effect of metabolic reprogramming and immune suppression in the microenvironment(\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe interactive regulatory network between ferroptosis and the immune microenvironment has become a current research hotspot. Through weighted gene co-expression network analysis (WGCNA), researchers discovered a strong association between the cholesterol synthesis pathway and the ferroptosis-resistant phenotype in NPC(\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). ACSL3 as a critical node linking lipid metabolic reprogramming and ferroptosis. Additionally, bioinformatics-driven drug repositioning strategies have identified metabolic modulators such as metformin, which can enhance the anti-tumor effects of ferroptosis inducers by inhibiting the mTORC1 signaling pathway.\u003c/p\u003e \u003cp\u003eThe integration of multi-modal strategies based on bioinformatics is set to advance NPC research to a higher dimension. By constructing a dynamic network model of ferroptosis-immune regulation, real-time simulations of TME evolution under therapeutic interventions can be performed. Furthermore, the fusion of spatial multi-omics technology and digital pathology is expected to elucidate the immune-related mechanisms of microenvironment remodeling induced by ferroptosis at single-cell resolution. This interdisciplinary paradigm not only provides a precise roadmap for personalized treatment of NPC but also lays a methodological foundation for the systemic regulation of the metabolic-immune axis in solid tumor therapies.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The discrepancy analyse of ferroptosis-related genes\u003c/h2\u003e \u003cp\u003eIn this study, we obtained two samples (GSE12452). After integrating data through standardization, the R software analysis package was used for differential analysis. DEGs with∣log2FoldChange∣ \u0026gt;1 and P.adjust \u0026lt; 0.05 as screening conditions(\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Identification of ferroptosis correlation genes based on WGCNA\u003c/h2\u003e \u003cp\u003eThe software package \"WGCNA\" was employed to build multiple gene models. Subsequently, a set of gene network connections were assessed using Tom. The dynamic cutting method was then utilized to partition different modules, with a cutting confidence value of 0.9 being chosen. Through the R package \"WGCNA,\" various clustering algorithms were executed, and treemaps were generated to examine the correlation among modules.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eEnrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed using the \u0026ldquo;ClusterProfiler\u0026rdquo; package of R software. A critical value of error detection rate\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.Gene set variation analysis (GSVA) is a special type of gene set enrichment method, which converts gene expression data from the level of individual genes to the enrichment degree of gene sets by calculating the enrichment level of gene sets in each sample(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Immune infiltration and functional analysis from ssGSEA\u003c/h2\u003e \u003cp\u003eThe \"GSVA\" R package was used to perform ssGSEA on the samples to calculate the absolute enrichment scores of immune cells and immune-related functions in the samples, and the correlation between key genes and immune infiltration was explored by correlation analysis(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Kaplan-Meier Survival Analysis\u003c/h2\u003e \u003cp\u003eThe Kaplan-Meier survival curve serves as a non-parametric method to evaluate survival outcomes, with its primary utility lying in visualizing the temporal dynamics of survival probabilities through event occurrence data. This method quantifies survival processes using a step-function algorithm: the x-axis represents continuous observation time, while the y-axis denotes cumulative survival probability. Each horizontal step reflects survival status within specific time intervals, with vertical drops at step termini indicating event occurrences. In this study, we employed this approach to predict prognostic outcomes in NPC by analyzing the co-expression patterns of ferroptosis-associated genes (GSTZ1, MT1G, PTGS2) combined with the proliferation marker MKi67.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll data processing and statistical analysis were conducted using R software version 3.6.1. Student's t-test was used to determine differences between groups and a p-value less than 0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identifies Tumor-Associated Gene Dysregulation and Biomarker Potential\u003c/h2\u003e \u003cp\u003eBioinformatics analysis of the GSE12452 dataset revealed 1,351 differentially expressed genes (DEGs) using stringent threshold criteria of |logFC| \u0026gt; 1 and adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The DEGs comprised 1,037 upregulated genes and 314 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). All identified DEGs were establish a foundation for subsequent investigations. The identification of these molecular signatures not only highlights critical transcriptional differences between tumor and normal tissues, but also provides critical insights for functional enrichment analyses and potential biomarker discovery. These findings offer valuable resources for understanding tumor pathogenesis and developing targeted therapeutic strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2WGCNA Unravels Key Modules and Driver ferroptosis-related gene Linked to NPC Progression\u003c/h2\u003e \u003cp\u003eWeighted Gene Co-expression Network Analysis (WGCNA) was employed to identify critical driver genes and risk-associated modules involved in NPC progression. During this process, we established an optimal soft threshold power of 16, which minimized scale-free topology model fitting errors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). Through centrality analysis of these co-expression modules, three distinct modules (skyblue, salmon, green, darkred, magenta, and grey60) exhibited significant differential expression patterns in NPC tissues compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Subsequent module-trait association analysis revealed strong correlations between these modules and clinically relevant NPC characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Notably, the skyblue and salmon modules demonstrated the highest connectivity with tumor progression markers, suggesting their potential roles as regulatory hubs in NPC pathogenesis. These findings provide a framework for prioritizing candidate genes and pathways underlying NPC development.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIntersection analysis between the identified hub genes and ferroptosis-related gene sets revealed 43 conserved ferroptosis-associated genes, establishing a crucial molecular framework for subsequent mechanistic investigations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). These candidate genes will be prioritized for functional validation studies to elucidate their precise roles in NPC progression, particularly focusing on ferroptosis regulation and therapeutic vulnerability. The integrated analytical pipeline not only delineates characteristic ferroptotic signatures in NPC pathogenesis but also provides a translational roadmap for developing targeted therapies. Preliminary pathway enrichment analysis suggests these genes predominantly cluster in iron metabolism and oxidative stress response pathways, potentially serving as dual biomarkers and therapeutic targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of ferroptosis hub marker GSTZ1/PTGS2/MT1G genes in NPC\u003c/h2\u003e \u003cp\u003eTo further screen hub characteristic genes, we employed multiple machine learning algorithms including Lasso regression, support vector machine (SVM), and Boruta algorithm for comprehensive analysis. This integrated analytical strategy aimed to precisely identify critical feature genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-D). Notably, GSTZ1, PTGS2, and MT1G were consistently identified as risk factors potentially accelerating disease progression. Subsequent residual analysis through boxplots revealed key distribution characteristics, with median values, interquartile ranges, and outlier patterns systematically examined (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The reverse cumulative distribution plot of sample residuals demonstrated that over 85% of residuals were concentrated within the 0.00-0.25 interval, indicating close alignment between model predictions and actual observations with minimal prediction errors. Model performance was further validated by ROC curve analysis, showing excellent discriminatory capacity with AUC values exceeding 0.9 across all models. Feature importance analysis from multiple algorithms consistently highlighted several genes with prominent relative importance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). These findings establish a robust theoretical foundation for subsequent biological validation and clinical translation, underscoring the potential pathogenic roles of these genes in disease progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Ferroptosis related GSTZ1/MT1G/PTGS2 gene to Poor Prognosis in NPC\u003c/h2\u003e \u003cp\u003eOur evaluation of key gene expression profiles revealed significant differential expression in GSTZ1 (glutathione S-transferase zeta 1), PTGS2 (prostaglandin-endoperoxide synthase 2), and MT1G (metallothionein 1G). Specifically, PTGS2 exhibited downregulation, while GSTZ1 and MT1G showed marked upregulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Subsequent XGBoost analysis identified MT1G as the most enriched ferroptosis-related factor, followed by GSTZ1 and PTGS2(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Mechanistic investigation revealed that glutathione S-transferases (GSTs), a family of Phase II detoxification enzymes, catalyze the conjugation of glutathione (GSH) with endogenous or exogenous electrophilic compounds. As a critical member of the GST superfamily, abnormal GSTZ1 expression may promote ferroptosis through disrupted detoxification pathways. Notably, survival analysis demonstrated that combined overexpression of GSTZ1, PTGS2, MT1G, and the proliferation marker Ki-67 significantly correlated with poor clinical prognosis. These findings suggest a synergistic effect between ferroptosis-related genes and cellular proliferation in disease progression(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 TGF-βSignaling and Tryptophan Metabolic Reprogramming Synergistically Drive NPC Progression via EMT Induction and Immunosuppression\u003c/h2\u003e \u003cp\u003eThrough systematic KEGG pathway enrichment analysis of hub genes, we identified significant activation of the TGF-β signaling pathway in NPC specimens. Furthermore, metabolic pathway profiling revealed marked enrichment of tryptophan metabolism-related gene sets. These findings suggest that these critical hub genes may influence the TME through dual regulatory mechanisms: activating the TGF-β signaling axis to promote EMT. In addition immunosuppressive states via tryptophan metabolic reprogramming(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The dynamic interplay between this signaling transduction network and metabolic remodeling likely constitutes a pivotal mechanism driving malignant progression in NPC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Ferroptosis-related genes suppress Immunization cell functionality\u003c/h2\u003e \u003cp\u003eImmunoinfiltration analysis of the GSE12452 dataset revealed marked heterogeneity in the expression levels of different immune cell subpopulations, with significant correlations observed among specific immune indicators(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). Further investigation demonstrated that GSTZ1, PTGS2, and MT1G genes exhibit specific regulatory roles in shaping the immune microenvironment: GSTZ1 showed significant positive correlations with B cells and CD4\u0026thinsp;+\u0026thinsp;tissue-resident memory T cells, while displaying negative associations with M1 macrophages and activated CD4\u0026thinsp;+\u0026thinsp;memory T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). MT1G specifically promoted mast cell infiltration(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D). PTGS2 manifested bidirectional regulatory characteristics, with its high expression positively correlated with resting CD4\u0026thinsp;+\u0026thinsp;memory T cells, M1 macrophages, and γδ T cells, but negatively associated with follicular helper T cells, memory B cells, and tissue-resident CD4\u0026thinsp;+\u0026thinsp;T cell activity(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-F). Notably, GSTZ1 and PTGS2 exhibited antagonistic regulatory patterns toward M1 macrophages, suggesting their potential involvement in inflammatory or tumor immune responses through macrophage polarization modulation. These findings provide novel insights into the regulatory networks of these genes within the immune microenvironment and their potential translational implications.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Pulsatilla chinensis Targets regulated Ferroptosis-Associated genens\u003c/h2\u003e \u003cp\u003eTo investigate the therapeutic effects of Pulsatilla chinensis on NPC, we first identified common therapeutic targets by intersecting Pulsatilla chinensis targets with NPC-related targets, yielding 139 overlapping therapeutic targets visualized through a Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Subsequent PPI network analysis revealed a complex interaction network comprising 138 nodes and 4,475 edges, with ferroptosis-associated genes including GSTZ1, PTGS2, and MT1G (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Furthermore, a comprehensive herb-component-target-disease network was constructed with 172 nodes and 420 interaction edges. Critical bioactive components were identified through degree value ranking, with the top five constituents being: Emodin, 3beta-Hydroxyurs-12-En-28-Syre, Colchicine, β-Sitosterol, and Isorhamnetin (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). These findings suggest these components may serve as primary active agents mediating Pulsatilla chinensis anti-tumor effects in nasopharyngeal carcinoma through multi-target regulatory mechanisms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study systematically revealed the regulatory networks of ferroptosis-related hub genes (GSTZ1, PTGS2, MT1G) and their mechanisms in remodeling the TME during the progression of NPC through the integration of multi-dimensional bioinformatics analyses and machine learning algorithms. These findings not only deepen the understanding of the molecular pathological features of NPC but also provide a theoretical basis for combined therapeutic strategies targeting ferroptosis and the immune microenvironment.\u003c/p\u003e \u003cp\u003eThe abnormal expression of GSTZ1, PTGS2, and MT1G as ferroptosis-related genes in NPC holds significant biological implications(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). GSTZ1, a key enzyme in glutathione metabolism, may promote ferroptosis by depleting the glutathione (GSH) pool, thereby exacerbating lipid peroxidation. This finding aligns with previous studies suggesting that imbalances in GSH metabolism are core drivers of ferroptosis. Notably, the significantly downregulated expression of PTGS2 (cyclooxygenase-2) in NPC contrasts with its reported pro-inflammatory and tumor-promoting roles in most solid tumors, indicating a unique regulatory pattern of ferroptosis in NPC(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The high expression of MT1G may indirectly affect iron homeostasis-related pathways by chelating intracellular free iron ions, although its specific regulatory mechanisms require further experimental validation.\u003c/p\u003e \u003cp\u003eIn addition, the KEGG enrichment analysis indicated that the activation of the TGF-β signaling pathway and tryptophan metabolic reprogramming serve as dual engines for the regulation of NPC progression by these hub genes. The role of the TGF-β pathway in promoting tumor invasion and metastasis through epithelial-mesenchymal transition (EMT) is widely recognized(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Our study found that its synergistic effect with tryptophan metabolism may drive immune evasion by depleting tryptophan and accumulating immunosuppressive metabolites via the kynurenine pathway. This proposed \"signal-metabolism axis\" provides a new perspective to elucidate the dynamic relationship between EMT and immune suppression within the NPC microenvironment.\u003c/p\u003e \u003cp\u003eOn the immunoregulation front, the specific modulation of immune cell subpopulations by GSTZ1, PTGS2, and MT1G highlights their multifaceted roles. The positive correlation of GSTZ1 with B cells and CD4\u0026thinsp;+\u0026thinsp;tissue-resident memory T cells suggests its potential role in inhibiting tumor progression by maintaining adaptive immune responses. However, its negative regulation on M1 macrophages might weaken innate immune killing, indicating a contradictory effect that could be linked to its dynamic expression across different stages of tumor progression. PTGS2's positive regulation of resting CD4\u0026thinsp;+\u0026thinsp;memory T cells and M1 macrophages is consistent with its classic pro-inflammatory function, while its suppression of follicular helper T cells may promote the formation of an immune-tolerant microenvironment. Importantly, the antagonistic regulation of M1 macrophages by GSTZ1 and PTGS2 may reflect the interplay between ferroptosis and inflammatory signaling during the polarization of tumor-associated macrophages (TAMs), offering a potential entry point for targeted therapies aimed at TAM polarization.\u003c/p\u003e \u003cp\u003eRecent studies have confirmed that inducing ferroptosis can enhance tumor immunogenicity and improve responses to immunotherapy, making the identified hub genes potential key targets for optimizing combination treatment regimens(\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). However, this study has certain limitations: The retrospective analysis based on public databases needs validation through in vitro experiments and clinical samples; The specific mechanisms of ferroptosis-related genes in metabolic/immune pathways are not fully elucidated; Immune infiltration analysis relies on computational models and requires further validation through flow cytometry or single-cell sequencing.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study has systematically illuminated the multidimensional regulatory networks of ferroptosis-related hub genes GSTZ1, PTGS2, and MT1G in nasopharyngeal carcinoma (NPC) through the integration of bioinformatics analysis, machine learning algorithms, and immune microenvironment characterization. For the first time, it proposed a mechanistic model in which the \"TGF-β signaling-tryptophan metabolic axis\" drives the malignant cycle of \"invasion-immune evasion\" in NPC, elucidating the dual roles of the TGF-β pathway in inducing EMT and expanding regulatory T cells (Tregs). The important thing is that Pulsatilla chinensis can target regulated Ferroptosis-Associated genens to inhibit tumor progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.Data availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study can be found in GEO (https://www.ncbi.nlm.nih.gov/geo/), Xena (https://xena.ucsc.edu/). The datasets used and analyzed in this study are available from the corresponding authors of this study upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.Conflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.Author contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZheng Ma and Xinran Niu conceived the study.x drafted the manuscript. Zheng Ma, Xinran Niu, Weijie Liu, Liping Zhang, Moran Liu, Ping Chen and Li Hou performed the literature search and collected the data. Ping Chen and Li Hou analyzed and visualized the data. Li Hou helped with the final revision of this manuscript. All authors reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9.Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to the support of Natural Science Foundation of Ningxia (2023AAC02065,2024AAC03622), Key R\u0026amp;D Programme of the\u0026nbsp;Ningxia\u0026nbsp;Autonomous Region(2022BEG03105).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10.Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no need for any special permissions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e11.Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e12.Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGuo R, Mao YP, Tang LL, Chen L, Sun Y, Ma J. 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FLOT2 promotes nasopharyngeal carcinoma progression through suppression of TGF-\u0026beta; pathway via facilitating CD109 expression. iScience. 2023 Nov 25;27(1):108580.\u003c/li\u003e\n \u003cli\u003eTong X, Tang R, Xiao M, Xu J, Wang W, Zhang B, et al. Targeting cell death pathways for cancer therapy: recent developments in necroptosis, pyroptosis, ferroptosis, and cuproptosis research. J Hematol Oncol. 2022 Dec 8;15(1):174.\u003c/li\u003e\n \u003cli\u003eMa X, Xiao L, Liu L, Ye L, Su P, Bi E, et al. CD36-mediated ferroptosis dampens intratumoral CD8+ T cell effector function and impairs their antitumor ability. Cell Metab. 2021 May 4;33(5):1001-12. e5.\u003c/li\u003e\n \u003cli\u003eYang P, Yang W, Wei Z, Li Y, Yang Y, Wang J. Novel targets for gastric cancer: The tumor microenvironment (TME), N6-methyladenosine (m6A), pyroptosis, autophagy, ferroptosis and cuproptosis. 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Trends Pharmacol Sci. 2023 Aug;44(8):542-52.\u003c/li\u003e\n\u003c/ol\u003e"}],"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":"[email protected]","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":"Ferroptosis, tumor microenvironment (TME), Nasopharyngeal carcinoma (NPC), GSTZ1, PTGS2, MT1G, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-6741407/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6741407/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eNasopharyngeal carcinoma (NPC) progression involves dynamic interactions between ferroptosis and tumor microenvironment (TME) remodeling, yet the regulatory roles of ferroptosis-related genes (GSTZ1, PTGS2, MT1G) remain poorly characterized. This study aimed to dissect their multidimensional networks and therapeutic implications in NPC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThrough bioinformatics analysis and machine learning algorithms (including XGBoost-driven prognostic modeling), we systematically investigated the expression patterns, pathway interactions, and immune modulation of these hub genes. KEGG enrichment, immune infiltration deconvolution, and survival analyses were performed in NPC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe ferroptosis-associated genes exhibited NPC-specific dysregulation: GSTZ1 and MT1G are lowly expressed in NPC, while PTGS2 is highly expressed.The prognostic model integrating these genes achieved superior predictive accuracy (AUC \u0026gt;0.9). In addition A novel TGF-β‒tryptophan metabolic axis was identified, coordinating epithelial-mesenchymal transition (EMT) and immunosuppression.Immunologically, GSTZ1 showed dual regulation—positively correlating with B cells/CD4+ TRM cells but suppressing M1 macrophages, whereas PTGS2 promoted M1 polarization while inhibiting follicular helper T cells. Interestingly, Pulsatilla chinensis can target regulated Ferroptosis-Associated genens to inhibit tumor progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis research found that Ferroptose-related genes GSTZ1、PTGS2、MT1G as multifunctional regulators bridging ferroptosis, metabolic reprogramming, and immune evasion in NPC.we also found thatthese demonstrated co-promote NPC TME.The TGFβ signaling pathway, as a connecting bridge, provides a deeper understanding of the important molecular mechanisms by which ferroptosis induces the progression of NPC\u003c/p\u003e","manuscriptTitle":"Ferroptosis-Associated Genes GSTZ1 and MT1G Orchestrate Multidimensional Remodeling of the Nasopharyngeal Carcinoma Microenvironment via the TGF-β Signaling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 09:35:04","doi":"10.21203/rs.3.rs-6741407/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"3bb5b8f0-d556-4e53-bbaf-8c19a590ad76","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-27T09:24:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 09:35:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6741407","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6741407","identity":"rs-6741407","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}