Integrated multi-omics reveals the ITGB2-IL2RB-NK axis in promoting papillary thyroid carcinoma progression and immune microenvironment crosstalk

Integrated multi-omics reveals the ITGB2-IL2RB-NK axis in promoting papillary thyroid carcinoma progression and immune microenvironment crosstalk | 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 Integrated multi-omics reveals the ITGB2-IL2RB-NK axis in promoting papillary thyroid carcinoma progression and immune microenvironment crosstalk Jianxiong Xu, CHEN GAO This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7218110/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Introduction : Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with some cases exhibiting aggressive features and therapeutic resistance. Although dysregulated protein interaction networks and immune microenvironment remodeling have been increasingly recognized as critical in PTC progression, the specific genetic-protein-immune regulatory networks remain to be systematically elucidated. Methodology : This study adopted a multi-stage integrated analysis strategy combining Mendelian randomization (MR), mediation analysis, and bioinformatics validation. First, MR was applied using pQTL data from the deCODE and UKB-PP databases alongside PTC GWAS data from FinnGen to screen PTC-associated proteins. Second, a two-step mediation analysis was conducted to construct protein-protein-immune microenvironment regulatory networks. Finally, differential expression and co-expression analyses using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were performed to validate functional relevance. Results : We identified the ITGB2-IL2RB-NK cell regulatory axis, which was significantly associated with PTC risk. This regulatory axis demonstrated marked differential expression and co-expression relationships in PTC tissues, confirming its functional relevance. Conclusion : This study systematically revealed the tumor-promoting role of the ITGB2-IL2RB-NK cell axis in PTC for the first time, providing a theoretical basis for developing combined immunotherapy strategies targeting the tumor microenvironment. Figures Figure 1 Figure 2 Figure 3 Introduction Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, accounting for approximately 80%-85% of all thyroid cancers. Although most PTC patients have favorable prognoses, a subset of cases exhibit aggressive characteristics such as local invasion, metastasis, and resistance to radioiodine therapy. Current standard therapies demonstrate limited efficacy in advanced cases, highlighting the urgent need to elucidate the pathogenesis of PTC [ ] . Despite the identification of high-frequency driver genetic alterations, critical knowledge gaps persist in understanding its molecular basis. Recent studies have revealed that dysregulated protein interaction networks and immune microenvironment remodeling are closely associated with the initiation and progression of PTC, yet the regulatory networks remain undefined. Protein interaction networks play critical roles in PTC by influencing tumor growth, invasion, and apoptosis [ ] . Their dysregulation is linked to abnormal cellular proliferation, migration, and apoptosis, and contributes to tumor microenvironment modulation [ ] . Deciphering these networks could facilitate the discovery of molecular mechanisms underlying PTC, identify potential therapeutic targets, and provide novel strategies to improve patient outcomes. Integrating proteomics with genetic studies may deepen our understanding of PTC complexity and advance therapeutic development. In recent studies, the immune microenvironment has been identified as a pivotal determinant of PTC progression and clinical outcomes [ ] . Key components of this interaction include diverse immune cell types that interact with tumor cells to regulate immune responses. Proteins and receptors involved in immune regulation are essential for modulating the immune microenvironment in PTC. The impact of key protein-immune cell regulatory axes within the immune microenvironment on PTC is profound, as they not only dictate tumor behavior and progression but also influence therapeutic outcomes [ ] . Dissecting the intricate interplay between tumor cells and the immune environment may uncover novel therapeutic targets and strategies to enhance anti-thyroid cancer immunity. Understanding these dynamics could pave the way for developing more effective immunotherapies and personalized approaches to improve PTC management. Mendelian randomization (MR) addresses confounding biases and reverse causation in observational studies by utilizing genetic variants as instrumental variables, enabling robust inference of causal relationships between specific proteins and diseases [ ] . Mediation analysis further dissects whether such effects are direct or mediated indirectly through other proteins. The integration of these approaches is particularly suited for investigating protein interaction networks, as it allows differentiation between direct and indirect causal pathways, identification of key mediator proteins, and discovery of therapeutic targets [ ][ ] . This combined strategy provides a powerful analytical framework for unraveling disease mechanisms and developing precision therapies. Together, these methods not only delineate exposure-outcome causal pathways but also elucidate underlying mechanisms, thereby decoding the "gene-protein-phenotype" cascade. Through a multi-stage integrative analysis, this study systematically dissected the genetic regulatory network connecting proteins and the immune microenvironment in PTC. First, we screened proteins significantly associated with PTC risk using pQTL data from the Icelandic deCODE cohort and PTC GWAS data from the Finnish FinnGen database. Second, upstream regulatory factors were identified by integrating UK Biobank pQTL data with FinnGen GWAS. Subsequently, a two-step mediation analysis confirmed a critical protein regulatory axis (ITGB2-IL2RB). Further mediation analysis revealed that IL2RB influences PTC through downstream NK cells. Our study demonstrated the pivotal role of the ITGB2-IL2RB-NK cell axis in PTC. Differential expression and correlation analyses using TCGA and GTEx databases further validated this regulatory relationship. This multidimensional "gene-protein-immune phenotype" evidence chain not only elucidates the role of the ITGB2-IL2RB-NK cell axis in PTC but also provides a theoretical foundation for targeted precision therapy. Methodology 2.1 Study Design Figure 1 visually illustrates the overall study flowchart. Briefly, we conducted two-sample Mendelian randomization (MR) analyses using pQTL data from the deCODE and UKB-PP databases as exposures and PTC genome-wide association study (GWAS) data as outcomes to investigate causal relationships between proteins and PTC. Subsequently, molecular regulatory axes were screened through mediation analysis and further validated via bioinformatics and correlation analyses. Next, downstream mechanistic exploration of these regulatory axes was performed using mediation analysis, ultimately confirming the genetic regulatory network connecting proteins and the immune microenvironment. Throughout the analysis, single nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) under strict inclusion/exclusion criteria, and a series of sensitivity analyses were conducted to ensure MR quality. Our analytical strategy adhered to rigorous ethical standards, as all data used in this study had obtained ethical approvals and participant consents in their original studies. 2.2 Data Sources Proteomic data were obtained from two sources: (1) pQTL data for the Icelandic population from the deCODE database ( https://www.decode.com/summarydata/ ) [ ] ; (2) plasma proteome pQTL data from the UK Biobank Pharma Proteomics Project (UKB-PPP) ( https://www.synapse.org/Synapse:syn51364943/wiki/622119 ) [ ] . Immunological trait data (accession numbers: GCST0001391-GCST0002121) were retrieved from the GWAS Catalog, comprising summary statistics for 731 immune features: absolute counts (AC, n = 118), median fluorescence intensity (MFI, n = 389), morphological parameters (MP, n = 32), and relative counts (RC, n = 192). A complete list of immune traits is provided in Table S1 [ ] . PTC GWAS statistics were derived from the FinnGen consortium R10 release, including 1,472 cases and 314,193 controls. 2.3 Instrumental Variable (IV) Selection According to MR assumptions [ ] , we implemented a strict selection process for SNPs in each gene. First, SNPs were selected using a stringent and uniform threshold to ensure only those with P-values below the genome-wide significance threshold (5.0 × 10⁻⁸) were considered. Next, to obtain a set of independent SNPs, we performed clumping based on 1000 Genomes Project European population data for each gene, setting the linkage disequilibrium (LD) threshold to r² < 0.1 with a clumping window of 10,000 kb [ ] . Third, SNPs with incompatible alleles between exposure and outcome were excluded. Palindromic SNPs were resolved using allele frequency information to infer forward-strand alleles; those without allele frequency data were directly excluded. Additionally, SNPs with F-statistics < 10 were removed to avoid weak instrument bias [ ] . 2.4 Mendelian Randomization Analysis, Sensitivity Analysis, and Directionality Testing This study performed two-sample MR analyses using the TwoSampleMR package. We utilized the inverse-variance weighted (IVW) method [ ] as the primary algorithm and excluded exposures for which IVW results could not be calculated. Furthermore, we applied the MR-Egger intercept test [ ] to detect pleiotropy and Cochran’s Q test [ ] to assess heterogeneity. Simultaneously, Steiger testing was conducted to evaluate potential bias, aiming to enhance study reliability and minimize bias caused by reverse causation. 2.5 Bioinformatics Analysis We queried differential expression data for PTC in TCGA and GTEx databases using the GEPIA2 database ( http://gepia2.cancer-pku.cn/#index ), with thresholds set as absolute logFC > 0.585 (1.5-fold difference) and FDR-adjusted P < 0.05. Validation of differential expression in PTC further confirmed our conclusions. Additionally, we employed the correlation analysis module in GEPIA2 to further verify and refine our interaction network. 2.6 Mediation Analysis Upstream analysis: We applied a two-step MR mediation analysis [ ][ ] to explore protein regulatory networks. First, we calculated the effect of upstream proteins on disease, denoted as beta_all. Second, we calculated the effect of upstream proteins on downstream proteins (beta1) and the effect of downstream proteins on disease (beta2). Finally, the mediated effect (beta12) was calculated as beta1 multiplied by beta2, and the mediation proportion (beta12_p) was derived as (beta12 / beta_all) × 100%. Results with mediation proportions exceeding 20% were selected for subsequent investigation (Fig. 2 A). Downstream analysis: We employed mediation analysis to explore potential downstream mechanisms of proteins. First, we calculated the effect of proteins on PTC (beta_all). Second, we calculated the effect of proteins on immune cells (beta1) and the effect of immune cells on PTC (beta2). Finally, the mediated effect (beta12) was calculated as beta1 multiplied by beta2, and the mediation proportion (beta12_p) was derived as (beta12 / beta_all) × 100%. Results with mediation proportions exceeding 20% were selected (Fig. 2 B). Results 3.1 Identification of Differentially Expressed Genes By querying differential expression data from TCGA and GTEx databases with thresholds of absolute log2FC > 0.585 (1.5-fold change) and adjusted P < 0.05, we obtained 8,419 differentially expressed genes (details in Table S2 ). 3.2 Screening of Outcome-Associated Molecules Mendelian randomization (MR) analysis was performed using the inverse-variance weighted (IVW) method as the primary approach, with pQTLs from the deCODE database as exposures, to screen proteins causally associated with PTC (Table S3 ). Based on the consistency between the directional effects of pQTLs on PTC and the differential expression data, 93 outcome-associated molecules were ultimately identified. 3.3 Screening of Upstream Molecules Using the UKB-PPP database, MR analysis was conducted to screen associations between UKB-PPP pQTLs and PTC (Table S4 ). Combined with differential expression analysis, 53 molecules were initially identified. Subsequently, MR analysis was applied to identify upstream regulatory molecules for outcome-associated proteins in the deCODE database, yielding 1,595 causal molecule pairs (Table S5 ). 3.4 Mediation Analysis and Bioinformatics Validation of Molecular Regulatory Axes Mediation analysis was employed to further screen candidate molecules by calculating mediation effects and proportions (Table S6 ). A total of 1,595 mediator pairs were identified, with those exhibiting stronger mediation effects selected for downstream analysis. Differential expression and correlation analyses were then performed to validate candidate molecules. Ultimately, the ITGB2-IL2RB regulatory axis was confirmed (Fig. 2 , Tables 1 – 3 ). Table 1 Causal Relationships of PTC–Associated Proteins and Upstream/Downstream Regulators with PTC Identified by Mendelian Randomization Analysis Exposure Outcome Mendelian randomization analysis method p beta OR OR(95%CI) deCODE FinnGen IL2RB PTC IVW 0.0115 0.255 1.29 (1.05,1.57) UKB-PPP FinnGen ITGB2 PTC IVW 0.0172 0.215 1.24 (1.04,1.48) UKB-PPP deCODE ITGB2 IL2RB IVW 0.0301 0.525 1.69 (1.45,1.97) Table 2 Sensitivity Analysis and Directionality Test of Causal Relationships in Mendelian Randomization for PTC–Associated Proteins and Upstream/Downstream Regulators. Exposure Outcome SNP Steiger direction Steiger P value Heterogeneity Pleiotropy deCODE FinnGen IL2RB PTC rs3184504 TRUE 1.47e-88 0.187 0.083 UKB-PPP FinnGen ITGB2 PTC rs11574639 TRUE 3.10e-49 0.294 0.307 UKB-PPP deCODE ITGB2 IL2RB rs55714927 TRUE 7.39e-12 7.22e-132 0.163 3.5 Mediation Analysis of Downstream Mechanisms for the ITGB2-IL2RB Axis To investigate downstream mechanisms, mediation analysis was conducted by calculating the proportion of immune cell phenotypes mediating the effect between proteins and PTC. First, MR analysis identified immune cell phenotypes associated with PTC. Next, MR analysis was applied to assess relationships between the IL2RB protein and these immune cell phenotypes (Table S7 , Tables 1 – 2 ). Finally, integrating protein-PTC association results, we quantified the extent to which immune cells mediated causal effects of proteins on PTC, as summarized in Table 3 . Table 3 Mediation Effect of the ITGB2-IL2RB-NK cell Regulatory Axis in PTC. Exposure Mediator Proportion mediated beta_all beta1 beta2 beta12/beta_all ITGB2 IL2RB 0.215 0.525 0.255 62.3% IL2RB NK AC 0.255 0.187 0.329 24.1% Discussion This study systematically deciphered the genetic regulatory network of the ITGB2-IL2RB-NK cell axis in papillary thyroid carcinoma (PTC) through multi-stage integrative analyses. This "genetic-protein-phenotype" multidimensional evidence chain not only revealed the tumor-promoting role and mechanisms of the ITGB2-IL2RB-NK cell axis in PTC but also provided a theoretical foundation for targeted precision therapy. Protein-protein interaction (PPI) networks play a pivotal role in PTC pathogenesis. Previous studies employing high-throughput proteomics and bioinformatics approaches have identified differentially expressed proteins and their interactions in PTC [ ][ ] . In this study, we found that proteins such as integrin β2 (ITGB2) and IL-2 receptor β (IL2RB) exerted critical functions during PTC development. ITGB2 promotes cancer cell invasion and lymph node metastasis via integrin signaling pathways, with its expression being closely associated with pro-inflammatory states in the tumor microenvironment. Meanwhile, IL2RB modulates IL-2 signaling to regulate immune responses of NK cells and T cells [ ] . The interplay between these proteins forms a complex network that may either suppress tumors or promote progression under specific conditions. For instance, ITGB2 overexpression induces extracellular matrix (ECM) remodeling to enhance cancer cell migration [ ] , whereas IL2RB activation alters immune microenvironment homeostasis through NK cells or regulatory T cells (Tregs) [ ][ ] . Therefore, deconstructing PPI networks facilitates the identification of potential biomarkers and provides novel therapeutic strategies, such as developing small-molecule inhibitors or immunomodulatory approaches targeting the ITGB2-IL2RB axis. Natural killer (NK) cells exhibit dual pro- and anti-tumor roles in PTC, contingent upon microenvironmental conditions. In early-stage PTC or immunocompetent patients, NK cells eliminate tumor cells through direct cytotoxicity or antibody-dependent cellular cytotoxicity (ADCC). Additionally, interferon-γ (IFN-γ) secreted by NK cells enhances antigen presentation and potentiates T cell-mediated anti-tumor responses, demonstrating tumor-suppressive functions. However, under chronic inflammation or advanced PTC, inhibitory factors in the tumor microenvironment drive NK cell exhaustion [ ] . These exhausted NK cells may switch to secreting pro-inflammatory cytokines, thereby promoting angiogenesis and stromal remodeling. Notably, interactions between NK cells and cancer-associated fibroblasts (CAFs) exacerbate fibrosis, forming immune-excluded barriers that reinforce pro-tumorigenic NK cell functions [ ] . Therapeutic strategies targeting NK cells require precise modulation, such as combining PD-1 inhibitors to reverse exhaustion or utilizing IL-15 superagonists to enhance cytotoxicity. Concurrently, inhibiting pro-inflammatory axes may restore NK cell anti-tumor activity. Elucidating this dual role provides refined immunotherapeutic strategies for PTC. The mechanism of the ITGB2-IL2RB-NK cell axis in papillary thyroid carcinoma (PTC) involves multi-level regulation, primarily centered on pro-inflammatory microenvironment formation and immune dysfunction. As a member of the integrin family, ITGB2 mediates cell adhesion and signaling to influence immune cell recruitment and activation. Studies demonstrate that tumor microenvironments with high ITGB2 expression exhibit significantly increased immune cell infiltration. IL2RB, a critical component of the IL-2 receptor, may be regulated by ITGB2-dependent cell adhesion signals. ITGB2 potentially enhances STAT5 phosphorylation via FAK/SRC pathway activation, where STAT5 serves as a key transcriptional regulator of IL2RB. Additionally, ITGB2 may indirectly upregulate IL2RB surface expression on immune cells through exosome-mediated delivery of pro-inflammatory factors [ ] .IL2RB is a shared subunit of IL-2 and IL-15 receptors, essential for NK cell proliferation and survival. Upon IL-2/IL-15 binding to IL2RB, the JAK-STAT pathway activates proliferation-related genes, thereby promoting NK cell expansion. In PTC, elevated IL2RB expression may drive excessive NK cell proliferation, yet their function becomes dysregulated due to inhibitory factors in the tumor microenvironment [ ][ ] . This "quantity increase but functional exhaustion" phenomenon correlates with chronic inflammatory states, where persistent IL-2 signaling may induce inhibitory receptor expression on NK cells, impairing cytotoxicity. Although NK cells theoretically exert anti-tumor effects, their function may be reprogrammed into a pro-inflammatory phenotype in the PTC-specific microenvironment. For instance, activated NK cells secrete interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α), further recruiting macrophages and myeloid-derived suppressor cells (MDSCs) to form a feedback loop that exacerbates inflammation. Furthermore, tumor-associated NK cells may promote angiogenesis via growth factor secretion or facilitate invasion through matrix metalloproteinases (MMPs). This mechanism explains why increased NK cell abundance in PTC patients associates with poor prognosis—NK cells themselves are not inherently pro-tumorigenic, but the microenvironment "hijacks" their function to serve tumor progression [ ] .The ITGB2-IL2RB-NK cell axis collectively drives PTC progression through integrin-mediated immune cell activation, IL-2-dependent NK cell expansion, and inflammatory microenvironment reprogramming. Targeting this axis may represent a novel therapeutic direction. The molecular mechanisms of PTC involve complex protein interaction networks and imbalanced immune microenvironment regulation. Our study first revealed the critical role of the ITGB2-IL2RB-NK cell axis in PTC, which constitutes the first report in this field. The breakthrough lies in integrating protein interaction networks with tumor immune regulation, uncovering the novel mechanism by which ITGB2 modulates NK cells via IL2RB. In PTC, the protein interaction network, ITGB2-IL2RB-NK axis, and NK cell functional regulation collectively form a dynamic protein-immune-tumor interaction system whose disrupted balance determines tumor outcomes. These findings not only expand our understanding of PTC pathogenesis but also provide a critical foundation for developing precision therapies targeting tumor microenvironment modulation. The strengths of this study lie in its multi-level and multi-dimensional analytical strategy. First, we employed an integrated approach combining Mendelian randomization (MR), mediation analysis, and bioinformatics to construct a comprehensive "gene-protein-immune phenotype" evidence chain, thereby enhancing the robustness and reliability of the findings. Second, the utilization of data from multiple large-scale databases—including the deCODE database, UK Biobank Pharma Proteomics Project (UKB-PPP), GWAS Catalog, and FinnGen consortium—ensured broad applicability and representativeness of the discoveries. Additionally, rigorous sensitivity analyses were implemented to minimize potential biases and strengthen result validity. Finally, this study first revealed the tumor-promoting role of the ITGB2-IL2RB-NK cell axis in PTC, an innovative discovery that provides a novel theoretical foundation for developing precision therapies targeting tumor microenvironment modulation, with significant clinical implications. Despite these advancements, several limitations should be acknowledged. First, the data primarily originated from European populations. While this ensures reliability and representativeness within this demographic, it may limit generalizability to other ethnic and geographic groups. Future studies should validate these findings in diverse populations to confirm their universal applicability. Second, causal inferences were predominantly derived from bioinformatics and MR analyses. Given the complexity of dynamic protein interactions across biological states, heterogeneity remains inevitable. However, our MR analyses passed stringent sensitivity tests, indicating that causal estimates were robust to horizontal pleiotropy. Furthermore, core interacting genes demonstrated significant co-expression in differential expression analyses, reinforcing their biological relevance. Third, although rigorous sensitivity analyses were applied to reduce bias, in vitro and in vivo experimental validations are warranted to elucidate specific molecular mechanisms. Additionally, while MR provides evidence for causality, results might be influenced by residual confounding. Despite strict covariate adjustments, residual effects cannot be entirely excluded. Lastly, this study focused on the ITGB2-IL2RB-NK cell axis, but its mechanisms may involve interactions with additional molecules and pathways. Future investigations should explore these potential networks to comprehensively decipher the genetic regulation of protein-immune microenvironment interactions in PTC. Conclusion This study first revealed the tumor-promoting role of the ITGB2-IL2RB-NK cell axis in papillary thyroid carcinoma (PTC) through multi-stage integrative analyses. By integrating pQTL data, GWAS data, Mendelian randomization (MR), and bioinformatics approaches, we established a comprehensive "gene-protein-immune phenotype" multi-dimensional evidence chain, providing novel insights into PTC pathogenesis. This discovery not only enriches the theoretical understanding of dysregulated protein interaction networks and immune microenvironment remodeling in cancer but also lays a critical theoretical foundation for precision therapies targeting tumor microenvironment modulation.Future research should focus on validating the specific molecular mechanisms of this regulatory axis and exploring its potential roles in other cancer types, thereby expanding therapeutic possibilities. Furthermore, developing inhibitors targeting ITGB2 and IL2RB may open new avenues for PTC treatment, holding significant promise 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 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. Consent to Participate declaration Not applicable. Consent to Publish declaration Not applicable. References Boucai L,Zafereo M,Cabanillas ME. Thyroid Cancer: A Review. JAMA. 2024;331 (5):425-435. doi:10.1001/jama.2023.26348 Krishnan A,Berthelet J,Renaud E, et al. Proteogenomics analysis unveils a TFG-RET gene fusion and druggable targets in papillary thyroid carcinomas. Nat Commun. 2020;11 (1):2056. doi:10.1038/s41467-020-15955-w Sabins NC,Harman BC,Barone LR, et al. 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Int J Endocrinol. 2017;2017:8471235. doi:10.1155/2017/8471235 Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviews received at journal 10 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers invited by journal 13 Aug, 2025 Editor assigned by journal 26 Jul, 2025 Submission checks completed at journal 26 Jul, 2025 First submitted to journal 25 Jul, 2025 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. 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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-7218110","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502502258,"identity":"d216f935-d6da-4cd3-a23e-a65ea889548c","order_by":0,"name":"Jianxiong Xu","email":"","orcid":"","institution":"The First Hospital of Putian City","correspondingAuthor":false,"prefix":"","firstName":"Jianxiong","middleName":"","lastName":"Xu","suffix":""},{"id":502502259,"identity":"259a11a8-4ecb-46c3-9269-036ab4895578","order_by":1,"name":"CHEN GAO","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYLACCYaEejb25oMPEipqiNLA2ADUksDHcyzZ4MGZY0RqYQBqkZPIMZN82MJMWL28++HjDyzb0vLYGBLMKhIb2Bj427sT8GoxPJOW2CDZllPMxnAg7UbiDhkGiTNnN+DX0pBj2CC5rYKxjbHh2I3EM2wMBhK5BLT0v4FqYWZsK0hsYyasRV4CbEtOYhsbMxsDUVoMJJ4lzpD8l2bMxsPGLJFw5hgPQb/I9ycf+CxxJllOfv77jx9/VNTI8bf3ErDlAAMDswSSAA9e5WBbGoBx+YGgslEwCkbBKBjRAAAzq0rQQD8CxgAAAABJRU5ErkJggg==","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"CHEN","middleName":"","lastName":"GAO","suffix":""}],"badges":[],"createdAt":"2025-07-26 03:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7218110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7218110/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89592675,"identity":"465b5903-ea03-44a2-b1ed-73e008315b5b","added_by":"auto","created_at":"2025-08-21 16:12:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56667,"visible":true,"origin":"","legend":"\u003cp\u003eStudy workflow. MR, mendelian randomization; pQTL, protein quantitative trait locus.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7218110/v1/82bd7b0c0cb325b53cf098d2.jpg"},{"id":89592676,"identity":"2dd3901b-3493-424d-b098-74284b321718","added_by":"auto","created_at":"2025-08-21 16:12:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41109,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of mediation analysis for regulatory axis screening.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7218110/v1/20b145c25ee7ba26ed26f1fb.jpg"},{"id":89593433,"identity":"1198b8f3-6561-47e3-8c4f-f4b80c7dc164","added_by":"auto","created_at":"2025-08-21 16:20:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76015,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Expression and Co-expression Patterns of the ITGB2-IL2RB Regulatory Axis in PTC.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7218110/v1/f7b58cd10b3ea31ee04db90b.jpg"},{"id":89594377,"identity":"fab7be9d-38bb-4688-b83c-4224df68979f","added_by":"auto","created_at":"2025-08-21 16:28:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1012832,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7218110/v1/9e9f9053-193b-4789-b381-5f692e8018bf.pdf"},{"id":89592683,"identity":"32fb227d-133b-40b9-8dfe-818557264083","added_by":"auto","created_at":"2025-08-21 16:12:53","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":48171,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7218110/v1/efdf5f832fde35f492324810.xlsx"},{"id":89593436,"identity":"696ce1e9-02d0-4056-874c-364e40161434","added_by":"auto","created_at":"2025-08-21 16:20:53","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":565079,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7218110/v1/f0a56ba279b735ea628250b6.xlsx"},{"id":89592686,"identity":"9a1a10c3-293d-4a7d-8f45-5fd3276af5e9","added_by":"auto","created_at":"2025-08-21 16:12:53","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":60694,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7218110/v1/23cbf5b5c4bf26d5df035452.xlsx"},{"id":89593440,"identity":"b8673e00-ec77-4584-a42e-ed0df3f57146","added_by":"auto","created_at":"2025-08-21 16:20:53","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":46471,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7218110/v1/d1a886be9b86c8720ec09392.xlsx"},{"id":89594376,"identity":"5900a2ba-2814-4288-8b18-4e76d55dd761","added_by":"auto","created_at":"2025-08-21 16:28:53","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":950379,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7218110/v1/dd184f2484003ad952ec4ac9.xlsx"},{"id":89592694,"identity":"5843f46f-9887-4a32-bdb6-e33b56505573","added_by":"auto","created_at":"2025-08-21 16:12:53","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":608368,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7218110/v1/ce75911018f96480dcc3652e.xlsx"},{"id":89593442,"identity":"df3be008-0bed-4f43-92e3-94ac43d1b1c4","added_by":"auto","created_at":"2025-08-21 16:20:53","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":14325,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7218110/v1/e7f1e087c946070f1b23a53d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated multi-omics reveals the ITGB2-IL2RB-NK axis in promoting papillary thyroid carcinoma progression and immune microenvironment crosstalk","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePapillary thyroid carcinoma (PTC) is the most common thyroid malignancy, accounting for approximately 80%-85% of all thyroid cancers. Although most PTC patients have favorable prognoses, a subset of cases exhibit aggressive characteristics such as local invasion, metastasis, and resistance to radioiodine therapy. Current standard therapies demonstrate limited efficacy in advanced cases, highlighting the urgent need to elucidate the pathogenesis of PTC\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Despite the identification of high-frequency driver genetic alterations, critical knowledge gaps persist in understanding its molecular basis.\u003c/p\u003e\u003cp\u003eRecent studies have revealed that dysregulated protein interaction networks and immune microenvironment remodeling are closely associated with the initiation and progression of PTC, yet the regulatory networks remain undefined. Protein interaction networks play critical roles in PTC by influencing tumor growth, invasion, and apoptosis\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Their dysregulation is linked to abnormal cellular proliferation, migration, and apoptosis, and contributes to tumor microenvironment modulation\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Deciphering these networks could facilitate the discovery of molecular mechanisms underlying PTC, identify potential therapeutic targets, and provide novel strategies to improve patient outcomes. Integrating proteomics with genetic studies may deepen our understanding of PTC complexity and advance therapeutic development.\u003c/p\u003e\u003cp\u003eIn recent studies, the immune microenvironment has been identified as a pivotal determinant of PTC progression and clinical outcomes\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Key components of this interaction include diverse immune cell types that interact with tumor cells to regulate immune responses. Proteins and receptors involved in immune regulation are essential for modulating the immune microenvironment in PTC. The impact of key protein-immune cell regulatory axes within the immune microenvironment on PTC is profound, as they not only dictate tumor behavior and progression but also influence therapeutic outcomes\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Dissecting the intricate interplay between tumor cells and the immune environment may uncover novel therapeutic targets and strategies to enhance anti-thyroid cancer immunity. Understanding these dynamics could pave the way for developing more effective immunotherapies and personalized approaches to improve PTC management.\u003c/p\u003e\u003cp\u003eMendelian randomization (MR) addresses confounding biases and reverse causation in observational studies by utilizing genetic variants as instrumental variables, enabling robust inference of causal relationships between specific proteins and diseases\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Mediation analysis further dissects whether such effects are direct or mediated indirectly through other proteins. The integration of these approaches is particularly suited for investigating protein interaction networks, as it allows differentiation between direct and indirect causal pathways, identification of key mediator proteins, and discovery of therapeutic targets\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e\u003csup\u003e][\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn8\" id=\"#FNLinkFn8\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. This combined strategy provides a powerful analytical framework for unraveling disease mechanisms and developing precision therapies. Together, these methods not only delineate exposure-outcome causal pathways but also elucidate underlying mechanisms, thereby decoding the \"gene-protein-phenotype\" cascade.\u003c/p\u003e\u003cp\u003eThrough a multi-stage integrative analysis, this study systematically dissected the genetic regulatory network connecting proteins and the immune microenvironment in PTC. First, we screened proteins significantly associated with PTC risk using pQTL data from the Icelandic deCODE cohort and PTC GWAS data from the Finnish FinnGen database. Second, upstream regulatory factors were identified by integrating UK Biobank pQTL data with FinnGen GWAS. Subsequently, a two-step mediation analysis confirmed a critical protein regulatory axis (ITGB2-IL2RB). Further mediation analysis revealed that IL2RB influences PTC through downstream NK cells. Our study demonstrated the pivotal role of the ITGB2-IL2RB-NK cell axis in PTC. Differential expression and correlation analyses using TCGA and GTEx databases further validated this regulatory relationship. This multidimensional \"gene-protein-immune phenotype\" evidence chain not only elucidates the role of the ITGB2-IL2RB-NK cell axis in PTC but also provides a theoretical foundation for targeted precision therapy.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Methodology","content":"\u003ch2\u003e2.1 Study Design\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e visually illustrates the overall study flowchart. Briefly, we conducted two-sample Mendelian randomization (MR) analyses using pQTL data from the deCODE and UKB-PP databases as exposures and PTC genome-wide association study (GWAS) data as outcomes to investigate causal relationships between proteins and PTC. Subsequently, molecular regulatory axes were screened through mediation analysis and further validated via bioinformatics and correlation analyses. Next, downstream mechanistic exploration of these regulatory axes was performed using mediation analysis, ultimately confirming the genetic regulatory network connecting proteins and the immune microenvironment. Throughout the analysis, single nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) under strict inclusion/exclusion criteria, and a series of sensitivity analyses were conducted to ensure MR quality. Our analytical strategy adhered to rigorous ethical standards, as all data used in this study had obtained ethical approvals and participant consents in their original studies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003e2.2 Data Sources\u003c/h2\u003e\u003cp\u003eProteomic data were obtained from two sources: (1) pQTL data for the Icelandic population from the deCODE database (\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)\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn9\" id=\"#FNLinkFn9\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e; (2) plasma proteome pQTL data from the UK Biobank Pharma Proteomics Project (UKB-PPP) (\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)\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn10\" id=\"#FNLinkFn10\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Immunological trait data (accession numbers: GCST0001391-GCST0002121) were retrieved from the GWAS Catalog, comprising summary statistics for 731 immune features: absolute counts (AC, n = 118), median fluorescence intensity (MFI, n = 389), morphological parameters (MP, n = 32), and relative counts (RC, n = 192). A complete list of immune traits is provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn11\" id=\"#FNLinkFn11\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. PTC GWAS statistics were derived from the FinnGen consortium R10 release, including 1,472 cases and 314,193 controls.\u003c/p\u003e\u003ch2\u003e2.3 Instrumental Variable (IV) Selection\u003c/h2\u003e\u003cp\u003eAccording to MR assumptions\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn12\" id=\"#FNLinkFn12\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e, we implemented a strict selection process for SNPs in each gene. First, SNPs were selected using a stringent and uniform threshold to ensure only those with P-values below the genome-wide significance threshold (5.0 × 10⁻⁸) were considered. Next, to obtain a set of independent SNPs, we performed clumping based on 1000 Genomes Project European population data for each gene, setting the linkage disequilibrium (LD) threshold to r² \u0026lt; 0.1 with a clumping window of 10,000 kb\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn13\" id=\"#FNLinkFn13\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Third, SNPs with incompatible alleles between exposure and outcome were excluded. Palindromic SNPs were resolved using allele frequency information to infer forward-strand alleles; those without allele frequency data were directly excluded. Additionally, SNPs with F-statistics \u0026lt; 10 were removed to avoid weak instrument bias\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn14\" id=\"#FNLinkFn14\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003e2.4 Mendelian Randomization Analysis, Sensitivity Analysis, and Directionality Testing\u003c/h2\u003e\u003cp\u003eThis study performed two-sample MR analyses using the TwoSampleMR package. We utilized the inverse-variance weighted (IVW) method\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn15\" id=\"#FNLinkFn15\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e as the primary algorithm and excluded exposures for which IVW results could not be calculated. Furthermore, we applied the MR-Egger intercept test\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn16\" id=\"#FNLinkFn16\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e to detect pleiotropy and Cochran’s Q test\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn17\" id=\"#FNLinkFn17\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e to assess heterogeneity. Simultaneously, Steiger testing was conducted to evaluate potential bias, aiming to enhance study reliability and minimize bias caused by reverse causation.\u003c/p\u003e\u003ch2\u003e2.5 Bioinformatics Analysis\u003c/h2\u003e\u003cp\u003eWe queried differential expression data for PTC in TCGA and GTEx databases using 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), with thresholds set as absolute logFC \u0026gt; 0.585 (1.5-fold difference) and FDR-adjusted P \u0026lt; 0.05. Validation of differential expression in PTC further confirmed our conclusions. Additionally, we employed the correlation analysis module in GEPIA2 to further verify and refine our interaction network.\u003c/p\u003e\u003ch2\u003e2.6 Mediation Analysis\u003c/h2\u003e\u003cp\u003eUpstream analysis: We applied a two-step MR 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 to explore protein regulatory networks. First, we calculated the effect of upstream proteins on disease, denoted as beta_all. Second, we calculated the effect of upstream proteins on downstream proteins (beta1) and the effect of downstream proteins on disease (beta2). Finally, the mediated effect (beta12) was calculated as beta1 multiplied by beta2, and the mediation proportion (beta12_p) was derived as (beta12 / beta_all) × 100%. Results with mediation proportions exceeding 20% were selected for subsequent investigation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eDownstream analysis: We employed mediation analysis to explore potential downstream mechanisms of proteins. First, we calculated the effect of proteins on PTC (beta_all). Second, we calculated the effect of proteins on immune cells (beta1) and the effect of immune cells on PTC (beta2). Finally, the mediated effect (beta12) was calculated as beta1 multiplied by beta2, and the mediation proportion (beta12_p) was derived as (beta12 / beta_all) × 100%. Results with mediation proportions exceeding 20% were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Identification of Differentially Expressed Genes\u003c/h2\u003e\u003cp\u003eBy querying differential expression data from TCGA and GTEx databases with thresholds of absolute log2FC\u0026thinsp;\u0026gt;\u0026thinsp;0.585 (1.5-fold change) and adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, we obtained 8,419 differentially expressed genes (details in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Screening of Outcome-Associated Molecules\u003c/h2\u003e\u003cp\u003eMendelian randomization (MR) analysis was performed using the inverse-variance weighted (IVW) method as the primary approach, with pQTLs from the deCODE database as exposures, to screen proteins causally associated with PTC (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Based on the consistency between the directional effects of pQTLs on PTC and the differential expression data, 93 outcome-associated molecules were ultimately identified.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Screening of Upstream Molecules\u003c/h2\u003e\u003cp\u003eUsing the UKB-PPP database, MR analysis was conducted to screen associations between UKB-PPP pQTLs and PTC (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Combined with differential expression analysis, 53 molecules were initially identified. Subsequently, MR analysis was applied to identify upstream regulatory molecules for outcome-associated proteins in the deCODE database, yielding 1,595 causal molecule pairs (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Mediation Analysis and Bioinformatics Validation of Molecular Regulatory Axes\u003c/h2\u003e\u003cp\u003eMediation analysis was employed to further screen candidate molecules by calculating mediation effects and proportions (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). A total of 1,595 mediator pairs were identified, with those exhibiting stronger mediation effects selected for downstream analysis. Differential expression and correlation analyses were then performed to validate candidate molecules. Ultimately, the ITGB2-IL2RB regulatory axis was confirmed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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 PTC\u0026ndash;Associated Proteins and Upstream/Downstream 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\u003eIL2RB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\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\u003e0.0115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.05,1.57)\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\u003eITGB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\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\u003e0.0172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.04,1.48)\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\u003edeCODE\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\u003eITGB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIL2RB\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\u003e0.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.45,1.97)\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\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSensitivity Analysis and Directionality Test of Causal Relationships in Mendelian Randomization for PTC\u0026ndash;Associated Proteins and Upstream/Downstream Regulators.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSteiger direction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSteiger P value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHeterogeneity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePleiotropy\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\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL2RB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers3184504\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\u003e1.47e-88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.083\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\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eITGB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers11574639\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\u003e3.10e-49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.307\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\u003edeCODE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eITGB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIL2RB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers55714927\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\u003e7.39e-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.22e-132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.163\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=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Mediation Analysis of Downstream Mechanisms for the ITGB2-IL2RB Axis\u003c/h2\u003e\u003cp\u003eTo investigate downstream mechanisms, mediation analysis was conducted by calculating the proportion of immune cell phenotypes mediating the effect between proteins and PTC. First, MR analysis identified immune cell phenotypes associated with PTC. Next, MR analysis was applied to assess relationships between the IL2RB protein and these immune cell phenotypes (Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Finally, integrating protein-PTC association results, we quantified the extent to which immune cells mediated causal effects of proteins on PTC, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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 ITGB2-IL2RB-NK cell 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=\"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\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\u003ebeta_all\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ebeta1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ebeta2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ebeta12/beta_all\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eITGB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIL2RB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL2RB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNK AC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.1%\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 deciphered the genetic regulatory network of the ITGB2-IL2RB-NK cell axis in papillary thyroid carcinoma (PTC) through multi-stage integrative analyses. This \"genetic-protein-phenotype\" multidimensional evidence chain not only revealed the tumor-promoting role and mechanisms of the ITGB2-IL2RB-NK cell axis in PTC but also provided a theoretical foundation for targeted precision therapy.\u003c/p\u003e\u003cp\u003eProtein-protein interaction (PPI) networks play a pivotal role in PTC pathogenesis. Previous studies employing high-throughput proteomics and bioinformatics approaches have identified differentially expressed proteins and their interactions in PTC\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. In this study, we found that proteins such as integrin β2 (ITGB2) and IL-2 receptor β (IL2RB) exerted critical functions during PTC development. ITGB2 promotes cancer cell invasion and lymph node metastasis via integrin signaling pathways, with its expression being closely associated with pro-inflammatory states in the tumor microenvironment. Meanwhile, IL2RB modulates IL-2 signaling to regulate immune responses of NK cells and T cells\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn22\" id=\"#FNLinkFn22\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. The interplay between these proteins forms a complex network that may either suppress tumors or promote progression under specific conditions. For instance, ITGB2 overexpression induces extracellular matrix (ECM) remodeling to enhance cancer cell migration\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn23\" id=\"#FNLinkFn23\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e, whereas IL2RB activation alters immune microenvironment homeostasis through NK cells or regulatory T cells (Tregs)\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn24\" id=\"#FNLinkFn24\"\u003e\u003c/a\u003e\u003csup\u003e][\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn25\" id=\"#FNLinkFn25\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Therefore, deconstructing PPI networks facilitates the identification of potential biomarkers and provides novel therapeutic strategies, such as developing small-molecule inhibitors or immunomodulatory approaches targeting the ITGB2-IL2RB axis.\u003c/p\u003e\u003cp\u003eNatural killer (NK) cells exhibit dual pro- and anti-tumor roles in PTC, contingent upon microenvironmental conditions. In early-stage PTC or immunocompetent patients, NK cells eliminate tumor cells through direct cytotoxicity or antibody-dependent cellular cytotoxicity (ADCC). Additionally, interferon-γ (IFN-γ) secreted by NK cells enhances antigen presentation and potentiates T cell-mediated anti-tumor responses, demonstrating tumor-suppressive functions. However, under chronic inflammation or advanced PTC, inhibitory factors in the tumor microenvironment drive NK cell exhaustion\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn26\" id=\"#FNLinkFn26\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. These exhausted NK cells may switch to secreting pro-inflammatory cytokines, thereby promoting angiogenesis and stromal remodeling. Notably, interactions between NK cells and cancer-associated fibroblasts (CAFs) exacerbate fibrosis, forming immune-excluded barriers that reinforce pro-tumorigenic NK cell functions\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn27\" id=\"#FNLinkFn27\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. Therapeutic strategies targeting NK cells require precise modulation, such as combining PD-1 inhibitors to reverse exhaustion or utilizing IL-15 superagonists to enhance cytotoxicity. Concurrently, inhibiting pro-inflammatory axes may restore NK cell anti-tumor activity. Elucidating this dual role provides refined immunotherapeutic strategies for PTC.\u003c/p\u003e\u003cp\u003eThe mechanism of the ITGB2-IL2RB-NK cell axis in papillary thyroid carcinoma (PTC) involves multi-level regulation, primarily centered on pro-inflammatory microenvironment formation and immune dysfunction. As a member of the integrin family, ITGB2 mediates cell adhesion and signaling to influence immune cell recruitment and activation. Studies demonstrate that tumor microenvironments with high ITGB2 expression exhibit significantly increased immune cell infiltration. IL2RB, a critical component of the IL-2 receptor, may be regulated by ITGB2-dependent cell adhesion signals. ITGB2 potentially enhances STAT5 phosphorylation via FAK/SRC pathway activation, where STAT5 serves as a key transcriptional regulator of IL2RB. Additionally, ITGB2 may indirectly upregulate IL2RB surface expression on immune cells through exosome-mediated delivery of pro-inflammatory factors\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn28\" id=\"#FNLinkFn28\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e.IL2RB is a shared subunit of IL-2 and IL-15 receptors, essential for NK cell proliferation and survival. Upon IL-2/IL-15 binding to IL2RB, the JAK-STAT pathway activates proliferation-related genes, thereby promoting NK cell expansion. In PTC, elevated IL2RB expression may drive excessive NK cell proliferation, yet their function becomes dysregulated due to inhibitory factors in the tumor microenvironment\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn29\" id=\"#FNLinkFn29\"\u003e\u003c/a\u003e\u003csup\u003e][\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn30\" id=\"#FNLinkFn30\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. This \"quantity increase but functional exhaustion\" phenomenon correlates with chronic inflammatory states, where persistent IL-2 signaling may induce inhibitory receptor expression on NK cells, impairing cytotoxicity. Although NK cells theoretically exert anti-tumor effects, their function may be reprogrammed into a pro-inflammatory phenotype in the PTC-specific microenvironment. For instance, activated NK cells secrete interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α), further recruiting macrophages and myeloid-derived suppressor cells (MDSCs) to form a feedback loop that exacerbates inflammation. Furthermore, tumor-associated NK cells may promote angiogenesis via growth factor secretion or facilitate invasion through matrix metalloproteinases (MMPs). This mechanism explains why increased NK cell abundance in PTC patients associates with poor prognosis\u0026mdash;NK cells themselves are not inherently pro-tumorigenic, but the microenvironment \"hijacks\" their function to serve tumor progression\u003csup\u003e[\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn31\" id=\"#FNLinkFn31\"\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e.The ITGB2-IL2RB-NK cell axis collectively drives PTC progression through integrin-mediated immune cell activation, IL-2-dependent NK cell expansion, and inflammatory microenvironment reprogramming. Targeting this axis may represent a novel therapeutic direction.\u003c/p\u003e\u003cp\u003eThe molecular mechanisms of PTC involve complex protein interaction networks and imbalanced immune microenvironment regulation. Our study first revealed the critical role of the ITGB2-IL2RB-NK cell axis in PTC, which constitutes the first report in this field. The breakthrough lies in integrating protein interaction networks with tumor immune regulation, uncovering the novel mechanism by which ITGB2 modulates NK cells via IL2RB. In PTC, the protein interaction network, ITGB2-IL2RB-NK axis, and NK cell functional regulation collectively form a dynamic protein-immune-tumor interaction system whose disrupted balance determines tumor outcomes. These findings not only expand our understanding of PTC pathogenesis but also provide a critical foundation for developing precision therapies targeting tumor microenvironment modulation.\u003c/p\u003e\u003cp\u003eThe strengths of this study lie in its multi-level and multi-dimensional analytical strategy. First, we employed an integrated approach combining Mendelian randomization (MR), mediation analysis, and bioinformatics to construct a comprehensive \"gene-protein-immune phenotype\" evidence chain, thereby enhancing the robustness and reliability of the findings. Second, the utilization of data from multiple large-scale databases\u0026mdash;including the deCODE database, UK Biobank Pharma Proteomics Project (UKB-PPP), GWAS Catalog, and FinnGen consortium\u0026mdash;ensured broad applicability and representativeness of the discoveries. Additionally, rigorous sensitivity analyses were implemented to minimize potential biases and strengthen result validity. Finally, this study first revealed the tumor-promoting role of the ITGB2-IL2RB-NK cell axis in PTC, an innovative discovery that provides a novel theoretical foundation for developing precision therapies targeting tumor microenvironment modulation, with significant clinical implications.\u003c/p\u003e\u003cp\u003eDespite these advancements, several limitations should be acknowledged. First, the data primarily originated from European populations. While this ensures reliability and representativeness within this demographic, it may limit generalizability to other ethnic and geographic groups. Future studies should validate these findings in diverse populations to confirm their universal applicability. Second, causal inferences were predominantly derived from bioinformatics and MR analyses. Given the complexity of dynamic protein interactions across biological states, heterogeneity remains inevitable. However, our MR analyses passed stringent sensitivity tests, indicating that causal estimates were robust to horizontal pleiotropy. Furthermore, core interacting genes demonstrated significant co-expression in differential expression analyses, reinforcing their biological relevance. Third, although rigorous sensitivity analyses were applied to reduce bias, in vitro and in vivo experimental validations are warranted to elucidate specific molecular mechanisms. Additionally, while MR provides evidence for causality, results might be influenced by residual confounding. Despite strict covariate adjustments, residual effects cannot be entirely excluded. Lastly, this study focused on the ITGB2-IL2RB-NK cell axis, but its mechanisms may involve interactions with additional molecules and pathways. Future investigations should explore these potential networks to comprehensively decipher the genetic regulation of protein-immune microenvironment interactions in PTC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study first revealed the tumor-promoting role of the ITGB2-IL2RB-NK cell axis in papillary thyroid carcinoma (PTC) through multi-stage integrative analyses. By integrating pQTL data, GWAS data, Mendelian randomization (MR), and bioinformatics approaches, we established a comprehensive \u0026quot;gene-protein-immune phenotype\u0026quot; multi-dimensional evidence chain, providing novel insights into PTC pathogenesis. This discovery not only enriches the theoretical understanding of dysregulated protein interaction networks and immune microenvironment remodeling in cancer but also lays a critical theoretical foundation for precision therapies targeting tumor microenvironment modulation.Future research should focus on validating the specific molecular mechanisms of this regulatory axis and exploring its potential roles in other cancer types, thereby expanding therapeutic possibilities. Furthermore, developing inhibitors targeting ITGB2 and IL2RB may open new avenues for PTC treatment, holding significant promise for clinical translation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\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 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\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\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\u003e\u003cstrong\u003eEthics and Consent Statements\u003c/strong\u003e\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\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\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\u003e\u003cstrong\u003eClinical Trial Registration Statement \u003c/strong\u003e\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\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\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\u003e\u003cstrong\u003eConsent to Participate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBoucai L,Zafereo M,Cabanillas ME. Thyroid Cancer: A Review. 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The Immune Interplay between Thyroid Papillary Carcinoma and Hepatic Fibrosis. PLoS One. 2015;10 (7):e0132463. doi:10.1371/journal.pone.0132463\u003c/li\u003e\n\u003cli\u003eZhou M,Feng Y,Zhang X, et al. Platelet-derived microparticles adoptively transfer integrin \u0026beta;3 to promote antitumor effect of tumor-infiltrating T cells. Oncoimmunology. 2024;13 (1):2304963. doi:10.1080/2162402X.2024.2304963\u003c/li\u003e\n\u003cli\u003eWong VA,Dinh KN,Chen G, et al. IL-2R\u0026alpha; KO mice exhibit maternal microchimerism and reveal nuclear localization of IL-2R\u0026alpha; in lymphoid and non-lymphoid cells. Front Immunol. 2024;15:1369818. doi:10.3389/fimmu.2024.1369818\u003c/li\u003e\n\u003cli\u003eFernandez IZ,Baxter RM,Garcia-Perez JE, et al. A novel human IL2RB mutation results in T and NK cell-driven immune dysregulation. J Exp Med. 2019;216 (6):1255-1267. doi:10.1084/jem.20182015\u003c/li\u003e\n\u003cli\u003eManatakis DK,Tseleni-Balafouta S,Balalis D, et al. Association of Baseline Neutrophil-to-Lymphocyte Ratio with Clinicopathological Characteristics of Papillary Thyroid Carcinoma. Int J Endocrinol. 2017;2017:8471235. doi:10.1155/2017/8471235\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7218110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7218110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eIntroduction\u003c/b\u003e: Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with some cases exhibiting aggressive features and therapeutic resistance. Although dysregulated protein interaction networks and immune microenvironment remodeling have been increasingly recognized as critical in PTC progression, the specific genetic-protein-immune regulatory networks remain to be systematically elucidated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethodology\u003c/b\u003e: This study adopted a multi-stage integrated analysis strategy combining Mendelian randomization (MR), mediation analysis, and bioinformatics validation. First, MR was applied using pQTL data from the deCODE and UKB-PP databases alongside PTC GWAS data from FinnGen to screen PTC-associated proteins. Second, a two-step mediation analysis was conducted to construct protein-protein-immune microenvironment regulatory networks. Finally, differential expression and co-expression analyses using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were performed to validate functional relevance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e: We identified the ITGB2-IL2RB-NK cell regulatory axis, which was significantly associated with PTC risk. This regulatory axis demonstrated marked differential expression and co-expression relationships in PTC tissues, confirming its functional relevance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e: This study systematically revealed the tumor-promoting role of the ITGB2-IL2RB-NK cell axis in PTC for the first time, providing a theoretical basis for developing combined immunotherapy strategies targeting the tumor microenvironment.\u003c/p\u003e","manuscriptTitle":"Integrated multi-omics reveals the ITGB2-IL2RB-NK axis in promoting papillary thyroid carcinoma progression and immune microenvironment crosstalk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 16:12:48","doi":"10.21203/rs.3.rs-7218110/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-19T09:45:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-10T14:37:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T21:41:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20799164945493734528691292565946325555","date":"2025-08-27T16:34:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52073293383594682720063455398684494001","date":"2025-08-21T15:55:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-14T01:48:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-26T06:58:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-26T06:57:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-07-26T03:10:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb383729-dff5-4c8c-aeaf-0a396c68e9c1","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T08:38:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-21 16:12:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7218110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7218110","identity":"rs-7218110","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}