The Role of DISP3 in Thyroid Cancer Progression: Implications for Immune Microenvironment Modulation

The Role of DISP3 in Thyroid Cancer Progression: Implications for Immune Microenvironment Modulation | 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 The Role of DISP3 in Thyroid Cancer Progression: Implications for Immune Microenvironment Modulation Yanchu Tong, Wenkui Chen, Shun Deng, Lu Qin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9394485/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 Objective: To investigate the role of DISP3 in thyroid cancer (TC) progression and its modulation of the tumor immune microenvironment. Methods: Bioinformatics analyzed DISP3 expression and its prognostic significance in TC. The biological impact of DISP3 on TC cells was validated through in vitro assays and in vivo BALB/c mice models. Flow cytometry and cytokine assays were performed to evaluate the regulatory mechanism of DISP3 on dendritic cell (DC) maturation and macrophage polarization. Results: DISP3 was overexpressed in TC and significantly correlated with poorer prognosis. Bioinformatics revealed that DISP3 expression negatively correlated with DC and macrophage infiltration. Experimentally, knocking down DISP3 significantly inhibited TC cell proliferation and migration both in vitro and in vivo. Furthermore, DISP3 knockdown enhanced anti-tumor cytokine secretion, promoted DC maturation, and induced a phenotypic shift of macrophages toward the M1 (anti-tumor) phenotype. Conclusion: DISP3 promotes TC progression by modulating the immune microenvironment, specifically by suppressing DC maturation and M1 macrophage polarization. These findings suggest DISP3 as a promising novel target for TC immunotherapy. DISP3 Thyroid cancer Tumor immune microenvironment Macrophage polarization dendritic cell Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Thyroid cancer (TC) is a frequent endocrine malignancy, with a steadily increasing prevalence of differentiated TC (DTC) worldwide over the past three decades(1). Although most patients achieve favorable outcomes with current treatments, about 30% experience disease progression due to recurrence and become unresponsive to current therapies(2). Frequently, TC displays local immune responses, creating a tumor microenvironment (TME) where tumor and host cells coexist(3). The immune cell composition within this microenvironment influences the tumor's clinical aggressiveness(4-6). In cancer immune surveillance, the innate immune response initially targets and eliminates tumor cells, followed by macrophages phagocytosing the cell fragments(7, 8). Dendritic cells (DCs) process neoantigens from these cells, secrete inflammatory cytokines, and present tumor antigens to T cells, thereby facilitating an adaptive immune response(9). This activates and expands T cells and produces tumor-specific antibodies by B cells to eliminate the tumor. However, research into the TME reveals that tumors can re-educate immune cells to support tumor development(10). Reversing this trend is indispensable for tumor immunotherapy success(11). DISP3, part of the Dispatched family, primarily regulates Hedgehog signaling, which is crucial for embryonic development, cell differentiation, and tissue homeostasis(12). Over-activation of Hedgehog signaling is linked to malignancy development, including gastric, pancreatic, basal cell, and prostate cancers(13). Nonetheless, the DISP3 function in TC remains unexplored. Materials and Methods Patient clinical samples Between June and December 2024, 10 paired TC and adjacent non-cancerous tissues were collected from Jingzhou Hospital Affiliated to Yangtze University and maintained at –80 °C. The study was authorized by the hospital's Ethics Committee and complied with their guidelines. Bioinformatics analysis Transcriptomic data (TPM-normalized) and corresponding clinical information for the Thyroid Carcinoma project (TCGA-THCA, n=510) and normal thyroid tissues (n=58) were downloaded from the TCGA database. For validation, the GEO database was utilized to ensure the reproducibility of DISP3 expression patterns. Differential expression analysis between high- and low-DISP3 expression groups (stratified by the median) was performed using the DESeq2 package in R. Significant differentially expressed genes (DEGs) were identified using the criteria of |log2 FoldChange| > 1.5 and adjusted P 1.2, P < 0.05). Functional enrichment was conducted using the clusterProfiler package. Overlapping genes between DISP3-correlated genes (2,197 genes), prognosis-related genes (3,214 genes), and immune-related genes from the ImmPort database (2,271 genes) were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. To quantify the infiltration of 24 types of immune cells, the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm was employed. The correlation between DISP3 expression and immune cell abundance, including dendritic cells (DCs) and macrophages, was assessed using Spearman correlation analysis. Cell culture The culture of BCPAP, FTC133, K-1, and TPC-1 cell lines (Wuhan Institute of Cell Biology, China) was conducted in 1640 complete medium that contained 10% fetal bovine serum in a 5% CO 2 incubator at 37 °C. Pathological sample processing Tumor and adjacent non-cancerous tissues were fixed in 10% neutral buffered formalin, embedded in paraffin, and sectioned into 5μm slices. Following deparaffinization and rehydration through a graded ethanol series, heat-induced antigen retrieval was performed using microwave heating in citrate buffer (pH 6.0). To block non-specific binding, sections were incubated with 5% bovine serum albumin (BSA) and subsequently incubated with primary antibodies at 4 °C overnight. After washing, the sections were treated with horseradish peroxidase (HRP)-conjugated secondary antibodies and visualized using a 3,3'-diaminobenzidine (DAB) substrate. Finally, the slides were counterstained with hematoxylin, dehydrated, and mounted. Total RNA was isolated from tissues or cells using TRIzol reagent according to the manufacturer’s protocol. The concentration and purity of the RNA were determined spectrophotometrically. Complementary DNA (cDNA) was synthesized using a reverse transcription kit, and quantitative real-time PCR (qRT-PCR) was performed using SYBR Green Master Mix. The primer sequences were as follows: DISP3 Forward: 5'-CCAAAGAATGACAGGAACACTGAG-3', Reverse: 5'-CTCATCTACATACCAGTAGAACTCG-3'; GAPDH Forward: 5'-CTGCTCCTCCTGTTCGACAGT-3', Reverse: 5'-CCGTTGACTCCGACCTTCAC-3'. The PCR thermal cycling conditions consisted of an initial denaturation at 95 degrees Celsius for 5 minutes, followed by 40 cycles of 95 degrees Celsius for 15 seconds and 60 degrees Celsius for 30 seconds. Target gene expression levels were normalized to GAPDH and calculated using the 2-Delta Delta Ct method CCK8 assay Cells from each group were digested and resuspended in a complete medium. Cell growth was evaluated at 24, 48, 72, and 96 h by CCK-8 assay, measuring optical density at 450 nm via an enzyme marker. Clone formation assay Two thousand cells were seeded into six-well plates, with half of the medium refreshed daily. After 10 days, cells were collected, blotted to remove the excess medium, fixed in 4% paraformaldehyde for 10 min, and stained using crystal violet. Transwell assay Initially, 10,000 cells were placed in the Transwell system upper chamber (Corning, USA), incubated for 24 h at 37 °C, and stained using crystal violet for 10 min, followed by washing to remove excess stain. The migrated cells were then counted and photographed. Scratch test FTC133, K-1, and TPC-1 TC cells were grown in IBIDI two-well inserts placed in 24-well plates for 24 h. The inserts were removed using forceps, and 1 mL of low serum medium was introduced to each well. The healing process of the scratches was monitored and photographed at the start and 24 h after the inserts were removed. Tumor formation in BALB/c mice Twelve 6-week-old female BALB/c mice (average weight 24 g) were randomly assigned into two groups. Each mouse received an injection of approximately 10 million tumor cells into the left subcutaneous fat pad. Xenograft size was assessed every other day using the formula V = 0.5 × L × W², where L, tumor length; W, tumor width. Twenty days post-experiment, each BALB/c mouse was euthanized for tumor excision and weighing. The Jingzhou Central Hospital Animal Ethics Committee authorized the study. Enzyme-linked immunosorbent assay An ELISA assay was utilized to determine specific cytokine levels in the sub-cavitary culture fluid from TC and macrophage co-cultures. Herein, we used enzyme markers to determine the absorbance at 450 nm and, calculated the concentration from the calculation curve provided by the reagent vendor and expressed the results in pg/mL. Immune cell ratio analysis Mouse tumors were mechanically minced and incubated with collagenase IV and DNA enzyme at 37 degrees Celsius for 30 minutes with shaking. The resulting cell suspension was filtered through a 70 micrometer mesh. To prevent non-specific binding, cells were incubated with an anti-mouse CD16/32 antibody (Fc Block, BioLegend, Cat# 101302). Subsequently, the cells were stained with specific fluorophore-conjugated antibodies at 4 degrees Celsius for 20 minutes in the dark. For macrophage polarization analysis, the following antibodies were used: APC anti-mouse F4/80 (Clone BM8, BioLegend, Cat# 123115), PE anti-mouse CD86 (Clone GL-1, BioLegend, Cat# 105007), and FITC anti-mouse CD206 (Clone C068C2, BioLegend, Cat# 141707). For mature dendritic cell (DC) identification, the antibodies included: BV421 anti-mouse CD80 (Clone 16-10A1, BioLegend, Cat# 104725) and PE anti-mouse CD86 (Clone GL-1, BioLegend, Cat# 105007). The stained cells were analyzed using LSRFortessa flow cytometry (BD Biosciences) and FlowJo software. Statistical analysis Data analyses were conducted through one-way ANOVA with Bonferroni's post hoc test or an unpaired two-sided Student's t-test using GraphPad Prism 5.0, reporting results as mean ± standard deviation. P < 0.05 indicated statistical significance: no significant (N.S), * P <0.05, ** P <0.01, and *** P <0.001. Results DISP3 overexpression and its clinical significance Analysis of TCGA-THCA data revealed DISP3 overexpression in TC samples, both unpaired and paired ( Figures 1A–B ), and in ten other malignancies: BLCA, BRCA, CHOL, HNSC, KIRC, LIHC, LUAD, PCPG, PRAD, and UCEC ( Figure 1C ). Clinical analysis results indicated that DISP3 overexpression is not associated with the T or N stage in TC patients but is higher in M1 compared to M0 patients ( Figures 1D–F ). Patients with DISP3 overexpression had a significantly worse prognosis ( HR = 8.94, p = 0.004, Figure 1G ). The ROC curve area of 0.724 indicated DISP3's potential as a diagnostic biomarker for TC ( Figure 1H ). Correlation and enrichment analyses To ascertain DISP3's impact on TC prognosis, we performed a single-gene correlation analysis to identify co-expressed genes. The top 20 correlated genes were visualized in a heatmap, identifying the intersection of DISP3 co-expressed genes with TC prognosis-related genes ( Figures 2A-B ). The GO/KEGG enrichment analysis suggested that DISP3 may function through cell projection membranes and glycosyltransferase activity ( Figure 2C ). For further exploration of DISP3's role in tumor immunity, an enrichment analysis was conducted by intersecting DISP3-related immunity genes with TC prognosis-related genes ( Figure 2D ). The results suggested that DISP3 might promote TC development by affecting cytokine activity and cytokine-cytokine receptor interactions ( Figures 2E–F ). DISP3 and its association with immune cell infiltration (ICI) Building on the enrichment analysis, which highlighted DISP3's role in cytokine signaling within the tumor immune microenvironment (IME), the relation between DISP3 expression and ICI was examined. The DISP3 overexpression was correlated with increased pDC and NK CD56bright cell infiltration, whereas DC, Th17, aDC, and macrophage infiltration decreased (figure 3A) . Specifically, in the DISP3 high-expression group, overall DC infiltration was reduced, with an escalation in pDC content and a reduction in aDC infiltration (figure 3B-E) . Ex vivo and in vitro experiments confirm DISP3 overexpression and its role in TC progression Herein, we conducted ex vivo experiments to assess DISP3's impact on TC, revealing significantly overexpressed DISP3 in TC cell lines, unlike the normal thyroid cell line Nthy-ori3-1 (Figure 4A) . Immunohistochemical analysis of paired samples from 10 TC patients showed higher DISP3 levels in cancerous tissues than in adjacent normal tissues (Figures 4B-C) . Due to the aggressive nature of follicular TC and the prevalence of papillary thyroid carcinoma (PTC), we utilized FTC133, K-1, and TPC-1 cell lines for in vitro studies. Moreover, we selected appropriate siRNAs to achieve DISP3 knockdown in these cell lines, achieving satisfactory efficiencies (Figure 4D) . Clone formation assay results revealed that DISP3 knockdown significantly inhibited TC cell proliferation (Figure 4E) . Additionally, scratch and Transwell assay outcomes showcased DISP3 knockdown reduced cell migration and invasion (Figures 4F-G) . These experiments were repeated three times and analyzed statistically (Figures 4H–J) . The CCK-8 and BALB/c mice tumor formation assay results manifested a significant inhibition in TC growth following DISP3 knockdown (Figures 4K–O) . DISP3 modulates TC-IME To explore DISP3's impact on the TC-IME, we developed a model co-culturing TC cells with M0 macrophages (Figure 5A) . In this setup, tumor cells in the Transwell's upper compartment secrete cytokines that migrate to the lower compartment, influencing macrophages mimicking the TME's interaction between cancer and immune cells. Then, we assessed cytokine levels in the lower chamber cultures of both groups using ELISA. The study revealed that DISP3 knockdown in tumor cells resulted in elevated production of pro-inflammatory cytokines, including IL-1β / 4 and TNF-α, while reducing those of anti-inflammatory cytokines like IL-6 / 10 (Figure 5B) . Additionally, flow cytometry analysis of immune cells in tumors from mice demonstrated that DISP3 knockdown increased M1 macrophage infiltration, decreased M2 macrophages, and significantly elevated mature DCs (Figures 5C–F) . Discussion On a global scale, TC represents a common endocrine malignancy with rapidly increasing incidence. While most patients with DTC have a favorable prognosis, some may develop aggressive forms that resist standard treatments such as surgery, radioactive iodine, and thyroid hormone suppression. Recently, immunotherapy has become a promising approach for managing TC through the immune system to target and eliminate cancer cells. Pembrolizumab(14) and nivolumab(15), targeting PD-1 and CTLA-4 proteins, have shown efficacy in certain patients with advanced, treatment-resistant conditions by boosting immune responses against tumors. Nonetheless, their limited success indicates significant potential for advancing immunotherapy in TC. The TME encompasses both the internal and external conditions where tumor cells develop, proliferate, and metastasize and includes cancer cells, immune cells, cytokines, chemokines, and extracellular matrix. The tumor IME, a subset of the TME, consists of innate and adaptive immune cells, extracellular immune factors, and cell surface molecules, playing a pivotal role in tumor initiation and progression through complex interactions. Immune cells in the TME have been reported to be related to TC aggressiveness. Tumor-associated macrophages (TAMs) are particularly connected with clinical outcomes(16). Anaplastic TC, known for its poor prognosis, exhibits a high TAM density. In TC, TAMs are predominantly M2 macrophages, which promote a microenvironment conducive to tumor growth, survival, and angiogenesis(17, 18). DCs, essential for antigen presentation and cytokine secretion, are usually rare in normal thyroid tissue but are more prevalent in PTC(19). These DCs often display an immature phenotype, possibly due to secreting immunosuppressive cytokines such as IL-10 by the tumor cells(20). Immature DCs exhibit limited antigen-presenting capabilities and struggle to activate the cytotoxic T cells' tumor-killing function(21). This study utilized bioinformatics to examine DISP3 expression, prognosis, and fundamental characteristics in TC. Through the integration of analyses on co-expressed, prognosis-related, and immune-related genes, we clarified the mechanism of DISP3, a potential pathogenic gene in TC, and conducted both in vivo and in vitro experiments. DISP3 is recognized as a key regulator of the Hedgehog signaling pathway, which is crucial for embryonic development and tissue homeostasis. Over-activation of Hedgehog signaling has been linked to the development of various malignancies. It is highly plausible that DISP3 exerts its effects on the tumor immune microenvironment through the Hedgehog pathway, as this signaling cascade is known to play a role in modulating cytokine networks and immune cell recruitment. Herein, DISP3 knockdown improved the immune response within the TME and decreased TC malignant behavior. Elevated pro-inflammatory cytokines, TNF-α, and diminished secretion of immune-suppressive factors, IL-10, indicated an enhanced immune response. Concurrently, the increase in mature DCs improved tumor antigen presentation. Additionally, the shift from M2 to M1 macrophages strengthened innate immunity, transforming 'cold' immune tumors and potentially enhancing T-cell response to other immunotherapies(22, 23). While the immunohistochemical analysis in our study confirmed higher DISP3 levels in cancerous tissues than in adjacent normal tissues , it is important to acknowledge the limitation of the relatively small clinical sample size (n=10) used for this validation. Although these paired samples provided statistically significant preliminary evidence, future validation in larger, multi-center cohorts is necessary to firmly establish the diagnostic and prognostic value of DISP3 in clinical practice. Additionally, our findings that DISP3 levels are higher in M1 stage patients compared to M0 stage patients further highlight its potential as a marker for advanced disease. The shortcoming of this study is that the tumor IME involves more interactions of immune cells, cytokines, and extracellular matrix, and more complex and realistic models of the human body are needed to simulate this process. In addition, the effectiveness of targeted DISP3 therapy in synergy with other immunotherapies needs further validation. Abbreviations BLCA bladder cancer BRCA breast cancer CHOL bile duct cancer DCs dendritic cells DTC differentiated thyroid cancer HNSC head and neck cancer IME immune microenvironment KIRC kidney clear cell carcinoma LIHC liver cancer LUAD lung adenocarcinoma PCPG Pheochromocytoma & Paraganglioma PRAD prostate cancer PTC papillary thyroid carcinoma TC thyroid cancer UCEC Endometrioid Cancer Declarations Ethics approval and consent to participate The studies involving humans were approved by Ethics Committee of jingzhou central hospital (Tz20240724). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. The animal study was approved by Ethics Committee of jingzhou central hospital (Tz20240819). The study was conducted in accordance with the local legislation and institutional requirements. Consent for publication Not applicable. Availability of data and material The data that support the findings of this study are available in The Cancer Genome Atlas (TCGA) database (https://tcia.at/home) and Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding Not applicable. Authors' contributions LQ and YT obtained clinical specimens; YT performed the data analysis; WC performed the formal analysis; WC and YTperformed immunohistochemical analysis; LQ and SD wrote the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12-49. Pizzato M, Li M, Vignat J, Laversanne M, Singh D, La Vecchia C, et al. 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Chen J, Duan Y, Che J, Zhu J. Dysfunction of dendritic cells in tumor microenvironment and immunotherapy. Cancer Commun (Lond). 2024;44(9):1047-70. Poschke I, Mougiakakos D, Kiessling R. Camouflage and sabotage: tumor escape from the immune system. Cancer Immunol Immunother. 2011;60(8):1161-71. Cunha LL, Morari EC, Guihen AC, Razolli D, Gerhard R, Nonogaki S, et al. Infiltration of a mixture of immune cells may be related to good prognosis in patients with differentiated thyroid carcinoma. Clin Endocrinol (Oxf). 2012;77(6):918-25. Beatty GL, Gladney WL. Immune escape mechanisms as a guide for cancer immunotherapy. Clin Cancer Res. 2015;21(4):687-92. 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. 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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-9394485","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626491633,"identity":"daa3d76a-304a-49af-88ab-8d81e01e7927","order_by":0,"name":"Yanchu Tong","email":"","orcid":"","institution":"Jingzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanchu","middleName":"","lastName":"Tong","suffix":""},{"id":626491635,"identity":"70b49ff7-c85b-4511-92cd-6697505adb28","order_by":1,"name":"Wenkui Chen","email":"","orcid":"","institution":"Jingzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenkui","middleName":"","lastName":"Chen","suffix":""},{"id":626491638,"identity":"3c12ca07-9057-4ef1-9c92-e0f3569206c2","order_by":2,"name":"Shun Deng","email":"","orcid":"","institution":"Jingzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shun","middleName":"","lastName":"Deng","suffix":""},{"id":626491639,"identity":"1258ab59-3764-4e22-9c02-14a82cadb548","order_by":3,"name":"Lu Qin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDACZgbGAwwGNnL8zMyHHxCrheEAQ0GasWQ7W5oB0RYdYPhwOHHDeR4FCaKU8x3nPXCYx+Bw4ubDPAwGDDU20QS1SB7mSwBqSTfedpj3wAOGY2m5DYS0GICs4DGwlt0G1GvA2HCYaC3MjJubeQwkSNHirLiBmVgtkkD1B+cYpBlLHAYGcgIxfuE7f8bwwZs/wKjsP3z4wYcaG8JagJGCBBIIKsfQMgpGwSgYBaMAGwAAPHI/erX825YAAAAASUVORK5CYII=","orcid":"","institution":"Jingzhou Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"Qin","suffix":""}],"badges":[],"createdAt":"2026-04-12 13:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9394485/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9394485/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107488396,"identity":"2004ea01-20df-4723-a852-64c0d59828dd","added_by":"auto","created_at":"2026-04-22 02:44:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":477193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of DISP3 in thyroid cancer (TC) and prognosis\u003c/strong\u003e. \u003cstrong\u003e(A–C)\u003c/strong\u003e DISP3 expression in TC unpaired \u003cstrong\u003e(A)\u003c/strong\u003e, paired samples \u003cstrong\u003e(B)\u003c/strong\u003e and pan-cancer \u003cstrong\u003e(C)\u003c/strong\u003e. \u003cstrong\u003e(D–F)\u003c/strong\u003e Correlation of DISP3 with T-\u003cstrong\u003e(D)\u003c/strong\u003e, N- \u003cstrong\u003e(E)\u003c/strong\u003e, and M-stages \u003cstrong\u003e(F)\u003c/strong\u003e in TC patients. \u003cstrong\u003e(G)\u003c/strong\u003e Correlation of DISP3 expression with OS, a prognostic indicator in TC patients. \u003cstrong\u003e(H) ROC curve\u003c/strong\u003e: DISP3 in TC. \u003cstrong\u003eData interpretation:\u003c/strong\u003e mean ± \u003cem\u003eSD\u003c/em\u003e. ns no significant, \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003csup\u003e****\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9394485/v1/9b078a25392644db892a1d8b.png"},{"id":107419488,"identity":"90691e87-b489-44ab-afc9-a869e6d4b61e","added_by":"auto","created_at":"2026-04-21 10:14:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":511386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO/KEGG analyses of DISP3-related genes. (A)\u003c/strong\u003e Intersection of DISP3 co-expressed genes with thyroid cancer (TC) prognosis-related genes. \u003cstrong\u003e(B)\u003c/strong\u003e \u003cstrong\u003eHeatmap\u003c/strong\u003e: DISP3 co-expressed genes. \u003cstrong\u003e(C) GO/KEGG analysis\u003c/strong\u003e: Intersection of DISP3 co-expressed genes with TC prognosis-related genes. \u003cstrong\u003e(D)\u003c/strong\u003eIntersection of TC prognosis-related genes with immune-related genes. \u003cstrong\u003e(E-F)\u003c/strong\u003eFull GO \u003cstrong\u003e(E)\u003c/strong\u003e and KEGG analyses \u003cstrong\u003e(F)\u003c/strong\u003e of prognosis-immunity gene intersections.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9394485/v1/cfd52cf0d344435c6b5500b0.png"},{"id":107419490,"identity":"8d4e7d16-7bc3-42f0-b704-9c459a5320b4","added_by":"auto","created_at":"2026-04-21 10:14:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":512568,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between DISP3 and immune infiltration (ICI) in thyroid cancer (TC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Interconnection of DISP3 expression with various ICI in TC. \u003cstrong\u003e(B–E)\u003c/strong\u003e Correlation of DISP3 expression with DC \u003cstrong\u003e(B)\u003c/strong\u003e, pDC \u003cstrong\u003e(C)\u003c/strong\u003e, aDC \u003cstrong\u003e(D),\u003c/strong\u003e and macrophage \u003cstrong\u003e(E)\u003c/strong\u003e in TC. \u003cstrong\u003eData interpretation:\u003c/strong\u003e mean ± \u003cem\u003eSD\u003c/em\u003e. ns no significant, \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9394485/v1/abcd59da6fd3be33d528fb7a.png"},{"id":107490291,"identity":"3a400676-42a5-44a8-9941-7d38ccb7ce92","added_by":"auto","created_at":"2026-04-22 02:51:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2363518,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDISP3 knockdown inhibits thyroid cancer (TC) malignant biological behavior\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e DISP3 expression in various cell lines of the thyroid gland. \u003cstrong\u003e(B-C)\u003c/strong\u003e Immunohistochemical typical pictures \u003cstrong\u003e(B)\u003c/strong\u003e and statistical analysis \u003cstrong\u003e(C)\u003c/strong\u003e of paired tissues from patients in our center. \u003cstrong\u003e(D)\u003c/strong\u003e \u003cstrong\u003eqRT-PCR\u003c/strong\u003e: Efficiency of DISP3 knockdown. \u003cstrong\u003e(E) Clone formation assay\u003c/strong\u003e: Proliferative capacity of TC. \u003cstrong\u003e(F) Scratch assay\u003c/strong\u003e: Migration ability of TC. \u003cstrong\u003e(G) Transwell assay\u003c/strong\u003e: Invasion ability of TC. \u003cstrong\u003e(H) Quantitative analysis\u003c/strong\u003e: Clone formation assay. \u003cstrong\u003e(I-J) Quantitative analysis\u003c/strong\u003e: Scratch and \u003cstrong\u003e(J)\u003c/strong\u003e Transwell experiments. \u003cstrong\u003e(K–M) CCK-8 assay\u003c/strong\u003e: Growth potential of FTC-133, K1, and TPC-1. \u003cstrong\u003e(N-O)\u003c/strong\u003e Size of tumor formation in BALB/c mice \u003cstrong\u003e(N)\u003c/strong\u003e and statistical analysis \u003cstrong\u003e(O)\u003c/strong\u003e. Data interpretation: mean ± \u003cem\u003eSD\u003c/em\u003e. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9394485/v1/db5d6d17c88f782dcd632fd0.png"},{"id":107419492,"identity":"69076492-e7e7-4d03-a7c2-7474676a0f58","added_by":"auto","created_at":"2026-04-21 10:14:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":476561,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDISP3 reverses the pro-tumor immune microenvironment of thyroid cancer (TC)\u003c/strong\u003e. \u003cstrong\u003e(A) \u003c/strong\u003eSchematic of the co-culture model of TC with macrophages. \u003cstrong\u003e(B) ELISA\u003c/strong\u003e: Cytokines in the lower chamber of Transwell. \u003cstrong\u003e(C–F) Flow cytometry:\u003c/strong\u003e M1 macrophages \u003cstrong\u003e(C)\u003c/strong\u003e, M2 macrophages \u003cstrong\u003e(D),\u003c/strong\u003e and mature DCs \u003cstrong\u003e(E)\u003c/strong\u003e in control and experimental tumors and quantitative analysis \u003cstrong\u003e(F)\u003c/strong\u003e. \u003cstrong\u003eData interpretation:\u003c/strong\u003e mean ± \u003cem\u003eSD\u003c/em\u003e. ns no significant, \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9394485/v1/9f1564061f4f7bdbb37a1368.png"},{"id":107490432,"identity":"9f22a8d2-079d-4a91-aa3d-89da2af7a588","added_by":"auto","created_at":"2026-04-22 02:52:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4994131,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9394485/v1/6c709fe9-03fc-4cef-94da-25e46ac67faa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of DISP3 in Thyroid Cancer Progression: Implications for Immune Microenvironment Modulation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThyroid cancer (TC) is a frequent endocrine malignancy, with a steadily increasing prevalence of differentiated TC (DTC) worldwide over the past three decades(1). Although most patients achieve favorable outcomes with current treatments, about 30% experience disease progression due to recurrence and become unresponsive to current therapies(2). Frequently, TC displays local immune responses, creating a tumor microenvironment (TME) where tumor and host cells coexist(3). The immune cell composition within this microenvironment influences the tumor\u0026apos;s clinical aggressiveness(4-6).\u003c/p\u003e\n\u003cp\u003eIn cancer immune surveillance, the innate immune response initially targets and eliminates tumor cells, followed by macrophages phagocytosing the cell fragments(7, 8). Dendritic cells (DCs) process neoantigens from these cells, secrete inflammatory cytokines, and present tumor antigens to T cells, thereby facilitating an adaptive immune response(9). This activates and expands T cells and produces tumor-specific antibodies by B cells to eliminate the tumor. However, research into the TME reveals that tumors can re-educate immune cells to support tumor development(10). Reversing this trend is indispensable for tumor immunotherapy success(11).\u003c/p\u003e\n\u003cp\u003eDISP3, part of the Dispatched family, primarily regulates Hedgehog signaling, which is crucial for embryonic development, cell differentiation, and tissue homeostasis(12). Over-activation of Hedgehog signaling is linked to malignancy development, including gastric, pancreatic, basal cell, and prostate cancers(13). Nonetheless, the DISP3 function in TC remains unexplored.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003ePatient clinical samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween June and December 2024, 10 paired TC and adjacent non-cancerous tissues were collected from Jingzhou Hospital Affiliated to Yangtze University and maintained at \u0026ndash;80 \u0026deg;C. The study was authorized by the hospital\u0026apos;s Ethics Committee and complied with their guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatics analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptomic data (TPM-normalized) and corresponding clinical information for the Thyroid Carcinoma project (TCGA-THCA, n=510) and normal thyroid tissues (n=58) were downloaded from the TCGA database. For validation, the GEO database was utilized to ensure the reproducibility of DISP3 expression patterns.\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis between high- and low-DISP3 expression groups (stratified by the median) was performed using the DESeq2 package in R. Significant differentially expressed genes (DEGs) were identified using the criteria of |log2 FoldChange| \u0026gt; 1.5 and adjusted P \u0026lt; 0.05. To evaluate the prognostic value, univariate Cox proportional hazards regression analysis was used to calculate hazard ratios (HR) and identify prognosis-related genes (HR \u0026gt; 1.2, P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eFunctional enrichment was conducted using the clusterProfiler package. Overlapping genes between DISP3-correlated genes (2,197 genes), prognosis-related genes (3,214 genes), and immune-related genes from the ImmPort database (2,271 genes) were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.\u003c/p\u003e\n\u003cp\u003eTo quantify the infiltration of 24 types of immune cells, the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm was employed. The correlation between DISP3 expression and immune cell abundance, including dendritic cells (DCs) and macrophages, was assessed using Spearman correlation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe culture of BCPAP, FTC133, K-1, and TPC-1 cell lines (Wuhan Institute of Cell Biology, China) was conducted in 1640 complete medium that contained 10% fetal bovine serum in a 5% CO\u003csub\u003e2\u003c/sub\u003e incubator at 37 \u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathological sample processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor and adjacent non-cancerous tissues were fixed in 10% neutral buffered formalin, embedded in paraffin, and sectioned into 5\u0026mu;m slices. Following deparaffinization and rehydration through a graded ethanol series, heat-induced antigen retrieval was performed using microwave heating in citrate buffer (pH 6.0). To block non-specific binding, sections were incubated with 5% bovine serum albumin (BSA) and subsequently incubated with primary antibodies at 4 \u0026deg;C overnight. After washing, the sections were treated with horseradish peroxidase (HRP)-conjugated secondary antibodies and visualized using a 3,3\u0026apos;-diaminobenzidine (DAB) substrate. Finally, the slides were counterstained with hematoxylin, dehydrated, and mounted.\u003c/p\u003e\n\u003cp\u003eTotal RNA was isolated from tissues or cells using TRIzol reagent according to the manufacturer\u0026rsquo;s protocol. The concentration and purity of the RNA were determined spectrophotometrically. Complementary DNA (cDNA) was synthesized using a reverse transcription kit, and quantitative real-time PCR (qRT-PCR) was performed using SYBR Green Master Mix. The primer sequences were as follows: DISP3 Forward: 5\u0026apos;-CCAAAGAATGACAGGAACACTGAG-3\u0026apos;, Reverse: 5\u0026apos;-CTCATCTACATACCAGTAGAACTCG-3\u0026apos;; GAPDH Forward: 5\u0026apos;-CTGCTCCTCCTGTTCGACAGT-3\u0026apos;, Reverse: 5\u0026apos;-CCGTTGACTCCGACCTTCAC-3\u0026apos;. The PCR thermal cycling conditions consisted of an initial denaturation at 95 degrees Celsius for 5 minutes, followed by 40 cycles of 95 degrees Celsius for 15 seconds and 60 degrees Celsius for 30 seconds. Target gene expression levels were normalized to GAPDH and calculated using the 2-Delta Delta Ct method\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCCK8 assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells from each group were digested and resuspended in a complete medium. Cell growth was evaluated at 24, 48, 72, and 96 h by CCK-8 assay, measuring optical density at 450 nm via an enzyme marker.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClone formation assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo thousand cells were seeded into six-well plates, with half of the medium refreshed daily. After 10 days, cells were collected, blotted to remove the excess medium, fixed in 4% paraformaldehyde for 10 min, and stained using crystal violet.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranswell assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInitially, 10,000 cells were placed in the Transwell system upper chamber (Corning, USA), incubated for 24 h at 37 \u0026deg;C, and stained using crystal violet for 10 min, followed by washing to remove excess stain. The migrated cells were then counted and photographed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScratch test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFTC133, K-1, and TPC-1 TC cells were grown in IBIDI two-well inserts placed in 24-well plates for 24 h. The inserts were removed using forceps, and 1 mL of low serum medium was introduced to each well. The healing process of the scratches was monitored and photographed at the start and 24 h after the inserts were removed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor formation in BALB/c mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwelve 6-week-old female BALB/c mice (average weight 24 g) were randomly assigned into two groups. Each mouse received an injection of approximately 10 million tumor cells into the left subcutaneous fat pad. Xenograft size was assessed every other day using the formula V = 0.5 \u0026times; L \u0026times; W\u0026sup2;, where L, tumor length; W, tumor width. Twenty days post-experiment, each BALB/c mouse was euthanized for tumor excision and weighing. The Jingzhou Central Hospital Animal Ethics Committee authorized the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnzyme-linked immunosorbent assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn ELISA assay was utilized to determine specific cytokine levels in the sub-cavitary culture fluid from TC and macrophage co-cultures. Herein, we used enzyme markers to determine the absorbance at 450 nm and, calculated the concentration from the calculation curve provided by the reagent vendor and expressed the results in pg/mL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune cell ratio analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMouse tumors were mechanically minced and incubated with collagenase IV and DNA enzyme at 37 degrees Celsius for 30 minutes with shaking. The resulting cell suspension was filtered through a 70 micrometer mesh. To prevent non-specific binding, cells were incubated with an anti-mouse CD16/32 antibody (Fc Block, BioLegend, Cat# 101302). Subsequently, the cells were stained with specific fluorophore-conjugated antibodies at 4 degrees Celsius for 20 minutes in the dark.\u003c/p\u003e\n\u003cp\u003eFor macrophage polarization analysis, the following antibodies were used: APC anti-mouse F4/80 (Clone BM8, BioLegend, Cat# 123115), PE anti-mouse CD86 (Clone GL-1, BioLegend, Cat# 105007), and FITC anti-mouse CD206 (Clone C068C2, BioLegend, Cat# 141707). For mature dendritic cell (DC) identification, the antibodies included: BV421 anti-mouse CD80 (Clone 16-10A1, BioLegend, Cat# 104725) and PE anti-mouse CD86 (Clone GL-1, BioLegend, Cat# 105007). The stained cells were analyzed using LSRFortessa flow cytometry (BD Biosciences) and FlowJo software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analyses were conducted through one-way ANOVA with Bonferroni\u0026apos;s post hoc test or an unpaired two-sided Student\u0026apos;s t-test using GraphPad Prism 5.0, reporting results as mean \u0026plusmn; standard deviation. P \u0026lt; 0.05 indicated statistical significance: no significant (N.S), \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, and \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDISP3 overexpression and its clinical significance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of TCGA-THCA data revealed DISP3 overexpression\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ein TC samples, both unpaired and paired (\u003cstrong\u003eFigures 1A\u0026ndash;B\u003c/strong\u003e), and in ten other malignancies: BLCA, BRCA, CHOL, HNSC, KIRC, LIHC, LUAD, PCPG, PRAD, and UCEC (\u003cstrong\u003eFigure 1C\u003c/strong\u003e). Clinical analysis results indicated that DISP3 overexpression is not associated with the T or N stage in TC patients but is higher in M1 compared to M0 patients (\u003cstrong\u003eFigures 1D\u0026ndash;F\u003c/strong\u003e). Patients with DISP3 overexpression had a significantly worse prognosis (\u003cstrong\u003eHR = 8.94, \u003cem\u003ep\u003c/em\u003e = 0.004, Figure 1G\u003c/strong\u003e). The ROC curve area of 0.724 indicated DISP3\u0026apos;s potential as a diagnostic biomarker for TC (\u003cstrong\u003eFigure 1H\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation and enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ascertain DISP3\u0026apos;s impact on TC prognosis, we performed a single-gene correlation analysis to identify co-expressed genes. The top 20 correlated genes were visualized in a heatmap, identifying the intersection of DISP3 co-expressed genes with TC prognosis-related genes (\u003cstrong\u003eFigures 2A-B\u003c/strong\u003e). The GO/KEGG enrichment analysis suggested that DISP3 may function through cell projection membranes and glycosyltransferase activity (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). For further exploration of DISP3\u0026apos;s role in tumor immunity, an enrichment analysis was conducted by intersecting DISP3-related immunity genes with TC prognosis-related genes (\u003cstrong\u003eFigure 2D\u003c/strong\u003e). The results suggested that DISP3 might promote TC development by affecting cytokine activity and cytokine-cytokine receptor interactions (\u003cstrong\u003eFigures 2E\u0026ndash;F\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDISP3 and its association with immune cell infiltration (ICI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBuilding on the enrichment analysis, which highlighted DISP3\u0026apos;s role in cytokine signaling within the tumor immune microenvironment (IME), the relation between DISP3 expression and ICI was examined.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eThe DISP3 overexpression was correlated with increased pDC and NK CD56bright cell infiltration, whereas DC, Th17, aDC, and macrophage infiltration decreased \u003cstrong\u003e(figure 3A)\u003c/strong\u003e. Specifically, in the DISP3 high-expression group, overall DC infiltration was reduced, with an escalation in pDC content and a reduction in aDC infiltration \u003cstrong\u003e(figure 3B-E)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEx vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e experiments confirm DISP3 overexpression and its role in TC progression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHerein, we conducted \u003cem\u003eex vivo\u003c/em\u003e experiments to assess DISP3\u0026apos;s impact on TC, revealing significantly overexpressed DISP3 in TC cell lines, unlike the normal thyroid cell line Nthy-ori3-1 \u003cstrong\u003e(Figure 4A)\u003c/strong\u003e. Immunohistochemical analysis of paired samples from 10 TC patients showed higher DISP3 levels in cancerous tissues than in adjacent normal tissues \u003cstrong\u003e(Figures 4B-C)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eDue to the aggressive nature of follicular TC and the prevalence of papillary thyroid carcinoma (PTC), we utilized FTC133, K-1, and TPC-1 cell lines for \u003cem\u003ein vitro\u003c/em\u003e studies. Moreover, we selected appropriate siRNAs to achieve DISP3 knockdown in these cell lines, achieving satisfactory efficiencies \u003cstrong\u003e(Figure 4D)\u003c/strong\u003e. Clone formation assay results revealed that DISP3 knockdown significantly inhibited TC cell proliferation \u003cstrong\u003e(Figure 4E)\u003c/strong\u003e. Additionally, scratch and Transwell assay outcomes showcased DISP3 knockdown reduced cell migration and invasion \u003cstrong\u003e(Figures 4F-G)\u003c/strong\u003e. These experiments were repeated three times and analyzed statistically \u003cstrong\u003e(Figures 4H\u0026ndash;J)\u003c/strong\u003e. The CCK-8 and BALB/c mice tumor formation assay results manifested a significant inhibition in TC growth following DISP3 knockdown \u003cstrong\u003e(Figures 4K\u0026ndash;O)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDISP3 modulates TC-IME\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore DISP3\u0026apos;s impact on the TC-IME, we developed a model co-culturing TC cells with M0 macrophages \u003cstrong\u003e(Figure 5A)\u003c/strong\u003e. In this setup, tumor cells in the Transwell\u0026apos;s upper compartment secrete cytokines that migrate to the lower compartment, influencing macrophages mimicking the TME\u0026apos;s interaction between cancer and immune cells. Then, we assessed cytokine levels in the lower chamber cultures of both groups using ELISA. The study revealed that DISP3 knockdown in tumor cells resulted in elevated production of pro-inflammatory cytokines, including IL-1\u0026beta;\u003cspan dir=\"RTL\"\u003e/\u003c/span\u003e4 and TNF-\u0026alpha;, while reducing those of anti-inflammatory cytokines like IL-6\u003cspan dir=\"RTL\"\u003e/\u003c/span\u003e10 \u003cstrong\u003e(Figure 5B)\u003c/strong\u003e. Additionally, flow cytometry analysis of immune cells in tumors from mice demonstrated that DISP3 knockdown increased M1 macrophage infiltration, decreased M2 macrophages, and significantly elevated mature DCs \u003cstrong\u003e(Figures 5C\u0026ndash;F)\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOn a global scale, TC represents a common endocrine malignancy with rapidly increasing incidence. While most patients with DTC have a favorable prognosis, some may develop aggressive forms that resist standard treatments such as surgery, radioactive iodine, and thyroid hormone suppression. Recently, immunotherapy has become a promising approach for managing TC through the immune system to target and eliminate cancer cells. Pembrolizumab(14) and nivolumab(15), targeting PD-1 and CTLA-4 proteins, have shown efficacy in certain patients with advanced, treatment-resistant conditions by boosting immune responses against tumors. Nonetheless, their limited success indicates significant potential for advancing immunotherapy in TC.\u003c/p\u003e\n\u003cp\u003eThe TME encompasses both the internal and external conditions where tumor cells develop, proliferate, and metastasize and includes cancer cells, immune cells, cytokines, chemokines, and extracellular matrix. The tumor IME, a subset of the TME, consists of innate and adaptive immune cells, extracellular immune factors, and cell surface molecules, playing a pivotal role in tumor initiation and progression through complex interactions. Immune cells in the TME have been reported to be related to TC aggressiveness. Tumor-associated macrophages (TAMs) are particularly connected with clinical outcomes(16). Anaplastic TC, known for its poor prognosis, exhibits a high TAM density. In TC, TAMs are predominantly M2 macrophages, which promote a microenvironment conducive to tumor growth, survival, and angiogenesis(17, 18).\u003c/p\u003e\n\u003cp\u003eDCs, essential for antigen presentation and cytokine secretion, are usually rare in normal thyroid tissue but are more prevalent in PTC(19). These DCs often display an immature phenotype, possibly due to secreting immunosuppressive cytokines such as IL-10 by the tumor cells(20). Immature DCs exhibit limited antigen-presenting capabilities and struggle to activate the cytotoxic T cells\u0026apos; tumor-killing function(21).\u003c/p\u003e\n\u003cp\u003eThis study utilized bioinformatics to examine DISP3 expression, prognosis, and fundamental characteristics in TC. Through the integration of analyses on co-expressed, prognosis-related, and immune-related genes, we clarified the mechanism of DISP3, a potential pathogenic gene in TC, and conducted both \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e experiments. DISP3 is recognized as a key regulator of the Hedgehog signaling pathway, which is crucial for embryonic development and tissue homeostasis. Over-activation of Hedgehog signaling has been linked to the development of various malignancies. It is highly plausible that DISP3 exerts its effects on the tumor immune microenvironment through the Hedgehog pathway, as this signaling cascade is known to play a role in modulating cytokine networks and immune cell recruitment. Herein, DISP3 knockdown improved the immune response within the TME and decreased TC malignant behavior. Elevated pro-inflammatory cytokines, TNF-\u0026alpha;, and diminished secretion of immune-suppressive factors, IL-10, indicated an enhanced immune response. Concurrently, the increase in mature DCs improved tumor antigen presentation. Additionally, the shift from M2 to M1 macrophages strengthened innate immunity, transforming \u0026apos;cold\u0026apos; immune tumors and potentially enhancing T-cell response to other immunotherapies(22, 23). While the immunohistochemical analysis in our study confirmed higher DISP3 levels in cancerous tissues than in adjacent normal tissues , it is important to acknowledge the limitation of the relatively small clinical sample size (n=10) used for this validation. Although these paired samples provided statistically significant preliminary evidence, future validation in larger, multi-center cohorts is necessary to firmly establish the diagnostic and prognostic value of DISP3 in clinical practice. Additionally, our findings that DISP3 levels are higher in M1 stage patients compared to M0 stage patients further highlight its potential as a marker for advanced disease.\u003c/p\u003e\n\u003cp\u003eThe shortcoming of this study is that the tumor IME involves more interactions of immune cells, cytokines, and extracellular matrix, and more complex and realistic models of the human body are needed to simulate this process. In addition, the effectiveness of targeted DISP3 therapy in synergy with other immunotherapies needs further validation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eBLCA\u003c/strong\u003e bladder cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBRCA\u003c/strong\u003e breast cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCHOL\u003c/strong\u003e bile duct cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDCs\u003c/strong\u003e dendritic cells\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDTC\u003c/strong\u003e differentiated thyroid cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHNSC\u003c/strong\u003e head and neck cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIME\u003c/strong\u003e immune microenvironment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKIRC\u003c/strong\u003e kidney clear cell carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLIHC\u003c/strong\u003e liver cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLUAD\u003c/strong\u003e lung adenocarcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCPG\u0026nbsp;\u003c/strong\u003ePheochromocytoma \u0026amp; Paraganglioma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePRAD\u003c/strong\u003e prostate cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePTC\u003c/strong\u003e papillary thyroid carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e thyroid cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUCEC\u003c/strong\u003e Endometrioid Cancer\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving humans were approved by Ethics Committee of jingzhou central hospital (Tz20240724). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. The animal study was approved by Ethics Committee of jingzhou central hospital (Tz20240819). The study was conducted in accordance with the local legislation and institutional requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available in The Cancer Genome Atlas (TCGA) database (https://tcia.at/home) and Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLQ and YT obtained clinical specimens; YT performed the data analysis; WC performed the formal analysis; WC and YTperformed immunohistochemical analysis; LQ and SD wrote the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12-49.\u003c/li\u003e\n\u003cli\u003ePizzato M, Li M, Vignat J, Laversanne M, Singh D, La Vecchia C, et al. The epidemiological landscape of thyroid cancer worldwide: GLOBOCAN estimates for incidence and mortality rates in 2020. Lancet Diabetes Endocrinol. 2022;10(4):264-72.\u003c/li\u003e\n\u003cli\u003eCunha LL, Ward LS. Translating the immune microenvironment of thyroid cancer into clinical practice. Endocr Relat Cancer. 2022;29(6):R67-R83.\u003c/li\u003e\n\u003cli\u003eBalkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet. 2001;357(9255):539-45.\u003c/li\u003e\n\u003cli\u003eColotta F, Allavena P, Sica A, Garlanda C, Mantovani A. Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability. Carcinogenesis. 2009;30(7):1073-81.\u003c/li\u003e\n\u003cli\u003ePalucka AK, Coussens LM. The Basis of Oncoimmunology. Cell. 2016;164(6):1233-47.\u003c/li\u003e\n\u003cli\u003eKim R, Emi M, Tanabe K. Cancer immunoediting from immune surveillance to immune escape. Immunology. 2007;121(1):1-14.\u003c/li\u003e\n\u003cli\u003eFinn OJ. Immuno-oncology: understanding the function and dysfunction of the immune system in cancer. Ann Oncol. 2012;23 Suppl 8(Suppl 8):viii6-9.\u003c/li\u003e\n\u003cli\u003eHilly O, Koren R, Raz R, Rath-Wolfson L, Mizrachi A, Hamzany Y, et al. The role of s100-positive dendritic cells in the prognosis of papillary thyroid carcinoma. Am J Clin Pathol. 2013;139(1):87-92.\u003c/li\u003e\n\u003cli\u003eWillimsky G, Blankenstein T. Sporadic immunogenic tumours avoid destruction by inducing T-cell tolerance. Nature. 2005;437(7055):141-6.\u003c/li\u003e\n\u003cli\u003eRabinovich GA, Gabrilovich D, Sotomayor EM. Immunosuppressive strategies that are mediated by tumor cells. Annu Rev Immunol. 2007;25:267-96.\u003c/li\u003e\n\u003cli\u003eIngham PW. Hedgehog signaling. Curr Top Dev Biol. 2022;149:1-58.\u003c/li\u003e\n\u003cli\u003eJiang J. Hedgehog signaling mechanism and role in cancer. Semin Cancer Biol. 2022;85:107-22.\u003c/li\u003e\n\u003cli\u003eHarrington KJ, Burtness B, Greil R, Soulieres D, Tahara M, de Castro G, Jr., et al. Pembrolizumab With or Without Chemotherapy in Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma: Updated Results of the Phase III KEYNOTE-048 Study. J Clin Oncol. 2023;41(4):790-802.\u003c/li\u003e\n\u003cli\u003eFerris RL, Blumenschein G, Jr., Fayette J, Guigay J, Colevas AD, Licitra L, et al. Nivolumab vs investigator\u0026apos;s choice in recurrent or metastatic squamous cell carcinoma of the head and neck: 2-year long-term survival update of CheckMate 141 with analyses by tumor PD-L1 expression. Oral Oncol. 2018;81:45-51.\u003c/li\u003e\n\u003cli\u003eRyder M, Ghossein RA, Ricarte-Filho JC, Knauf JA, Fagin JA. Increased density of tumor-associated macrophages is associated with decreased survival in advanced thyroid cancer. Endocr Relat Cancer. 2008;15(4):1069-74.\u003c/li\u003e\n\u003cli\u003eJung KY, Cho SW, Kim YA, Kim D, Oh BC, Park DJ, et al. Cancers with Higher Density of Tumor-Associated Macrophages Were Associated with Poor Survival Rates. J Pathol Transl Med. 2015;49(4):318-24.\u003c/li\u003e\n\u003cli\u003eFang W, Ye L, Shen L, Cai J, Huang F, Wei Q, et al. Tumor-associated macrophages promote the metastatic potential of thyroid papillary cancer by releasing CXCL8. Carcinogenesis. 2014;35(8):1780-7.\u003c/li\u003e\n\u003cli\u003eTran Janco JM, Lamichhane P, Karyampudi L, Knutson KL. Tumor-infiltrating dendritic cells in cancer pathogenesis. J Immunol. 2015;194(7):2985-91.\u003c/li\u003e\n\u003cli\u003eChen J, Duan Y, Che J, Zhu J. Dysfunction of dendritic cells in tumor microenvironment and immunotherapy. Cancer Commun (Lond). 2024;44(9):1047-70.\u003c/li\u003e\n\u003cli\u003ePoschke I, Mougiakakos D, Kiessling R. Camouflage and sabotage: tumor escape from the immune system. Cancer Immunol Immunother. 2011;60(8):1161-71.\u003c/li\u003e\n\u003cli\u003eCunha LL, Morari EC, Guihen AC, Razolli D, Gerhard R, Nonogaki S, et al. Infiltration of a mixture of immune cells may be related to good prognosis in patients with differentiated thyroid carcinoma. Clin Endocrinol (Oxf). 2012;77(6):918-25.\u003c/li\u003e\n\u003cli\u003eBeatty GL, Gladney WL. Immune escape mechanisms as a guide for cancer immunotherapy. Clin Cancer Res. 2015;21(4):687-92.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"DISP3, Thyroid cancer, Tumor immune microenvironment, Macrophage polarization, dendritic cell","lastPublishedDoi":"10.21203/rs.3.rs-9394485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9394485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo investigate the role of DISP3 in thyroid cancer (TC) progression and its modulation of the tumor immune microenvironment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eBioinformatics analyzed DISP3 expression and its prognostic significance in TC. The biological impact of DISP3 on TC cells was validated through in vitro assays and in vivo BALB/c mice models. Flow cytometry and cytokine assays were performed to evaluate the regulatory mechanism of DISP3 on dendritic cell (DC) maturation and macrophage polarization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDISP3 was overexpressed in TC and significantly correlated with poorer prognosis. Bioinformatics revealed that DISP3 expression negatively correlated with DC and macrophage infiltration. Experimentally, knocking down DISP3 significantly inhibited TC cell proliferation and migration both in vitro and in vivo. Furthermore, DISP3 knockdown enhanced anti-tumor cytokine secretion, promoted DC maturation, and induced a phenotypic shift of macrophages toward the M1 (anti-tumor) phenotype.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eDISP3 promotes TC progression by modulating the immune microenvironment, specifically by suppressing DC maturation and M1 macrophage polarization. These findings suggest DISP3 as a promising novel target for TC immunotherapy.\u003c/p\u003e","manuscriptTitle":"The Role of DISP3 in Thyroid Cancer Progression: Implications for Immune Microenvironment Modulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 10:14:18","doi":"10.21203/rs.3.rs-9394485/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":"bd772611-2138-4a07-b546-fb7b7b2ce784","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-25T16:24:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 10:14:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9394485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9394485","identity":"rs-9394485","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}