Immune cell score model is a new tumor marker for predicting prognosis and selecting therapeutic drugs in thyroid cancer

Immune cell score model is a new tumor marker for predicting prognosis and selecting therapeutic drugs in thyroid cancer | 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 Immune cell score model is a new tumor marker for predicting prognosis and selecting therapeutic drugs in thyroid cancer Youlin Yu, Junlin Yu, Ting Lu, Chan Xiao, Chaohua Hu, Haoyuan Shen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8329405/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 Immune cells are widely distributed in the microenvironment of thyroid cancer, which promote or inhibit tumor development at different stages of tumorigenesis. The immune microenvironment of tumors may play an important role in tumor development. The number and function of immune cells infiltrated in different compartments of the tumor tissue affect the prognosis of tumor patients. The characteristics and clinical value of the immune microenvironment of thyroid cancer are unknown. In this study, the characteristic of the immune microenvironment of thyroid cancer was analyzed. The immune cell score was used to construct a risk model, and further investigated relationship between the model and the prognosis and drug sensitivity of thyroid cancer. The correlation between immune cells and immune genes was studied in the model. The model predicts the prognosis of thyroid cancer, identifies high-risk groups of thyroid cancer, and screens dominant populations benefiting from immunotherapy. It provides reference for prognosis evaluation and treatment strategy of thyroid cancer. Thyroid cancer Tumor microenvironment Immune cells Risk model Prognosis Drug sensitivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Please have a look at courier new font provided for text in article. Thyroid cancer is the most common endocrine malignancy, and its incidence is slowly increasing worldwide [ 1 – 6 ] . The therapeutic efficacy and prognosis of thyroid cancer are widely concerned. Currently, there are some deficiencies in predicting prognosis and guiding treatment decisions by the TNM staging system. In the process of long-term clinical practice, it is found that the patients younger than 55 years old with differentiated thyroid cancer had invasion of local tissues and organs and distant metastasis, and they still had a long survival period with tumor. However, the patients with poorly differentiated and undifferentiated thyroid cancer have rapid tumor progression and death after surgery, even if local tissue invasion occurs. There are significant differences in the clinical outcomes of thyroid cancer in the same tumor histological stage, different age stages, and different tumor types. It can be inferred that the traditional tumor TNM staging system focuses on the predictive value of the tumor itself for disease progression, ignoring the influence of the immune status of the host and the influence of the tumor microenvironment [ 7 – 8 ] .The studies have confirmed that the immune microenvironment of tumors may play an important role in tumor development [ 9 – 11 ] . Tumor progression is a result of the imbalance of organism anti-tumor immunity. The human immune system cannot eliminate or control tumor cells alone, and tumors enter the stage of immune escape. Immune cells are widely distributed in the microenvironment of thyroid cancer, which promote or inhibit tumor development at different stages of tumorigenesis [ 12 – 13 ] . It is particularly important to understand the role of immune cells in the tumor microenvironment. In the tumor microenvironment, immune cells constitute an immune barrier in the tumor parenchyma, the surrounding matrix of the tumor, the edge of tumor invasion, and the vicinity of the tumor [ 14 ] .The number and function of immune cells infiltrated in different compartments of the tumor tissue affect the prognosis of tumor patients [ 15 – 16 ] . Based on this, Angell H et al researchers quantitatively analyzed the immune cells in the tumor microenvironment by immunohistochemistry, and proposed the immunoscore (IS) system to predict the survival of tumor patients [ 17 ] . This study has limitations. Immunohistochemical technique is a semi-quantitative detection technique, and quantitative scoring is performed only on T cells. Neglecting the many other different immune cells of the tumor microenvironment. Our previous research results have shown that the prognostic evaluation model of thyroid cancer was constructed based on clinic pathological features, immune genes, the model has high accuracy. The model can better predict the prognosis of thyroid cancer [ 18 – 19 ] . Therefore, this study further analyzed the characteristics of thyroid cancer immune microenvironment at the cellular level, and the effect of immune microenvironment on tumor prognosis. The prognostic model of thyroid cancer was constructed by the different immune cell scores. The model predicts the prognosis of thyroid cancer, identifies high-risk groups of thyroid cancer, and screens dominant populations benefiting from immunotherapy. It provides reference for prognosis evaluation and treatment strategy of thyroid cancer. Materials and Methods Materials 1.1 Data acquisition The gene expression feature set of 22 immune cells was obtained from the previous literature [ 20 ] , and the infiltration score of each immune cell was calculated by the CIBERSORT algorithm ( https://cibersortx.stanford.edu/ ). Gene expression profiles and clinical information of patients were obtained by TCGA ( https://portal.gdc.cancer.gov/ ). 1.2 Differences in immune cell scores and clinical correlation The difference of immune cell score between tumor and normal tissue was analyzed by R software. The relationship between immune cell score and clinical characteristics was further researched. 1.3 Prognostic risk model was constructed by immune cell score Univariate COX analysis was used to screen prognostic-related immune cells (P < 0.1).The prognostic risk model was constructed by multivariate analysis. Prognostic risk score = Σ (immune cell infiltration score * correlation coefficient). 1.4 Prognostic value of prognostic risk model in thyroid cancer Univariate and multivariate COX regression analysis was used to analyze the independent prognostic predictive value of the prognostic risk model. The area under the ROC curve (AUC) was used to evaluate the predictive efficacy of the model. The K-M curve was used to evaluate the difference in overall survival (OS) between the high and low risk groups of the model. 1.5 Prognostic risk model enrichment analysis GSEA enrichment analysis ( https://www.gsea-msigdb.org/gsea/index.jsp ) was performed on the high and low risk groups of the model by the KEGG and HALLMARK gene set. 1.6 Differences in immune cell function, immunotherapy and drug sensitivity between high-risk and low-risk groups in the prognostic risk model In the model, the differences in immune cell function, PD-L1 expression, anti-PD-1 and CTLA4 treatment response (TCIA database, https://www.tcia.at/home),an d common targeted and chemotherapeutic drugs IC50 (half maximal inhibitory concentration value) between the high-risk group and the low-risk group (GDSC database, https://www.cancerrxgene.org/ ) was analyzed by R language. 1.7 Prognostic risk model construction and prognostic value verification of immune genes Six immune related genes (IRG) were obtained from our previous study [ 19 ] .The prognostic risk model was constructed by univariate COX and LASSO regression. Risk score = Σ (gene expression * correlation coefficient). And further verify the prognostic value of the model. 1.8 Correlation analysis between immune cells and immune genes The correlation between immune cell score and immune gene expression was studied by R language. Results 2.1 The difference of immune cell infiltration in thyroid carcinoma Naive B cells, memory B cells, CD8 T cells, gamma delta T cells, M0 macrophages, M1 macrophages, M2 macrophages, naive dendritic cells, activated dendritic cells, naive mast cells, activated mast cells, eosinophils. These 12 types immune cells score were different between tumor and normal tissues (P < 0.05, Fig. 1 A). Among them, CD8 T cells, M1 macrophages scored lower in the tumor group, while M2 macrophages scored higher in the tumor group. The results of correlation between immune cells showed that CD8 T cells score was positively correlated with M1 macrophages score and negatively correlated with M2 macrophages score( Fig. 1 B ). Immune cells score was correlated with tumor stage. The score of CD8 T cells and M1 macrophages was lower, the score of M2 macrophages was higher in stage III and IV tumors compared with stage I tumors (Fig. 1 C-E). 2.2 Risk model constructed by immune cell score predicts the prognosis of patients with thyroid cancer Univariate COX analysis showed that 6 types immune cells were associated with the prognosis of patients (P < 0.1, Table 1 ), including memory B cells, plasma cells, CD8 T cells, M0 macrophages, activated dendritic cells and mast cells. Further multivariate COX regression constructed a risk model (Fig. 2 A). Risk score of immune cells = memory B cell score * 15.20656 + plasma cell score * (-27.43278) + CD8 T cell score * (-6.06323) + M0 macrophage cell score * 2.50137 + activated dendritic cell score * 5.08962 + activated mast cells * 221.74345. The risk model was divided into high-risk group and low-risk group according to the median score. The overall survival (OS) of the high-risk group patient was shorter than that of the risk group (P = 0.008, Fig. 2 B). The 10-year OS was 82.6% in the high-risk group patient, while that of was 100% in the low-risk group. The AUC of the risk score for predicting 5-year, 8-year, and 10-year survival was respectively 0.886, 0.862, and 0.896(Fig. 2 C). There were differences in the scores of six types immune cells in the high and low risk groups (Fig. 2 D). Table 1 Cox univariate analysis of six immune cells in thyroid cancer Immune cell HR(95%CI) pvalue B cell memory 1.6e + 05(0.3-8.9e + 10) 0.075 Plasma cells 8.2e-20(4.8e-40-14.2) 0.065 T cells CD8 3.0e-8(3.6e-15-0.3) 0.033 Macrophages M0 121.6(1.1-1.4e + 4) 0.046 Dendritic cells activated 3.1e + 08(0.2-5.3e + 17) 0.072 Mast cells activated 2.0e + 124(1.4e + 33-3.1e + 215) 0.008 2.3 Differences in immune function, immunotherapeutic response and drug sensitivity between high and low risk group in the model There were differences in immune function between high and low risk group in the model (Fig. 3 A, P < 0.05). B cells and CD8T cells functioned more poorly, the response to anti-PD-1 or CTLA4 inhibitors was worse (Fig. 3 B-E, P < 0.001), the IC50 values to cyclophosphamide and darafenib were higher in the high-risk group than in the low-risk group (Fig. 3 F-G, P < 0.001). 2.4 GSEA pathway enrichment analysis in the model The gene enrichment pathways in the high-risk group in the model were mainly concentrated in ECM-receptor interaction, focal adhesion, tumorigenesis, PPAR signaling pathway and TGF-β signaling pathway (Fig. 4 ). 2.5 The risk model of immune gene construction predicts the prognosis of patients with thyroid cancer Six types prognosis-related immune genes (CXCL5, COLEC10, S100A9, MMP12, APOD, FGF7) were obtained from previous studies [ 19 ] . The prognostic risk model was constructed by univariate COX and LASSO regression (Fig. 5 A-C). Risk score of immune gene = CXCL5exp * 0.74626 + COLEC10exp * 1.54694 + S100A9exp * 0.00784 + MMP12exp * (-0.06699) + APODexp * 0.02822 + FGF7exp * 0.34368. The OS of the high-risk group patient was shorter than that of the low-risk group (P < 0.001, Fig. 5 D). The AUC of the 5-year, 8-year, and 10-year risk scores were respectively 0.830, 0.826, and 0.830 (Fig. 5 E). 2.6 Correlation between immune cell score and immune gene The correlation between immune cells score and immune gene expression in the risk model was analyzed, and its results were divided into negative correlation group and negative correlation group (Fig. 6 A-M). In the negative correlation group, plasma cells vs APOD ( R = -0.21 ), CD8 T cells vs CXCL5 ( R = -0.25 ), plasma cells vs CXCL5 ( R = -0.21 ), plasma cells vs FGF7 ( R = -0.17 ), CD8 T cells vs S100A9 ( R = -0.18 ). In the positive correlation group, CD8 T cells vs COLEC10 ( R = 0.21 ), M0 macrophages vs CXCL5 ( R = 0.2 ), activated dendritic cells vs CXCL5 ( R = 0.21 ), memory B cells vs FGF7 ( R = 0.21 ), activated dendritic cells vs S100A9 ( R = 0.28 ), M0 macrophages vs MMP12 ( R = 0.15 ), activated mast cells vs S100A9 ( R = 0.69 ). Discussion Tumor microenvironment (TME) is a structured ecosystem that consists of tumor cells, abundant and diverse immune cells, cancer-associated fibroblasts (CAFs), endothelial cells (ECs) and other types of cells and extracellular matrix [ 21 ] . The cells and cell molecules in TME play a key role in tumor development, and they have become the most attractive therapeutic targets [ 22 – 24 ] . The immune cells in TME play a dual role of promoting or inhibiting tumor growth at different stages of tumor development, and also participate in tumor immune escape [ 25 – 28 ] . In the tumor microenvironment, CD8 T cells are cytotoxic T lymphocytes that specifically eliminate tumor cells, playing a crucial role in anti-tumor immune responses.M1 macrophages also contribute to anti-tumor immune responses by releasing proinflammatory cytokines ( such as IL-12, TNF-a ) to inhibit tumor cells proliferation. However, M2 macrophages promote tumor proliferation and metastasis by secreting anti-inflammatory cytokines (including IL-10, TGF-β), which suppress the anti-tumor immune response [ 29 ] . The results of this study indicate that CD8 T cells and M1 macrophages score was lower, while the M2 macrophages score was higher in tumor tissues compared with normal tissues. Additionally, the CD8 T cells score was positively correlated with the M1 macrophages score, while the CD8 T cells score was negatively correlated with the M2 macrophages score in tumor tissues. This suggests that there are more inhibitory immune cells infiltrating around the tumor tissue, which is easy to form an immunosuppressive tumor microenvironment. Furthermore, the correlation analysis between immune cells score and tumor stage revealed that CD8 T cells and M1 macrophages had lower score and M2 macrophages had higher score in stage III and IV tumors than in stage I tumors. It predicates that the lower the CD8 T-cells score and M1 macrophages score, the later the tumor stage, and the poorer the prognosis of the patients. The other previous studies have utilized immunohistochemistry to quantitatively analyze immune cells in the tumor microenvironment, and have proposed an immunoscore (IS) system to predict the survival and recurrence rates of tumor patients, as well as the timing of therapeutic interventions [ 17 , 30 – 31 ] . However, these studies have limitations. Immunohistochemistry is a semi-quantitative technique that only analyzes only CD8 + T cells, and CD3 + T cells in relation to patient prognosis. In contrast, our study identified prognostic relevance of several different immune cells. Each immune cell was quantitatively scored to construct a scoring model, which was used to stratify the prognostic risk of thyroid cancer and predict patient survival. This model also has the potential to identify drug treatment-sensitive populations and guide drug selection. The results of our study showed that a risk model constructed by the scores of six types of immune cells (memory B cells, plasma cells, CD8 T cells, M0 macrophages, activated dendritic cells and mast cells), the model can effectively divide patients into high-risk and low-risk group according to the scores, and the OS of patients in high-risk group was significantly shorter than that of patients in the low-risk group, The 10-year OS of patients was 82.6% in high-risk group and 100% in the low-risk group. Furthermore, our risk score demonstrated high accuracy in predicting the 5-year, 8-year and 10-year survival of patients, and the AUC values of the model at 5-years, 8-years and 10-years were 0.886, 0.862 and 0.896 respectively. Immunotherapy is gradually being used in the treatment of various cancers [ 32 – 33 ] , and has also been applied and made some advancements in thyroid cancer [ 34 – 36 ] . However, how to find the target population for therapeutic benefit is still an urgent problem. This study revealed that individuals in the high-risk group of the immune cell score risk model, the function of B-cells and CD8 + T-cells was weaker. Their therapeutic response to PD-1 or CTLA4 inhibitors was poorer, and IC50 values for cyclophosphamide and darafenib were higher. These findings suggest that the patients in the high-risk group were less likely to benefit from immunotherapy. Furthermore, the high-risk patients showed lower sensitivity to drugs such as cyclophosphamide and dabrafenib. Therefore, it may be inappropriate for high-risk patients to receive the above drugs. These findings can guide us to adjust the treatment strategy clinically. In tumor microenvironment, the infiltration abundance and function of immune cells are strongly related to the prognosis of tumor patients. It also affects prognosis of patient. It was reported that highly invasive CD8 + T cells are related to the favorable prognosis of patients with thyroid cancer [ 36 ] , while the inhibition of CD8 + T cell function can promote the progression of thyroid cancer [ 37 ] . The same findings were found in this study that the number of infiltrating CD8 T cells was less in thyroid cancer tissue in comparison with normal tissues, and a later clinical stage in patients with lower levels of infiltrating CD8 + T cells. In addition, this study has also revealed a significant correlation between the prognosis of patients and six types immune cell, these cell types included memory B cells, plasma cells, activated dendritic cells, M0 macrophages and mast cells. High infiltration of memory B cells, activated dendritic cells and plasma cells in tumor tissue indicate that the favorable prognosis of tumor patients, the better therapeutic effect of immunotherapy [ 38 – 45 ] . M0 macrophages are non-polarized cells [ 46 ] .Interaction of M0 macrophages with naïve CD4 + T cells promote tumor progression through paracrine mechanisms and intercellular adhesion interactions. Blocking the communication between M0 macrophages and naïve CD4 + T cells inhibited the generation of an immunosuppressive microenvironment in cervical cancer, leading to improved patient prognosis [ 47 ] . In the thyroid cancer microenvironment, CXCL5 activates macrophages [ 48 ] . Mast cell (MC) is an innate immune cell derived from bone marrow stem cells. In tumor microenvironment (TME), MCs promote and inhibit tumor growth. MCs play different roles in different tumor types and different stages of tumor development. MCs degrade the extracellular matrix by activating matrix metalloproteinases to promote tumor growth and metastasis. In addition, MCs release VEGF, PDGF-β and IL-6 to stimulate tumor angiogenesis and cell proliferation. Furthermore, mast cells secrete chemokines such as CXCL10, CLL3 and CCL5, which recruit CD8 + T cells and CD4 + T cells to inhibit tumor growth [ 49 – 51 ] . MCs promote the progression of colorectal cancer, angiogenesis, and lymphangiogenesis through the c-kit/ SCF pathway and histamine production [ 52 ] . MCs also participate in tumor immunotherapy resistance [ 53 ] . The interaction between IL-33 and ST2 in gastric cancer activates mast cells through P38MAPK pathway, and mast cells secrete cytokine IL-2 to induce the transformation of naïve CD4 + T cells into ICOS + Treg cells, and COS + Treg cells significantly inhibit the proliferation and function of cytotoxic CD8 + T cells, thus resulting in gastric cancer immune tolerance [ 54 ] . However, Mast cells play an anti-tumor immune role by recruiting and activating T and B cells. The combination of the activated mast cells and PD-L1 inhibitor blocked the progress of breast cancer [ 55 ] . MC is activated to inhibit tumor progression in the colorectal cancer microenvironment [ 56 ] . The studies have showed that tumor-associated mast cells promote thyroid cancer progression by inhibition of CD8 + T-cell function through hemagglutinin-9 [ 57 ] . Additionally, mast cells induce EMT and stem cell characteristics in human thyroid cancer cells through the IL-8-Akt-Slug pathway [ 58 ] . Plasma cells secrete different types of antibodies that modulate the host immune response and influence patient prognosis. The secretion of IgG1 and IgG3 by plasma cells is associated with good prognosis for patients. Plasma cells secrete IgG1/IgG3 in combination with FcγR1/FcγRIV to activate natural killer cells (NK cells) and macrophages, which mediate antibody-dependent cell cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) effects and directly kill tumor cells. Conversely, the secretion of IgA, IgM or IgG4 by plasma cells is linked to poor prognosis for patients [ 59 – 60 ] . In colorectal liver metastasis (CRLM), IgA + plasma cells are recruited by the CCR10-CCL28 axis to deactivate granulocyte-myeloid-derived suppressor cells (G-MDSC) and inhibit the host's anti-tumor immunity. In hepatocellular carcinoma, IgG + plasma cells are recruited by the CXCR3-CXCL10 axis to promote tumor macrophage infiltration and inhibit immune response [ 61 ] . In the tumor microenvironment, IgG antibodies secreted from infiltrating plasma cells that maintain the self-renewal of tumor stem cells and promote the progression of glioblastoma by activating the FcγRIIA-AKT-mTOR signaling pathway [ 62 ] . The activation and suppression of immune genes significantly affect the function of immune cells immune cell function during tumorigenesis and development. The expression of immune genes is closely related to the prognosis of tumor patients. The results of the research have displayed that the infiltration degree of CD8 T cells is negatively correlated with the expression of immune genes S100A9 and CXCL5, while positively correlated with COLEC10, It suggests that these genes regulate CD8 T cells. CXCL5 is a CXC chemokine and a CXCR2 receptor agonist. In the tumor microenvironment, CXCL5 promotes tumor angiogenesis and participates in tumor growth and metastasis. Overexpression of CXCL5 indicate poor prognosis for patients with tumors [ 63 ] . The previous studies have demonstrated that CXCL5 induces the accumulation of mature neutrophils in lung cancer tissues and inhibits the differentiation of CD8 T cells [ 64 ] . The overexpression of CXCL5 in papillary thyroid carcinoma cells promote tumor cells proliferation by activating the CXCL5-CXCR2 axis [ 65 ] .S100A9 is a calcium-binding protein. S100A9 promotes tumor proliferation, migration and invasion by recruiting myeloid-derived suppressor cells (MDSC) [ 66 ] .Glioma-derived S100A9 inhibits the immunosuppressive effect of CD8 + T lymphocytes by αvβ3 integrin / AKT1 / TGFβ1 polarized M2 microglial cells [ 67 ] . COLEC10 is a sensor of innate immunity and triggering complement cascade, which activates the innate immune response of the host's to resist tumors. COLEC10 inhibits hepatocellular carcinoma(HCC) progression by regulating GRP78-mediated the endoplasmic reticulum stress signaling [ 68 ] . COLEC10 inhibits the stemness of HCC by downregulating the Wnt/β-catenin pathway [ 69 ] . COLEC10 inhibits proliferation, migration and invasion of HCC cells by suppressing epithelial-mesenchymal transition and modulating the PI3K-AKT pathway [ 70 ] .Our previous research also presented that the low expression of COLEC10 was related to the late staging and lymph node metastasis in thyroid cancer, and the overexpression of CXCL5 and S100A9 suggested that the prognosis of thyroid cancer was poor [ 19 ] . Furthermore, the results of this research showed that plasma cells were negatively correlated with CXCL5, FGF7 and APOD, while memory B cells were positively correlated with FGF7. The activated dendritic cells were positively correlated with CXCL5 and S100A9, M0 macrophages were positively correlated with CXCL5 and MMP12. The activated mast cells were positively correlated with S100A9. CXCL5 is a member of the chemokine family, which is secreted by various types of cells, including tumor cells, immune cells (macrophages), and other non-immune cells. CXCL5 secreted by immune cells participates in the formation of immunosuppressive microenvironment and promotes tumor metastasis. CXCL5 derived from tumor recruits myeloid-derived suppressor cells (MDSC) to inhibit the function of T cells and NK cells, and prevent the host’s anti-tumor immune response [ 71 ] . CXCL5 enhances the migration and invasion of thyroid cancer cells by promoting epithelial mesenchymal transformation through CXCL5-CXCR2 signal axis [ 72 ] . Apolipoprotein D (APOD) encoded by the APOD gene is a multifunctional lipoprotein. APOD is a member of the APO family and plays an important role in lipid transport, inflammation, antioxidant response, and tumorigenesis [ 73 ] . SACC-derived APOD elevated cancer cell a migration and invasion along peripheral nerves in vivo [ 74 ] . In thyroid cancer, high expression of APOD is closely linked to larger tumor size, later clinical stage and lymph node metastasis [ 19 ] . FGF7, also known as keratinocyte growth factor (KGF), plays a crucial role in tissue development and tumorigenesis. It binds to FGFR2-IIIb to regulate the proliferation, differentiation and repair of epithelial cells by activating the RAS-MAPK and PI3K-AKT pathways. Otherwise, FGF7 is involved in the formation of organs such as lung, breast and prostate during embryonic development. In addition, FGF7 promotes cancer cell proliferation through the autocrine loop. FGF7 secreted by stromal cells stimulates tumor growth through the paracrine loop [ 75 ] . FGF-7 enhances tumor invasion and macrophage infiltration and promotes tumor progression [ 76 ] . FGF7 derived from cancer-associated fibroblasts (CAF) mediates epithelial-mesenchyme transition (EMT) to promote tumor metastasis by activating FGF7/HIF-1α pathway [ 77 ] . Memory B cells are located in spleen, blood, other lymphoid organs and barrier tissues, and are the key storage of plasma cell production in secondary immune response. FGF7 is essential for organogenesis and tissue differentiation [ 75 ] . Our research has discovered that memory B cells are positively correlated with FGF7. It can be inferred that FGF7 positively regulates the function and differentiation of memory B cells. CXCL5, FGF7 and APOD in tumor tissues have negative impact on anti-tumor immunity. While plasma cells have positive impact. The overexpression of CXCL5, FGF7 and APOD in thyroid cancer is associated with poor prognosis. CXCL5, FGF7 and APOD can be used as potential prognostic biomarkers [ 19 ] . Matrix metalloproteinase 12 (MMP-12) is a member of matrix metalloproteinase family, which is mainly produced by macrophages. MMP-12 promotes tumor proliferation and metastasis by inhibiting the activation of T cells and B cells [ 78 ] . Calceolarioside B inhibits MMP12 M2-like TAMs polarization and infiltration in the tumor microenvironment. It reverses immunosuppression and effectively inhibits tumor progression [ 79 ] . There is usually a connection between immune cells, and it is a deficiency that the infiltration abundance of a single immune cell predicts the prognosis of thyroid cancer patients. In this study, the risk model was constructed using six different types of immune cells. This model categorized patients into high-risk group and low-risk group according to the immune cell score. The prognosis of patients in the high-risk group was worse than that in the low-risk group. The gene enrichment of patients in the high-risk group was mainly in ECM-receptor interaction, focal adhesion, tumorigenesis, PPAR signaling pathway and TGF-β signaling pathway. These pathways are frequently activated in tumorigenesis and tumor progression. In conclusion, the risk model was constructed using the infiltration scores of six types immune cells in the study. The model can identify the high-risk group, predict survival, screen the population benefiting from drug treatment. The correlation between immune cells and six immune-related genes has also been confirmed in the study. These findings are helpful for the prognosis evaluation of thyroid cancer and guide drug selection in clinical practice. The genes related to immune cells can be used as potential targets for immunotherapy. The efficacy of this scoring model still needs further validation in multicenter clinical practice. The application of Multi-omics findings combined with artificial intelligence will be the direction of future research. Declarations Data availability statement The datasets analysed in this study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo/), TCGA database(https: //portal. gdc.cancer.gov /),GSEA enrichment analysis (https://www.gsea-msigdb.org/gsea/index.jsp),TCIA database(https://www.tcia.at/home),GDSC database(https://www.cancerrxgene.org/),CIBERSORT algorithm (https://cibersortx.stanford.edu/). Generative AI statement The author(s) confirm that no generative AI tools were utilized in the preparation of this manuscript Clinical trial number Not applicable. Consent to Publish declaration Not applicable. Declarations Ethical approval Not applicable. Consent to participate Not applicable. Consent to publish Not applicable. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships. Authors' Contributions Included conception and study design (YYL and YJL), data collection or acquisition (XC, LT, HCH, SHY, LXR, SZB), statistical analysis (YJL, YYL, XC, and SZB), interpre­tation of results (YYL, YJL, XC, LT, HCH, SHY, LXR, and SZB), drafting the manuscript work (YYL and YJL) and approval of final version to be published and agreement to be accountable for the integrity and ac­curacy of all aspects of the work (all the authors). *Correspondence to: Youlin Yu, Email: [email protected] . Junlin Yu. 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Single-Cell RNA Sequencing Revealing that MMP12 + Macrophages are Associated with Cancer Liver Metastasis. Curr Med Chem. 2024. https://doi.org/10.2174/0109298673337385240827061539 . 10.2174/0109298673337385240827061539. Advance online publication. Cao L, Shao M, Gu Y, et al. Calceolarioside B targets MMP12 in the tumor microenvironment to inhibit M2 macrophage polarization and suppress hepatocellular carcinoma progression. Phytomedicine: Int J phytotherapy phytopharmacology. 2025;142:156805. https://doi.org/10.1016/j.phymed.2025.156805 . Advance online publication. Conclusions. In this study, the characteristic of the immune microenvironment of thyroid cancer was analyzed. The immune cell score was used to construct a risk model, and further investigated relationship between the model and the prognosis and drug sensitivity of thyroid cancer. The correlation between immune cells and immune genes was studied in the model. The model predicts the prognosis of thyroid cancer, identifies high-risk groups of thyroid cancer, and screens dominant populations benefiting from immunotherapy. It provides reference for prognosis evaluation and treatment strategy of thyroid cancer. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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11:39:19","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25898,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8329405/v1/b983f5a995824eee9afe057a.png"},{"id":100396262,"identity":"dfde724f-f5aa-4660-806d-c0fdea5a8280","added_by":"auto","created_at":"2026-01-16 11:40:18","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":34449,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8329405/v1/bafe6f5bc6ae12538cf72931.png"},{"id":100396028,"identity":"0f0eb198-068b-4176-a71b-510e5879e681","added_by":"auto","created_at":"2026-01-16 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13:02:50","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172117,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8329405/v1/bfb687cfa9af37160e5dc36b.html"},{"id":100395678,"identity":"400885ba-c90b-4694-b50a-589770c8657c","added_by":"auto","created_at":"2026-01-16 11:39:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3180454,"visible":true,"origin":"","legend":"\u003cp\u003eInfiltration of 22 immune cells in the tumor microenvironment and clinical correlation. A Distribution map of the differences in immune cell infiltration between tumor and normal tissues; B Correlation of infiltration abundance between different immune cells; C-E Correlation of T cells CD8, M1 macrophage, and M2 macrophage infiltration abundance with clinical stage, respectively.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8329405/v1/c190a18ebcb4449cc57cba8a.png"},{"id":100395869,"identity":"e7ebc74d-ab6d-4dd8-9d86-38f5eed71144","added_by":"auto","created_at":"2026-01-16 11:39:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1547135,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and prognostic value of a risk model based on immune cell infiltration score A Forest plot of multivariate Cox regression analysis B Risk model K-M survival plot C Risk model ROC curve D Heatmap of immune cell distribution.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8329405/v1/5fb1d0e7df8570a2e75cddb7.png"},{"id":100396101,"identity":"98f0c261-62b1-43d3-9470-d955e98be155","added_by":"auto","created_at":"2026-01-16 11:39:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1371904,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in immune function (A) immunotherapy sensitivity (B-E), and drug sensitivity (F-G) between risk groups\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8329405/v1/50b6147aeb0176c63243e759.png"},{"id":100396500,"identity":"22791ad9-6cb4-4da9-b0d4-7c13351a8e55","added_by":"auto","created_at":"2026-01-16 11:40:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":594133,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA analysis in the high-risk group\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8329405/v1/c5e3f11acf22dfe6a4d1ed2b.png"},{"id":100396285,"identity":"c8f5f936-3db0-46e1-90b2-da4a23ae7cda","added_by":"auto","created_at":"2026-01-16 11:40:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":640764,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and prognostic validation of risk model based on immune-related genes A Forest plot of multivariate Cox regression analysis. B LASSO cross-validation curve. C LASSO coefficient pathway plot. D K-M survival plot, E Risk score ROC curve.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8329405/v1/ba75a7e5c9daac509b89dc2a.png"},{"id":100396355,"identity":"0471d81f-ec95-41ac-8810-96ed0877e109","added_by":"auto","created_at":"2026-01-16 11:40:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1329850,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between immune cell infiltration score and expression of immune-related genes\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8329405/v1/6e77331e2fb201f96451e193.png"},{"id":107868292,"identity":"0db84916-e119-47db-bb2b-1a29584d1229","added_by":"auto","created_at":"2026-04-27 07:09:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9055531,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8329405/v1/2e760d0d-979e-475d-bec1-675ae6c6dec1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Immune cell score model is a new tumor marker for predicting prognosis and selecting therapeutic drugs in thyroid cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003ePlease have a look at courier new font provided for text in article.\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThyroid cancer is the most common endocrine malignancy, and its incidence is slowly increasing worldwide \u003csup\u003e[\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The therapeutic efficacy and prognosis of thyroid cancer are widely concerned. Currently, there are some deficiencies in predicting prognosis and guiding treatment decisions by the TNM staging system. In the process of long-term clinical practice, it is found that the patients younger than 55 years old with differentiated thyroid cancer had invasion of local tissues and organs and distant metastasis, and they still had a long survival period with tumor. However, the patients with poorly differentiated and undifferentiated thyroid cancer have rapid tumor progression and death after surgery, even if local tissue invasion occurs. There are significant differences in the clinical outcomes of thyroid cancer in the same tumor histological stage, different age stages, and different tumor types. It can be inferred that the traditional tumor TNM staging system focuses on the predictive value of the tumor itself for disease progression, ignoring the influence of the immune status of the host and the influence of the tumor microenvironment\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.The studies have confirmed that the immune microenvironment of tumors may play an important role in tumor development\u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Tumor progression is a result of the imbalance of organism anti-tumor immunity. The human immune system cannot eliminate or control tumor cells alone, and tumors enter the stage of immune escape. Immune cells are widely distributed in the microenvironment of thyroid cancer, which promote or inhibit tumor development at different stages of tumorigenesis\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. It is particularly important to understand the role of immune cells in the tumor microenvironment. In the tumor microenvironment, immune cells constitute an immune barrier in the tumor parenchyma, the surrounding matrix of the tumor, the edge of tumor invasion, and the vicinity of the tumor \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.The number and function of immune cells infiltrated in different compartments of the tumor tissue affect the prognosis of tumor patients \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Based on this, Angell H et al researchers quantitatively analyzed the immune cells in the tumor microenvironment by immunohistochemistry, and proposed the immunoscore (IS) system to predict the survival of tumor patients \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. This study has limitations. Immunohistochemical technique is a semi-quantitative detection technique, and quantitative scoring is performed only on T cells. Neglecting the many other different immune cells of the tumor microenvironment. Our previous research results have shown that the prognostic evaluation model of thyroid cancer was constructed based on clinic pathological features, immune genes, the model has high accuracy. The model can better predict the prognosis of thyroid cancer \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Therefore, this study further analyzed the characteristics of thyroid cancer immune microenvironment at the cellular level, and the effect of immune microenvironment on tumor prognosis. The prognostic model of thyroid cancer was constructed by the different immune cell scores. The model predicts the prognosis of thyroid cancer, identifies high-risk groups of thyroid cancer, and screens dominant populations benefiting from immunotherapy. It provides reference for prognosis evaluation and treatment strategy of thyroid cancer.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eMaterials\u003c/b\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Data acquisition\u003c/h2\u003e \u003cp\u003eThe gene expression feature set of 22 immune cells was obtained from the previous literature\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, and the infiltration score of each immune cell was calculated by the CIBERSORT algorithm (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersortx.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersortx.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Gene expression profiles and clinical information of patients were obtained by TCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Differences in immune cell scores and clinical correlation\u003c/h2\u003e \u003cp\u003eThe difference of immune cell score between tumor and normal tissue was analyzed by R software. The relationship between immune cell score and clinical characteristics was further researched.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Prognostic risk model was constructed by immune cell score\u003c/h2\u003e \u003cp\u003eUnivariate COX analysis was used to screen prognostic-related immune cells (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1).The prognostic risk model was constructed by multivariate analysis. Prognostic risk score\u0026thinsp;=\u0026thinsp;Σ (immune cell infiltration score * correlation coefficient).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Prognostic value of prognostic risk model in thyroid cancer\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate COX regression analysis was used to analyze the independent prognostic predictive value of the prognostic risk model. The area under the ROC curve (AUC) was used to evaluate the predictive efficacy of the model. The K-M curve was used to evaluate the difference in overall survival (OS) between the high and low risk groups of the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.5 Prognostic risk model enrichment analysis\u003c/h2\u003e \u003cp\u003eGSEA enrichment analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was performed on the high and low risk groups of the model by the KEGG and HALLMARK gene set.\u003c/p\u003e \u003cp\u003e \u003cb\u003e1.6 Differences in immune cell function, immunotherapy and drug sensitivity between high-risk and low-risk groups in the prognostic risk model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the model, the differences in immune cell function, PD-L1 expression, anti-PD-1 and CTLA4 treatment response (TCIA database, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tcia.at/home),an\u003c/span\u003e\u003cspan address=\"https://www.tcia.at/home),an\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ed common targeted and chemotherapeutic drugs IC50 (half maximal inhibitory concentration value) between the high-risk group and the low-risk group (GDSC database, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was analyzed by R language.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.7 Prognostic risk model construction and prognostic value verification of immune genes\u003c/h2\u003e \u003cp\u003eSix immune related genes (IRG) were obtained from our previous study \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.The prognostic risk model was constructed by univariate COX and LASSO regression. Risk score\u0026thinsp;=\u0026thinsp;Σ (gene expression * correlation coefficient). And further verify the prognostic value of the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e1.8 Correlation analysis between immune cells and immune genes\u003c/h2\u003e \u003cp\u003eThe correlation between immune cell score and immune gene expression was studied by R language.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The difference of immune cell infiltration in thyroid carcinoma\u003c/h2\u003e \u003cp\u003eNaive B cells, memory B cells, CD8 T cells, gamma delta T cells, M0 macrophages, M1 macrophages, M2 macrophages, naive dendritic cells, activated dendritic cells, naive mast cells, activated mast cells, eosinophils. These 12 types immune cells score were different between tumor and normal tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Among them, CD8 T cells, M1 macrophages scored lower in the tumor group, while M2 macrophages scored higher in the tumor group. The results of correlation between immune cells showed that CD8 T cells score was positively correlated with M1 macrophages score and negatively correlated with M2 macrophages score( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB ). Immune cells score was correlated with tumor stage. The score of CD8 T cells and M1 macrophages was lower, the score of M2 macrophages was higher in stage III and IV tumors compared with stage I tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Risk model constructed by immune cell score predicts the prognosis of patients with thyroid cancer\u003c/h2\u003e \u003cp\u003eUnivariate COX analysis showed that 6 types immune cells were associated with the prognosis of patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), including memory B cells, plasma cells, CD8 T cells, M0 macrophages, activated dendritic cells and mast cells. Further multivariate COX regression constructed a risk model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Risk score of immune cells\u0026thinsp;=\u0026thinsp;memory B cell score * 15.20656\u0026thinsp;+\u0026thinsp;plasma cell score * (-27.43278)\u0026thinsp;+\u0026thinsp;CD8 T cell score * (-6.06323)\u0026thinsp;+\u0026thinsp;M0 macrophage cell score * 2.50137\u0026thinsp;+\u0026thinsp;activated dendritic cell score * 5.08962\u0026thinsp;+\u0026thinsp;activated mast cells * 221.74345. The risk model was divided into high-risk group and low-risk group according to the median score. The overall survival (OS) of the high-risk group patient was shorter than that of the risk group (P\u0026thinsp;=\u0026thinsp;0.008, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The 10-year OS was 82.6% in the high-risk group patient, while that of was 100% in the low-risk group. The AUC of the risk score for predicting 5-year, 8-year, and 10-year survival was respectively 0.886, 0.862, and 0.896(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). There were differences in the scores of six types immune cells in the high and low risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\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\u003e\u003cb\u003eCox univariate analysis of six immune cells in thyroid cancer\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmune cell\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB cell memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6e\u0026thinsp;+\u0026thinsp;05(0.3-8.9e\u0026thinsp;+\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2e-20(4.8e-40-14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT cells CD8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0e-8(3.6e-15-0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrophages M0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121.6(1.1-1.4e\u0026thinsp;+\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDendritic cells activated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1e\u0026thinsp;+\u0026thinsp;08(0.2-5.3e\u0026thinsp;+\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMast cells activated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0e\u0026thinsp;+\u0026thinsp;124(1.4e\u0026thinsp;+\u0026thinsp;33-3.1e\u0026thinsp;+\u0026thinsp;215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\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 \u003cp\u003e \u003cb\u003e2.3 Differences in immune function, immunotherapeutic response and drug sensitivity between high and low risk group in the model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere were differences in immune function between high and low risk group in the model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). B cells and CD8T cells functioned more poorly, the response to anti-PD-1 or CTLA4 inhibitors was worse (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-E, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the IC50 values to cyclophosphamide and darafenib were higher in the high-risk group than in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-G, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.4 GSEA pathway enrichment analysis in the model\u003c/h2\u003e \u003cp\u003eThe gene enrichment pathways in the high-risk group in the model were mainly concentrated in ECM-receptor interaction, focal adhesion, tumorigenesis, PPAR signaling pathway and TGF-β signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.5 The risk model of immune gene construction predicts the prognosis of patients with thyroid cancer\u003c/h2\u003e \u003cp\u003eSix types prognosis-related immune genes (CXCL5, COLEC10, S100A9, MMP12, APOD, FGF7) were obtained from previous studies \u003csup\u003e[ 19 ]\u003c/sup\u003e. The prognostic risk model was constructed by univariate COX and LASSO regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). Risk score of immune gene\u0026thinsp;=\u0026thinsp;CXCL5exp * 0.74626\u0026thinsp;+\u0026thinsp;COLEC10exp * 1.54694\u0026thinsp;+\u0026thinsp;S100A9exp * 0.00784\u0026thinsp;+\u0026thinsp;MMP12exp * (-0.06699)\u0026thinsp;+\u0026thinsp;APODexp * 0.02822\u0026thinsp;+\u0026thinsp;FGF7exp * 0.34368. The OS of the high-risk group patient was shorter than that of the low-risk group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The AUC of the 5-year, 8-year, and 10-year risk scores were respectively 0.830, 0.826, and 0.830 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Correlation between immune cell score and immune gene\u003c/h2\u003e \u003cp\u003eThe correlation between immune cells score and immune gene expression in the risk model was analyzed, and its results were divided into negative correlation group and negative correlation group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-M). In the negative correlation group, plasma cells vs APOD ( R = -0.21 ), CD8 T cells vs CXCL5 ( R = -0.25 ), plasma cells vs CXCL5 ( R = -0.21 ), plasma cells vs FGF7 ( R = -0.17 ), CD8 T cells vs S100A9 ( R = -0.18 ). In the positive correlation group, CD8 T cells vs COLEC10 ( R\u0026thinsp;=\u0026thinsp;0.21 ), M0 macrophages vs CXCL5 ( R\u0026thinsp;=\u0026thinsp;0.2 ), activated dendritic cells vs CXCL5 ( R\u0026thinsp;=\u0026thinsp;0.21 ), memory B cells vs FGF7 ( R\u0026thinsp;=\u0026thinsp;0.21 ), activated dendritic cells vs S100A9 ( R\u0026thinsp;=\u0026thinsp;0.28 ), M0 macrophages vs MMP12 ( R\u0026thinsp;=\u0026thinsp;0.15 ), activated mast cells vs S100A9 ( R\u0026thinsp;=\u0026thinsp;0.69 ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTumor microenvironment (TME) is a structured ecosystem that consists of tumor cells, abundant and diverse immune cells, cancer-associated fibroblasts (CAFs), endothelial cells (ECs) and other types of cells and extracellular matrix\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The cells and cell molecules in TME play a key role in tumor development, and they have become the most attractive therapeutic targets\u003csup\u003e[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The immune cells in TME play a dual role of promoting or inhibiting tumor growth at different stages of tumor development, and also participate in tumor immune escape\u003csup\u003e[\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the tumor microenvironment, CD8 T cells are cytotoxic T lymphocytes that specifically eliminate tumor cells, playing a crucial role in anti-tumor immune responses.M1 macrophages also contribute to anti-tumor immune responses by releasing proinflammatory cytokines ( such as IL-12, TNF-a ) to inhibit tumor cells proliferation. However, M2 macrophages promote tumor proliferation and metastasis by secreting anti-inflammatory cytokines (including IL-10, TGF-β), which suppress the anti-tumor immune response\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe results of this study indicate that CD8 T cells and M1 macrophages score was lower, while the M2 macrophages score was higher in tumor tissues compared with normal tissues. Additionally, the CD8 T cells score was positively correlated with the M1 macrophages score, while the CD8 T cells score was negatively correlated with the M2 macrophages score in tumor tissues. This suggests that there are more inhibitory immune cells infiltrating around the tumor tissue, which is easy to form an immunosuppressive tumor microenvironment. Furthermore, the correlation analysis between immune cells score and tumor stage revealed that CD8 T cells and M1 macrophages had lower score and M2 macrophages had higher score in stage III and IV tumors than in stage I tumors. It predicates that the lower the CD8 T-cells score and M1 macrophages score, the later the tumor stage, and the poorer the prognosis of the patients.\u003c/p\u003e \u003cp\u003eThe other previous studies have utilized immunohistochemistry to quantitatively analyze immune cells in the tumor microenvironment, and have proposed an immunoscore (IS) system to predict the survival and recurrence rates of tumor patients, as well as the timing of therapeutic interventions\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. However, these studies have limitations. Immunohistochemistry is a semi-quantitative technique that only analyzes only CD8\u0026thinsp;+\u0026thinsp;T cells, and CD3\u0026thinsp;+\u0026thinsp;T cells in relation to patient prognosis. In contrast, our study identified prognostic relevance of several different immune cells. Each immune cell was quantitatively scored to construct a scoring model, which was used to stratify the prognostic risk of thyroid cancer and predict patient survival. This model also has the potential to identify drug treatment-sensitive populations and guide drug selection. The results of our study showed that a risk model constructed by the scores of six types of immune cells (memory B cells, plasma cells, CD8 T cells, M0 macrophages, activated dendritic cells and mast cells), the model can effectively divide patients into high-risk and low-risk group according to the scores, and the OS of patients in high-risk group was significantly shorter than that of patients in the low-risk group, The 10-year OS of patients was 82.6% in high-risk group and 100% in the low-risk group. Furthermore, our risk score demonstrated high accuracy in predicting the 5-year, 8-year and 10-year survival of patients, and the AUC values of the model at 5-years, 8-years and 10-years were 0.886, 0.862 and 0.896 respectively. Immunotherapy is gradually being used in the treatment of various cancers\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, and has also been applied and made some advancements in thyroid cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. However, how to find the target population for therapeutic benefit is still an urgent problem. This study revealed that individuals in the high-risk group of the immune cell score risk model, the function of B-cells and CD8\u0026thinsp;+\u0026thinsp;T-cells was weaker. Their therapeutic response to PD-1 or CTLA4 inhibitors was poorer, and IC50 values for cyclophosphamide and darafenib were higher. These findings suggest that the patients in the high-risk group were less likely to benefit from immunotherapy. Furthermore, the high-risk patients showed lower sensitivity to drugs such as cyclophosphamide and dabrafenib. Therefore, it may be inappropriate for high-risk patients to receive the above drugs. These findings can guide us to adjust the treatment strategy clinically.\u003c/p\u003e \u003cp\u003eIn tumor microenvironment, the infiltration abundance and function of immune cells are strongly related to the prognosis of tumor patients. It also affects prognosis of patient. It was reported that highly invasive CD8\u0026thinsp;+\u0026thinsp;T cells are related to the favorable prognosis of patients with thyroid cancer\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, while the inhibition of CD8\u0026thinsp;+\u0026thinsp;T cell function can promote the progression of thyroid cancer\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. The same findings were found in this study that the number of infiltrating CD8 T cells was less in thyroid cancer tissue in comparison with normal tissues, and a later clinical stage in patients with lower levels of infiltrating CD8\u0026thinsp;+\u0026thinsp;T cells.\u003c/p\u003e \u003cp\u003eIn addition, this study has also revealed a significant correlation between the prognosis of patients and six types immune cell, these cell types included memory B cells, plasma cells, activated dendritic cells, M0 macrophages and mast cells. High infiltration of memory B cells, activated dendritic cells and plasma cells in tumor tissue indicate that the favorable prognosis of tumor patients, the better therapeutic effect of immunotherapy\u003csup\u003e[\u003cspan additionalcitationids=\"CR39 CR40 CR41 CR42 CR43 CR44\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eM0 macrophages are non-polarized cells\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e.Interaction of M0 macrophages with na\u0026iuml;ve CD4\u0026thinsp;+\u0026thinsp;T cells promote tumor progression through paracrine mechanisms and intercellular adhesion interactions. Blocking the communication between M0 macrophages and na\u0026iuml;ve CD4\u0026thinsp;+\u0026thinsp;T cells inhibited the generation of an immunosuppressive microenvironment in cervical cancer, leading to improved patient prognosis \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. In the thyroid cancer microenvironment, CXCL5 activates macrophages\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMast cell (MC) is an innate immune cell derived from bone marrow stem cells. In tumor microenvironment (TME), MCs promote and inhibit tumor growth. MCs play different roles in different tumor types and different stages of tumor development. MCs degrade the extracellular matrix by activating matrix metalloproteinases to promote tumor growth and metastasis. In addition, MCs release VEGF, PDGF-β and IL-6 to stimulate tumor angiogenesis and cell proliferation. Furthermore, mast cells secrete chemokines such as CXCL10, CLL3 and CCL5, which recruit CD8\u0026thinsp;+\u0026thinsp;T cells and CD4\u0026thinsp;+\u0026thinsp;T cells to inhibit tumor growth \u003csup\u003e[\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. MCs promote the progression of colorectal cancer, angiogenesis, and lymphangiogenesis through the c-kit/ SCF pathway and histamine production \u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. MCs also participate in tumor immunotherapy resistance \u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. The interaction between IL-33 and ST2 in gastric cancer activates mast cells through P38MAPK pathway, and mast cells secrete cytokine IL-2 to induce the transformation of na\u0026iuml;ve CD4\u0026thinsp;+\u0026thinsp;T cells into ICOS\u0026thinsp;+\u0026thinsp;Treg cells, and COS\u0026thinsp;+\u0026thinsp;Treg cells significantly inhibit the proliferation and function of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells, thus resulting in gastric cancer immune tolerance\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. However, Mast cells play an anti-tumor immune role by recruiting and activating T and B cells. The combination of the activated mast cells and PD-L1 inhibitor blocked the progress of breast cancer \u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. MC is activated to inhibit tumor progression in the colorectal cancer microenvironment \u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. The studies have showed that tumor-associated mast cells promote thyroid cancer progression by inhibition of CD8\u0026thinsp;+\u0026thinsp;T-cell function through hemagglutinin-9\u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. Additionally, mast cells induce EMT and stem cell characteristics in human thyroid cancer cells through the IL-8-Akt-Slug pathway \u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003ePlasma cells secrete different types of antibodies that modulate the host immune response and influence patient prognosis. The secretion of IgG1 and IgG3 by plasma cells is associated with good prognosis for patients. Plasma cells secrete IgG1/IgG3 in combination with FcγR1/FcγRIV to activate natural killer cells (NK cells) and macrophages, which mediate antibody-dependent cell cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) effects and directly kill tumor cells. Conversely, the secretion of IgA, IgM or IgG4 by plasma cells is linked to poor prognosis for patients\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. In colorectal liver metastasis (CRLM), IgA\u0026thinsp;+\u0026thinsp;plasma cells are recruited by the CCR10-CCL28 axis to deactivate granulocyte-myeloid-derived suppressor cells (G-MDSC) and inhibit the host's anti-tumor immunity. In hepatocellular carcinoma, IgG\u0026thinsp;+\u0026thinsp;plasma cells are recruited by the CXCR3-CXCL10 axis to promote tumor macrophage infiltration and inhibit immune response \u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e. In the tumor microenvironment, IgG antibodies secreted from infiltrating plasma cells that maintain the self-renewal of tumor stem cells and promote the progression of glioblastoma by activating the FcγRIIA-AKT-mTOR signaling pathway\u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe activation and suppression of immune genes significantly affect the function of immune cells immune cell function during tumorigenesis and development. The expression of immune genes is closely related to the prognosis of tumor patients. The results of the research have displayed that the infiltration degree of CD8 T cells is negatively correlated with the expression of immune genes S100A9 and CXCL5, while positively correlated with COLEC10, It suggests that these genes regulate CD8 T cells. CXCL5 is a CXC chemokine and a CXCR2 receptor agonist. In the tumor microenvironment, CXCL5 promotes tumor angiogenesis and participates in tumor growth and metastasis. Overexpression of CXCL5 indicate poor prognosis for patients with tumors \u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e. The previous studies have demonstrated that CXCL5 induces the accumulation of mature neutrophils in lung cancer tissues and inhibits the differentiation of CD8 T cells \u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e. The overexpression of CXCL5 in papillary thyroid carcinoma cells promote tumor cells proliferation by activating the CXCL5-CXCR2 axis \u003csup\u003e[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e.S100A9 is a calcium-binding protein. S100A9 promotes tumor proliferation, migration and invasion by recruiting myeloid-derived suppressor cells (MDSC) \u003csup\u003e[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e .Glioma-derived S100A9 inhibits the immunosuppressive effect of CD8\u0026thinsp;+\u0026thinsp;T lymphocytes by αvβ3 integrin / AKT1 / TGFβ1 polarized M2 microglial cells \u003csup\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e. COLEC10 is a sensor of innate immunity and triggering complement cascade, which activates the innate immune response of the host's to resist tumors. COLEC10 inhibits hepatocellular carcinoma(HCC) progression by regulating GRP78-mediated the endoplasmic reticulum stress signaling\u003csup\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e. COLEC10 inhibits the stemness of HCC by downregulating the Wnt/β-catenin pathway \u003csup\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/sup\u003e. COLEC10 inhibits proliferation, migration and invasion of HCC cells by suppressing epithelial-mesenchymal transition and modulating the PI3K-AKT pathway \u003csup\u003e[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e.Our previous research also presented that the low expression of COLEC10 was related to the late staging and lymph node metastasis in thyroid cancer, and the overexpression of CXCL5 and S100A9 suggested that the prognosis of thyroid cancer was poor\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, the results of this research showed that plasma cells were negatively correlated with CXCL5, FGF7 and APOD, while memory B cells were positively correlated with FGF7. The activated dendritic cells were positively correlated with CXCL5 and S100A9, M0 macrophages were positively correlated with CXCL5 and MMP12. The activated mast cells were positively correlated with S100A9.\u003c/p\u003e \u003cp\u003eCXCL5 is a member of the chemokine family, which is secreted by various types of cells, including tumor cells, immune cells (macrophages), and other non-immune cells. CXCL5 secreted by immune cells participates in the formation of immunosuppressive microenvironment and promotes tumor metastasis. CXCL5 derived from tumor recruits myeloid-derived suppressor cells (MDSC) to inhibit the function of T cells and NK cells, and prevent the host\u0026rsquo;s anti-tumor immune response \u003csup\u003e[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/sup\u003e. CXCL5 enhances the migration and invasion of thyroid cancer cells by promoting epithelial mesenchymal transformation through CXCL5-CXCR2 signal axis \u003csup\u003e[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/sup\u003e. Apolipoprotein D (APOD) encoded by the APOD gene is a multifunctional lipoprotein. APOD is a member of the APO family and plays an important role in lipid transport, inflammation, antioxidant response, and tumorigenesis \u003csup\u003e[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/sup\u003e. SACC-derived APOD elevated cancer cell a migration and invasion along peripheral nerves in vivo \u003csup\u003e[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/sup\u003e. In thyroid cancer, high expression of APOD is closely linked to larger tumor size, later clinical stage and lymph node metastasis \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFGF7, also known as keratinocyte growth factor (KGF), plays a crucial role in tissue development and tumorigenesis. It binds to FGFR2-IIIb to regulate the proliferation, differentiation and repair of epithelial cells by activating the RAS-MAPK and PI3K-AKT pathways. Otherwise, FGF7 is involved in the formation of organs such as lung, breast and prostate during embryonic development. In addition, FGF7 promotes cancer cell proliferation through the autocrine loop. FGF7 secreted by stromal cells stimulates tumor growth through the paracrine loop \u003csup\u003e[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/sup\u003e. FGF-7 enhances tumor invasion and macrophage infiltration and promotes tumor progression\u003csup\u003e[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/sup\u003e. FGF7 derived from cancer-associated fibroblasts (CAF) mediates epithelial-mesenchyme transition (EMT) to promote tumor metastasis by activating FGF7/HIF-1α pathway \u003csup\u003e[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]\u003c/sup\u003e. Memory B cells are located in spleen, blood, other lymphoid organs and barrier tissues, and are the key storage of plasma cell production in secondary immune response. FGF7 is essential for organogenesis and tissue differentiation \u003csup\u003e[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/sup\u003e. Our research has discovered that memory B cells are positively correlated with FGF7. It can be inferred that FGF7 positively regulates the function and differentiation of memory B cells. CXCL5, FGF7 and APOD in tumor tissues have negative impact on anti-tumor immunity. While plasma cells have positive impact. The overexpression of CXCL5, FGF7 and APOD in thyroid cancer is associated with poor prognosis. CXCL5, FGF7 and APOD can be used as potential prognostic biomarkers \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMatrix metalloproteinase 12 (MMP-12) is a member of matrix metalloproteinase family, which is mainly produced by macrophages. MMP-12 promotes tumor proliferation and metastasis by inhibiting the activation of T cells and B cells \u003csup\u003e[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/sup\u003e. Calceolarioside B inhibits MMP12 M2-like TAMs polarization and infiltration in the tumor microenvironment. It reverses immunosuppression and effectively inhibits tumor progression \u003csup\u003e[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere is usually a connection between immune cells, and it is a deficiency that the infiltration abundance of a single immune cell predicts the prognosis of thyroid cancer patients. In this study, the risk model was constructed using six different types of immune cells. This model categorized patients into high-risk group and low-risk group according to the immune cell score. The prognosis of patients in the high-risk group was worse than that in the low-risk group. The gene enrichment of patients in the high-risk group was mainly in ECM-receptor interaction, focal adhesion, tumorigenesis, PPAR signaling pathway and TGF-β signaling pathway. These pathways are frequently activated in tumorigenesis and tumor progression.\u003c/p\u003e \u003cp\u003eIn conclusion, the risk model was constructed using the infiltration scores of six types immune cells in the study. The model can identify the high-risk group, predict survival, screen the population benefiting from drug treatment. The correlation between immune cells and six immune-related genes has also been confirmed in the study. These findings are helpful for the prognosis evaluation of thyroid cancer and guide drug selection in clinical practice. The genes related to immune cells can be used as potential targets for immunotherapy. The efficacy of this scoring model still needs further validation in multicenter clinical practice. The application of Multi-omics findings combined with artificial intelligence will be the direction of future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed in this study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo/), TCGA database(https: //portal. gdc.cancer.gov /),GSEA enrichment analysis (https://www.gsea-msigdb.org/gsea/index.jsp),TCIA database(https://www.tcia.at/home),GDSC database(https://www.cancerrxgene.org/),CIBERSORT algorithm (https://cibersortx.stanford.edu/).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative AI statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) confirm that no generative AI tools were utilized in the preparation of this manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships.\u003c/p\u003e\n\u003cp skip=\"true\"\u003e\u003cstrong\u003eAuthors' Contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp skip=\"true\"\u003eIncluded conception and study design (YYL and YJL), data collection or acquisition (XC, LT, HCH, SHY, LXR, SZB), statistical analysis (YJL, YYL, XC, and SZB), interpre­tation of results (YYL, YJL, XC, LT, HCH, SHY, LXR, and SZB), drafting the manuscript work (YYL and YJL) and approval of final version to be published and agreement to be accountable for the integrity and ac­curacy of all aspects of the work (all the authors). *Correspondence to: Youlin Yu, Email: \u003cem\[email protected]\u003c/em\u003e. Junlin Yu. Email: \u003cem\[email protected]\u003c/em\u003e. Xiaogan Hospital Affiliated to Wuhan University of Science and Technology, Xiaogan, Hubei Province 432000, China.\u003c/p\u003e\n\u003cpre\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/pre\u003e\n\u003cp skip=\"true\"\u003eThis research was supported by the Natural Science Foundation of Hubei Province (No.22CFB522).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge TCGA, GEO,\u0026nbsp;TCIA ,and\u0026nbsp;GDSC database\u0026nbsp;of the studies for\u003c/p\u003e\n\u003cp\u003eproviding free data.This work was supported by Xiaogan Hospital Affiliated to Wuhan University of Science and Technology.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Kratzer TB, Giaquinto AN, et al. Cancer statistics, 2025. 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Calceolarioside B targets MMP12 in the tumor microenvironment to inhibit M2 macrophage polarization and suppress hepatocellular carcinoma progression. Phytomedicine: Int J phytotherapy phytopharmacology. 2025;142:156805. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.phymed.2025.156805\u003c/span\u003e\u003cspan address=\"10.1016/j.phymed.2025.156805\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Advance online publication.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConclusions.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIn this study, the characteristic of the immune microenvironment of thyroid cancer was analyzed. The immune cell score was used to construct a risk model, and further investigated relationship between the model and the prognosis and drug sensitivity of thyroid cancer. The correlation between immune cells and immune genes was studied in the model. The model predicts the prognosis of thyroid cancer, identifies high-risk groups of thyroid cancer, and screens dominant populations benefiting from immunotherapy. It provides reference for prognosis evaluation and treatment strategy of thyroid cancer.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Thyroid cancer, Tumor microenvironment, Immune cells, Risk model, Prognosis, Drug sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-8329405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8329405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImmune cells are widely distributed in the microenvironment of thyroid cancer, which promote or inhibit tumor development at different stages of tumorigenesis. The immune microenvironment of tumors may play an important role in tumor development. The number and function of immune cells infiltrated in different compartments of the tumor tissue affect the prognosis of tumor patients. The characteristics and clinical value of the immune microenvironment of thyroid cancer are unknown. In this study, the characteristic of the immune microenvironment of thyroid cancer was analyzed. The immune cell score was used to construct a risk model, and further investigated relationship between the model and the prognosis and drug sensitivity of thyroid cancer. The correlation between immune cells and immune genes was studied in the model. The model predicts the prognosis of thyroid cancer, identifies high-risk groups of thyroid cancer, and screens dominant populations benefiting from immunotherapy. It provides reference for prognosis evaluation and treatment strategy of thyroid cancer.\u003c/p\u003e","manuscriptTitle":"Immune cell score model is a new tumor marker for predicting prognosis and selecting therapeutic drugs in thyroid cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 08:25:04","doi":"10.21203/rs.3.rs-8329405/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":"56ac1041-5cd1-4bde-b1ae-3168d958e255","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T19:24:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 08:25:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8329405","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8329405","identity":"rs-8329405","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}