Over-Expression and Prognostic Significance of FN1, Correlating with Immune Infiltrates in Thyroid Cancer

Over-Expression and Prognostic Significance of FN1, Correlating with Immune Infiltrates 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 Over-Expression and Prognostic Significance of FN1, Correlating with Immune Infiltrates in Thyroid Cancer Qi-Shun Geng, Zhi-Bo Shen, Li-Feng Li, Jie Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-506313/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jan, 2022 Read the published version in Frontiers in Medicine → Version 1 posted You are reading this latest preprint version Abstract Background. Thyroid cancer (THCA) is a malignancy affecting the endocrine system, which currently has no effective treatment due to a limited number of suitable drugs and prognostic markers. Methods. Three Gene Expression Omnibus (GEO) datasets were selected to identify differentially expressed genes (DEGs) between THCA and normal thyroid samples using GEO2R tools of National Center for Biotechnology Information. We identified hub gene FN1 using functional enrichment and protein–protein interaction network analyses. Subsequently, we evaluated the importance of gene expression on clinical prognosis using The Cancer Genome Atlas (TCGA) database and GEO datasets. MEXPRESS was used to investigate the correlation between gene expression and DNA methylation; the correlations between FN1 and cancer immune infiltrates were investigated using CIBERSORT. In addition, we assessed the effect of silencing FN1 expression, using an in vitro cellular model of THCA. Immunohistochemical(IHC) was used to elevate the correlation between CD276 and FN1 . Results. FN1 expression was highly correlated with progression-free survival and moderately to strongly correlated with the infiltration levels of M2 macrophages and resting memory CD4 + T cells, as well as with CD276 expression. We suggest promoter hypermethylation as the mechanism underlying the observed changes in FN1 expression, as 20 CpG sites in 507 THCA cases in TCGA database showed a negative correlation with FN1 expression. In addition, silencing FN1 expression suppressed clonogenicity, motility, invasiveness, and expression of CD276 in vitro. The correlation between FN1 and CD276 was further confirmed by immunohistochemical. Conclusion. Our findings show that FN1 expression levels correlate with prognosis and immune infiltration levels in THCA, suggesting that FN1 expression be used as immunity-related biomarker and therapeutic target in THCA. Pathology thyroid cancer biomarker FN1 immunity survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Thyroid carcinoma (THCA) is the most common type of endocrine cancer [ 1 ]. Its prevalence has sharply increased in recent decades. In the US, the annual incidence of thyroid cancer increased from 4.9/100,000 in 1975 to 14.3/100,000 in 2009 [ 2 ]. The observed increase in the incidence is partly due to the increased detection rate. In Beijing, China, the detection rate significantly increased from 16.8% in 1994 to 69.8% in 2015 ( P < 0.01) [ 3 ]. THCA can be subclassified into several histological subtypes, which include papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), undifferentiated or anaplastic thyroid carcinoma (ATC), and medullary thyroid carcinoma (MTC). THCA occurs mostly in young adults (the average age of diagnosis is 40 years), and more frequently in females [ 4 ]. The most common treatment of thyroid cancer consists of surgical resection combined with radiotherapy or chemotherapy. However, for PTC, ATC, and MTC, a satisfactory resection is not always feasible, and even after radiotherapy, the risk of cancer recurrence is still high [ 5 , 6 ]. In addition, recently personalized therapy approaches directed against specific targets have become available, only a few suitable targets have been identified thus far. Although the survival rate of patients with thyroid cancer is very high, with the rapid increase in THCA incidence, this disease poses a serious threat for human health [ 7 ]. Immune-related processes play an important role in the development of thyroid cancer, and hence, immunotherapy strategies are considered the most promising candidates for the treatment of thyroid cancer in the future [ 8 ]. Many studies have shown that tumor-infiltrating lymphocytes, such as tumor-associated macrophages, tumor-associated dendritic cells and tumor-infiltrating neutrophils, affect the prognosis of THCA patients, as well as the efficacy of chemotherapy and immunotherapy [ 9 ]. Therefore, there is an urgent need to understand which immune cells play a role in the development of THCA, as well as to explore novel immune-related biomarkers that could aid in the diagnosis and prognosis of this disease. Fibronectin 1 ( FN1 ) encodes a glycoprotein present in a soluble dimeric form in the plasma, and in a dimeric or multimeric form at the cell surface and in the extracellular matrix. FN1 is involved in cell adhesion and migration processes during embryogenesis, wound healing, blood coagulation, host defense, and metastasis, as well as in cell proliferation [ 10 ]. Multiple studies have established its involvement in the development of cancer, including oral squamous cell carcinoma [ 11 ], renal cancer [ 12 ], and thyroid cancer [ 13 ]. Previous studies have indicated that FN1 is involved in NKp46 receptor-mediated interferon-γ (IFN-γ) production by natural killer cells, with respect to the control of tumor architecture and metastasis [ 14 ]. In addition, FN1 plays an important role in glioblastoma growth and invasion [ 15 ]. These findings suggest that FN1 has multifaceted functional roles in tumor progression. In this study, we comprehensively analyzed the correlation between FN1 expression with the prognosis of patients with THCA, as well as with the presence of tumor-infiltrating immune cells. Our findings highlight the important role of FN1 expression in THCA, and suggest a potential correlation between FN1 expression and tumor-immune interactions. Furthermore, the results were validated by immunohistochemistry and cell biology experiments, which indicate that FN1 is a potential prognostic and immunity-related biomarker in THCA and could potentially be used as drug target in THCA therapy. 1 Material And Methods 1.1 Cell lines and reagents The cell lines B-CPAP and KTC-1, belonging to the thyroid cancer cell lines, were obtained from the Institute of Biochemistry and Cell Biology of the Chinese Academy of Sciences, Shanghai, China. All cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium (Gibco, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (10%FBS), 100 U/mL penicillin, and 100 mg/mL streptomycin (Invitrogen, Carlsbad, CA, USA). Cells were incubated in a 5% CO 2 /95% O 2 in a humidified atmosphere at 37 °C. 1.2 Data sources and identification of differentially expressed genes (DEGs) The Cancer Genome Atlas (TCGA) is a landmark cancer genomics program that characterized over 20,000 primary cancer samples, spanning 33 cancer types and matched to the respective normal samples. Samples are molecularly characterized, and multi-omics data are provided, including gene transcripts, miRNA expression data, and DNA methylation state data. Additionally, it contains abundant and standardized clinical data. All datasets used were downloaded from the Cancer Genomics Browser website of the University of California Santa Cruz [16]. The GEO database (http://www.ncbi.nlm.nih.gov/geo/) stores original submitter-supplied records (series, samples, and platforms), as well as the curated datasets. Using the selection criteria of a number of samples greater than 20 and less than 1000, we selected three gene expression profiles (GSE33630 [17], GSE58545 [18], and GSE60542 [19]). Among of them, GSE33630 and GSE60542 are based on the GPL570 platform, and GSE58545 was obtained with the GPL96 platform. According to the commonly used threshold parameters (adjusted P < 0.05, |log 2 FoldChange| ≥ 2.0) [20], we determined the DEGs between THCA and normal thyroid samples using the GEO2R online analysis tool, accessible via the National Center for Biotechnology Information website. Then, the interactive tool Venny2.1.0 [21] was used to create a Venn diagram of the DEGs to determine the common DEGs between the three analyzed gene expression profiles. 1.3 Functional enrichment analysis and identification of hub genes Gene Ontology (GO) is an initiative that provides a standardized classification of genes by accounting for their functions, biological pathways they participate in, and the cell localization of the corresponding proteins. Genes are categorized into three domains: biological process (BP), molecular function (MF), and cellular component (CC) [22]. We annotated the identified DEGs according to the GO classification system using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool (https://david.ncifcrf.gov/) [23]. The threshold criteria to determine the significantly enriched GO terms were P < 0.05 and gene counts ≥ 5. To identify the hub genes, a protein-protein interaction (PPI) network was constructed for the DEGs that had been annotated in the category BP ( P < 0.05) using Search Tool for the Retrieval of Interacting Genes (STRING) (http://string-db.org/) [24]. PPI pairs with a combined confidence score ≥ 0.4, were visualized using Cytoscape (version 3.7.2) [25]. The Cytoscape plugin Molecular Complex Detection (MCODE) (version 1.4.2), an app to cluster any given network, was used to identify the most important module in the PPI network, and the plugin CytoHubba was used to identify the hub genes in the PPI networks by calculating the degree of connectivity between DEGs. The selection criteria were as follows: MCODE score > 5 points, degree cut-off = 2, node score cut-off = 0.2, maximum depth = 100, and k-score = 2. 1.4 Correlation between gene expression and survival ONCOMINE is an online cancer microarray database (www.oncomine.org) [26]. Gene expression profiles from the website were used to analyze the transcription levels of FN1 in THCA. Furthermore, the correlation between FN1 expression and progression-free survival (PFS) and clinical parameters was analyzed using TCGA database. In addition, the UALCAN [27], a web resource to analyze cancer OMICS data, was used to investigate correlation between FN1 expression and cancer stage, and between FN1 expression and promoter methylation level. 1.5 Methylation and immunity correlation analysis MEXPRESS (https://mexpress) is a data visualization tool designed for easy visualization of TCGA expression, DNA methylation, and clinical data, as well as the correlations between them [28, 29]. We used this tool to investigate the correlation between hub gene expression and the degree of methylation of the gene promoters. CIBERSORT is an analytical tool used to estimate the abundance of cell types in a mixed cell population using gene expression data [30]. We used this tool to assess the degree of immune infiltration. The co-expression analysis of FN1 and B7 family members (including CD274, CD80, CD86, CD276, CD273, CD275, B7-H4, B7-H5, CD28, B7-H7, CD152, CD279, CD278, TLT-2 , and NKp30 ) was assessed in the normal thyroid samples and in the THCA samples from TCGA database. The correlation between FN1, CD273, CD274, CD275, B7-H4 , and CD276 was further analyzed in the THCA cohort. GEPIA (http://gepia.cancer-pku.cn/detail.php) [31] and TIMER [32] were used to plot the expression scatterplots between any pair of genes in a given cancer type, while including the Spearman correlation and the statistical significance. 1.6 Silencing of FN1 by small interfering RNA (siRNA) The siRNA targeting human FN1 (siFN1) and a non-specific scramble siRNA sequence (siNC) were purchased from Shanghai Gene Pharma and transiently transfected into B-CPAP and KTC-1 cells using Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. The target sequence in FN1 was: 5’-CAGUCAAAGCAAGCCCGGUUGUUAU-3’. Subsequently, assays were performed 48 h after the transfection. Cell viability was assessed 24, 48, or 72 h after transfection using the commercial kit Cell Counting Kit-8 (CCK-8, Beyotime Biotechnology, China) according to manufacturer’s instructions. 1.7 Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) RNA was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA, USA), followed by transfer into RNA-free EP tubes and storage at −80 °C. Complementary DNA (cDNA) was synthesized from total RNA using the PrimeScript RT reagent Kit (Takara, Dalian, China), and PCR was performed using the SYBR Green RT-PCR Kit (Takara). PCR was performed on the StepOne Plus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). Data were analyzed using the 2 -ΔΔCT method. 1.8 Immunohistochemistry (IHC) Staining Samples from cancer patients were obtained from thyroid cancer arrays (DC-Thy11004 Avira Biotechnology Co., Ltd., China), which included 24 cases of thyroid cancer and corresponding paracancerous tissues. Tumor tissues chip were deparaffinized and rehydrated, followed by antigen retrieval. The sections were then blocked with 5% BSA in PBS and incubated with FN1 antibody (1mg/mL, 1:200; Affinity) and CD276 antibody (1mg/mL, 1:200; Affinity) at 4 °C overnight. After three times washing, tissue sections were incubated with the secondary antibody conjugated with streptavidin–horseradish peroxidases for 1h at room temperature. The slides were stained with 3, 3-diaminobenzidine tetrahydrochloride (DAB) and thenuclei were counterstained with hematoxylin. Marker density was scored independently by two investigators as follows: 0, negative; 1, weak; 2, moderate; or 3, strong. 1.9 Statistical analyses All statistical analyses were performed using GraphPad Prism 5.0 (San Diego, CA, USA) and R version 3.6.1. Data from three independent experiments performed in triplicate are presented as mean ± standard deviation (SD). Multivariate survival analysis was carried out for all parameters that were significant in the univariate analysis using the Cox regression model. To analyze the significance of differences between groups, unpaired two-tailed Student’s t-test and one-way analysis of variance (ANOVA) were performed, and multiple comparison was accounted for using Bonferroni's correction. Differences were considered significant at P < 0.05. 2 Results 2.1 Identification of the common DEGs between the datasets used We obtained three gene expression profiles (GSE33630, GSE58545, and GSE60542) from the GEO database. GSE33630 includes 45 normal thyroid samples and 60 THCA samples; GSE58545 includes 18 normal thyroid samples and 27 THCA samples; GSE60542 includes 30 normal thyroid samples and 33 THCA samples. According to the conventional criteria (adjusted P < 0.05 and |log 2 FoldChange| ≥ 2.0), 263 genes were identified as DEGs in GSE33630, of which 133 were downregulated and 130 were upregulated; moreover, GSE58545 included 270 DEGs, including 144 downregulated genes and 126 upregulated genes, and GSE60542 contained 228 DEGs, including 107 downregulated genes and 121 upregulated genes (Figure 1A-C). A Venn diagram showed that 98 genes were differentially expressed in the three data sets, of which 46 were downregulated and 52 were upregulated (Figure 1D, E). 2.2 Functional enrichment analysis of the common DEGs In this study, we performed GO functional analysis of the common DEGs using the tool DAVID. Then, we filtered the results to improve the confidence according to the standard criteria ( P < 0.05 and gene counts ≥ 5) (Table S1). GO analysis showed that the common DEGs were mainly enriched in the CC category, including plasma membrane, extracellular exosome, extracellular region, and extracellular space. DEGs annotated as BP were enriched in cell adhesion, signal transduction, extracellular matrix organization, positive regulation of gene expression, positive regulation of cell proliferation, blood coagulation, wound healing, platelet degranulation, and nervous system development, all of which are processes associated with the occurrence and development of tumors. 2.3 Identification and analysis of the hub genes We selected 46 DEGs involved in specific BP ( P < 0.05) to build the PPI network (Figure 2A, Table 1). The most important module was obtained using the plugin MCODE in Cytoscape (Figure 2B). The top eight genes, including FN1 , TIMP1 , SERPINA1 , COMP , PROS1 , MMRN1 , KIT , and TNFRSF11B , were identified as potential hub genes according to the degree score generated by the plugin CytoHubba (Figure 2C, Table 2). This was consistent with their enrichment in the top module determined using MCODE.Among of them, FN1 had the highest degree of connectivity in the PPI network. Furthermore, logrank regression and multivariate Cox regression analysis were used to calculate the correlation between gene expression and PFS (Figure D-E), indicating FN1 appeared to be the most attractive drug target and prognostic marker., GSEA was used to perform kegg analysis for FN1. The results suggested that the most of the involved significant pathways included chemokine signaling pathway, cytokine cytokine receptor interaction, leishmania infection, natural killer cell mediated cytotoxicity, and T cell receptor signaling pathway, as it has been established that FN1 plays a crucial role in tumor architecture and controls metastasis (Figure 2F) (14). 2.4 Expression levels of FN1 in THCA and evaluation of its value as a prognostic marker We analyzed the expression levels of FN1 in THCA using the Oncomine database. We found that FN1 expression was upregulated in almost all different subtypes of THCA, including PTC and ATC (Figure 3A). We validated these results using TCGA database, in which FN1 was also significantly more expressed in THCA samples than in normal thyroid samples ( P < 0.05) (Figure 3B). FN1 expression levels were correlated with PFS ( P < 0.05) (Figure 3C). The area under the curve (AUC) of FN1 from TCGA dataset was 0.8971 (Figure 3D), highlighting the value of FN1 as a diagnostic marker in THCA. We compared the clinical characteristics (including age, sex, location, clinical stage, progression state, lymph node metastasis, and distant metastasis) between the FN1 -high expression and FN1 -low expression groups, and observed statistically significant differences in lymph node metastasis, distant metastasis, and progression state, although no significant differences were identified for other clinical features (Table 3). Furthermore, univariate and multivariate Cox regression models revealed that clinical stage and FN1 expression were independent prognostic factors for PFS in patients with THCA (Table 4). In addition, analysis of the UALCAN database showed that FN1 expression levels were closely correlated to cancer stage (Figure 3E) and that the degree of methylation of the FN1 promoter was lower in THCA than that in normal tissues (Figure 3F), indicating that FN1 may be involved in the development of THCA. 2.5 Analysis of the potential genetic and epigenetic alterations underlying FN1 dysregulation Next, we investigated the underlying mechanism of FN1 dysregulation in THCA. To determine whether copy number alterations (CNAs) are responsible for the abnormal expression of FN1 in THCA, we analyzed 397 cases from TCGA database for which CNAs data was available. No differences were observed for CNAs in the FN1-high and FN1-low groups (Figure 4A). Another mechanism that could underlie the altered FN1 expression profile observed in THCA samples is promoter hypermethylation, which plays an important role in the occurrence and development of several types of tumors [33, 34]. To investigate whether DNA methylation results in FN1 dysfunction, we examined the status of CpG sites in 507 THCA cases from TCGA database using the tool MEXPRESS. We found that 27 CpG sites had associated data; among which 20 CpG sites showed a negative correlation with FN1 expression (Figure 4C). The Pearson correlation coefficient was calculated for the five CpG sites with the highest correlation coefficient, including cg21494132, cg09040552, cg11309217, cg03228449, and cg03228449 (Figure 4D, all P < 0.05). Our results demonstrate the correlation between DNA methylation and abnormal expression levels of FN1 in THCA, highlighting the need for further investigation of the underlying mechanism. 2.6 Correlation between FN1 and inflammatory activities Considering the strong association between FN1 expression levels and THCA prognosis, we hypothesized that FN1 may be associated with inflammatory responses, leading to enhanced survival rates. To identify the FN1 associated immune signature in THCA, we assessed the degree of immune infiltration with CIBERSORT. FN1 expression was closely correlated to the degree of M2 macrophages, resting memory CD4 + T cells, follicular helper T cells, and CD8 + T cell infiltration. The increase in FN1 expression was associated with an increase of the proportion of M2 macrophages and resting memory CD4 + T cells and a decrease of the proportion of follicular helper T cells and CD8 + T cells (Figure 5A). To further investigate the correlation between FN1 and inflammation, we analyzed the co-expression of FN1 and members of the B7-CD28 ligand-receptor family, including CD274, CD80, CD86, CD276, CD273, CD275, B7-H4, B7-H5, CD28, B7-H7, CD152, CD279, CD278, TLT-2 , and NKp30 , which are closely correlated to T cell function. The result demonstrated that FN1 exhibited a significant co-expression trend with CD273, CD274, CD275, CD276 and B7-H4 (Figure 5B). Furthermore, we investigated the correlation between FN1, CD273, CD274, CD275, CD276 , and B7-H4 expression in the THCA cohort, and found a close positive correlation between FN1 and CD276 expression (R = 0.55, P = 0) (Figure 5C-D). This result was validated using TIMER and GEPIA, which provided R values of 0.682 and 0.79, respectively, for the correlation between FN1 and CD276 in THCA ( P < 0.001) (Figure 5E-F). 2.7 Down-regulation of FN1 inhibited cell proliferation and invasion and decreased CD276 expression levels in THCA samples To explore the biological significance of FN1 in THCA tumorigenesis, KTC-1 and B-CPAP cells were transfected with siRNA targeting FN1 (siFN1) or negative control siRNA (siNC). Efficient depletion of FN1 expression was confirmed via RT-qPCR ( P < 0.05, Figure 6A). Moreover, we found that downregulation of FN1 significantly reduced the expression of CD276 in KTC-1 and B-CPAP cells compared with siNC transfection (Figure 6A). Then, we studied the effect of FN1 on THCA cell proliferation and invasion in vitro . The wound-healing assay, cell viability and cytotoxicity CCK-8 assay, and clone formation assay revealed that downregulation of FN1 in both cell types significantly inhibited cell proliferation and invasion compared to that in the control cells ( P < 0.05, Figure 6B-D). These data suggest that downregulation of FN1 reduces the viability of THCA cell lines. 2.8 FN1 is positively correlated with CD276 In order to assess FN1 correlation with CD276, we analyzed FN1 and CD276 expression in tumor sites and the adjacent no-tumor samples (Figure 7A). We found that FN1 and CD276 showed significantly higher expression in tumor sites than in the adjacent no-tumor samples ( P <0.05, Figure 7C-D). Furthermore, we divided the tumor sites into two groups according to FN1 expression and found that CD276 levels were positively correlated with the level of FN1( P <0.05, Figure 7B, E). 3 Discussion FN1 is a member of the FN family that is widely expressed in multiple cell types and is involved in cellular adhesion and migration processes [ 35 ]. Here, we report that variations in FN1 expression levels correlate with prognosis in THCA patients. High expression levels of FN1 are associated with a poorer prognosis in THCA, indicating that FN1 expression could be used as a predictor of tumor prognosis. Furthermore, our analyses show that immune infiltration levels and immune markers correlate with FN1 expression levels in THCA samples, suggesting a potential role of FN1 in tumor immunology and its possible use as a cancer biomarker. In this study, we screened DEGs from three GEO datasets based on functional enrichment analysis and PPI network maps. FN1 , which is closely correlated to disease-free survival, was identified as a hub gene. In addition, we found that the promoter region of FN1 had significantly lower methylation levels ( P < 0.05) in THCA than in normal thyroid tissues. GSEA analysis also showed that FN1 plays a role in chemokine signaling pathway, cytokine cytokine receptor interaction, natural killer cell mediated cytotoxicity, and T cell receptor signaling pathway, which are closely correlated to tumorigenesis. Importantly, immune infiltration analysis showed that immune infiltration levels and diverse immune marker sets were correlated with FN1 expression levels. Therefore, FN1 can be a potential immunity-related biomarker and therapeutic target in THCA. Moreover, using quantitative proteomic approaches, previous studies have proved that FN1 can be a potential novel candidate prognostic biomarker in THCA [ 36 ]. However, these studies did not specify the exact range of effects of genes on disease prognosis, lacked certain clinical significance. In this study, three GEO datasets were combined to screen the hub genes, providing results with a high statistical and clinical significance. Our research further demonstrates that FN1 is a potential prognostic biomarker and therapeutic target in THCA from the perspective of DNA methylation and tumor immunology. The important aspect of this study is that FN1 expression is correlated with diverse immune infiltration levels in THCA. An increase in FN1 expression levels was positively correlated with the proportion of M2 macrophages and resting memory CD4 + T cells but negatively correlated with the proportion of follicular helper T cells and CD8 + T cells. To further investigate the correlation between FN1 and inflammatory activities, the correlation between FN1 and members of the B7-CD28 ligand-receptor family was analyzed, and a close positive correlation between FN1 and CD276 was identified. CD276 , a member of the B7 superfamily, has been previously identified as a poor prognostic factor. A previous study demonstrated that CD276 , expressed in multiple tumor lines, tumor-infiltrating dendritic cells, and macrophages, can inhibit T-cell activation and autoimmunity [ 37 ]. Therefore, the interactions between FN1 and CD276 could be a potential mechanism underlying the correlation of FN1 expression with immune infiltration and poor prognosis in THCA. In summary, increased FN1 expression correlated with poor prognosis and altered immune infiltration levels in THCA, indicating that FN1 is a potential immunity-related biomarker. This study was based on statistical analysis of bioinformatics methods, and the conclusions obtained were supported by experimental data and multiple database verification. Therefore, it is reasonable to believe that FN1 plays an important role in the diagnosis, treatment, and prognosis of THCA. 4 Conclusion In summary, we performed a comprehensive analysis using TCGA dataset and multiple online databases and identified FN1 as a potential immunity-related biomarker in THCA, as well as a potential drug target in THCA, highlighting the necessity of future clinical research in the topic. 5 Abbreviations THCA Thyroid cancer GEO the Gene Expression Omnibus database DEGs the differentially expressed genes DAVID the Database for Annotation, Visualization and Integrated Discovery STRING the Retrieval of Interacting Genes PPI the protein–protein interaction GEPIA Gene expression profiling and interactive analyses PTC papillary thyroid carcinoma FTC follicular thyroid carcinoma ATC undifferentiated thyroid carcinoma MTC medullary thyroid carcinoma IHC Immunohistochemistry 6 Declarations Ethics approval and consent to participate All the patients’ data involved in this study is open source which is freely available in the public research databases including the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The application of the public data is already properly anonymized and informed consent was also obtained at the time of the original data collection. Samples from cancer patients were obtained from thyroid cancer arrays (DC-Thy11004 Avira Biotechnology Co., Ltd., China). The data is already properly anonymized and informed consent was also obtained at the time of the original data collection. Consent for publication Not applicable. Availability of data and materials The data used to support the findings of this study are included in the article. Competing interests The authors have declared that they have no competing interests. Funding The National Key Research and Development Program of China (Grant No. 2017YFC0909900). The National Natural Science Foundation of China (Grant No. 82002433), and Science and Technology Project of Henan Provincial Department of Education (Grant No. 18A320044，21A320036), Henan Province Medical Science and Technology Research Project Joint Construction Project (Grant No. LHGJ20190003, LHGJ20190055). The National Natural Science Foundation of China (82002998), the First-class Postdoctoral Research Grant in Henan Province (201901007), Medical Science and Technology Research Project in Henan Province (LHGJ20190042), and Youth Talent Support Project of Henan Province (2021HYTP045). Authors' contributions All authors of this research paper participated directly in the planning and execution of the study, and in the analysis of the results. Acknowledgments Not applicable. References Bonhomme B, Godbert Y, Perot G, Al GA, Bardet S, Belleannee G, et al. Molecular Pathology of Anaplastic Thyroid Carcinomas: A Retrospective Study of 144 Cases. THYROID. 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Platinum-Based Chemotherapy Induces Methylation Changes in Blood DNA Associated with Overall Survival in Patients with Ovarian Cancer. CLIN CANCER RES. [Journal Article]. 2017 2017-05-01;23(9):2213–22. Ruoslahti E. Fibronectin in cell adhesion and invasion. Cancer Metastasis Rev. [Journal Article; Research Support US, Gov't PHS. Review]. 1984 1984-01-19;3(1):43–51. Zhan S, Li J, Wang T, Ge W. Quantitative Proteomics Analysis of Sporadic Medullary Thyroid Cancer Reveals FN1 as a Potential Novel Candidate Prognostic Biomarker. ONCOLOGIST. [Journal Article; Research Support, Non-U.S. Gov't]. 2018 2018-12-01;23(12):1415–25. Lee YH, Martin-Orozco N, Zheng P, Li J, Zhang P, Tan H, et al. Inhibition of the B7-H3 immune checkpoint limits tumor growth by enhancing cytotoxic lymphocyte function. CELL RES. [Journal Article]. 2017 2017-08-01;27(8):1034–45. Tables Table 1 The information related to biological processes of statistical significance. Term Describe P value Gene Count GO:0007155 cell adhesion 1.61E-06 PLXNC1, CYP1B1, CLDN10, MMRN1, CDH3, NCAM1, LAMB3, LYVE1, CDH16, SORBS2, COMP, ENTPD1, DPT, FN1 14 GO:0007165 signal transduction 0.012464 GNA14, EDN3, CRABP1, RAP1GAP, KIT, HMGA2, LYVE1, TNFRSF11B, CXCL14, ANK2, TENM1, IGSF1, GDF15, PLAU 14 GO:0030198 extracellular matrix organization 0.000814 CSGALNACT1, TNFRSF11B, LAMB3, COL9A3, COL13A1, COMP, FN1 7 GO:0010628 positive regulation of gene expression 0.00353 ANK2, KIT, HMGA2, CDH3, AGR2, CITED1, FN1 7 GO:0008284 positive regulation of cell proliferation 0.046698 EDN3, TIAM1, TGFA, KIT, DPP4, FN1, TIMP1 7 GO:0007596 blood coagulation 0.046698 SERPINA1, MMRN1, ENTPD1, PROS1, PAPSS2, PLAU 6 GO:0042060 wound healing 0.046698 TGFA, TFF3, CDH3, FN1, TIMP1 5 GO:0002576 platelet degranulation 0.046698 SERPINA1, MMRN1, PROS1, FN1, TIMP1 5 GO:0007399 nervous system development 0.046698 CSGALNACT1, CHRDL1, TENM1, BEX1, MPPED2 5 Table 2 Hub genes with higher degree of connectivity Gene symbol Gene title Degree Gene nature FN1 Fibronectin 1 17 UP TIMP1 TIMP metallopeptidase inhibitor 1 9 UP SERPINA1 Serpin family A member 1 6 UP COMP Cartilage oligomeric matrix protein 5 UP PROS1 Protein S (alpha) 4 UP MMRN1 Multimerin 1 4 DOWN KIT KIT proto-oncogene receptor tyrosine kinase 4 DOWN TNFRSF11B TNF receptor superfamily member 11b 4 DOWN Table 3 Comparison of clinical characteristics between low FN1 group and high FN1 group in THCA cohort. Variable Case NO. (%) FN1 P High Low Sample 505 253 252 Age(Year) ≥ 60 118 62 56 0.544 < 60 387 191 196 Gender Female 366 182 184 0.786 Male 139 71 68 Clinical stage Ⅰ 287 136 151 Ⅱ 52 13 39 < 0.001 Ⅲ 111 65 46 Ⅳ 55 39 16 Lymph node metastasis Yes 227 84 143 No 229 152 77 < 0.001 Unknown 49 17 32 Distant metastases Yes 280 157 123 No 9 3 6 0.009 Unknown 216 93 123 Location Left lobe 176 89 87 Right lobe 219 105 114 0.348 Bilateral 88 44 44 Isthmus 22 15 7 Progression state Yes 53 35 18 0.014 No 452 218 234 Table 4 Univariate and Multivariate Cox logistic regression analysis of FN1 for predicting PFS in TCGA cohort (PFS: progression free survival; TCGA: The Cancer Genome Atlas) Variable Univariate Multivariate HR (95% CI) P HR (95% CI) P Age(ref.<60) 2.10 1.20 ~ 3.70 0.007 1.49 0.78 ~ 2.80 0.225 Gender (ref. Male) 0.58 0.33 ~ 1.00 0.055 0.69 0.39 ~ 1.20 0.211 Clinical stage (ref. I-II) 2.60 1.50 ~ 4.40 < 0.001 1.94 1.03 ~ 3.60 0.039 Lymph node metastasis (ref. No) 1.60 0.91 ~ 2.80 0.11 1.13 0.62 ~ 2.10 0.687 Distant metastases (ref. No) 1.50 0.88 ~ 2.60 0.13 1.65 0.95 ~ 2.90 0.076 Location(ref. Left&Right lode) 0.90 0.45 ~ 1.80 0.77 0.84 0.42 ~ 1.70 0.632 FN1 expression (ref. low) 1.90 1.10 ~ 3.40 0.022 1.83 1.01 ~ 3.30 0.046 Supplementary Files FigureS1.tif Figure S1 Flow diagram of the study. TableS1.docx Cite Share Download PDF Status: Published Journal Publication published 24 Jan, 2022 Read the published version in Frontiers in Medicine → 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. 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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-506313","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":25827569,"identity":"75adff99-c069-46d7-8574-9a46ce0038a4","order_by":0,"name":"Qi-Shun Geng","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qi-Shun","middleName":"","lastName":"Geng","suffix":""},{"id":25827570,"identity":"60f60520-d3f5-4db6-b2d5-5c297ca3bc02","order_by":1,"name":"Zhi-Bo Shen","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhi-Bo","middleName":"","lastName":"Shen","suffix":""},{"id":25827571,"identity":"e144ad6f-d44e-42cd-b15b-8718d4275a57","order_by":2,"name":"Li-Feng Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li-Feng","middleName":"","lastName":"Li","suffix":""},{"id":25827572,"identity":"8b4a897d-9388-452a-8c34-97002a72989c","order_by":3,"name":"Jie Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACCRA2YJCD8NhI0GJMohYgSGwgWov87OaHDywKbNLn958xYPhQdpiBf3YDfi2Mc44ZG0gYpOU2NpwxYJxx7jCDxJ0D+LUwSySYSUgYHM5tZuwxYOZtO8xgIJGAXwubRPo3oJb/6WzMPAbMf4nRwiORA7LlQAIPG1ALIzFaJCRyioF+STacwcNWcLDnXDqPxA0CWuRnpG98LPHHTl6+//DGBz/KrOX4ZxDQAgLM0LhhOAByKWH1QMD4gShlo2AUjIJRMGIBAFZ/N/+Rc0/8AAAAAElFTkSuQmCC","orcid":"","institution":"The first affiliated hospital of zhengzhou university","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2021-05-08 14:46:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-506313/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-506313/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3389/fmed.2021.812278","type":"published","date":"2022-01-24T05:21:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":9184289,"identity":"f1031b62-f3db-46f4-b042-0ed8aeac81a5","added_by":"auto","created_at":"2021-05-14 15:13:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139896,"visible":true,"origin":"","legend":"Identification of differentially expressed gene(DEG). Volcano plot showing the differentially expressed genes of GSE33630(A), GSE58545(B) and GSE60542(C). Veen diagram of downregulated(D) and upregulated(E) common DEGs to three gene expression profiles.","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-506313/v1/a0bba6a8e81e529f13be9498.png"},{"id":9184522,"identity":"0cce9d2a-8e6d-4550-bf35-2f3b37ce2373","added_by":"auto","created_at":"2021-05-14 15:16:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":190851,"visible":true,"origin":"","legend":"The Screening and identification of key genes. (A) PPI network constructed with the DEGs in biological progress. (B) The most significant module obtained from PPI network. (C) The top eight hub genes. The logrank analysis of hub genes(D) and the multivariate cox logistic regression analysis(E) of hub genes based on the TCGA database. (F) The significant kegg pathways of FN1 in THCA obtained by GSEA.","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-506313/v1/8612aa9286b9c26eb635ab77.png"},{"id":9184288,"identity":"6124673a-66a0-41b7-a5dc-e07dc53631fc","added_by":"auto","created_at":"2021-05-14 15:13:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102524,"visible":true,"origin":"","legend":"Transcriptional expressions of FN1 significantly correlated with poor survival outcomes in THCA patients from TCGA cohort. (A)The expression of FN1 in different subtypes of THCA. (B) The expressions of FN1 in THCA patients from TCGA cohort (C)The correlation between the expression of FN1 and PFS, *p\u003c0.05. (D) ROC curve with an AUC of 0.8971. Transcriptional expression of FN1 was significantly correlated with individual cancer stages(E) and promoter methylation(F), *p\u003c0.05, **p\u003c0.01, ***p\u003c0.001.","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-506313/v1/d4ec48422a731093c772cf4c.png"},{"id":9183900,"identity":"69f65a7a-995e-4277-9dd8-82500c18644b","added_by":"auto","created_at":"2021-05-14 15:07:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1833479,"visible":true,"origin":"","legend":"Analysis of the potential genetic and epigenetic alterations associated with FN1 dysregulation. (A-B) The CNAs in FN1-high and low expression groups. (C) Analysis of CpG island methylation and abnormal FN1 expression using TCGA dataset. (D) Correlation between FN1 expression and CpG island methylation was performed. ***p\u003c0.001.","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-506313/v1/4cbbc257e31dafab94d446e2.png"},{"id":9184004,"identity":"12193926-df31-48b7-8f25-145639c4524a","added_by":"auto","created_at":"2021-05-14 15:10:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":224174,"visible":true,"origin":"","legend":"The correlation between FN1 and inflammatory activities. (A) The heatmap of immune infiltration analysis. (B) The coexpression between FN1 and the B7-CD28 ligand-receptor family. (C) The correlation between FN1, CD273, CD274, CD275, CD276 and B7-H4 in THCA cohort. The correlation between FN1 and CD276 analyzed by THCA datasets(D), TIMER(E) and GEPIA(F).","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-506313/v1/1371bdb7f373fdba0445a63a.png"},{"id":9184291,"identity":"2c04f2c6-d79b-4542-b33a-85335b0ba210","added_by":"auto","created_at":"2021-05-14 15:13:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":287658,"visible":true,"origin":"","legend":"Down-regulation of FN1 suppressed THCA cell migration and invasion in vitro. (A) qRT-PCR showing the efficient depletion of FN1 expression and the expression of CD276 in B-CPAP and KTC-1 cells compared with siNC transfection. Representative photo-images (left) and histograms (right) of the effect of siFN1 on the clone formation(B) and migration(D) of B-CPAP and KTC-1 cells. (C) The proliferative ability of B-CPAP and KTC-1 cells after transfection was evaluated by CCK-8 assay. * p \u003c 0.05, **p\u003c0.001.","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-506313/v1/9e1580e84593fa315bff7019.png"},{"id":9183902,"identity":"20bcef73-d642-4a78-88a1-b90f71e4bc56","added_by":"auto","created_at":"2021-05-14 15:07:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":866222,"visible":true,"origin":"","legend":"The correlation between FN1 and CD276 in THCA. (A)Tumors and adjacent no-tumor tissues from patients with THCA were stained for CD276 expression. (B) Tumors and adjacent no-tumor tissues from patients with THCA were stained for FN1 expression. (C) CD276 showed high expression in tumor tissues with high FN1 expression. IHC score of CD276 (D) and FN1 (E) in adjacent no-tumor tissues and tumor tissues form patients with THCA. (F) IHC score of CD276 in tumor tissues form FN1-high and low patient groups, *p\u003c0.05, **P \u003c 0.01.","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-506313/v1/4456908911cb63f4159566dd.png"},{"id":17578835,"identity":"b41b94dd-c825-4e69-9967-f3a3e589e48d","added_by":"auto","created_at":"2022-01-24 05:21:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4726115,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-506313/v1/56cf2827-710f-4c7c-a7ba-da84121de54c.pdf"},{"id":9183894,"identity":"05b7d662-4421-4386-b7b8-918508aad11e","added_by":"auto","created_at":"2021-05-14 15:07:02","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":290520,"visible":true,"origin":"","legend":"Figure S1 Flow diagram of the study.","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-506313/v1/2ba0251d84b6ff0259b3fd7f.tif"},{"id":9184002,"identity":"0c2dd25c-dcdd-4c60-b397-b5ccffd8e404","added_by":"auto","created_at":"2021-05-14 15:10:02","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16199,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-506313/v1/1eedf475f5a535d0bf174252.docx"}],"financialInterests":"","formattedTitle":"Over-Expression and Prognostic Significance of FN1, Correlating with Immune Infiltrates in Thyroid Cancer","fulltext":[{"header":"Introduction","content":" \u003cp\u003eThyroid carcinoma (THCA) is the most common type of endocrine cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its prevalence has sharply increased in recent decades. In the US, the annual incidence of thyroid cancer increased from 4.9/100,000 in 1975 to 14.3/100,000 in 2009 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The observed increase in the incidence is partly due to the increased detection rate. In Beijing, China, the detection rate significantly increased from 16.8% in 1994 to 69.8% in 2015 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. THCA can be subclassified into several histological subtypes, which include papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), undifferentiated or anaplastic thyroid carcinoma (ATC), and medullary thyroid carcinoma (MTC). THCA occurs mostly in young adults (the average age of diagnosis is 40 years), and more frequently in females [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The most common treatment of thyroid cancer consists of surgical resection combined with radiotherapy or chemotherapy. However, for PTC, ATC, and MTC, a satisfactory resection is not always feasible, and even after radiotherapy, the risk of cancer recurrence is still high [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, recently personalized therapy approaches directed against specific targets have become available, only a few suitable targets have been identified thus far. Although the survival rate of patients with thyroid cancer is very high, with the rapid increase in THCA incidence, this disease poses a serious threat for human health [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImmune-related processes play an important role in the development of thyroid cancer, and hence, immunotherapy strategies are considered the most promising candidates for the treatment of thyroid cancer in the future [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Many studies have shown that tumor-infiltrating lymphocytes, such as tumor-associated macrophages, tumor-associated dendritic cells and tumor-infiltrating neutrophils, affect the prognosis of THCA patients, as well as the efficacy of chemotherapy and immunotherapy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, there is an urgent need to understand which immune cells play a role in the development of THCA, as well as to explore novel immune-related biomarkers that could aid in the diagnosis and prognosis of this disease.\u003c/p\u003e \u003cp\u003eFibronectin 1 (\u003cem\u003eFN1\u003c/em\u003e) encodes a glycoprotein present in a soluble dimeric form in the plasma, and in a dimeric or multimeric form at the cell surface and in the extracellular matrix. FN1 is involved in cell adhesion and migration processes during embryogenesis, wound healing, blood coagulation, host defense, and metastasis, as well as in cell proliferation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Multiple studies have established its involvement in the development of cancer, including oral squamous cell carcinoma [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], renal cancer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and thyroid cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Previous studies have indicated that FN1 is involved in NKp46 receptor-mediated interferon-γ (IFN-γ) production by natural killer cells, with respect to the control of tumor architecture and metastasis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, FN1 plays an important role in glioblastoma growth and invasion [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These findings suggest that FN1 has multifaceted functional roles in tumor progression.\u003c/p\u003e \u003cp\u003eIn this study, we comprehensively analyzed the correlation between \u003cem\u003eFN1\u003c/em\u003e expression with the prognosis of patients with THCA, as well as with the presence of tumor-infiltrating immune cells. Our findings highlight the important role of \u003cem\u003eFN1\u003c/em\u003e expression in THCA, and suggest a potential correlation between \u003cem\u003eFN1\u003c/em\u003e expression and tumor-immune interactions. Furthermore, the results were validated by immunohistochemistry and cell biology experiments, which indicate that \u003cem\u003eFN1\u003c/em\u003e is a potential prognostic and immunity-related biomarker in THCA and could potentially be used as drug target in THCA therapy.\u003c/p\u003e "},{"header":"1 Material And Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1 Cell lines and reagents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cell lines B-CPAP and KTC-1, belonging to the thyroid cancer cell lines, were obtained from the Institute of Biochemistry and Cell Biology of the Chinese Academy of Sciences, Shanghai, China. All cells were cultured in Roswell Park Memorial Institute\u0026nbsp;(RPMI) 1640 medium (Gibco, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (10%FBS), 100 U/mL penicillin, and 100 mg/mL streptomycin (Invitrogen, Carlsbad, CA, USA). Cells were incubated in a 5% CO\u003csub\u003e2\u003c/sub\u003e/95% O\u003csub\u003e2\u003c/sub\u003e in a humidified atmosphere at 37 \u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Data sources and identification of differentially expressed genes (DEGs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Cancer Genome Atlas (TCGA) is a landmark cancer genomics program that characterized over 20,000 primary cancer samples, spanning 33 cancer types and matched to the respective normal samples. Samples are molecularly characterized, and multi-omics data are provided, including gene transcripts, miRNA expression data, and DNA methylation state data. Additionally, it contains abundant and standardized clinical data. All datasets used were downloaded from the Cancer Genomics Browser website of the University of California Santa Cruz [16]. The GEO database (http://www.ncbi.nlm.nih.gov/geo/) stores original submitter-supplied records (series, samples, and platforms), as well as the curated datasets. Using the selection criteria of a number of samples greater than 20 and less than 1000, we selected three gene expression profiles (GSE33630 [17], GSE58545 [18], and GSE60542 [19]). Among of them, GSE33630 and GSE60542 are based on the GPL570 platform, and GSE58545 was obtained with the GPL96 platform. According to the commonly used threshold parameters (adjusted \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, |log\u003csub\u003e2\u003c/sub\u003eFoldChange| \u0026ge; 2.0) [20], we determined the DEGs between THCA and normal thyroid samples using the GEO2R online analysis tool, accessible via the National Center for Biotechnology Information website. Then, the interactive tool Venny2.1.0 [21] was used to create a Venn diagram of the DEGs to determine the common DEGs between the three analyzed gene expression profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Functional enrichment analysis \u003c/strong\u003e\u003cstrong\u003eand\u003c/strong\u003e\u003cstrong\u003e identification of hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) is an initiative that provides a standardized classification of genes by accounting for their functions, biological pathways they participate in, and the cell localization of the corresponding proteins. Genes are categorized into three domains: biological process (BP), molecular function (MF), and cellular component (CC) [22]. We annotated the identified DEGs according to the GO classification system using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool (https://david.ncifcrf.gov/) [23]. The threshold criteria to determine the significantly enriched GO terms were \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 and gene counts \u0026ge; 5. To identify the hub genes, a protein-protein interaction (PPI) network was constructed for the DEGs that had been annotated in the category BP (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05) using Search Tool for the Retrieval of Interacting Genes (STRING) (http://string-db.org/) [24]. PPI pairs with a combined confidence score \u0026ge; 0.4, were visualized using Cytoscape (version 3.7.2) [25]. The Cytoscape plugin Molecular Complex Detection (MCODE) (version 1.4.2), an app to cluster any given network, was used to identify the most important module in the PPI network, and the plugin CytoHubba was used to identify the hub genes in the PPI networks by calculating the degree of connectivity between DEGs. The selection criteria were as follows: MCODE score \u0026gt; 5 points, degree cut-off = 2, node score cut-off = 0.2, maximum depth = 100, and k-score = 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Correlation between gene expression and survival \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eONCOMINE is an online cancer microarray database (www.oncomine.org) [26]. Gene expression profiles from the website were used to analyze the transcription levels of \u003cem\u003eFN1\u003c/em\u003e in THCA. Furthermore, the correlation between \u003cem\u003eFN1\u003c/em\u003e expression and progression-free survival (PFS) and clinical parameters was analyzed using TCGA database. In addition, the UALCAN [27], a web resource to analyze cancer OMICS data, was used to investigate correlation between\u003cem\u003e FN1\u003c/em\u003e expression and cancer stage, and between \u003cem\u003eFN1\u003c/em\u003e expression and promoter methylation level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Methylation and \u003c/strong\u003e\u003cstrong\u003eimmunity correlation\u003c/strong\u003e\u003cstrong\u003e analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMEXPRESS (https://mexpress) is a data visualization tool designed for easy visualization of TCGA expression, DNA methylation, and clinical data, as well as the correlations between them [28, 29]. We used this tool to investigate the correlation between hub gene expression and the degree of methylation of the gene promoters. CIBERSORT is an analytical tool used to estimate the abundance of cell types in a mixed cell population using gene expression data [30]. We used this tool to assess the degree of immune infiltration. The co-expression analysis of \u003cem\u003eFN1\u003c/em\u003e and B7 family members (including \u003cem\u003eCD274, CD80, CD86, CD276, CD273, CD275, B7-H4, B7-H5, CD28, B7-H7, CD152,\u003c/em\u003e\u003cem\u003eCD279, CD278, TLT-2\u003c/em\u003e, and \u003cem\u003eNKp30\u003c/em\u003e) was assessed in the normal thyroid samples and in the THCA samples from TCGA database. The correlation between \u003cem\u003eFN1, CD273, CD274, CD275, B7-H4\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e and\u003cem\u003e CD276\u003c/em\u003e was further analyzed in the THCA cohort. GEPIA (http://gepia.cancer-pku.cn/detail.php) [31] and TIMER [32] were used to plot the expression scatterplots between any pair of genes in a given cancer type, while including the Spearman correlation and the statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.6 Silencing of FN1 by small interfering RNA (siRNA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe siRNA targeting human\u003cem\u003e FN1 \u003c/em\u003e(siFN1) and a non-specific scramble siRNA sequence (siNC) were purchased from Shanghai Gene Pharma and transiently transfected into B-CPAP and KTC-1 cells using Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) according to the manufacturer\u0026rsquo;s instructions. The target sequence in \u003cem\u003eFN1\u003c/em\u003e was: 5\u0026rsquo;-CAGUCAAAGCAAGCCCGGUUGUUAU-3\u0026rsquo;. Subsequently, assays were performed 48 h after the transfection. Cell viability was assessed 24, 48, or 72 h after transfection using the commercial kit Cell Counting Kit-8 (CCK-8, Beyotime Biotechnology, China) according to manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.7 Quantitative reverse transcription-polymerase chain reaction (RT-qPCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA, USA), followed by transfer into RNA-free EP tubes and storage at \u0026minus;80 \u0026deg;C. Complementary DNA (cDNA) was synthesized from total RNA using the PrimeScript RT reagent Kit (Takara, Dalian, China), and PCR was performed using the SYBR Green RT-PCR Kit (Takara). PCR was performed on the StepOne Plus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). Data were analyzed using the 2\u003csup\u003e-\u0026Delta;\u0026Delta;CT\u003c/sup\u003e method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.8 Immunohistochemistry (IHC) Staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples from cancer patients were obtained from thyroid cancer arrays (DC-Thy11004 Avira Biotechnology Co., Ltd., China), which included 24 cases of thyroid cancer and corresponding paracancerous tissues. Tumor tissues chip were deparaffinized and rehydrated, followed by antigen retrieval. The sections were then blocked with 5% BSA in PBS and incubated with FN1 antibody (1mg/mL, 1:200; Affinity) and CD276 antibody (1mg/mL, 1:200; Affinity) at 4 \u0026deg;C overnight. After three times washing, tissue sections were incubated with the secondary antibody conjugated with streptavidin\u0026ndash;horseradish peroxidases for 1h at room temperature. The slides were stained with 3, 3-diaminobenzidine tetrahydrochloride (DAB) and thenuclei were counterstained with hematoxylin. Marker density was scored independently by two investigators as follows: 0, negative; 1, weak; 2, moderate; or 3, strong.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.9 Statistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using GraphPad Prism 5.0 (San Diego, CA, USA) and R version 3.6.1. Data from three independent experiments performed in triplicate are presented as mean \u0026plusmn; standard deviation (SD). Multivariate survival analysis was carried out for all parameters that were significant in the univariate analysis using the Cox regression model. To analyze the significance of differences between groups, unpaired two-tailed Student\u0026rsquo;s t-test and one-way analysis of variance (ANOVA) were performed, and multiple comparison was accounted for using Bonferroni's correction. Differences were considered significant at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Identification of the common DEGs between the datasets used\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained three gene expression profiles (GSE33630, GSE58545, and GSE60542) from the GEO database. GSE33630 includes 45 normal thyroid samples and 60 THCA samples; GSE58545 includes 18 normal thyroid samples and 27 THCA samples; GSE60542 includes 30 normal thyroid samples and 33 THCA samples. According to the conventional criteria (adjusted \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 and |log\u003csub\u003e2\u003c/sub\u003eFoldChange| \u0026ge; 2.0), 263 genes were identified as DEGs in GSE33630, of which 133 were downregulated and 130 were upregulated; moreover, GSE58545 included 270 DEGs, including 144 downregulated genes and 126 upregulated genes, and GSE60542 contained 228 DEGs, including 107 downregulated genes and 121 upregulated genes (Figure 1A-C). A Venn diagram showed that 98 genes were differentially expressed in the three data sets, of which 46 were downregulated and 52 were upregulated (Figure 1D, E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Functional enrichment analysis of the common DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we performed GO functional analysis of the common DEGs using the tool DAVID. Then, we filtered the results to improve the confidence according to the standard criteria (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 and gene counts \u0026ge; 5) (Table S1). GO analysis showed that the common DEGs were mainly enriched in the CC category, including plasma membrane, extracellular exosome, extracellular region, and extracellular space. DEGs annotated as BP were enriched in cell adhesion, signal transduction, extracellular matrix organization, positive regulation of gene expression, positive regulation of cell proliferation, blood coagulation, wound healing, platelet degranulation, and nervous system development, all of which are processes associated with the occurrence and development of tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Identification and analysis of the \u003c/strong\u003e\u003cstrong\u003ehub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected 46 DEGs involved in specific BP (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) to build the PPI network (Figure 2A, Table 1). The most important module was obtained using the plugin MCODE in Cytoscape (Figure 2B). The top eight genes, including \u003cem\u003eFN1\u003c/em\u003e\u003cem\u003e, TIMP1\u003c/em\u003e\u003cem\u003e, SERPINA1\u003c/em\u003e\u003cem\u003e, COMP\u003c/em\u003e\u003cem\u003e, PROS1\u003c/em\u003e\u003cem\u003e, MMRN1\u003c/em\u003e\u003cem\u003e, KIT\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e and\u003cem\u003e TNFRSF11B\u003c/em\u003e, were identified as potential hub genes according to the degree score generated by the plugin CytoHubba (Figure 2C, Table 2). This was consistent with their enrichment in the top module determined using MCODE.Among of them, \u003cem\u003eFN1\u003c/em\u003e had the highest degree of connectivity in the PPI network. Furthermore, logrank regression and multivariate Cox regression analysis were used to calculate the correlation between gene expression and PFS (Figure D-E), indicating \u003cem\u003eFN1\u003c/em\u003e appeared to be the most attractive drug target and prognostic marker., GSEA was used to perform kegg analysis for FN1. The results suggested that the most of the involved significant pathways included chemokine signaling pathway, cytokine cytokine receptor interaction, leishmania infection, natural killer cell mediated cytotoxicity, and T cell receptor signaling pathway, as it has been established that FN1 plays a crucial role in tumor architecture and controls metastasis (Figure 2F) (14).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Expression levels of \u003cem\u003eFN1 \u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003ein THCA and evaluation of its value as a prognostic marker\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the expression levels of \u003cem\u003eFN1\u003c/em\u003e in THCA using the Oncomine database. We found that \u003cem\u003eFN1\u003c/em\u003e expression was upregulated in almost all different subtypes of THCA, including PTC and ATC (Figure 3A). We validated these results using TCGA database, in which \u003cem\u003eFN1\u003c/em\u003e was also significantly more expressed in THCA samples than in normal thyroid samples (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Figure 3B). \u003cem\u003eFN1 \u003c/em\u003eexpression levels were correlated with PFS (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Figure 3C). The area under the curve (AUC) of\u003cem\u003e FN1\u003c/em\u003e from TCGA dataset was 0.8971 (Figure 3D), highlighting the value of \u003cem\u003eFN1\u003c/em\u003e as a diagnostic marker in THCA. We compared the clinical characteristics (including age, sex, location, clinical stage, progression state, lymph node metastasis, and distant metastasis) between the \u003cem\u003eFN1\u003c/em\u003e-high expression and \u003cem\u003eFN1\u003c/em\u003e-low expression groups, and observed statistically significant differences in lymph node metastasis, distant metastasis, and progression state, although no significant differences were identified for other clinical features (Table 3). Furthermore, univariate and multivariate Cox regression models revealed that clinical stage and \u003cem\u003eFN1\u003c/em\u003e expression were independent prognostic factors for PFS in patients with THCA (Table 4). In addition, analysis of the UALCAN database showed that \u003cem\u003eFN1 \u003c/em\u003eexpression levels were closely correlated to cancer stage (Figure 3E) and that the degree of methylation of the \u003cem\u003eFN1\u003c/em\u003e promoter was lower in THCA than that in normal tissues (Figure 3F), indicating that \u003cem\u003eFN1\u003c/em\u003e may be involved in the development of THCA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Analysis of the potential genetic and\u003c/strong\u003e\u003cstrong\u003eepigenetic alterations underlying \u003cem\u003eFN1\u003c/em\u003e dysregulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we investigated the underlying mechanism of \u003cem\u003eFN1\u003c/em\u003e dysregulation in THCA. To determine whether copy number alterations (CNAs) are responsible for the abnormal expression of \u003cem\u003eFN1\u003c/em\u003e in THCA, we analyzed 397 cases from TCGA database for which CNAs data was available. No differences were observed for CNAs in the FN1-high and FN1-low groups (Figure 4A). Another mechanism that could underlie the altered\u003cem\u003e FN1\u003c/em\u003e expression profile observed in THCA samples is promoter hypermethylation, which plays an important role in the occurrence and development of several types of tumors [33, 34]. To investigate whether DNA methylation results in FN1 dysfunction, we examined the status of CpG sites in 507 THCA cases from TCGA database using the tool MEXPRESS. We found that 27 CpG sites had associated data; among which 20 CpG sites showed a negative correlation with \u003cem\u003eFN1\u003c/em\u003e expression (Figure 4C). The Pearson correlation coefficient was calculated for the five CpG sites with the highest correlation coefficient, including cg21494132, cg09040552, cg11309217, cg03228449, and cg03228449 (Figure 4D, all \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05). Our results demonstrate the correlation between DNA methylation and abnormal expression levels of \u003cem\u003eFN1\u003c/em\u003e in THCA, highlighting the need for further investigation of the underlying mechanism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Correlation between FN1 and inflammatory activities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering the strong association between \u003cem\u003eFN1 \u003c/em\u003eexpression levels and THCA prognosis, we hypothesized that FN1 may be associated with inflammatory responses, leading to enhanced survival rates. To identify the FN1 associated immune signature in THCA, we assessed the degree of immune infiltration with CIBERSORT. \u003cem\u003eFN1\u003c/em\u003e expression was closely correlated to the degree of M2 macrophages, resting memory CD4\u003csup\u003e+\u003c/sup\u003e T cells, follicular helper T cells, and CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration. The increase in \u003cem\u003eFN1\u003c/em\u003e expression was associated with an increase of the proportion of M2 macrophages and resting memory CD4\u003csup\u003e+\u003c/sup\u003e T cells and a decrease of the proportion of follicular helper T cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells (Figure 5A). To further investigate the correlation between \u003cem\u003eFN1\u003c/em\u003e and inflammation, we analyzed the co-expression of \u003cem\u003eFN1\u003c/em\u003e and members of the B7-CD28 ligand-receptor family, including \u003cem\u003eCD274, CD80, CD86, CD276, CD273, CD275, B7-H4, B7-H5, CD28, B7-H7, CD152, CD279, CD278, TLT-2\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e and \u003cem\u003eNKp30\u003c/em\u003e, which are closely correlated to T cell function. The result demonstrated that FN1 exhibited a significant co-expression trend with CD273, CD274, CD275, CD276 and B7-H4 (Figure 5B). Furthermore, we investigated the correlation between \u003cem\u003eFN1, CD273, CD274, CD275, CD276\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e and \u003cem\u003eB7-H4\u003c/em\u003e expression in the THCA cohort, and found a close positive correlation between \u003cem\u003eFN1\u003c/em\u003e and \u003cem\u003eCD276\u003c/em\u003e expression (R = 0.55, P = 0) (Figure 5C-D). This result was validated using TIMER and GEPIA, which provided R values of 0.682 and 0.79, respectively, for the correlation between \u003cem\u003eFN1\u003c/em\u003e and \u003cem\u003eCD276 \u003c/em\u003ein THCA (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Figure 5E-F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Down-regulation of \u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eFN1\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e inhibited cell proliferation and invasion and decreased \u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eCD276\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e expression levels in THCA samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the biological significance of \u003cem\u003eFN1\u003c/em\u003e in THCA tumorigenesis, KTC-1 and B-CPAP cells were transfected with siRNA targeting \u003cem\u003eFN1\u003c/em\u003e (siFN1) or negative control siRNA (siNC). Efficient depletion of \u003cem\u003eFN1\u003c/em\u003e expression was confirmed via RT-qPCR (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Figure 6A). Moreover, we found that downregulation of \u003cem\u003eFN1\u003c/em\u003e significantly reduced the expression of \u003cem\u003eCD276\u003c/em\u003e in KTC-1 and B-CPAP cells compared with siNC transfection (Figure 6A). Then, we studied the effect of \u003cem\u003eFN1\u003c/em\u003e on THCA cell proliferation and invasion \u003cem\u003ein vitro\u003c/em\u003e. The wound-healing assay, cell viability and cytotoxicity CCK-8 assay, and clone formation assay revealed that downregulation of \u003cem\u003eFN1\u003c/em\u003e in both cell types significantly inhibited cell proliferation and invasion compared to that in the control cells (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Figure 6B-D). These data suggest that downregulation of \u003cem\u003eFN1\u003c/em\u003e reduces the viability of THCA cell lines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 FN1 is positively correlated with CD276\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to assess FN1 correlation with CD276, we analyzed FN1 and CD276 expression in tumor sites and the adjacent no-tumor samples (Figure 7A). We found that FN1 and CD276 showed significantly higher expression in tumor sites than in the adjacent no-tumor samples (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, Figure 7C-D). Furthermore, we divided the tumor sites into two groups according to FN1 expression and found that CD276 levels were positively correlated with the level of FN1(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, Figure 7B, E).\u003c/p\u003e"},{"header":"3 Discussion","content":" \u003cp\u003e \u003cem\u003eFN1\u003c/em\u003e is a member of the FN family that is widely expressed in multiple cell types and is involved in cellular adhesion and migration processes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Here, we report that variations in \u003cem\u003eFN1\u003c/em\u003e expression levels correlate with prognosis in THCA patients. High expression levels of \u003cem\u003eFN1\u003c/em\u003e are associated with a poorer prognosis in THCA, indicating that \u003cem\u003eFN1\u003c/em\u003e expression could be used as a predictor of tumor prognosis. Furthermore, our analyses show that immune infiltration levels and immune markers correlate with \u003cem\u003eFN1\u003c/em\u003e expression levels in THCA samples, suggesting a potential role of \u003cem\u003eFN1\u003c/em\u003e in tumor immunology and its possible use as a cancer biomarker.\u003c/p\u003e \u003cp\u003eIn this study, we screened DEGs from three GEO datasets based on functional enrichment analysis and PPI network maps. \u003cem\u003eFN1\u003c/em\u003e, which is closely correlated to disease-free survival, was identified as a hub gene. In addition, we found that the promoter region of \u003cem\u003eFN1\u003c/em\u003e had significantly lower methylation levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in THCA than in normal thyroid tissues. GSEA analysis also showed that \u003cem\u003eFN1\u003c/em\u003e plays a role in chemokine signaling pathway, cytokine cytokine receptor interaction, natural killer cell mediated cytotoxicity, and T cell receptor signaling pathway, which are closely correlated to tumorigenesis. Importantly, immune infiltration analysis showed that immune infiltration levels and diverse immune marker sets were correlated with \u003cem\u003eFN1\u003c/em\u003e expression levels. Therefore, \u003cem\u003eFN1\u003c/em\u003e can be a potential immunity-related biomarker and therapeutic target in THCA. Moreover, using quantitative proteomic approaches, previous studies have proved that \u003cem\u003eFN1\u003c/em\u003e can be a potential novel candidate prognostic biomarker in THCA [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, these studies did not specify the exact range of effects of genes on disease prognosis, lacked certain clinical significance. In this study, three GEO datasets were combined to screen the hub genes, providing results with a high statistical and clinical significance. Our research further demonstrates that \u003cem\u003eFN1\u003c/em\u003e is a potential prognostic biomarker and therapeutic target in THCA from the perspective of DNA methylation and tumor immunology.\u003c/p\u003e \u003cp\u003eThe important aspect of this study is that \u003cem\u003eFN1\u003c/em\u003e expression is correlated with diverse immune infiltration levels in THCA. An increase in \u003cem\u003eFN1\u003c/em\u003e expression levels was positively correlated with the proportion of M2 macrophages and resting memory CD4\u003csup\u003e+\u003c/sup\u003e T cells but negatively correlated with the proportion of follicular helper T cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells. To further investigate the correlation between \u003cem\u003eFN1\u003c/em\u003e and inflammatory activities, the correlation between \u003cem\u003eFN1\u003c/em\u003e and members of the B7-CD28 ligand-receptor family was analyzed, and a close positive correlation between \u003cem\u003eFN1\u003c/em\u003e and \u003cem\u003eCD276\u003c/em\u003e was identified. \u003cem\u003eCD276\u003c/em\u003e, a member of the B7 superfamily, has been previously identified as a poor prognostic factor. A previous study demonstrated that \u003cem\u003eCD276\u003c/em\u003e, expressed in multiple tumor lines, tumor-infiltrating dendritic cells, and macrophages, can inhibit T-cell activation and autoimmunity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, the interactions between \u003cem\u003eFN1\u003c/em\u003e and \u003cem\u003eCD276\u003c/em\u003e could be a potential mechanism underlying the correlation of \u003cem\u003eFN1\u003c/em\u003e expression with immune infiltration and poor prognosis in THCA.\u003c/p\u003e \u003cp\u003eIn summary, increased \u003cem\u003eFN1\u003c/em\u003e expression correlated with poor prognosis and altered immune infiltration levels in THCA, indicating that \u003cem\u003eFN1\u003c/em\u003e is a potential immunity-related biomarker. This study was based on statistical analysis of bioinformatics methods, and the conclusions obtained were supported by experimental data and multiple database verification. Therefore, it is reasonable to believe that FN1 plays an important role in the diagnosis, treatment, and prognosis of THCA.\u003c/p\u003e "},{"header":"4 Conclusion","content":" \u003cp\u003eIn summary, we performed a comprehensive analysis using TCGA dataset and multiple online databases and identified \u003cem\u003eFN1\u003c/em\u003e as a potential immunity-related biomarker in THCA, as well as a potential drug target in THCA, highlighting the necessity of future clinical research in the topic.\u003c/p\u003e "},{"header":"5 Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTHCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThyroid cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Gene Expression Omnibus database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe differentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAVID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Database for Annotation, Visualization and Integrated Discovery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTRING\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Retrieval of Interacting Genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe protein\u0026ndash;protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEPIA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene expression profiling and interactive analyses\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epapillary thyroid carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efollicular thyroid carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eundifferentiated thyroid carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emedullary thyroid carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmunohistochemistry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"6 Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cstrong\u003eand consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the patients\u0026rsquo; data involved in this study is open source which is freely available in the public research databases including the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The application of the public data is already properly anonymized and informed consent was also obtained at the time of the original data collection. Samples from cancer patients were obtained from thyroid cancer arrays (DC-Thy11004 Avira Biotechnology Co., Ltd., China). The data is already properly anonymized and informed consent was also obtained at the time of the original data collection.\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 materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used to support the findings of this study are included in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Key Research and Development Program of China (Grant No. 2017YFC0909900). The National Natural Science Foundation of China (Grant No. 82002433), and Science and Technology Project of Henan Provincial Department of Education (Grant No. 18A320044，21A320036), Henan Province Medical Science and Technology Research Project Joint Construction Project (Grant No. LHGJ20190003, LHGJ20190055). The National Natural Science Foundation of China (82002998), the First-class Postdoctoral Research Grant in Henan Province (201901007), Medical Science and Technology Research Project in Henan Province (LHGJ20190042), and Youth Talent Support Project of Henan Province (2021HYTP045).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors of this research paper participated directly in the planning and execution of the study, and in the analysis of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBonhomme B, Godbert Y, Perot G, Al GA, Bardet S, Belleannee G, et al. Molecular Pathology of Anaplastic Thyroid Carcinomas: A Retrospective Study of 144 Cases. THYROID. 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Fibronectin in cell adhesion and invasion. Cancer Metastasis Rev. [Journal Article; Research Support US, Gov't PHS. Review]. 1984 1984-01-19;3(1):43\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhan S, Li J, Wang T, Ge W. Quantitative Proteomics Analysis of Sporadic Medullary Thyroid Cancer Reveals FN1 as a Potential Novel Candidate Prognostic Biomarker. ONCOLOGIST. [Journal Article; Research Support, Non-U.S. Gov't]. 2018 2018-12-01;23(12):1415\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee YH, Martin-Orozco N, Zheng P, Li J, Zhang P, Tan H, et al. Inhibition of the B7-H3 immune checkpoint limits tumor growth by enhancing cytotoxic lymphocyte function. CELL RES. [Journal Article]. 2017 2017-08-01;27(8):1034\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe information related to biological processes of statistical significance.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTerm\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescribe\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCount\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0007155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ecell adhesion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.61E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLXNC1, CYP1B1, CLDN10, MMRN1, CDH3, NCAM1, LAMB3, LYVE1, CDH16, SORBS2, COMP, ENTPD1, DPT, FN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0007165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003esignal transduction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012464\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGNA14, EDN3, CRABP1, RAP1GAP, KIT, HMGA2, LYVE1, TNFRSF11B, CXCL14, ANK2, TENM1, IGSF1, GDF15, PLAU\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0030198\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eextracellular matrix organization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000814\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCSGALNACT1, TNFRSF11B, LAMB3, COL9A3, COL13A1, COMP, FN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0010628\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epositive regulation of gene expression\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00353\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eANK2, KIT, HMGA2, CDH3, AGR2, CITED1, FN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0008284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epositive regulation of cell proliferation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.046698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEDN3, TIAM1, TGFA, KIT, DPP4, FN1, TIMP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0007596\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eblood coagulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.046698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSERPINA1, MMRN1, ENTPD1, PROS1, PAPSS2, PLAU\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0042060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ewound healing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.046698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTGFA, TFF3, CDH3, FN1, TIMP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0002576\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eplatelet degranulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.046698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSERPINA1, MMRN1, PROS1, FN1, TIMP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0007399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003enervous system development\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.046698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCSGALNACT1, CHRDL1, TENM1, BEX1, MPPED2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eHub genes with higher degree of connectivity\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene symbol\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene title\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDegree\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene nature\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFibronectin 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUP\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTIMP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTIMP metallopeptidase inhibitor 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUP\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSERPINA1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerpin family A member 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUP\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCOMP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCartilage oligomeric matrix protein\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUP\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePROS1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProtein S (alpha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUP\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMRN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMultimerin 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDOWN\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKIT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKIT proto-oncogene receptor tyrosine kinase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDOWN\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTNFRSF11B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTNF receptor superfamily member 11b\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDOWN\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" style=\"width: 339px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of clinical characteristics between low FN1 group and high FN1 group in THCA cohort.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 119px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 86px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCase NO. (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 55px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eFN1\u0026nbsp;\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 47px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e505\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e253\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge(Year)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.544\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e387\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e191\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e196\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e366\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e182\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.786\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e139\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eClinical stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eⅠ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e136\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e151\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eⅡ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eⅢ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eⅣ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 307px;\" colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLymph node metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e143\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDistant metastases\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e280\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e216\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eLeft lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eRight lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e219\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e114\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.348\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eBilateral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eIsthmus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eProgression state\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 47px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 119px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86px;\" align=\"left\"\u003e\n\u003cp\u003e452\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n\u003cp\u003e218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 26px;\" align=\"left\"\u003e\n\u003cp\u003e234\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eUnivariate and Multivariate Cox logistic regression analysis of FN1 for predicting PFS in TCGA cohort (PFS: progression free survival; TCGA: The Cancer Genome Atlas)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnivariate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMultivariate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge(ref.\u0026lt;60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.20\u0026thinsp;~\u0026thinsp;3.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78\u0026thinsp;~\u0026thinsp;2.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.225\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender (ref. Male)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.33\u0026thinsp;~\u0026thinsp;1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.055\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.39\u0026thinsp;~\u0026thinsp;1.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.211\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinical stage (ref. I-II)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.50\u0026thinsp;~\u0026thinsp;4.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.03\u0026thinsp;~\u0026thinsp;3.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLymph node metastasis (ref. No)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.91\u0026thinsp;~\u0026thinsp;2.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.62\u0026thinsp;~\u0026thinsp;2.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.687\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDistant metastases (ref. No)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.88\u0026thinsp;~\u0026thinsp;2.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95\u0026thinsp;~\u0026thinsp;2.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLocation(ref. Left\u0026amp;Right lode)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.45\u0026thinsp;~\u0026thinsp;1.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.42\u0026thinsp;~\u0026thinsp;1.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.632\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFN1 expression (ref. low)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.10\u0026thinsp;~\u0026thinsp;3.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.01\u0026thinsp;~\u0026thinsp;3.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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, biomarker, FN1, immunity, survival","lastPublishedDoi":"10.21203/rs.3.rs-506313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-506313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eThyroid cancer (THCA) is a malignancy affecting the endocrine system, which currently has no effective treatment due to a limited number of suitable drugs and prognostic markers.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eThree Gene Expression Omnibus (GEO) datasets were selected to identify differentially expressed genes (DEGs) between THCA and normal thyroid samples using GEO2R tools of National Center for Biotechnology Information. We identified hub gene \u003cem\u003eFN1\u003c/em\u003e using functional enrichment and protein\u0026ndash;protein interaction network analyses. Subsequently, we evaluated the importance of gene expression on clinical prognosis using The Cancer Genome Atlas (TCGA) database and GEO datasets. MEXPRESS was used to investigate the correlation between gene expression and DNA methylation; the correlations between \u003cem\u003eFN1\u003c/em\u003e and cancer immune infiltrates were investigated using CIBERSORT. In addition, we assessed the effect of silencing \u003cem\u003eFN1\u003c/em\u003e expression, using an \u003cem\u003ein vitro\u003c/em\u003e cellular model of THCA. Immunohistochemical(IHC) was used to elevate the correlation between \u003cem\u003eCD276\u003c/em\u003e and \u003cem\u003eFN1\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003e \u003cem\u003eFN1\u003c/em\u003e expression was highly correlated with progression-free survival and moderately to strongly correlated with the infiltration levels of M2 macrophages and resting memory CD4\u0026thinsp;+\u0026thinsp;T cells, as well as with \u003cem\u003eCD276\u003c/em\u003e expression. We suggest promoter hypermethylation as the mechanism underlying the observed changes in \u003cem\u003eFN1\u003c/em\u003e expression, as 20 CpG sites in 507 THCA cases in TCGA database showed a negative correlation with \u003cem\u003eFN1\u003c/em\u003e expression. In addition, silencing FN1 expression suppressed clonogenicity, motility, invasiveness, and expression of \u003cem\u003eCD276 in vitro.\u003c/em\u003e The correlation between FN1 and CD276 was further confirmed by immunohistochemical.\u003c/p\u003e\u003ch2\u003eConclusion.\u003c/h2\u003e \u003cp\u003eOur findings show that \u003cem\u003eFN1\u003c/em\u003e expression levels correlate with prognosis and immune infiltration levels in THCA, suggesting that \u003cem\u003eFN1\u003c/em\u003e expression be used as immunity-related biomarker and therapeutic target in THCA.\u003c/p\u003e","manuscriptTitle":"Over-Expression and Prognostic Significance of FN1, Correlating with Immune Infiltrates in Thyroid Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-14 15:07:00","doi":"10.21203/rs.3.rs-506313/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":"0791bf23-ad96-48bd-8956-ad10dddb9359","owner":[],"postedDate":"May 14th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":4271488,"name":"Pathology"}],"tags":[],"updatedAt":"2022-01-24T05:21:12+00:00","versionOfRecord":{"articleIdentity":"rs-506313","link":"https://doi.org/10.3389/fmed.2021.812278","journal":{"identity":"frontiers-in-medicine","isVorOnly":true,"title":"Frontiers in Medicine"},"publishedOn":"2022-01-24 05:21:12","publishedOnDateReadable":"January 24th, 2022"},"versionCreatedAt":"2021-05-14 15:07:00","video":"","vorDoi":"10.3389/fmed.2021.812278","vorDoiUrl":"https://doi.org/10.3389/fmed.2021.812278","workflowStages":[]},"version":"v1","identity":"rs-506313","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-506313","identity":"rs-506313","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}