DPP4 Promotes Papillary Thyroid Cancer Progression by Regulating the Infiltration and Exhaustion of CD8+ T cells

DPP4 Promotes Papillary Thyroid Cancer Progression by Regulating the Infiltration and Exhaustion of CD8+ T cells | 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 DPP4 Promotes Papillary Thyroid Cancer Progression by Regulating the Infiltration and Exhaustion of CD8+ T cells Ren Jing, Nan Wu, Yang Wu, Qian Zhang, Jinlin Liu, Ying Zhao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4421908/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Papillary thyroid cancer (PTC) is one of the most prevalent endocrine malignancy with a rapidly increasing incidence worldwide, a special immune microenvironment of which is not well characterized. Thus, the aim of this study was to identify the key biomarkers that regulate immune cells for the development and recurrence of PTC. Methods: The expression of immune-associated differentially expressed genes (DEGs) in human PTC was examined by bioinformatics analysis of TCGA and GEO datasets. The CIBERSORT and TIMER tool was used to analyze the distribution of tumor[1]infiltrating immune cells in PTC. Furthermore, DEG expression and function for the infiltration of CD8+ T cells were explored using human PTC specimens. Results: In this study, we identified DPP4 as a key gene in PTC by differential expression analysis among four GEO datasets and TCGA dataset and validated its overexpression profile by data from the TCGA, HPA databases, WB and PCR analysis. DPP4 upregulation significantly correlated with advanced grades, stages, and poor progression-free survival.Based on TIMER and CIBERSORT analysis, DPP4 expression tightly correlated with the infiltration of diverse immune cell types, especially CD8+ T cell subtypes. Compared with benign thyroid tumor, the proportion of CD3+CD8+ T cells in peripheral blood of PTC patients was significantly decreased, while the CD3+CD8+DPP4+ T cells of PTC patients was increased. The relative expression of PD-L1 and CTLA-4 in the CD8+DPP4+ T cells of PTC patients was higher than that in the CD8+DPP4- T cells. In addition, CD8+DPP4+ T cells of PTC patients showed the lower expression of IFN-γ and increased expression of IL-13 than that in benign thyroid tumor. The relative expression of IFN-γ, TNF-α, IL-4, IL-5, and IL-13 in CD8+DPP4+ T cells were both lower than that in CD8+DPP4- T cells among PTC and benign thyroid tumor patients. Conclusion: Our work suggests that the immune-associated DEG DPP4 is upregulated in PTC tissues and is tightly correlated with clinical stages and outcomes and regulates immune infiltration, but in particular involves in CD8+ T cell evasion and exhaustion. These findings may offer a new prospect for targeting CD8+ T cell exhaustion therapies for the treatment of PTC. Papillary thyroid cancer DPP4 Immune infiltration T cell exhaustion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION Thyroid cancer (TC) has the ninth highest cancer incidence and been increasing in many countries and settings; it is the most common malignancy in adolescents and adults aged 16–33 years( 1 – 3 ). Approximately 75% of all patients with TC were women and the median age of diagnosis is nearly 50s( 2 ). Although mortality have remained stable at lower rates, around 5–30% of patients evolve in an unfavorable way due to distant metastases and Iodine-refractory TC( 4 , 5 ). Among all pathological types of TC, papillary TC (PTC) accounts for approximately 84% of cases and is often confined and asymptomatic( 5 ). Although the vast majority of PTC patients have a favorable prognosis, local recurrence and distant metastasis of advanced PTC still hamper the survival and clinical management in certain high-risk patients. The tumor immune microenvironment (TIME) of TC is the heterogeneous histological space in which tumor cells coexist with host cells, which is associated with the clinical aggressiveness characteristics of the neoplasm( 6 , 7 ). The BRAF V600E - induced immune suppression involves TBX3 re-activation, which in turn up-regulates CXCR2 ligands in a TLR2-NFκB dependent manner, leading to myeloid-derived suppressor cells recruitment into the PTC tumor microenvironment( 8 ). The receptor tyrosine kinase suppressed immune escape of TC by blocking the activation of the MAPK pathway and downregulating PD-L1( 9 ). Elevated PD-L1 status can be a prognostic indicator for survival in TC patients when comprehensively assessed using CD8 + expression, BRAF V600E mutation, and the patient's immune status( 10 ). Hence, the in-depth investigation of the molecular connections between immune and non-immune cells enriched in the TIME will allow the construction of new clinical tools to diagnose TC patients and provide more targeted therapeutic directions. Several multi-tyrosine kinase inhibitors (MKIs), or immune checkpoint inhibitors in combination with MKIs, have emerged as novel therapies for controlling the progression of advanced differentiated TC with recurrence, metastasis and iodine refractoriness, as well as more aggressive subtypes such as poorly differentiated TC (PDTC) and anaplastic thyroid cancer (ATC)( 11 ). Immunotherapy has achieved a certain effect in TC patients who are refractory to conventional therapy. Nevertheless, many TC patients remain insensitive to immunotherapy, and some who initially respond but eventually recurrence( 7 , 12 ). Although anti-PD-1 therapy has been effective in a small percentage of patients with advanced PTC and refractory ATC, the majority of the patients either do not respond or develop resistance to anti-PD-1 therapy due to up-regulated METTL3 expression that negatively correlated with CD70 expression and M2 macrophages, as well as regulatory T cell infiltration( 13 ). At present, the PTC tissue has a special TIME feature that is not well characterized. Thus, the aim of this study was to identify the hub biomarkers that regulate immune cells for the development and recurrence of PTC. 2. MATERIALS AND METHODS 2.1 GEO data set selection and data processing The gene expression profiles of PTC in GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ) were obtained. The screening criteria included (a) papillary thyroid tumor, tumour, cancer, or carcinoma; (b) samples containing tumor and normal thyroid tissues; (c) Homo sapiens as the organism; (d) expression profiling by array as the study type; (e) the sample size was more than 10 samples in each group. A total of four GEO datasets based on the GPL570 platform were selected, including GSE33630 ( n = 94), GSE35570 ( n = 83), GSE60542 ( n = 47), and GSE29265 ( n = 40). When multiple probes corresponded to one gene, their average expression level was considered. In addition, the mRNA expression matrix file of TCGA thyroid cancer was acquired from the Gene Expression Profiling Interactive Analysis (GEPIA) ( http://gepia.cancer-pku.cn/ ). 2.2 Identification of differentially expressed genes (DEGs) in each GEO data set The raw expression data in these four GEO datasets were preprocessed into expression matrices using R software. Gene expression intensities were performed using quantile normalisation method with normalizeBetweenArrays package (Figure S1). The DEGs in each GEO data set was then identified by the limma package in R( 14 ). The cut-off criteria of |log2 fold change (FC)| > 1.2 and p < 0.01 were considered to be statistically significant. The DEGs of TCGA thyroid cancer was acquired from GEPIA using limma methods in the section of Differential Expression Analysis. The common DEGs of the aforementioned five DEG sets were identified and shown in the Venn diagram by using ClustVis. 2.3 Protein-protein interaction (PPI) network construction The STRING (version 10.5) database was used to construct the PPI network( 15 ). The parameter of interactive relationships among these common DEGs was set as high confidence > 0.7. Cytoscape (version 3.6.1) software ( http://www.cytoscape.org/ ) was used to visualize and analyse the PPI network. The plug-in cytoHubba of Cytoscape was used to screen significant modules of the PPI network (the parameters were set to default). Maximal clique centrality (MCC) and other 11 computing methods were used to screen significant hub genes of the PPI network (the parameters were set to default). 2.4 RRA method and marker gene identification To obtain robust hub DEGs (rhDEGs), RRA method was used to identify genes that were ranked consistently better than expected by running the R package RobustRankAggreg ( 16 ). In brief, based on the results of the aforementioned 12 computing methods from the plug-in cytoHubba of Cytoscape, the genes were ordered by their scores. The aggregation based on the ranks of genes in different methods were then performed. Using core algorithm of RobustRankAggreg package, the screening criteria of Frequencies ≥ 12 and Score < 0.05 were considered statistically significant for the identification of robust DEGs. 2.5 Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses To characterize the functional roles of the top 10 rhDEGs, DAVID (version 6.8) database ( https://david.ncifcrf.gov/ ) was used for GO enrichment analysis of biological process (BP), molecular function (MF), and cellular component (CC) with a cut-off of p < 0.05 and count ≥ 3. The KEGG databases (KEGG: Kyoto Encyclopedia of Genes and Genomes) was used to explore KEGG pathways analysis, a web tool for gene functional enrichment. 2.6 Expression and prognostic analysis. The GEPIA database was used for gene expression and survival analysis of these rhDEGs in TC. The violin plot was showed the expression patterns between tumour and normal samples of these marker genes. The Uniprot, BioGPS (BioGPS-your Gene Portal System) and KEGG databases (KEGG: Kyoto Encyclopedia of Genes and Genomes) were then used to identify these rhDEGs that were associated with protein metabolism and immune function regulation. 2.7 Analysis of immune infiltration and gene correlation by the TIMER database and CIBERSORT The association of rhDEGs that is associated with immune function, infiltration level, and markers of common immune cells, such as B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells were analyzed using the the Tumor IMmune Estimation Resource (TIMER) database. Genomic expression data of PTC acquired from the TCGA database were divided into high and low expression groups by marker gene expression. The immune infiltration scores of a total of 22 subtypes of immune cells were calculated by CIBERSORT, and visualized by a violin graph. 2.8 Clinical tissues samples Ten patients diagnosed as PTC between September 2023 and January 2024 at South China Hospital of Shenzhen University (Shenzhen, China) were enrolled and this study. This study was approved by the ethical committee of the hospital. All patients had never received any treatment before sampling and signed written informed consent in advance. The tumor and adjacent nontumor tissues from six PTC patients were immediately transferred into liquid nitrogen after surgical resection, and stored at − 80°C for RNA extraction. Besides, the tumor and adjacent nontumor tissues from other four PTC patients were transferred into magnet-activated cell sorting (MACS) tissue storage solution (Cat. 130-100-008, Miltenyi Biotec, Cologne, German) after surgical resection, and stored at 4°C for mass cytometry. 2.9 Immunofluorescence The slides from the paraffin-embedded tumor tissues were dewaxed, hydrated, fixation with 3.7% paraformaldehyde (Cat.P804536-500g, Macklin, Shanghai, China), permeabilized with immunostaining permeabilization buffer with Triton X-100 (Cat.I997471-100ml, Macklin, Shanghai, China), used for antigen retrieval with SignalStain® Citrate Unmasking Solution (Cat.14746, Cell Signaling Technology, MA, USA), and blocked with immunofluorescence blocking buffer (Cat.12411, Cell Signaling Technology, MA, USA). Then, CD3/CD8 polycolonal antibody (Cat.PA5-102404, ThermoFisher Scientific, MA, USA) and anti-DPP4 antibody [OTI11D7] (Cat.Ab114033, Abcam, Cambridge, UK) were incubated as the primary antibody at 4°C overnight. Anti-mouse IgG (H + L), F(ab')2 Fragment (Alexa Fluor® 647 Conjugate) (Cat.4410S, Cell Signaling Technology, MA, USA) and anti-rabbit IgG (H + L), F(ab')2 Fragment (Alexa Fluor® 488 Conjugate) (Cat.4412S, Cell Signaling Technology, MA, USA) were served as secondary antibody for 45 min at room temperature (RT). The sections were finally treated with ProLong®Gold Antifade Reagent with 4’,6-diamidino-2-phenylindole (Cat.8961S, Cell Signaling Technology, MA, USA). All images were observed using multiplex confocal microscopy (LSM980, Zeiss, Oberkochen, Germany). In addition, the Human Protein Atlas (HPA) ( https://www.proteinatlas.org/ ), a proteomics database that provides information on the organization and cell distribution of 26,000 human proteins, was used to validate the expression of rhDEGs that is associated with immune function and protein metabolism. 2.10 RNA extraction and quantitative real-time polymerase chain reaction (qRT-PCR) analysis Total RNA was isolated from the tumor and adjacent nontumor tissues (NT) using Trizol™ reagent (Cat.15596026, ThermoFisher Scientific, MA, USA) according to the standard protocol. Complementary DNA (cDNA) was synthesized using the PrimeScript™ RT reagent Kit with the gDNA Eraser (Cat. RR047A,Takara, Dalian, China). RT-qPCR was performed using a qTOWER384G fluorescence RT-qPCR instrument (Analytik Jena AG, Jena, Germany) using the TB Green® Premix Ex Taq™ II (Tli RNaseH Plus; Cat. RR420A,Takara, Dalian, China). The qPCR primer sequences were listed in the Table 1 . Each cDNA sample was analyzed in triplicate for the quantitative assessment of RNA amplification. The level of each target gene was normalized relative to that of glyceraldehyde-3- phosphate dehydrogenase (GAPDH) in each sample using the ΔCt method. 2.11 Mass cytometry and flow cytometry After prepared of single-cell suspension with Maxpar® IMC™ Cell segmentation Kit (Cat. 201500, Fluidigm, CA, USA), the samples were stained with 2 µM cisplatin (Cat. 201064, Fluidigm, CA, USA) for 2 min before quenching with cell staining buffer (CSB; Cat.201068, Fluidigm, CA, USA). A Fix-I buffer (Cat. 201065, Fluidigm, CA, USA) was then used to fix cells for 15 min at RT, followed by washing three times with 1x phosphate buffer (PBS). The samples were stained with Cell-ID™ 20-Plex Pd Barcoding Kit (Cat. 201060, Fluidigm, CA, USA) to minimize internal cross reaction between samples. MaxPar × 8 Polymer Kits (Cat. 201321, Fluidigm, CA, USA) were used to conjugate with purified antibodies according to the manual. All metal-conjugated antibodies were titrated for optimal concentrations before use. For the surface protein staining, cells were adjusted to 1× 10 6 cell/mL in PBS and cultured with antibodies cock-tail in a total 50 µL CSB for 30 min at RT. After that, cells were washed and underwent permeabilization with 80% methanol for 15 min at 0°C and stained with an intracellular antibody cocktail for 30 min. After triple washes in CSB, cells were incubated with 0.125 µM iridium intercalator in fix and perm buffer (Cat. 201067, Fluidigm, CA, USA) at 4°C overnight. After cultured with intercalator, cells were washed with ice cold PBS and deionized water three times separately. Prior to acquisition, samples were resuspended in deionized water containing 10% EQ Four Element Caliboration Beads (Cat. 201078, Fluidigm, CA, USA) and cell concentrations were adjusted to 1×10 6 cell/mL. Data acquisition was performed on a Helios mass cytometer (Fluidigm, CA, USA). The original FCS data were normalized and .fcs files for every sample were collected. The whole blood from PTC patients was treated immediately with pre-warmed 1x RBC Lysis Solution (Cat. 420301, Biolegend, CA, USA) at 37°C for 15 min. After twice washes in CSB (Cat. 420201, Biolegend, CA, USA), cells were passed through a 45-µm strainer and stained with Human TruStain FcX™ (Cat. 422302, BioLegend, CA, USA) to block Fc-receptors. For the surface protein staining, cells were adjusted to 1× 10 6 cell/mL in CSB and cultured with antibodies cock-tail in a total 10 µL CSB for 45 min at 0°C. After that, cells were washed and fixed in 0.5 ml/tube Fixation Buffer (Cat. 420801, Biolegend, CA, USA) in the dark for 20 min at RT. The fixed cells were resuspended in Intracellular Staining Perm Wash Buffer (Cat. 421002, Biolegend, CA, USA) and centrifuge at 350xg for 5–10 min. Cells were resuspended in residual Intracellular Staining Perm Wash Buffer and add a predetermined optimum concentration of intracellular antibody cocktail for 45 min at 0°C. After triple washes in CSB, cells were sorted and analyzed using a BD FACSCanto II flow cytometer (BD Biosciences, NJ, USA). 2.12 Statistical analysis Data were analyzed using StataCorp LP (STATA Institute, Inc., College Station, TX, USA) and GraphPad Prism 9 (San Diego, CA, USA). Data were tested for normality with the Shapiro–Wilk test. Normally distributed data are presented as means ± S.E.M. Comparisons between two groups were performed using the two-tailed Student’s t-test, while comparisons between multiple groups were performed using one-way ANOVAs, followed by Tukey’s multiple comparisons test. A P -value < 0.05 was defined as statistically significant. Mass Cytometry data were firstly normalized using EQ Four Element Calibration Beads (EQ Beads, 201078, Fluidigm) according to manufacturer’s instructions, then the cell debris were removed according the 191Ir and 193Ir channel. Doublets were removed according to the Even Length. Furthermore, CD45 gate were used to isolate all the infiltrated leukocyte in PTC and adjacent nontumor tissues, then a clustering panel including CD3, CD4, CD8, CD11c, CD14, CD16, CD19, CD20, CD25, CD27, CD28, CD38, CD45, CD45RA, CD45RO, CD56, CD57, CD66b, CD103, CD123, CD127, CD161, CD294, CCR4, CCR6, CCR7, CXCR3, CXCR5, HLA-DR, IgD, and TCRyq were used to manually differentiate subpopulations of the infiltrated leukocytes. 3. RESULTS 3.1 Identification of DEGs in each GEO data set In present study, a multistep analysis were performed to explore key DEGs and their significant biological functions by integrated bioinformatics methods in PTC (Figure 1a). First, we selected and downloaded a total of four GEO datasets (GSE29265, GSE33630, GSE35570 and GSE60542) with gene expression profiles for PTC, as well as combined with TCGA dataset. A total of 127 PTC and 137 normal tissues from four GEO datasets, as well as 849 samples with 512 normal types and 337 tumor types from TCGA database, were obtained in this study. On the basis of the cut-off criteria, DEGs in each dataset were identified between TC and normal tissues using volcano plot (Figure 1b-f). There were 172 common DEGs, which was shown in a venn diagram of the distribution of DEGs in each dataset (Figure 1g). 3.2 Selection of rhDEGs by RRA method The PPI network of these common DEGs were constructed and uploaded to Cytoscape software. The top 50 hub DEGs in each computing method were identified and the PPI network of top 10 hub DEGs were shown in Figure S2. To explore rhDEGs in different computing methods, the RRA method was used, which is used to identify genes that are ranked consistently better than expected by chance. Finally, we determined 80 rhDEGs (Table 2). The relative expression (-log2(Score) and PPI network of top 10 rhDEGs were shown in Figure 1h-i. The expression heatmap of the top 10 rhDEGs is shown in Figure 2a. The above results proved that their expression patterns were consistent and that these rhDEGs were strong and robust. 3.3 Functional and pathway enrichment analyses To characterize the functional roles of the above top 10 rhDEGs, we used GO and KEGG pathway enrichment analyses (Table 3). The BP category of the GO analysis results showed that these rhDEGs were significantly enriched in blood coagulation, response to hypoxia, positive regulation of cell proliferation, and cell adhesion. For CC, these rhDEGs were significantly enriched in extracellular region and extracellular exosomes. Moreover, they were significantly enriched in protease binding, identical protein binding, and receptor binding in the MF categories (Figure 2b). According to KEGG pathway enrichment analysis, these rhDEGs were significantly enriched in proteoglycans in cancer, focal adhesion, and PI3K-Akt signaling pathway (Figure 2b). 3.4 Expression and prognostic analysis Next, we used the gene expression of above ten rhDEGs. As shown in Figure 2-l, we found that all of these rhDEGs were significantly differentially upregulated, which was consistent with our results above. Using the GEPIA database to explore the association between gene expression and survival, we found that fibronectin 1 (FN1; log-rank p = 0.026), dipeptidyl peptidase-4 (DPP4/CD26; log-rank p = 0.048), and integrin subunit alpha 2 (ITGA2; log-rank p = 0.032) were significantly correlated with the disease-free survival of thyroid cancer patients. In addition, the expression levels of these rhDEGs were also examined by qRT-PCR method in PTC and adjacent nontumor tissues. As shown in Figure S3, it can be seen that the significant upexpression of Fn1 ( p = 0.0031), Itga1 ( p = 0.0016), Dpp4 ( p = 0.0004), Fam20a ( p = 0.0155), Serpina1 ( p = 0.0013), Lgals3 ( p = 0.0072), Met ( p = 0.0020), and Plau ( p = 0.0030) were demonstrated in thyroid cancer tissues ( n = 10) compared with matched normal tissues (Figure S3a-c). These results demonstrated the accuracy of our above analysis, suggesting that Fn1, Itga1, Dpp4, Fam20a, Serpina1, Lgals3, Met , and Plau can be potential biomarkers for TC. Among these rhDEGs, we found that only DPP4 was significantly associated with immune function, which is specifically expressed in lymphatic vessels, smooth muscle, CD4+ and CD8+ T cells to regulated T-cell proliferation and NF-kappa-B activation. KEGG database reported that DPP4 is also involved in the pathway of protein digestion and absorption (Table 4). Relative expression of DPP4 protein in PTC tissues was higher than that in NT (Figure S3c). According to the results from the HPA database, DPP4 exhibits a significantly higher expression profile in PTC tissues than in NT (Figure S3d). 3.5 Manual gating and immune cell clusters To explore the multi-dimension mass cytometry data, we analyzed CD45+ infiltrated leukocytes through the implementation of manual gating and SPADE analysis. The gating strategy employed were displayed in Figure S4. The cells were visualized as a dimensional reduction plot using t-distributed stochastic neighbor embedding (t-SNE) between each NT and PTC (Figure 3a). A total of 29 cell clusters were then annotated according to the median expression of cell marker, including lymphocytes, monocytes, dendritic cells (DCs), and granulocytes (Table 5). The expressions of each cell marker were shown in common and t-SNE heatmap (Figure 3b-d). The characteristic changes in the number of cells in different clusters were observed by displaying the proportion of multiple cluster samples (Figure 4a). Compared with NT, the significantly increased range of clusters in PTC included 18 eosinophil cluster, 26 CD8 terminal effector cell cluster, 9 Th1-like cell cluster, 13 CD4 naive cell cluster, 15 regulatory T cell (Treg) cluster, and 12 later natural killer (NK) cell cluster. Meanwhile, the significantly decreased range of clusters in PTC included 3 NK T (NKT) cell cluster, 8 B naive cell cluster, 14 CD8 effector memory cell cluster, and 28 CD8 naive cell cluster compared with NT (Figure 4b). However, there were lack of statistical differences on inter group cluster difference analysis between NT and PTC (Figure 4c). To identify the clusters with similar phenotypes, Spearman correlation analysis was used. The cut-off criteria of |correlation coefficients| > 0.75 and p < 0.05 were considered to be statistically significant. We found that 1 CD4 terminal effector cell cluster is negatively correlated with 20 neutrophil cluster; 7 myeloid DCs (mDCs) cluster is negatively correlated with 17 cell cluster (CD45RO+TCR+CXCR3+CXCR5+), 18 eosinophil cluster, 19 Th1-like cell cluster, 24 classical monocyte cluster, but which was positively associated with 10 plasmacytoid DCs (pDCs) cluster; 8 B naive cell cluster is negatively correlated with 16 early NK cell cluster; 10 pDCs is negatively associated with 17 cell cluster, 18 eosinophil cluster, 24 classical monocyte cluster, and 29 CD8 central memory cell cluster; 11 pasmablast cluster is negatively correlated with 18 eosinophil cluster; 12 late NK cell cluster is positively associated with 15 Treg cluster; 13 CD4 naive T cell cluster is positively correlated with 18 eosinophil cluster and 19 Th1-like cell cluster; 17 cell cluster is positively correlated with 24 classical monocyte cluster and 29 CD8 central memory cell cluster; 18 eosinophil cluster s positively correlated with 9 Th1-like cell cluster, 24 classical monocyte cluster, and 29 CD8 central memory cell cluster; 19 Th1-like cell cluster is positively correlated with 29 CD8 central memory cell cluster (Figure 4d). 3.6 DPP4 expression is correlated with immune cell infiltration in PTC. In the present study, we analyzed the association between DPP4 expression and immune cell infiltration at the mRNA level. First, Spearman’ s correlation analysis showed that the DPP4 expression level was significantly, moderately to strongly, and positively correlated with the infiltration levels of B cells (r = 0.437, p = 7.40e-24), CD4+ T cells (r = 0.474, p = 1.04e-28), macrophages (r = 0.355, p = 5.68e-16), neutrophils (r = 0.446, p = 3.04e-25), and dendritic cells (r = 0.428, p = 5.01e-23). Moreover, moderate correlation was found between DPP4 and CD8+ T cells (r = -0.144, p = 1.39e-03) (Figure 5a). The “Survival” module showed that no significant differences were found among B cell, CD4+ T cell, CD8+ T cell, macrophage, neutrophil, and dendritic cells (Figure 5b). According to the COX regression model of survival, the survival of thyroid cancer patients is positively correlated with age (HR = 1.236, 95%CI, 1.126-1.357; p < 0.001) and stage 4 (HR = 21.587, 95%CI, 1.815-256.695; p = 0.015), while it is negatively correlated with CD8+ T cell (HR = 0, 95%CI, 0-0; p = 0.002), macrophage (HR = 0, 95%CI, 0-0.522; p = 0.045), and DPP4 expression (HR = 0.678, 95%CI, 0.472-0.973; p = 0.035) (Figure 5c). SCNA module showed that the infiltrated levels of B cell, CD4+ T cell, CD8+ T cell, macrophage, neutrophils, and DCs were both decreased in thyroid cancer tissues with arm-level deletion or arm-level gain of DPP4 compared with that with diploid/normal expression of DPP4 (Figure 5d). Second, we applied the CIBERSORT algorithm to further explore the correlation between the subtypes of the above six immune cells and different DPP4 expression levels. The immune infiltration scores of a total of 22 subtypes of immune cells were calculated in the high and low DPP4 groups (Figure 5e). Six immune cell subtypes were associated with DPP4 expression, including naive B cells, CD8+ T cells, M1 macrophages, M2 macrophages, resting DC, nuetrophils. The most noticeable differences were observed in the subtype of DCs, such as resting DCs. 3.7 DPP4 expression is correlated with CD8+ T cell function in PTC. To explore the role of DPP4 in CD8+ T cells, we compared with such negative immune checkpoint as PD-L1 and CTLA-4 between CD8+DPP4+ and CD8+DPP4- T cells (Figure 6a). Compared with nodular goiter (NG), the relative number of CD3+CD8+ T cells and CD3+CD8+DPP4- T cells in peripheral blood of PTC were both reduced while the relative number of CD3+CD8+DPP4+ T cells in peripheral blood of PTC was significantly increased (Figure 6b-c). The relative expression of PD-L1 and CTLA-4 in the CD3+CD8+DPP4+ T cells of PTC patients were both higher than that in the NG patients. However, there were lack of statistical differences on the relative expression of PD-L1 and CTLA-4 in the CD3+CD8+DPP4- T cells between PTC and nodular goiter patients. Simultaneously, among PTC patients, the relative expression of PD-L1 and CTLA-4 in the CD3+CD8+DPP4+ T cells were both higher than that in the CD3+CD8+DPP4- T cells (Figure 6e-f). In addition, we also found that the infiltration of CD8+DPP4+ T cells from tumor tissues was significantly increased compared with paratumor tissues of PTC patients (Figure 6d). We also compared the cytokines including interferon (IFN) -γ, tumor necrosis factor (TNF)-α, interleukin (IL)-4, IL-5, and IL-13 between CD8+DPP4+ and CD8+DPP4- T cells (Figure S5a-b). Compared with NG patients, the relative expression of IFN-γ in CD8+DPP4+ T cells of PTC patients was significantly decreased, but which in CD8+DPP4- T cells between PTC and NG patients was lack of statistical difference (Figure S5c). The relative expression of TNF-α, IL-4, and IL-5 in CD8+DPP4+ T cells between PTC and NG patients were not statistically different,and the relative expression of TNF-α and IL-4 in CD8+DPP4- T cells in CD8+DPP4- T cells of PTC patients were significantly lower than that in the NG patients. In addition, the relative expression of IL-5 in CD8+DPP4- T cells also not differed statistically between PTC and NG patients (Figure S5d-f). Compared with NG patients, the relative expression of IL-13 in CD8+DPP4+ T cells of PTC patients was significantly increased, but which in CD8+DPP4- T cells of PTC patients was reduced (Figure S5g). In total, the relative expression of these cytokines in CD8+DPP4+ T cells were both lower than that in CD8+DPP4- T cells among PTC and NG patients. 4. DISCUSSION Using classical bioinformatics analysis based on five microarray data, FN1, DPP4, and ITGA2 were identified as upregulated rhDEGs that are correlated with the disease-free survival of TC patients. After gene annotation and validation of the expression profile in clinical specimens, this study revealed that DPP4 was a potential oncogenic gene with prognostic value in TC. DPP4 is a significantly upregulated gene whose high expression predicts striking immune microenvironment remodeling, higher risk of immune evasion, and worse PFS in PTC patients. DPP4 is expressed in T cells, B cells, NK cells, DCs, and macrophages, and its expression level is also related to the activation status of immune cells. Cells marked by DPP4 expression are highly proliferative and multipotent progenitor cells (17, 18). Among these immune cells, DPP4 is mainly expressed in T cells and upregulated in malignant tumors such as TC(19). For example, DPP4 is stored in the secretory granules of human CD8+T cell populations and co-localizes with effector proteins such as granzyme, perforin, and granin(20). Here, we proved that the proportion of CD8+ T cells in the DPP4 high-expression PTC was significantly decreased in comparison with DPP4 low-expression PTC. DPP4 is a regulator to mediate the motility, activation, and effector function of T cells. DPP4 also promotes mitochondria-associated endoplasmic reticulum membrane formation and mitochondria calcium overload in regulatory T cells through binding to IGF2-R and activating PKA/SP1 signaling(21). DPP4+ chimeric antigen receptor T cells exhibited robust cytotoxicity against the DPP4+ T-cell lymphoma cells activated multiple effector functions and limited tumour progression(22). However, DPP4 deficiency reduces T-cell motility by suppressing the expression of microtubule associated protein midline-1 in T cells(23). Post-translational modification of chemokines mediated by DPP4 has been shown to negatively regulate lymphocyte trafficking, and its inhibition enhances T cell migration and tumor immunity(24). Administration of DPP4 inhibitor such as sitagliptin increased overall CXCR3-mediated CD8+ T-cell trafficking to the tumour and enhanced the activation and proliferation of CD8+ T-cells(24, 25). Anagliptin can enhance PD-L1 blockade efficacy in NSCLC by inhibiting macrophage differentiation and M2 macrophage polarization(26). Combining a DPP4 inhibitor with radiotherapy could promote chemokines expression and T cell infiltration in the tumor microenvironment, enhancing the anti-tumor effect of radiotherapy and anti-PD-1(27). In this study, DPP4+CD8+ T cells in peripheral blood of PTC patients showed the high expression of PD-L1, CTLA4, and IL-13, as well as low expression of IFN-γ than DPP4-CD8+ T cells, suggesting that DPP4 may be another negative immune checkpoint of CD8+ T cells. Among CD4+ T cells, DPP4 can identifies three T helper subsets with distinct immunological properties in both healthy individuals and cancer patients. For example, DPP4 neg T cells possess a regulatory phenotype, DPP4 int T cells are mainly naive, and DPP4 high T cells appear terminally differentiated and exhausted(28). Previous study(29) reported that DPP4+CD8+ T cells is the early effector memory T-cell subset, and that DPP4-mediated co-stimulation of CD8+ T cells exerts a cytotoxic effect preferentially via granzyme B, TNF-α, IFN-γ, and Fas ligand. However, this study showed that DPP4+CD8+ T cells may be the exhausted cytotoxicity T cells with higher expression of PD-L1 and CTLA-4, as well as down-regulation of IFN-γ for successful cancer immunotherapy involving checkpoint inhibitors and chimeric antigen receptor T cells(30, 31). DPP4 may be interacted with the secretion of IL-13(32, 33), which promotes the sphere formation, proliferation, and migration of cancer cells(34-36). This study firstly concentrated on the role of DPP4 in CD8+ T cells of PTC patients, and found that DPP4 is significantly up-regulated to be associated with infiltration, activation, and effector function of T cells. Simultaneously, DPP4 may be a negative immune checkpoint of CD8+ T cells with higher expression of PD-L1 and CTLA-4 as well as down-regulation of IFN-γ, However, the features of transcription profile, chemokine receptor profile, cellular developmental trajectory and stemness between DPP4+CD8+ and DPP4-CD8+ T cells should be further explored in our future study. 5. CONCLUSION In summary, the present study proved that DPP4 is upregulated in PTC tissues and is tightly correlated with clinical stages and outcomes, immune infiltration, and key molecules in CD8 + T cell evasion and exhaustion. These findings support the use of DPP4 inhibitors for stabilizing biologically active forms of chemokines and constraining CD8 + T cell exhaustion as a strategy to enhance tumor immunotherapy. Declarations Acknowledgments Not applicable. Author Contributions: Dr S.J. Yi had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: R. Jing, and S.J. Yi. Acquisition, analysis, or interpretation of data: R. Jing, N. Wu, Y. Wu, Q. Zhang, and S. Zeng. Drafting of the manuscript: R. Jing, N. Wu, and Y. Wu. Critical review of the manuscript for important intellectual content: N. Wu, Y. Wu, Q. Zhang. Statistical analysis: R. Jing and Q.K. Liang. Administrative, technical, or material support: J.L. Liu, and Y. Zhao. Supervision : S.J. Yi. Funding This work was supported by grants from the Youth Science Foundation of Foundation and Applied Basic Research Fund Project of Guangdong Province (2023A1515110149). Availability of data and materials The datasets analyzed in the current study are publicly available in the Gene Expression Omnibus (GEO) repository, with accession numbers of GSE29265, GSE33630, GSE35570, GSE60542,and another mRNA expression matrix file from TCGA database. The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate Informed consent was obtained from the patients before surgery. This study received approvement from the Ethical Committee of The South China Hospital of Shenzhen University (HNLS20240308003-A). Consent for publication Not applicable. Competing interests The authors state that they have no competing interests to declare in relation with this work. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Tables Table 1 qPCR primers Gene Direction Size (bp) Sequence (5’→3’) FN1 Forward 109 TGACCAGCAACACCGTGACAG Reverse 109 GAGACCCAGGAGACCACAAAGC ITGA1 Forward 150 GGAGCCTATGATTGGAATGGAACAG Reverse 150 AGAAGCAGTAGCAGAGTTTACAGTG DPP4 Forward 86 GCCCTGGTCGATGTTGGAGTG Reverse 86 TGTTGGTGTGCTGTGCTGCTAG APOE Forward 105 CTGCGTTGCTGGTCACATTCC Reverse 105 CGCTCTGCCACTCGGTCTG TIMP1 Forward 148 CCTGTTGTTGCTGTGGCTGATAG Reverse 148 ACGCTGGTATAAGGTGGTCTGG FAM20A Forward 109 AAATCTCCATCCTCTCGCCTCTC Reverse 109 CGCATCACATCGCTGAGTCTG SERPINA1 Forward 132 GAGTTCGCCTTCAGCCTATACCG Reverse 132 TTCATCGTGAGTGTCAGCCTTGG LGALS3 Forward 100 CCTTCCACTTTAACCCACGCTTC Reverse 100 ACCGACTGTCTTTCTTCCCTTCC MET Forward 129 GGTCCTTTGGCGTGCTCCTC Reverse 129 CTGGGCAGTATTCGGGTTGTAGG PLAU Forward 127 CGCTCAAGGCTTAACTCCAACAC Reverse 127 AACGGATCTTCAGCAAGGCAATG GAPDH Forward 115 GGCACAGTCAAGGCTGAGAAT G Reverse 115 ATGGTGGTGAGACGCCAGTA Table 2 Robust DEGs by RRA based on 12 computing methods of PPI network Name Score Freq FN1 7.21E-17 12 APOE 8.28E-10 12 SERPINA1 4.18E-08 12 PLAU 7.15E-07 12 DPP4 1.28E-06 12 LGALS3 1.28E-06 12 FAM20A 2.41E-06 12 MET 3.83E-06 12 TIMP1 1.51E-05 12 ITGA2 2.93E-05 12 LPL 4.06E-05 12 MMRN1 9.28E-05 12 CDH2 9.68E-05 12 CHI3L1 0.000129917 12 COMP 0.000137544 12 TGFA 0.000285189 12 CLDN1 0.000434868 12 SDC4 0.001103003 12 CTSC 0.005269011 12 MMP7 0.000278207 11 CEACAM6 0.057526643 11 CCL21 0.124420518 11 LRP1B 0.154701101 11 CCL20 0.235092492 11 ENTPD1 0.287435482 11 CFI 0.287435482 11 LAMB3 0.001982882 10 PROM1 0.087906412 10 ETV4 0.420011916 10 CDH3 0.42689841 10 RELN 1 10 APOD 0.113482868 9 ECM1 0.569922583 9 COL10A1 0.569922583 9 TFF3 1 9 CD55 1 9 SLIT1 1 9 LYVE1 1 9 DPP6 1 8 AGR2 1 8 DUSP6 1 8 NRCAM 1 8 MDK 1 8 PDZK1IP1 1 7 TREM1 1 7 CLDN16 1 7 MYOC 1 6 DPT 1 6 ALOX5 1 6 DLG2 1 6 SFN 1 6 GABRB2 1 5 KLK7 1 5 OGN 1 5 CD1A 1 5 GPM6A 1 4 SEMA3D 1 4 CTSH 1 4 ALOX15B 1 4 RYR2 1 3 EVA1A 1 3 SPX 1 3 STMN2 1 3 CYP1B1 1 3 TMPRSS4 1 3 SLC34A2 1 3 OCA2 1 3 PROS1 1 3 NPC2 1 3 LRRK2 1 3 FRMD3 1 3 DUSP4 1 3 CLDN10 1 3 AGR3 1 3 SLPI 1 3 CFD 1 2 ODAM 1 2 CHRDL1 1 1 DUSP5 1 1 KCNIP4 1 1 Table 4 Gene annotation by Uniprot, BioGPS, and KEGG database Gene Alternative name Protein Subcellular location Expression Function KEGG FN1 Cold-insoluble globulin (CIG) Fibronectin Secreted, extracellular space, extracellular matrix Plasma FN is secreted by hepatocytes; Cellular FN made by fibroblasts, epithelial and other cell types; Expressed in the inner limiting membrane and around blood vessels in the retina (at protein level). Smooth muscle, Cardiac myocytes, and adipocyte Cell adhesion, cell motility, opsonization, wound healing, and maintenance of cell shape; Osteoblast compaction and mineralization; Monocyte activation map04151 PI3K-Akt signaling pathway map04510 Focal adhesion map04512 ECM-receptor interaction map04810 Regulation of actin cytoskeleton map04933 AGE-RAGE signaling pathway in diabetic complications map05100 Bacterial invasion of epithelial cells map05135 Yersinia infection map05146 Amoebiasis map05165 Human papillomavirus infection map05200 Pathways in cancer map05205 Proteoglycans in cancer map05222 Small cell lung cancer ITGA2 CD49b Integrin alpha-2 Cell membrane Bronchial epithelial cells A receptor for laminin, collagen, collagen C-propeptides, fibronectin and E-cadherin. map03271 Virion - Rotavirus map04145 Phagosome map04151 PI3K-Akt signaling pathway map04510 Focal adhesion map04512 ECM-receptor interaction map04611 Platelet activation map04640 Hematopoietic cell lineage map04810 Regulation of actin cytoskeleton map05165 Human papillomavirus infection map05200 Pathways in cancer map05205 Proteoglycans in cancer map05222 Small cell lung cancer map05410 Hypertrophic cardiomyopathy map05412 Arrhythmogenic right ventricular cardiomyopathy map05414 Dilated cardiomyopathy DPP4 CD26 Dipeptidyl peptidase 4 Cell membrane, cell junction, membrane raft, secreted Expressed specifically in lymphatic vessels Smooth muscle, CD4+ and CD8+ T cells cell activation, T-cell proliferation and NF-kappa-B activation; Lymphocyte-epithelial cell adhesion; Pericellular proteolysis, migration and invasion of endothelial cells; Lymphatic endothelial cells adhesion, migration and tube formation; Regulates chemokines, mitogenic growth factors, neuropeptides and peptide hormones; A receptor for human coronavirus MERS-CoV-2 map04974 Protein digestion and absorption APOE ApoE4 Apolipoprotein E Secreted, extracellular space, extracellular matrix, endosome, multivesicular body Liver, adipocyte, amygdala, and adrenalgland An apolipoprotein associating with lipid particles; A core component of plasma lipoproteins and is involved in their production, conversion and clearance; Binds to the immune cell receptor LILRB4 to regulate innate and adaptive immune responses, etc. map04979 Cholesterol metabolism map05010 Alzheimer disease TIMP1 EPA Metalloproteinase inhibitor 1 Secreted Smooth muscle, Cardiac myocytes, and adipocyte Detected in rheumatoid synovial fluid (at protein level). Metalloproteinase inhibitor; A growth factor that regulates cell differentiation, migration and cell death and activates cellular signaling cascades via CD63 and ITGB1 map04066 HIF-1 signaling pathway FAM20A AI1G Pseudokinase FAM20A Secreted, Golgi apparatus, endoplasmic reticulum Highly expressed in lung and liver. Intermediate levels in thymus and ovary Pseudokinase that acts as an allosteric activator of the Golgi serine/threonine protein kinase FAM20C and is involved in biomineralization of teeth. SERPINA1 AAT Alpha-1-antitrypsin Secreted, endoplasmic reticulum Ubiquitous. Expressed in leukocytes and plasma. Inhibitor of serine proteases such as elastase, plasmin, and thrombin map04610 Complement and coagulation cascades LGALS3 Gal-3 Galectin-3 Secreted, cytoplasm, nucleus A major expression is found in the colonic epithelium. It is also abundant in the activated macrophages. Expressed in fetal membranes. Galactose-specific lectin which binds IgE; Involved in acute inflammatory responses including neutrophil activation and adhesion, chemoattraction of monocytes macrophages, opsonization of apoptotic neutrophils, and activation of mast cells, etc. MET c-Met Hepatocyte growth factor receptor Secreted, membrane Expressed in normal hepatocytes as well as in epithelial cells lining the stomach, the small and the large intestine. Found also in basal keratinocytes of esophagus and skin. High levels are found in liver, gastrointestinal tract, thyroid and kidney. Also present in the brain. Expressed in metaphyseal bone (at protein level) Receptor tyrosine kinase; Regulates many physiological processes including proliferation, scattering, morphogenesis and survival hsa01521 EGFR tyrosine kinase inhibitor resistance hsa04010 MAPK signaling pathway hsa04014 Ras signaling pathway hsa04015 Rap1 signaling pathway hsa04020 Calcium signaling pathway hsa04151 PI3K-Akt signaling pathway hsa04360 Axon guidance hsa04510 Focal adhesion hsa04520 Adherens junction hsa05100 Bacterial invasion of epithelial cells hsa05120 Epithelial cell signaling in Helicobacter pylori infection hsa05144 Malaria hsa05200 Pathways in cancer hsa05202 Transcriptional misregulation in cancer hsa05205 Proteoglycans in cancer hsa05206 MicroRNAs in cancer hsa05208 Chemical carcinogenesis - reactive oxygen species hsa05211 Renal cell carcinoma hsa05218 Melanoma hsa05223 Non-small cell lung cancer hsa05225 Hepatocellular carcinoma hsa05226 Gastric cancer hsa05230 Central carbon metabolism in cancer PLAU ATF, u-PA Urokinase-type plasminogen activator Secreted Expressed in the prostate gland and prostate cancers. Specifically cleaves the zymogen plasminogen to form the active enzyme plasmin. map04064 NF-kappa B signaling pathway map04610 Complement and coagulation cascades map05202 Transcriptional misregulation in cancer map05205 Proteoglycans in cancer map05206 MicroRNAs in cancer map05215 Prostate cancer Table 5 Cell cluster according to model phenotypes lndex Popuations Model phenotypes 1 Lymphocytes CD3 T cells + B cells +NK cells + plasmablasts 1.1 CD3 T cells CD8 T cells + CD4 T cells + γ T cells + MAIT/NKT cells 1.2 CD8 T cells CD3+CD66b-CD19-CD8+CD4-CD14-CD161-TCRgd-CD123-CD11c- 1.2.1 CD8 naïve CD8 T cells + CD45RA+CCR7+CD27+ 1.2.2 CD8 central memory CD8 T cells + CD45RA-CCR7+CD27+ 1.2.3 CD8 effector memory CD8 T cells + CCR7-CD27+ 1.2.4 CD8 terminal effector CD8 T cells + CCR7-CD27- 1.3 CD4 T cells CD66b-CD3+ CD8-CD4+ CD14-TCRgd-CD11c 1.3.1 CD4 naive CD4 T cells + CD45RA+CCR7+CD27+ 1.3.2 CD4 central memory CD4 T cells + CD45RA-CCR7+CD27+ 1.3.3 CD4 effector memory CD4 T cells + CD45RA-CCR7-CD27+ 1.3.4 CD4 terminal effector CD4 T cells + CD45RA-CCR7-CD27- 1.3.5 Tregs CD4 T cells + CD25+CD127-CCR4+ 1.3.6 Th1-like CD4 T cells + CXCR3+CCR6-CXCR5-CCR4- 1.3.7 Th2-like CD4 T cells + CXCR3-CCR6-CXCR5-CCR4+ 1.3.8 Th17-like CD4 T cells + CXCR3-CCR6+ CXCR5-CCR4+ 1.4 γ T cells CD66b-CD3+CD8dm-CD4-CD14-TCRgd dm,+ 1.5 MAIT/NKT cells CD66b-CD3+CD4-CD14-CD161+TCRgd-CD28+CD16- 1.6 B cells CD3-CD14-CD56-CD16 dim-CD19+CD20+HLA-DR dim,+ 1.6.1 B naive B cells + CD27- 1.6.2 B memory B cells + CD27+ 1.7 Pasmablasts CD3-CD14-CD16-dim CD66b-CD20-CD19+CD56-CD38++CD27+ 1.8 NK cells CD14-CD3-CD123-CD66b-CD45RA+CD56 dim+ 1.8.1 NKearly NK cells + CD57- 1.8.2 NK late NK cells + CD57+ 2 Monocytes CD3-CD19-CD56-CD66b-HLA-DR+CD11c+ 2.1 Monocytes classical Monocytes + CD14+CD38+ 2.2 Monocytes transitional Monocytes + CD14dm CD38 dm 2.3 Monocytes non-dassical Monocytes + CD14-CD38- 3 DCs pDCs + mDCs 3.1 pDCs CD3-CD19-CD14-CD20-CD66b-HLA-DR dim+ cD11c-CD123+ 3.2 mDCs CD3-CD19-CD14-CD20-HLA-DR dim+CD11cdim+CD123-CD16dim.-CD38dim+CD294-HLA-D 4 Granulocytes Neutrophils + basophils + cosinophils + CD66b- neutrophils 4.1 Neutrophils CD66b dm+cD16+ HL4-DR- 4.2 Basophils HLA-DR-CD66b-CD123 dim,+ cD38+CD294+ 4.3 Eosinophils CD14-CD3-CD19-HLA-DR-CD294+ CD66b dim+ 4.4 CD66b- neutrophils CD3-CD19-CD66b-CD56-HLA-DR-CD123-CD45- Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Figure S1 Results of quantile normalisation method to normalise gene expression intensities. FigureS2.tif Figure S2 Protein-protein interaction between top 10 hub genes using 12 computing method from the plug-in cytoHubba of Cytoscape FigureS3.tif Figure S3 Expression of top 10 rhDEGs and identification of DPP4 expression. (a-b) Relative expression of top 10 rhDEGs using RT-qPCR between PTC and para-tumor tissues. (c) Relative expression of DPP4 protein using western blotting between PTC and para-tumor tissues. (d) Validation of the DPP4 protein expression profile in the HPA database. rhDEGs, rhDEGs, robust hub differentially expressed genes. For (a-c) Representative data from three individual experiments. All data are presented as mean ± S.E.M and analyzed by a two-tailed unpaired t test. For (d), image is representative of three experiments. ns, not statistically; * P < 0.05; ** P < 0.01; *** P < 0.001. FigureS4.tif Figure S4 Gating strategy on multi-dimension mass cytometry FigureS5.tif Figure S5 Association between DPP4 expression and CD8+ T cells secreted cytokines. (a) Gating strategy on cytokines of DPP4+ and DPP4- CD8+ T cells in in peripheral blood of NG patients. (b) Gating strategy on cytokines of DPP4+ and DPP4- CD8+ T cells in in peripheral blood of PTC patients. (c) IFN-γ+ cells between DPP4+CD8+ and DPP4-CD8+ T cells. (d) Relative number of TNF-α+ cells between DPP4+CD8+ and DPP4-CD8+ T cells. (e) Relative number of IL-4+ cells between DPP4+CD8+ and DPP4-CD8+ T cells. (f) Relative number of IL-5+ cells between DPP4+CD8+ and DPP4-CD8+ T cells. (G) Relative number of IL-13+ cells between DPP4+CD8+ and DPP4-CD8+ T cells. NG, nodular goiter; PTC, papillary thyroid cancer; IFN-γ, Interferon-γ; TNF-α, tumor necrosis factor-α; IL, interleukin. For (a-b), image is representative of three experiments. For (c-g) Representative data from three individual experiments. All data are presented as mean ± S.E.M and analyzed by a two-tailed unpaired t test. ns, not statistically; * P < 0.05. TableS1GOandKEGGpathwayenrichment.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4421908","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304802250,"identity":"e55499b0-a79d-4915-8b73-3ad6876313be","order_by":0,"name":"Ren Jing","email":"","orcid":"","institution":"South China Hospital, Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Ren","middleName":"","lastName":"Jing","suffix":""},{"id":304802251,"identity":"f53eb915-3ba9-4974-8452-8d8e39ed714d","order_by":1,"name":"Nan Wu","email":"","orcid":"","institution":"South China Hospital, Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Wu","suffix":""},{"id":304802252,"identity":"547ce505-502a-40f6-bde5-94859106f195","order_by":2,"name":"Yang Wu","email":"","orcid":"","institution":"South China Hospital, Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Wu","suffix":""},{"id":304802253,"identity":"e9cfc584-ecc6-4894-984c-1d1d39f9f25c","order_by":3,"name":"Qian Zhang","email":"","orcid":"","institution":"Shenzhen Pingle Orthopedic Hospital (Shenzhen Pingshan Traditional Chinese Medicine Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zhang","suffix":""},{"id":304802254,"identity":"28601f7d-ee02-4a70-8278-a2a0730eb528","order_by":4,"name":"Jinlin Liu","email":"","orcid":"","institution":"South China Hospital, Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Jinlin","middleName":"","lastName":"Liu","suffix":""},{"id":304802255,"identity":"8fe4f16c-763b-4e73-a2ed-695b23e0e0e9","order_by":5,"name":"Ying Zhao","email":"","orcid":"","institution":"South China Hospital, Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhao","suffix":""},{"id":304802256,"identity":"bc6a42bc-97a7-42b5-b864-4de1e34741aa","order_by":6,"name":"Shan Zeng","email":"","orcid":"","institution":"South China Hospital, Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Zeng","suffix":""},{"id":304802257,"identity":"179aeac6-fe69-4c36-9dca-77b82d4a8834","order_by":7,"name":"Qiankun Liang","email":"","orcid":"","institution":"South China Hospital, Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Qiankun","middleName":"","lastName":"Liang","suffix":""},{"id":304802258,"identity":"a1004bf6-20b4-45b5-952e-ab10f3f4c0fa","order_by":8,"name":"Shijian Yi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYDACZgY2hgQeCSCD+eCDDxUQQQnitLCzJRvOOEOMFgagFjDg5zET5m0jQovBcfZnDx7IWMibM/OYMfPOq7M3OMB88DYPg10eTi2HecwNgA4z3NnMVvZw7rbDzAYH2JKteRiSi/FoYZMAamHccJh5u8HbbQfYDA7wmEnzMBxIbMCphf0ZSIv9hsMMZhK8c+p4DA7wfyOgBagSqCVxw2EWM0neBmYJoC1seLVIHuYBa0necBgUyMcOG0geZjO2nGOQjFML3/njzyR/9tTZbjh/GBiVNXX2fMebH954U2GHU4vCASDB2IMsxAx2MA71QCAPNusHbgWjYBSMglEwChgA+qZRu5pwtjoAAAAASUVORK5CYII=","orcid":"","institution":"South China Hospital, Shenzhen University","correspondingAuthor":true,"prefix":"","firstName":"Shijian","middleName":"","lastName":"Yi","suffix":""}],"badges":[],"createdAt":"2024-05-15 01:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4421908/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4421908/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57447356,"identity":"f70fdc35-e246-4520-9cec-fb28c0eca083","added_by":"auto","created_at":"2024-05-30 19:42:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3442026,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of the DEGs expression profile between PTC and normal thyroid tissue in the GEO and TCGA database. \u003c/strong\u003e(a) Flow diagram of the present study. (b-f) Volcano plots of the distribution of DEGs in each dataset. (g) Venn diagram of commonly DEGs. (h) Histogram of the top 10 rhDEGs after screening hub genes from PPI network. (i) PPI network constructing by the top 10 robust DEGs. DEG, differentially expressed genes; GEO, Gene Expression Omnibus; rhDEGs, robust hub DEGs; PPI, protein-protein interaction.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/146bda9ef55381e19140399b.jpg"},{"id":57447363,"identity":"61a0784b-5824-4af7-8757-a98de091f682","added_by":"auto","created_at":"2024-05-30 19:42:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2981915,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene annotation and expression of the 10 rhDEGs.\u003c/strong\u003e (a) Expression heatmap of the top 10 rhDEGs. (b) GO and KEGG pathway enrichment analyses. (c-i) Expression, tumor staging, and survival of the top 10 rhDEGs. rhDEGs, robust hub differentially expressed genes. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/1838a2a6ff897e61cadd9a93.jpg"},{"id":57447361,"identity":"abdc95a1-0382-4f0d-bf1c-234c3bcf7021","added_by":"auto","created_at":"2024-05-30 19:42:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6738369,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDimensional reduction plot of immune cells and its cell marker expression. \u003c/strong\u003e(a) t-distributed stochastic neighbor embedding (t-SNE) plot on immune cells between each NT and PTC. (b) Expression heatmap of each cell marker. (c) t-SNE heatmap of each cell marker in PTC tissues. (d) t-SNE heatmap of each cell marker in NT tissues.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/0433e269ac25079369f693f7.jpg"},{"id":57447827,"identity":"a1f0df7a-7b91-4795-ac33-50b3bca360fe","added_by":"auto","created_at":"2024-05-30 19:50:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2504259,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportion and correlation of multiple cell cluster samples.\u003c/strong\u003e (a) The proportion of multiple cell cluster samples between NT and PTC. (b) The proportion of multiple cell cluster samples of each NT and PTC sample. (c) Boxplot of multiple cell cluster samples between NT and PTC. (d) Spearman’ s correlation analysis between pairwise cell cluster.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/badc9e58204f7f963abac465.jpg"},{"id":57447829,"identity":"f3c41f8f-e73b-491b-9643-26b0ceb3073b","added_by":"auto","created_at":"2024-05-30 19:50:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2698504,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulation of DPP4 on the infiltration of immune cells in PTC. \u003c/strong\u003e(a) Spearman’ s correlation analysis between DPP4 and each immune cell. (b) Survival analysis between DPP4 and each immune cell. (c) Results and forest plot of the COX regression model of survival. (d) SCNA analysis on the infiltration levels of each immune cell between diploid/normal, arm-level deletion or arm-level gain of DPP4. (e) Immune infiltration scores of immune cells between the high and low DPP4 groups. HR, hazard ratio. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/03d9bf206bc5659942d78b7d.jpg"},{"id":57447360,"identity":"1eebfdaa-f07f-4fdf-98fe-882e5e4f53d4","added_by":"auto","created_at":"2024-05-30 19:42:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3017809,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between DPP4 expression and CD8+ T cells function. \u003c/strong\u003e(a) Gating strategy on DPP4+ and DPP4- CD8+ T cells in in peripheral blood between nodular goiter and PTC patients. (b) Relative number of lymphocytes. (c) Relative number of CD3+CD8+ T cells. (d) Relative number of DPP4+CD8+ and DPP4-CD8+ T cells. (e) Relative number of PD-L1+ cells between DPP4+CD8+ and DPP4-CD8+ T cells. (f) Relative number of CTLA-4+ cells between DPP4+CD8+ and DPP4-CD8+ T cells. (g) Infiltration of DPP4+CD8+ T cells between nodular goiter and PTC patients using immunofluorescence (Scar bar = 50 μm). NG, nodular goiter; PTC, papillary thyroid cancer; PD-L1, programmed cell death 1 ligand 1; CTLA-4, cytotoxic T-lymphocyte associated protein 4. For (a and g), image is representative of three experiments. For (b-c) Representative data from three individual experiments. All data are presented as mean ± S.E.M and analyzed by a two-tailed unpaired \u003cem\u003et\u003c/em\u003e test. For (d-f) Representative data from three individual experiments. All data are presented as mean ± S.E.M and analyzed by a two-way ANOVA with Bonferroni correction. ns, not statistically; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05;\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/84e5ab5a3cbb4f3ee54ae76c.jpg"},{"id":61091052,"identity":"6929f01c-f3a7-46b1-ae79-6e0e79ba5a88","added_by":"auto","created_at":"2024-07-25 13:07:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22719867,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/d1842061-9218-4edc-b8f2-2b8af5d79e2d.pdf"},{"id":57447365,"identity":"f3553248-ef9f-454d-94ce-f4aba547cbca","added_by":"auto","created_at":"2024-05-30 19:42:03","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1892996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1 Results of quantile normalisation method to normalise gene expression intensities.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/b6f6be615fa53e6ba0503ff5.tif"},{"id":57447367,"identity":"2f53a233-e5a3-4e38-b531-c7ba411a086e","added_by":"auto","created_at":"2024-05-30 19:42:03","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1351220,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2 Protein-protein interaction between top 10 hub genes using 12 computing method from the plug-in cytoHubba of Cytoscape\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/6aefd0a3f979ab60ead611c9.tif"},{"id":57447828,"identity":"97353500-79d7-42b3-9b99-76ae668df998","added_by":"auto","created_at":"2024-05-30 19:50:03","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18014048,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3 Expression of top 10 rhDEGs and identification of DPP4 expression. \u003c/strong\u003e(a-b) Relative expression of top 10 rhDEGs using RT-qPCR between PTC and para-tumor tissues. (c) Relative expression of DPP4 protein using western blotting between PTC and para-tumor tissues. (d) Validation of the DPP4 protein expression profile in the HPA database. rhDEGs, rhDEGs, robust hub differentially expressed genes. For (a-c) Representative data from three individual experiments. All data are presented as mean ± S.E.M and analyzed by a two-tailed unpaired \u003cem\u003et\u003c/em\u003e test. For (d), image is representative of three experiments. ns, not statistically; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/ae999889173cead11e8b07de.tif"},{"id":57447364,"identity":"f6fa2b2a-0458-45c7-b86f-2ab5f8fe8f5d","added_by":"auto","created_at":"2024-05-30 19:42:03","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15475074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4 Gating strategy on multi-dimension mass cytometry\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/101120586eeb57091ab7dc4a.tif"},{"id":57447359,"identity":"e9605873-1c10-493d-bd98-408a25fcb8a5","added_by":"auto","created_at":"2024-05-30 19:42:02","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":524088,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S5 Association between DPP4 expression and CD8+ T cells secreted cytokines. \u003c/strong\u003e(a) Gating strategy on cytokines of DPP4+ and DPP4- CD8+ T cells in in peripheral blood of NG patients. (b) Gating strategy on cytokines of DPP4+ and DPP4- CD8+ T cells in in peripheral blood of PTC patients. (c) IFN-γ+ cells between DPP4+CD8+ and DPP4-CD8+ T cells. (d) Relative number of TNF-α+ cells between DPP4+CD8+ and DPP4-CD8+ T cells. (e) Relative number of IL-4+ cells between DPP4+CD8+ and DPP4-CD8+ T cells. (f) Relative number of IL-5+ cells between DPP4+CD8+ and DPP4-CD8+ T cells. (G) Relative number of IL-13+ cells between DPP4+CD8+ and DPP4-CD8+ T cells. NG, nodular goiter; PTC, papillary thyroid cancer; IFN-γ, Interferon-γ; TNF-α, tumor necrosis factor-α; IL, interleukin. For (a-b), image is representative of three experiments. For (c-g) Representative data from three individual experiments. All data are presented as mean ± S.E.M and analyzed by a two-tailed unpaired \u003cem\u003et\u003c/em\u003e test. ns, not statistically; *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/0197e4aac575230c3262258c.tif"},{"id":57447362,"identity":"7e84396d-28f8-4bfd-adc9-4ffa35ca2532","added_by":"auto","created_at":"2024-05-30 19:42:02","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":16384,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1GOandKEGGpathwayenrichment.docx","url":"https://assets-eu.researchsquare.com/files/rs-4421908/v1/57345618284428563f99ea94.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"DPP4 Promotes Papillary Thyroid Cancer Progression by Regulating the Infiltration and Exhaustion of CD8+ T cells","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThyroid cancer (TC) has the ninth highest cancer incidence and been increasing in many countries and settings; it is the most common malignancy in adolescents and adults aged 16\u0026ndash;33 years(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Approximately 75% of all patients with TC were women and the median age of diagnosis is nearly 50s(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although mortality have remained stable at lower rates, around 5\u0026ndash;30% of patients evolve in an unfavorable way due to distant metastases and Iodine-refractory TC(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Among all pathological types of TC, papillary TC (PTC) accounts for approximately 84% of cases and is often confined and asymptomatic(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Although the vast majority of PTC patients have a favorable prognosis, local recurrence and distant metastasis of advanced PTC still hamper the survival and clinical management in certain high-risk patients.\u003c/p\u003e \u003cp\u003eThe tumor immune microenvironment (TIME) of TC is the heterogeneous histological space in which tumor cells coexist with host cells, which is associated with the clinical aggressiveness characteristics of the neoplasm(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The \u003cem\u003eBRAF\u003c/em\u003e\u003csup\u003e\u003cem\u003eV600E\u003c/em\u003e\u003c/sup\u003e - induced immune suppression involves TBX3 re-activation, which in turn up-regulates CXCR2 ligands in a TLR2-NFκB dependent manner, leading to myeloid-derived suppressor cells recruitment into the PTC tumor microenvironment(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The receptor tyrosine kinase suppressed immune escape of TC by blocking the activation of the MAPK pathway and downregulating PD-L1(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Elevated PD-L1 status can be a prognostic indicator for survival in TC patients when comprehensively assessed using CD8\u0026thinsp;+\u0026thinsp;expression, \u003cem\u003eBRAF\u003c/em\u003e\u003csup\u003e\u003cem\u003eV600E\u003c/em\u003e\u003c/sup\u003e mutation, and the patient's immune status(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Hence, the in-depth investigation of the molecular connections between immune and non-immune cells enriched in the TIME will allow the construction of new clinical tools to diagnose TC patients and provide more targeted therapeutic directions.\u003c/p\u003e \u003cp\u003eSeveral multi-tyrosine kinase inhibitors (MKIs), or immune checkpoint inhibitors in combination with MKIs, have emerged as novel therapies for controlling the progression of advanced differentiated TC with recurrence, metastasis and iodine refractoriness, as well as more aggressive subtypes such as poorly differentiated TC (PDTC) and anaplastic thyroid cancer (ATC)(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Immunotherapy has achieved a certain effect in TC patients who are refractory to conventional therapy. Nevertheless, many TC patients remain insensitive to immunotherapy, and some who initially respond but eventually recurrence(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Although anti-PD-1 therapy has been effective in a small percentage of patients with advanced PTC and refractory ATC, the majority of the patients either do not respond or develop resistance to anti-PD-1 therapy due to up-regulated METTL3 expression that negatively correlated with CD70 expression and M2 macrophages, as well as regulatory T cell infiltration(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). At present, the PTC tissue has a special TIME feature that is not well characterized. Thus, the aim of this study was to identify the hub biomarkers that regulate immune cells for the development and recurrence of PTC.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 GEO data set selection and data processing\u003c/h2\u003e\n \u003cp\u003eThe gene expression profiles of PTC in GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e) were obtained. The screening criteria included (a) papillary thyroid tumor, tumour, cancer, or carcinoma; (b) samples containing tumor and normal thyroid tissues; (c) Homo sapiens as the organism; (d) expression profiling by array as the study type; (e) the sample size was more than 10 samples in each group. A total of four GEO datasets based on the GPL570 platform were selected, including GSE33630 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;94), GSE35570 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;83), GSE60542 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;47), and GSE29265 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40). When multiple probes corresponded to one gene, their average expression level was considered. In addition, the mRNA expression matrix file of TCGA thyroid cancer was acquired from the Gene Expression Profiling Interactive Analysis (GEPIA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Identification of differentially expressed genes (DEGs) in each GEO data set\u003c/h2\u003e\n \u003cp\u003eThe raw expression data in these four GEO datasets were preprocessed into expression matrices using R software. Gene expression intensities were performed using quantile normalisation method with \u003cem\u003enormalizeBetweenArrays\u003c/em\u003e package (Figure S1). The DEGs in each GEO data set was then identified by the \u003cem\u003elimma\u003c/em\u003e package in R(\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e). The cut-off criteria of |log2 fold change (FC)| \u0026gt; 1.2 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were considered to be statistically significant. The DEGs of TCGA thyroid cancer was acquired from GEPIA using \u003cem\u003elimma\u003c/em\u003e methods in the section of Differential Expression Analysis. The common DEGs of the aforementioned five DEG sets were identified and shown in the Venn diagram by using ClustVis.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Protein-protein interaction (PPI) network construction\u003c/h2\u003e\n \u003cp\u003eThe STRING (version 10.5) database was used to construct the PPI network(\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e). The parameter of interactive relationships among these common DEGs was set as high confidence\u0026thinsp;\u0026gt;\u0026thinsp;0.7. Cytoscape (version 3.6.1) software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003c/span\u003e) was used to visualize and analyse the PPI network. The plug-in cytoHubba of Cytoscape was used to screen significant modules of the PPI network (the parameters were set to default). Maximal clique centrality (MCC) and other 11 computing methods were used to screen significant hub genes of the PPI network (the parameters were set to default).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 RRA method and marker gene identification\u003c/h2\u003e\n \u003cp\u003eTo obtain robust hub DEGs (rhDEGs), RRA method was used to identify genes that were ranked consistently better than expected by running the R package \u003cem\u003eRobustRankAggreg\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e). In brief, based on the results of the aforementioned 12 computing methods from the plug-in cytoHubba of Cytoscape, the genes were ordered by their scores. The aggregation based on the ranks of genes in different methods were then performed. Using core algorithm of \u003cem\u003eRobustRankAggreg\u003c/em\u003e package, the screening criteria of Frequencies\u0026thinsp;\u0026ge;\u0026thinsp;12 and Score\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant for the identification of robust DEGs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses\u003c/h2\u003e\n \u003cp\u003eTo characterize the functional roles of the top 10 rhDEGs, DAVID (version 6.8) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003c/span\u003e) was used for GO enrichment analysis of biological process (BP), molecular function (MF), and cellular component (CC) with a cut-off of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and count\u0026thinsp;\u0026ge;\u0026thinsp;3. The KEGG databases (KEGG: Kyoto Encyclopedia of Genes and Genomes) was used to explore KEGG pathways\u003c/p\u003e\n \u003cp\u003eanalysis, a web tool for gene functional enrichment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Expression and prognostic analysis.\u003c/h2\u003e\n \u003cp\u003eThe GEPIA database was used for gene expression and survival analysis of these rhDEGs in TC. The violin plot was showed the expression patterns between tumour and normal samples of these marker genes. The Uniprot, BioGPS (BioGPS-your Gene Portal System) and KEGG databases (KEGG: Kyoto Encyclopedia of Genes and Genomes) were then used to identify these rhDEGs that were associated with protein metabolism and immune function regulation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Analysis of immune infiltration and gene correlation by the TIMER\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003edatabase and CIBERSORT\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe association of rhDEGs that is associated with immune function, infiltration level, and markers of common immune cells, such as B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, neutrophils, macrophages, and dendritic cells were analyzed using the the Tumor IMmune Estimation Resource (TIMER) database. Genomic expression data of PTC acquired from the TCGA database were divided into high and low expression groups by marker gene expression. The immune infiltration scores of a total of 22 subtypes of immune cells were calculated by CIBERSORT, and visualized by a violin graph.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8 Clinical tissues samples\u003c/h2\u003e\n \u003cp\u003eTen patients diagnosed as PTC between September 2023 and January 2024 at South China Hospital of Shenzhen University (Shenzhen, China) were enrolled and this study. This study was approved by the ethical committee of the hospital. All patients had never received any treatment before sampling and signed written informed consent in advance. The tumor and adjacent nontumor tissues from six PTC patients were immediately transferred into liquid nitrogen after surgical resection, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for RNA extraction. Besides, the tumor and adjacent nontumor tissues from other four PTC patients were transferred into magnet-activated cell sorting (MACS) tissue storage solution (Cat. 130-100-008, Miltenyi Biotec, Cologne, German) after surgical resection, and stored at 4\u0026deg;C for mass cytometry.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.9 Immunofluorescence\u003c/h2\u003e\n \u003cp\u003eThe slides from the paraffin-embedded tumor tissues were dewaxed, hydrated, fixation with 3.7% paraformaldehyde (Cat.P804536-500g, Macklin, Shanghai, China), permeabilized with immunostaining permeabilization buffer with Triton X-100 (Cat.I997471-100ml, Macklin, Shanghai, China), used for antigen retrieval with SignalStain\u0026reg; Citrate Unmasking Solution (Cat.14746, Cell Signaling Technology, MA, USA), and blocked with immunofluorescence blocking buffer (Cat.12411, Cell Signaling Technology, MA, USA). Then, CD3/CD8 polycolonal antibody (Cat.PA5-102404, ThermoFisher Scientific, MA, USA) and anti-DPP4 antibody [OTI11D7] (Cat.Ab114033, Abcam, Cambridge, UK) were incubated as the primary antibody at 4\u0026deg;C overnight. Anti-mouse IgG (H\u0026thinsp;+\u0026thinsp;L), F(ab\u0026apos;)2 Fragment (Alexa Fluor\u0026reg; 647 Conjugate) (Cat.4410S, Cell Signaling Technology, MA, USA) and anti-rabbit IgG (H\u0026thinsp;+\u0026thinsp;L), F(ab\u0026apos;)2 Fragment (Alexa Fluor\u0026reg; 488 Conjugate) (Cat.4412S, Cell Signaling Technology, MA, USA) were served as secondary antibody for 45 min at room temperature (RT). The sections were finally treated with ProLong\u0026reg;Gold Antifade Reagent with 4\u0026rsquo;,6-diamidino-2-phenylindole (Cat.8961S, Cell Signaling Technology, MA, USA). All images were observed using multiplex confocal microscopy (LSM980, Zeiss, Oberkochen, Germany).\u003c/p\u003e\n \u003cp\u003eIn addition, the Human Protein Atlas (HPA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003c/span\u003e), a proteomics database that provides information on the organization and cell distribution of 26,000 human proteins, was used to validate the expression of rhDEGs that is associated with immune function and protein metabolism.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2.10 RNA extraction and quantitative real-time polymerase chain reaction (qRT-PCR) analysis\u003c/h2\u003e\n \u003cp\u003eTotal RNA was isolated from the tumor and adjacent nontumor tissues (NT) using Trizol\u0026trade; reagent (Cat.15596026, ThermoFisher Scientific, MA, USA) according to the standard protocol. Complementary DNA (cDNA) was synthesized using the PrimeScript\u0026trade; RT reagent Kit with the gDNA Eraser (Cat. RR047A,Takara, Dalian, China). RT-qPCR was performed using a qTOWER384G fluorescence RT-qPCR instrument (Analytik Jena AG, Jena, Germany) using the TB Green\u0026reg; Premix Ex Taq\u0026trade; II (Tli RNaseH Plus; Cat. RR420A,Takara, Dalian, China). The qPCR primer sequences were listed in the Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Each cDNA sample was analyzed in triplicate for the quantitative assessment of RNA amplification. The level of each target gene was normalized relative to that of glyceraldehyde-3- phosphate dehydrogenase (GAPDH) in each sample using the \u0026Delta;Ct method.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e2.11 Mass cytometry and flow cytometry\u003c/h2\u003e\n \u003cp\u003eAfter prepared of single-cell suspension with Maxpar\u0026reg; IMC\u0026trade; Cell segmentation Kit (Cat. 201500, Fluidigm, CA, USA), the samples were stained with 2 \u0026micro;M cisplatin (Cat. 201064, Fluidigm, CA, USA) for 2 min before quenching with cell staining buffer (CSB; Cat.201068, Fluidigm, CA, USA). A Fix-I buffer (Cat. 201065, Fluidigm, CA, USA) was then used to fix cells for 15 min at RT, followed by washing three times with 1x phosphate buffer (PBS). The samples were stained with Cell-ID\u0026trade; 20-Plex Pd Barcoding Kit (Cat. 201060, Fluidigm, CA, USA) to minimize internal cross reaction between samples. MaxPar \u0026times; 8 Polymer Kits (Cat. 201321, Fluidigm, CA, USA) were used to conjugate with purified antibodies according to the manual. All metal-conjugated antibodies were titrated for optimal concentrations before use. For the surface protein staining, cells were adjusted to 1\u0026times; 10\u003csup\u003e6\u003c/sup\u003e cell/mL in PBS and cultured with antibodies cock-tail in a total 50 \u0026micro;L CSB for 30 min at RT. After that, cells were washed and underwent permeabilization with 80% methanol for 15 min at 0\u0026deg;C and stained with an intracellular antibody cocktail for 30 min. After triple washes in CSB, cells were incubated with 0.125 \u0026micro;M iridium intercalator in fix and perm buffer (Cat. 201067, Fluidigm, CA, USA) at 4\u0026deg;C overnight. After cultured with intercalator, cells were washed with ice cold PBS and deionized water three times separately. Prior to acquisition, samples were resuspended in deionized water containing 10% EQ Four Element Caliboration Beads (Cat. 201078, Fluidigm, CA, USA) and cell concentrations were adjusted to 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e cell/mL. Data acquisition was performed on a Helios mass cytometer (Fluidigm, CA, USA). The original FCS data were normalized and .fcs files for every sample were collected.\u003c/p\u003e\n \u003cp\u003eThe whole blood from PTC patients was treated immediately with pre-warmed 1x RBC Lysis Solution (Cat. 420301, Biolegend, CA, USA) at 37\u0026deg;C for 15 min. After twice washes in CSB (Cat. 420201, Biolegend, CA, USA), cells were passed through a 45-\u0026micro;m strainer and stained with Human TruStain FcX\u0026trade; (Cat. 422302, BioLegend, CA, USA) to block Fc-receptors. For the surface protein staining, cells were adjusted to 1\u0026times; 10\u003csup\u003e6\u003c/sup\u003e cell/mL in CSB and cultured with antibodies cock-tail in a total 10 \u0026micro;L CSB for 45 min at 0\u0026deg;C. After that, cells were washed and fixed in 0.5 ml/tube Fixation Buffer (Cat. 420801, Biolegend, CA, USA) in the dark for 20 min at RT. The fixed cells were resuspended in Intracellular Staining Perm Wash Buffer (Cat. 421002, Biolegend, CA, USA) and centrifuge at 350xg for 5\u0026ndash;10 min. Cells were resuspended in residual Intracellular Staining Perm Wash Buffer and add a predetermined optimum concentration of intracellular antibody cocktail for 45 min at 0\u0026deg;C. After triple washes in CSB, cells were sorted and analyzed using a BD FACSCanto II flow cytometer (BD Biosciences, NJ, USA).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.12 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eData were analyzed using StataCorp LP (STATA Institute, Inc., College Station, TX, USA) and GraphPad Prism 9 (San Diego, CA, USA). Data were tested for normality with the Shapiro\u0026ndash;Wilk test. Normally distributed data are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;S.E.M. Comparisons between two groups were performed using the two-tailed Student\u0026rsquo;s t-test, while comparisons between multiple groups were performed using one-way ANOVAs, followed by Tukey\u0026rsquo;s multiple comparisons test. A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was defined as statistically significant.\u003c/p\u003e\n \u003cp\u003eMass Cytometry data were firstly normalized using EQ Four Element Calibration Beads (EQ Beads, 201078, Fluidigm) according to manufacturer\u0026rsquo;s instructions, then the cell debris were removed according the 191Ir and 193Ir channel. Doublets were removed according to the Even Length. Furthermore, CD45 gate were used to isolate all the infiltrated leukocyte in PTC and adjacent nontumor tissues, then a clustering panel including CD3, CD4, CD8, CD11c, CD14, CD16, CD19, CD20, CD25, CD27, CD28, CD38, CD45, CD45RA, CD45RO, CD56, CD57, CD66b, CD103, CD123, CD127, CD161, CD294, CCR4, CCR6, CCR7, CXCR3, CXCR5, HLA-DR, IgD, and TCRyq were used to manually differentiate subpopulations of the infiltrated leukocytes.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cstrong\u003e3.1 Identification of DEGs in each GEO data set\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn present study, a multistep analysis were performed to explore key DEGs and their significant biological functions by integrated bioinformatics methods in PTC (Figure 1a). First, we selected and downloaded a total of four GEO datasets (GSE29265, GSE33630, GSE35570 and GSE60542) with gene expression profiles for PTC, as well as combined with TCGA dataset. A total of 127 PTC and 137 normal tissues from four GEO datasets, as well as 849 samples with 512 normal types and 337 tumor types from TCGA database, were obtained in this study. On the basis of the cut-off criteria, DEGs in each dataset were identified between TC and normal tissues using volcano plot (Figure 1b-f). There were 172 common DEGs, which was shown in a venn diagram of the distribution of DEGs in each dataset (Figure 1g).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Selection of rhDEGs by RRA method \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PPI network of these common DEGs were constructed and uploaded to Cytoscape software. The top 50 hub DEGs in each computing method were identified and the PPI network of top 10 hub DEGs were shown in Figure S2. To explore rhDEGs in different computing methods, the RRA method was used, which is used to identify genes that are ranked consistently better than expected by chance. Finally, we determined 80 rhDEGs (Table 2). The relative expression (-log2(Score) and PPI network of top 10 rhDEGs were shown in Figure 1h-i. The expression heatmap of the top 10 rhDEGs is shown in Figure 2a. The above results proved that their expression patterns were consistent and that these rhDEGs were strong and robust.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Functional and pathway enrichment analyses \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize the functional roles of the above top 10 rhDEGs, we used GO and KEGG pathway enrichment analyses (Table 3). The BP category of the GO analysis results showed that these rhDEGs were significantly enriched in blood coagulation, response to hypoxia, positive regulation of cell proliferation, and cell adhesion. For CC, these rhDEGs were significantly enriched in extracellular region and extracellular exosomes. Moreover, they were significantly enriched in protease binding, identical protein binding, and receptor binding in the MF categories (Figure 2b). According to KEGG pathway enrichment analysis, these rhDEGs were significantly enriched in proteoglycans in cancer, focal adhesion, and PI3K-Akt signaling pathway (Figure 2b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Expression and prognostic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we used the gene expression of above ten rhDEGs. As shown in Figure 2-l, we found that all of these rhDEGs were significantly differentially upregulated, which was consistent with our results above. Using the GEPIA database to explore the association between gene expression and survival, we found that fibronectin 1 (FN1; log-rank \u003cem\u003ep\u003c/em\u003e = 0.026), dipeptidyl peptidase-4 (DPP4/CD26; log-rank \u003cem\u003ep\u003c/em\u003e = 0.048), and integrin subunit alpha 2 (ITGA2; log-rank \u003cem\u003ep\u003c/em\u003e = 0.032) were significantly correlated with the disease-free survival of thyroid cancer patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, the expression levels of these rhDEGs were also examined by qRT-PCR method in PTC and adjacent nontumor tissues. As shown in Figure S3, it can be seen that the significant upexpression of \u003cem\u003eFn1\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e = 0.0031), \u003cem\u003eItga1\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e = 0.0016), \u003cem\u003eDpp4\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e = 0.0004), \u003cem\u003eFam20a\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e = 0.0155), \u003cem\u003eSerpina1\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e = 0.0013), \u003cem\u003eLgals3\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e = 0.0072), \u003cem\u003eMet\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e = 0.0020), and \u003cem\u003ePlau\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e = 0.0030) were demonstrated in thyroid cancer tissues (\u003cem\u003en\u003c/em\u003e = 10) compared with matched normal tissues (Figure S3a-c). These results demonstrated the accuracy of our above analysis, suggesting that \u003cem\u003eFn1, Itga1, Dpp4, Fam20a, Serpina1, Lgals3, Met\u003c/em\u003e, and \u003cem\u003ePlau\u003c/em\u003e can be potential biomarkers for TC.\u003c/p\u003e\n\u003cp\u003eAmong these rhDEGs, we found that only DPP4 was significantly associated with immune function, which is specifically expressed in lymphatic vessels, smooth muscle, CD4+ and CD8+ T cells to regulated T-cell proliferation and NF-kappa-B activation. KEGG database reported that DPP4 is also involved in the pathway of protein digestion and absorption (Table 4). Relative expression of DPP4 protein in PTC tissues was higher than that in NT (Figure S3c). According to the results from the HPA database, DPP4 exhibits a significantly higher expression profile in PTC tissues than in NT (Figure S3d).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Manual gating and immune cell clusters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the multi-dimension mass cytometry data, we analyzed CD45+ infiltrated leukocytes through the implementation of manual gating and SPADE analysis. The gating strategy employed were displayed in Figure S4. The cells were visualized as a dimensional reduction plot using t-distributed stochastic neighbor embedding (t-SNE) between each NT and PTC (Figure 3a). A total of 29 cell clusters were then annotated according to the median expression of cell marker, including lymphocytes, monocytes, dendritic cells (DCs), and granulocytes (Table 5). The expressions of each cell marker were shown in common and t-SNE heatmap (Figure 3b-d).\u003c/p\u003e\n\u003cp\u003eThe characteristic changes in the number of cells in different clusters were observed by displaying the proportion of multiple cluster samples (Figure 4a). Compared with NT, the significantly increased range of clusters in PTC included 18 eosinophil cluster, 26 CD8 terminal effector cell cluster, 9 Th1-like cell cluster, 13 CD4 naive cell cluster, 15 regulatory T cell (Treg) cluster, and 12 later natural killer (NK) cell cluster. Meanwhile, the significantly decreased range of clusters in PTC included 3 NK T (NKT) cell cluster, 8 B naive cell cluster, 14 CD8 effector memory cell cluster, and 28 CD8 naive cell cluster compared with NT (Figure 4b). However, there were lack of statistical differences on inter group cluster difference analysis between NT and PTC (Figure 4c).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003eTo identify the clusters with similar phenotypes, Spearman correlation analysis was used. The cut-off criteria of |correlation coefficients| \u0026gt; 0.75 and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 were considered to be statistically significant. We found that 1 CD4 terminal effector cell cluster is negatively correlated with 20 neutrophil cluster; 7 myeloid DCs (mDCs) cluster is negatively correlated with 17 cell cluster (CD45RO+TCR+CXCR3+CXCR5+), 18 eosinophil cluster, 19 Th1-like cell cluster, 24 classical monocyte cluster, but which was positively associated with 10 plasmacytoid DCs (pDCs) cluster; 8 B naive cell cluster is negatively correlated with 16 early NK cell cluster; 10 pDCs is negatively associated with 17 cell cluster, 18 eosinophil cluster, 24 classical monocyte cluster, and 29 CD8 central memory cell cluster; 11 pasmablast cluster is negatively correlated with 18 eosinophil cluster; 12 late NK cell cluster is positively associated with 15 Treg cluster; 13 CD4 naive T cell cluster is positively correlated with 18 eosinophil cluster and 19 Th1-like cell cluster; 17 cell cluster is positively correlated with 24 classical monocyte cluster and 29 CD8 central memory cell cluster; 18 eosinophil cluster s positively correlated with 9 Th1-like cell cluster, 24 classical monocyte cluster, and 29 CD8 central memory cell cluster; 19 Th1-like cell cluster is positively correlated with 29 CD8 central memory cell cluster (Figure 4d).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 DPP4 expression is correlated with immune cell infiltration in PTC. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, we analyzed the association between DPP4 expression and immune cell infiltration at the mRNA level. First, Spearman\u0026rsquo; s correlation analysis showed that the DPP4 expression level was significantly, moderately to strongly, and positively correlated with the infiltration levels of B cells (r = 0.437, \u003cem\u003ep\u003c/em\u003e = 7.40e-24), CD4+ T cells (r = 0.474, \u003cem\u003ep\u003c/em\u003e = 1.04e-28), macrophages (r = 0.355, \u003cem\u003ep\u003c/em\u003e = 5.68e-16), neutrophils (r = 0.446, \u003cem\u003ep\u003c/em\u003e = 3.04e-25), and dendritic cells (r = 0.428, \u003cem\u003ep\u003c/em\u003e = 5.01e-23). Moreover, moderate correlation was found between DPP4 and CD8+ T cells (r = -0.144, \u003cem\u003ep\u003c/em\u003e = 1.39e-03) (Figure 5a). The \u0026ldquo;Survival\u0026rdquo; module showed that no significant differences were found among B cell, CD4+ T cell, CD8+ T cell, macrophage, neutrophil, and dendritic cells (Figure 5b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the COX regression model of survival, the survival of thyroid cancer patients is positively correlated with age (HR = 1.236, 95%CI, 1.126-1.357; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and stage 4 (HR = 21.587, 95%CI, 1.815-256.695; \u003cem\u003ep\u003c/em\u003e = 0.015), while it is negatively correlated with CD8+ T cell (HR = 0, 95%CI, 0-0; \u003cem\u003ep\u003c/em\u003e = 0.002), macrophage (HR = 0, 95%CI, 0-0.522; \u003cem\u003ep\u003c/em\u003e = 0.045), and DPP4 expression (HR = 0.678, 95%CI, 0.472-0.973; \u003cem\u003ep\u003c/em\u003e = 0.035) (Figure 5c). SCNA module showed that the infiltrated levels of B cell, CD4+ T cell, CD8+ T cell, macrophage, neutrophils, and DCs were both decreased in thyroid cancer tissues with arm-level deletion or arm-level gain of DPP4 compared with that with diploid/normal expression of DPP4 (Figure 5d).\u003c/p\u003e\n\u003cp\u003eSecond, we applied the CIBERSORT algorithm to further explore the correlation between the subtypes of the above six immune cells and different DPP4 expression levels. The immune infiltration scores of a total of 22 subtypes of immune cells were calculated in the high and low DPP4 groups (Figure 5e). Six immune cell subtypes were associated with DPP4 expression, including naive B cells, CD8+ T cells, M1 macrophages, M2 macrophages, resting DC, nuetrophils. The most noticeable differences were observed in the subtype of DCs, such as resting DCs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 DPP4 expression is correlated with CD8+ T cell function in PTC. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the role of DPP4 in CD8+ T cells, we compared with such negative immune checkpoint as PD-L1 and CTLA-4 between CD8+DPP4+ and CD8+DPP4- T cells (Figure 6a). Compared with nodular goiter (NG), the relative number of CD3+CD8+ T cells and CD3+CD8+DPP4- T cells in peripheral blood of PTC were both reduced while the relative number of CD3+CD8+DPP4+ T cells in peripheral blood of PTC was significantly increased (Figure 6b-c). The relative expression of PD-L1 and CTLA-4 in the CD3+CD8+DPP4+ T cells of PTC patients were both higher than that in the NG patients. However, there were lack of statistical differences on the relative expression of PD-L1 and CTLA-4 in the CD3+CD8+DPP4- T cells between PTC and nodular goiter patients. Simultaneously, among PTC patients, the relative expression of PD-L1 and CTLA-4 in the CD3+CD8+DPP4+ T cells were both higher than that in the CD3+CD8+DPP4- T cells (Figure 6e-f). In addition, we also found that the infiltration of CD8+DPP4+ T cells from tumor tissues was significantly increased compared with paratumor tissues of PTC patients (Figure 6d).\u003c/p\u003e\n\u003cp\u003eWe also compared the cytokines including interferon (IFN) -\u0026gamma;, tumor necrosis factor (TNF)-\u0026alpha;, interleukin (IL)-4, IL-5, and IL-13 between CD8+DPP4+ and CD8+DPP4- T cells (Figure S5a-b). Compared with NG patients, the relative expression of IFN-\u0026gamma; in CD8+DPP4+ T cells of PTC patients was significantly decreased, but which in CD8+DPP4- T cells between PTC and NG patients was lack of statistical difference (Figure S5c). The relative expression of TNF-\u0026alpha;, IL-4, and IL-5 in CD8+DPP4+ T cells between PTC and NG patients were not statistically different,and the relative expression of TNF-\u0026alpha; and IL-4 in CD8+DPP4- T cells in CD8+DPP4- T cells of PTC patients were significantly lower than that in the NG patients. In addition, the relative expression of IL-5 in CD8+DPP4- T cells also not differed statistically between PTC and NG patients (Figure S5d-f). Compared with NG patients, the relative expression of IL-13 in CD8+DPP4+ T cells of PTC patients was significantly increased, but which in CD8+DPP4- T cells of PTC patients was reduced (Figure S5g). In total, the relative expression of these cytokines in CD8+DPP4+ T cells were both lower than that in CD8+DPP4- T cells among PTC and NG patients.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eUsing classical bioinformatics analysis based on five microarray data, FN1, DPP4, and ITGA2 were identified as upregulated rhDEGs that are correlated with the disease-free survival of TC patients. After gene annotation and validation of the expression profile in clinical specimens, this study revealed that DPP4 was a potential oncogenic gene with prognostic value in TC. DPP4 is a significantly upregulated gene whose high expression predicts striking immune microenvironment remodeling, higher risk of immune evasion, and worse PFS in PTC patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDPP4 is expressed in T cells, B cells, NK cells, DCs, and macrophages, and its expression level is also related to the activation status of immune cells. Cells marked by DPP4 expression are highly proliferative and multipotent progenitor cells\u0026nbsp;(17, 18). Among these immune cells, DPP4 is mainly expressed in T cells and upregulated in malignant tumors such as TC(19). For example, DPP4 is stored in the secretory granules of human CD8+T cell populations and co-localizes with effector proteins such as granzyme, perforin, and granin(20). Here, we proved that the proportion of CD8+ T cells in the DPP4 high-expression PTC was significantly decreased in comparison with DPP4 low-expression PTC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDPP4 is a regulator to mediate the motility, activation, and effector function of T cells. DPP4 also promotes mitochondria-associated endoplasmic reticulum membrane formation and mitochondria calcium overload in regulatory T cells through binding to IGF2-R and activating PKA/SP1 signaling(21). DPP4+ chimeric antigen receptor T cells exhibited robust cytotoxicity against the DPP4+ T-cell lymphoma cells activated multiple effector functions and limited tumour progression(22). However, DPP4 deficiency reduces T-cell motility by suppressing the expression of microtubule associated protein midline-1 in T cells(23). Post-translational modification of chemokines mediated by DPP4 has been shown to negatively regulate lymphocyte trafficking, and its inhibition enhances T cell migration and tumor immunity(24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdministration of DPP4 inhibitor such as sitagliptin increased overall CXCR3-mediated CD8+ T-cell trafficking to the tumour and enhanced the activation and proliferation of CD8+ T-cells(24, 25). Anagliptin can enhance PD-L1 blockade efficacy in NSCLC by inhibiting macrophage differentiation and M2 macrophage polarization(26). Combining a DPP4 inhibitor with radiotherapy could promote chemokines expression and T cell infiltration in the tumor microenvironment, enhancing the anti-tumor effect of radiotherapy and anti-PD-1(27).\u0026nbsp;In this study, DPP4+CD8+ T cells in peripheral blood of PTC patients showed the high expression of PD-L1, CTLA4, and IL-13, as well as low expression of IFN-\u0026gamma; than DPP4-CD8+ T cells, suggesting that DPP4 may be another negative immune checkpoint of CD8+ T cells.\u003c/p\u003e\n\u003cp\u003eAmong CD4+ T cells, DPP4 can identifies three T helper subsets with distinct immunological properties in both healthy individuals and cancer patients. For example, DPP4\u003csup\u003eneg\u003c/sup\u003e T cells possess a regulatory phenotype, DPP4\u003csup\u003eint\u0026nbsp;\u003c/sup\u003eT cells are mainly naive, and DPP4\u003csup\u003ehigh\u003c/sup\u003e T cells appear terminally differentiated and exhausted(28). Previous study(29)\u0026nbsp;reported that DPP4+CD8+ T cells is the early effector memory T-cell subset, and that DPP4-mediated co-stimulation of CD8+ T cells exerts a cytotoxic effect preferentially via granzyme B, TNF-\u0026alpha;, IFN-\u0026gamma;, and Fas ligand. However, this study showed that DPP4+CD8+ T cells may be the exhausted cytotoxicity T cells with higher expression of PD-L1 and CTLA-4, as well as down-regulation of IFN-\u0026gamma; for successful cancer immunotherapy involving checkpoint inhibitors and chimeric antigen receptor T cells(30, 31). DPP4 may be interacted with the secretion of IL-13(32, 33), which promotes the sphere formation, proliferation, and migration of cancer cells(34-36).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study firstly concentrated on the role of DPP4 in CD8+ T cells of PTC patients, and found that DPP4 is significantly up-regulated to be associated with infiltration, activation, and effector function of T cells. Simultaneously, DPP4 may be a negative immune checkpoint of CD8+ T cells with higher expression of PD-L1 and CTLA-4 as well as down-regulation of IFN-\u0026gamma;, However, the features of transcription profile, chemokine receptor profile, cellular developmental trajectory and stemness between DPP4+CD8+ and DPP4-CD8+ T cells should be further explored in our future study.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eIn summary, the present study proved that DPP4 is upregulated in PTC tissues and is tightly correlated with clinical stages and outcomes, immune infiltration, and key molecules in CD8\u0026thinsp;+\u0026thinsp;T cell evasion and exhaustion. These findings support the use of DPP4 inhibitors for stabilizing biologically active forms of chemokines and constraining CD8\u0026thinsp;+\u0026thinsp;T cell exhaustion as a strategy to enhance tumor immunotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Dr S.J. Yi had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConcept and design:\u003c/em\u003e R. Jing, and S.J. Yi.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcquisition, analysis, or interpretation of data:\u003c/em\u003e R. Jing, N. Wu, Y. Wu, Q. Zhang, and S. Zeng.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDrafting of the manuscript:\u003c/em\u003e R. Jing, N. Wu, and Y. Wu.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCritical review of the manuscript for important intellectual content:\u003c/em\u003e N. Wu, Y. Wu, Q. Zhang.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis:\u003c/em\u003e R. Jing and Q.K. Liang.\u003c/p\u003e\n\u003cp\u003eAdministrative, technical, or material support: J.L. Liu, and Y. Zhao.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSupervision\u003c/em\u003e: S.J. Yi.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Youth Science Foundation of Foundation and Applied Basic Research Fund Project of Guangdong Province (2023A1515110149).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in the current study are publicly available in the Gene Expression Omnibus (GEO) repository, with accession numbers of GSE29265, GSE33630, GSE35570, GSE60542,and another mRNA expression matrix file from TCGA database. The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from the patients before surgery. This study received approvement from the Ethical Committee of The South China Hospital of Shenzhen University (HNLS20240308003-A).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that they have no competing interests to declare in relation with this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209-49.\u003c/li\u003e\n\u003cli\u003eChen DW, Lang BHH, McLeod DSA, Newbold K, Haymart MR. Thyroid cancer. 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Blood. 2022;140(26):2805-17.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 \u0026nbsp; \u0026nbsp;qPCR primers\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"489\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eDirection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003eSize (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eSequence (5\u0026rsquo;\u0026rarr;3\u0026rsquo;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003eFN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eTGACCAGCAACACCGTGACAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eGAGACCCAGGAGACCACAAAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003eITGA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eGGAGCCTATGATTGGAATGGAACAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eAGAAGCAGTAGCAGAGTTTACAGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003eDPP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eGCCCTGGTCGATGTTGGAGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eTGTTGGTGTGCTGTGCTGCTAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003eAPOE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eCTGCGTTGCTGGTCACATTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eCGCTCTGCCACTCGGTCTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003eTIMP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eCCTGTTGTTGCTGTGGCTGATAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eACGCTGGTATAAGGTGGTCTGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003eFAM20A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eAAATCTCCATCCTCTCGCCTCTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eCGCATCACATCGCTGAGTCTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003eSERPINA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eGAGTTCGCCTTCAGCCTATACCG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eTTCATCGTGAGTGTCAGCCTTGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003eLGALS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eCCTTCCACTTTAACCCACGCTTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eACCGACTGTCTTTCTTCCCTTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003eMET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eGGTCCTTTGGCGTGCTCCTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eCTGGGCAGTATTCGGGTTGTAGG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003ePLAU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eCGCTCAAGGCTTAACTCCAACAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eAACGGATCTTCAGCAAGGCAATG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eGGCACAGTCAAGGCTGAGAAT G\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.723926380368098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.38241308793456%\" valign=\"top\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.609406952965234%\" valign=\"top\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.284253578732105%\" valign=\"top\"\u003e\n \u003cp\u003eATGGTGGTGAGACGCCAGTA\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\u003cp\u003eTable 2 \u0026nbsp; \u0026nbsp;Robust DEGs by RRA based on 12 computing methods of PPI network\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"441\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003eFreq\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eFN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e7.21E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eAPOE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e8.28E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eSERPINA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e4.18E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003ePLAU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e7.15E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eDPP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1.28E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eLGALS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1.28E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eFAM20A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e2.41E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eMET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e3.83E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eTIMP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1.51E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eAGR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eDUSP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eNRCAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eMDK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003ePDZK1IP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eTREM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eCLDN16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eMYOC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eDPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eALOX5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eDLG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eSFN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eGABRB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eKLK7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eOGN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eCD1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eGPM6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eSEMA3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eCTSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eALOX15B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eRYR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eEVA1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eSPX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eSTMN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eCYP1B1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eTMPRSS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eSLC34A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eOCA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003ePROS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eNPC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eLRRK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eFRMD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eDUSP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eCLDN10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eAGR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eSLPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eCFD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eODAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eCHRDL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eDUSP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.798185941043084%\"\u003e\n \u003cp\u003eKCNIP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.35147392290249%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.85034013605442%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 \u0026nbsp; \u0026nbsp;Gene annotation by Uniprot, BioGPS, and KEGG database\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"974\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.213552361396303%\" valign=\"top\" style=\"width: 7.8605%;\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.731006160164272%\" valign=\"top\" style=\"width: 10.8599%;\"\u003e\n \u003cp\u003eAlternative name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"null; width: 12.5000%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.369609856262834%\" valign=\"top\" style=\"width: 12.1011%;\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936344969199178%\" valign=\"top\" style=\"width: 10.9634%;\"\u003e\n \u003cp\u003eSubcellular location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.016427104722794%\" valign=\"top\" style=\"width: 13.2388%;\"\u003e\n \u003cp\u003eExpression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.811088295687885%\" valign=\"top\" style=\"width: 13.4456%;\"\u003e\n \u003cp\u003eFunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.921971252566735%\" valign=\"top\" style=\"width: 19.0307%;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.213552361396303%\" valign=\"top\" style=\"width: 7.8605%;\"\u003e\n \u003cp\u003eFN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.731006160164272%\" valign=\"top\" style=\"width: 10.8599%;\"\u003e\n \u003cp\u003eCold-insoluble globulin\u0026nbsp;(CIG)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"null; width: 12.5000%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.369609856262834%\" valign=\"top\" style=\"width: 12.1011%;\"\u003e\n \u003cp\u003eFibronectin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936344969199178%\" valign=\"top\" style=\"width: 10.9634%;\"\u003e\n \u003cp\u003eSecreted, extracellular space, extracellular matrix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.016427104722794%\" valign=\"top\" style=\"width: 13.2388%;\"\u003e\n \u003cp\u003ePlasma FN is secreted by hepatocytes;\u003c/p\u003e\n \u003cp\u003eCellular FN made by fibroblasts, epithelial and other cell types;\u003c/p\u003e\n \u003cp\u003eExpressed in the inner limiting membrane and around blood vessels in the retina (at protein level).\u003c/p\u003e\n \u003cp\u003eSmooth muscle, Cardiac myocytes, and adipocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.811088295687885%\" valign=\"top\" style=\"width: 13.4456%;\"\u003e\n \u003cp\u003eCell adhesion, cell motility, opsonization, wound healing, and maintenance of cell shape;\u003c/p\u003e\n \u003cp\u003eOsteoblast compaction and mineralization;\u003c/p\u003e\n \u003cp\u003eMonocyte activation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.921971252566735%\" valign=\"top\" style=\"width: 19.0307%;\"\u003e\n \u003cp\u003emap04151 \u0026nbsp; \u0026nbsp; PI3K-Akt signaling pathway\u003c/p\u003e\n \u003cp\u003emap04510 \u0026nbsp; \u0026nbsp; Focal adhesion\u003c/p\u003e\n \u003cp\u003emap04512 \u0026nbsp; \u0026nbsp; ECM-receptor interaction\u003c/p\u003e\n \u003cp\u003emap04810 \u0026nbsp; \u0026nbsp; Regulation of actin cytoskeleton\u003c/p\u003e\n \u003cp\u003emap04933 \u0026nbsp; \u0026nbsp; AGE-RAGE signaling pathway in diabetic complications\u003c/p\u003e\n \u003cp\u003emap05100 \u0026nbsp; \u0026nbsp; Bacterial invasion of epithelial cells\u003c/p\u003e\n \u003cp\u003emap05135 \u0026nbsp; \u0026nbsp; Yersinia infection\u003c/p\u003e\n \u003cp\u003emap05146 \u0026nbsp; \u0026nbsp; Amoebiasis\u003c/p\u003e\n \u003cp\u003emap05165 \u0026nbsp; \u0026nbsp; Human papillomavirus infection\u003c/p\u003e\n \u003cp\u003emap05200 \u0026nbsp; \u0026nbsp; Pathways in cancer\u003c/p\u003e\n \u003cp\u003emap05205 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Proteoglycans in cancer\u003c/p\u003e\n \u003cp\u003emap05222 \u0026nbsp; \u0026nbsp; Small cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.213552361396303%\" valign=\"top\" style=\"width: 7.8605%;\"\u003e\n \u003cp\u003eITGA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.731006160164272%\" valign=\"top\" style=\"width: 10.8599%;\"\u003e\n \u003cp\u003eCD49b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"null; width: 12.5000%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.369609856262834%\" valign=\"top\" style=\"width: 12.1011%;\"\u003e\n \u003cp\u003eIntegrin alpha-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936344969199178%\" valign=\"top\" style=\"width: 10.9634%;\"\u003e\n \u003cp\u003eCell membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.016427104722794%\" valign=\"top\" style=\"width: 13.2388%;\"\u003e\n \u003cp\u003eBronchial epithelial cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.811088295687885%\" valign=\"top\" style=\"width: 13.4456%;\"\u003e\n \u003cp\u003eA receptor for laminin, collagen, collagen C-propeptides, fibronectin and E-cadherin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.921971252566735%\" valign=\"top\" style=\"width: 19.0307%;\"\u003e\n \u003cp\u003emap03271 \u0026nbsp; \u0026nbsp; Virion - Rotavirus\u003c/p\u003e\n \u003cp\u003emap04145 \u0026nbsp; \u0026nbsp; Phagosome\u003c/p\u003e\n \u003cp\u003emap04151 \u0026nbsp; \u0026nbsp; PI3K-Akt signaling pathway\u003c/p\u003e\n \u003cp\u003emap04510 \u0026nbsp; \u0026nbsp; Focal adhesion\u003c/p\u003e\n \u003cp\u003emap04512 \u0026nbsp; \u0026nbsp; ECM-receptor interaction\u003c/p\u003e\n \u003cp\u003emap04611 \u0026nbsp; \u0026nbsp; Platelet activation\u003c/p\u003e\n \u003cp\u003emap04640 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hematopoietic cell lineage\u003c/p\u003e\n \u003cp\u003emap04810 \u0026nbsp; \u0026nbsp; Regulation of actin cytoskeleton\u003c/p\u003e\n \u003cp\u003emap05165 \u0026nbsp; \u0026nbsp; Human papillomavirus infection\u003c/p\u003e\n \u003cp\u003emap05200 \u0026nbsp; \u0026nbsp; Pathways in cancer\u003c/p\u003e\n \u003cp\u003emap05205 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Proteoglycans in cancer\u003c/p\u003e\n \u003cp\u003emap05222 \u0026nbsp; \u0026nbsp; Small cell lung cancer\u003c/p\u003e\n \u003cp\u003emap05410 \u0026nbsp; \u0026nbsp; Hypertrophic cardiomyopathy\u003c/p\u003e\n \u003cp\u003emap05412 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Arrhythmogenic right ventricular cardiomyopathy\u003c/p\u003e\n \u003cp\u003emap05414 \u0026nbsp; \u0026nbsp; Dilated cardiomyopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.213552361396303%\" valign=\"top\" style=\"width: 7.8605%;\"\u003e\n \u003cp\u003eDPP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.731006160164272%\" valign=\"top\" style=\"width: 10.8599%;\"\u003e\n \u003cp\u003eCD26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"null; width: 12.5000%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.369609856262834%\" valign=\"top\" style=\"width: 12.1011%;\"\u003e\n \u003cp\u003eDipeptidyl peptidase 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936344969199178%\" valign=\"top\" style=\"width: 10.9634%;\"\u003e\n \u003cp\u003eCell membrane, cell junction, membrane raft,\u003c/p\u003e\n \u003cp\u003esecreted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.016427104722794%\" valign=\"top\" style=\"width: 13.2388%;\"\u003e\n \u003cp\u003eExpressed specifically in lymphatic vessels\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSmooth muscle, CD4+ and CD8+ T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.811088295687885%\" valign=\"top\" style=\"width: 13.4456%;\"\u003e\n \u003cp\u003ecell activation, T-cell proliferation and NF-kappa-B activation;\u003c/p\u003e\n \u003cp\u003eLymphocyte-epithelial cell adhesion;\u003c/p\u003e\n \u003cp\u003ePericellular proteolysis, migration and invasion of endothelial cells;\u003c/p\u003e\n \u003cp\u003eLymphatic endothelial cells adhesion, migration and tube formation;\u003c/p\u003e\n \u003cp\u003eRegulates chemokines, mitogenic growth factors, neuropeptides and peptide hormones;\u003c/p\u003e\n \u003cp\u003eA receptor for human coronavirus MERS-CoV-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.921971252566735%\" valign=\"top\" style=\"width: 19.0307%;\"\u003e\n \u003cp\u003emap04974 \u0026nbsp; \u0026nbsp; Protein digestion and absorption\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.213552361396303%\" valign=\"top\" style=\"width: 7.8605%;\"\u003e\n \u003cp\u003eAPOE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.731006160164272%\" valign=\"top\" style=\"width: 10.8599%;\"\u003e\n \u003cp\u003eApoE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"null; width: 12.5000%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.369609856262834%\" valign=\"top\" style=\"width: 12.1011%;\"\u003e\n \u003cp\u003eApolipoprotein E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936344969199178%\" valign=\"top\" style=\"width: 10.9634%;\"\u003e\n \u003cp\u003eSecreted, extracellular space, extracellular matrix,\u003c/p\u003e\n \u003cp\u003eendosome, multivesicular body\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.016427104722794%\" valign=\"top\" style=\"width: 13.2388%;\"\u003e\n \u003cp\u003eLiver, adipocyte, amygdala, and adrenalgland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.811088295687885%\" valign=\"top\" style=\"width: 13.4456%;\"\u003e\n \u003cp\u003eAn apolipoprotein associating with lipid particles;\u003c/p\u003e\n \u003cp\u003eA core component of plasma lipoproteins and is involved in their production, conversion and clearance;\u003c/p\u003e\n \u003cp\u003eBinds to the immune cell receptor LILRB4 to regulate innate and adaptive immune responses, etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.921971252566735%\" valign=\"top\" style=\"width: 19.0307%;\"\u003e\n \u003cp\u003emap04979 \u0026nbsp; \u0026nbsp; Cholesterol metabolism\u003c/p\u003e\n \u003cp\u003emap05010 \u0026nbsp; \u0026nbsp; Alzheimer disease\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.213552361396303%\" valign=\"top\" style=\"width: 7.8605%;\"\u003e\n \u003cp\u003eTIMP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.731006160164272%\" valign=\"top\" style=\"width: 10.8599%;\"\u003e\n \u003cp\u003eEPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"null; width: 12.5000%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.369609856262834%\" valign=\"top\" style=\"width: 12.1011%;\"\u003e\n \u003cp\u003eMetalloproteinase inhibitor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936344969199178%\" valign=\"top\" style=\"width: 10.9634%;\"\u003e\n \u003cp\u003eSecreted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.016427104722794%\" valign=\"top\" style=\"width: 13.2388%;\"\u003e\n \u003cp\u003eSmooth muscle, Cardiac myocytes, and adipocyte\u003c/p\u003e\n \u003cp\u003eDetected in rheumatoid synovial fluid (at protein level).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.811088295687885%\" valign=\"top\" style=\"width: 13.4456%;\"\u003e\n \u003cp\u003eMetalloproteinase inhibitor;\u003c/p\u003e\n \u003cp\u003eA growth factor that regulates cell differentiation, migration and cell death and activates cellular signaling cascades via CD63 and ITGB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.921971252566735%\" valign=\"top\" style=\"width: 19.0307%;\"\u003e\n \u003cp\u003emap04066 \u0026nbsp; \u0026nbsp; HIF-1 signaling pathway\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.213552361396303%\" valign=\"top\" style=\"width: 7.8605%;\"\u003e\n \u003cp\u003eFAM20A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.731006160164272%\" valign=\"top\" style=\"width: 10.8599%;\"\u003e\n \u003cp\u003eAI1G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"null; width: 12.5000%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.369609856262834%\" valign=\"top\" style=\"width: 12.1011%;\"\u003e\n \u003cp\u003ePseudokinase FAM20A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936344969199178%\" valign=\"top\" style=\"width: 10.9634%;\"\u003e\n \u003cp\u003eSecreted, Golgi apparatus, endoplasmic reticulum\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.016427104722794%\" valign=\"top\" style=\"width: 13.2388%;\"\u003e\n \u003cp\u003eHighly expressed in lung and liver. Intermediate levels in thymus and ovary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.811088295687885%\" valign=\"top\" style=\"width: 13.4456%;\"\u003e\n \u003cp\u003ePseudokinase that acts as an allosteric activator of the Golgi serine/threonine protein kinase FAM20C and is involved in biomineralization of teeth.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.921971252566735%\" valign=\"top\" style=\"width: 19.0307%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.213552361396303%\" valign=\"top\" style=\"width: 7.8605%;\"\u003e\n \u003cp\u003eSERPINA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.731006160164272%\" valign=\"top\" style=\"width: 10.8599%;\"\u003e\n \u003cp\u003eAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"null; width: 12.5000%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.369609856262834%\" valign=\"top\" style=\"width: 12.1011%;\"\u003e\n \u003cp\u003eAlpha-1-antitrypsin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936344969199178%\" valign=\"top\" style=\"width: 10.9634%;\"\u003e\n \u003cp\u003eSecreted, endoplasmic reticulum\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.016427104722794%\" valign=\"top\" style=\"width: 13.2388%;\"\u003e\n \u003cp\u003eUbiquitous. Expressed in leukocytes and plasma.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.811088295687885%\" valign=\"top\" style=\"width: 13.4456%;\"\u003e\n \u003cp\u003eInhibitor of serine proteases such as elastase, plasmin, and thrombin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.921971252566735%\" valign=\"top\" style=\"width: 19.0307%;\"\u003e\n \u003cp\u003emap04610 \u0026nbsp; \u0026nbsp; Complement and coagulation cascades\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.213552361396303%\" valign=\"top\" style=\"width: 7.8605%;\"\u003e\n \u003cp\u003eLGALS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.731006160164272%\" valign=\"top\" style=\"width: 10.8599%;\"\u003e\n \u003cp\u003eGal-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"null; width: 12.5000%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.369609856262834%\" valign=\"top\" style=\"width: 12.1011%;\"\u003e\n \u003cp\u003eGalectin-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936344969199178%\" valign=\"top\" style=\"width: 10.9634%;\"\u003e\n \u003cp\u003eSecreted, cytoplasm, nucleus\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.016427104722794%\" valign=\"top\" style=\"width: 13.2388%;\"\u003e\n \u003cp\u003eA major expression is found in the colonic epithelium.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIt is also abundant in the activated macrophages.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eExpressed in fetal membranes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.811088295687885%\" valign=\"top\" style=\"width: 13.4456%;\"\u003e\n \u003cp\u003eGalactose-specific lectin which binds IgE;\u003c/p\u003e\n \u003cp\u003eInvolved in acute inflammatory responses including neutrophil activation and adhesion, chemoattraction of monocytes macrophages, opsonization of apoptotic neutrophils, and activation of mast cells, etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.921971252566735%\" valign=\"top\" style=\"width: 19.0307%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.213552361396303%\" valign=\"top\" style=\"width: 7.8605%;\"\u003e\n \u003cp\u003eMET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.731006160164272%\" valign=\"top\" style=\"width: 10.8599%;\"\u003e\n \u003cp\u003ec-Met\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"null; width: 12.5000%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.369609856262834%\" valign=\"top\" style=\"width: 12.1011%;\"\u003e\n \u003cp\u003eHepatocyte growth factor receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936344969199178%\" valign=\"top\" style=\"width: 10.9634%;\"\u003e\n \u003cp\u003eSecreted, membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.016427104722794%\" valign=\"top\" style=\"width: 13.2388%;\"\u003e\n \u003cp\u003eExpressed in normal hepatocytes as well as in epithelial cells lining the stomach, the small and the large intestine.\u003c/p\u003e\n \u003cp\u003eFound also in basal keratinocytes of esophagus and skin.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHigh levels are found in liver, gastrointestinal tract, thyroid and kidney. Also present in the brain.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eExpressed in metaphyseal bone (at protein level)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.811088295687885%\" valign=\"top\" style=\"width: 13.4456%;\"\u003e\n \u003cp\u003eReceptor tyrosine kinase;\u003c/p\u003e\n \u003cp\u003eRegulates many physiological processes including proliferation, scattering, morphogenesis and survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.921971252566735%\" valign=\"top\" style=\"width: 19.0307%;\"\u003e\n \u003cp\u003ehsa01521 \u0026nbsp; \u0026nbsp; \u0026nbsp;EGFR tyrosine kinase inhibitor resistance\u003c/p\u003e\n \u003cp\u003ehsa04010 \u0026nbsp; \u0026nbsp; \u0026nbsp;MAPK signaling pathway\u003c/p\u003e\n \u003cp\u003ehsa04014 \u0026nbsp; \u0026nbsp; \u0026nbsp;Ras signaling pathway\u003c/p\u003e\n \u003cp\u003ehsa04015 \u0026nbsp; \u0026nbsp; \u0026nbsp;Rap1 signaling pathway\u003c/p\u003e\n \u003cp\u003ehsa04020 \u0026nbsp; \u0026nbsp; \u0026nbsp;Calcium signaling pathway\u003c/p\u003e\n \u003cp\u003ehsa04151 \u0026nbsp; \u0026nbsp; \u0026nbsp;PI3K-Akt signaling pathway\u003c/p\u003e\n \u003cp\u003ehsa04360 \u0026nbsp; \u0026nbsp; \u0026nbsp;Axon guidance\u003c/p\u003e\n \u003cp\u003ehsa04510 \u0026nbsp; \u0026nbsp; \u0026nbsp;Focal adhesion\u003c/p\u003e\n \u003cp\u003ehsa04520 \u0026nbsp; \u0026nbsp; \u0026nbsp;Adherens junction\u003c/p\u003e\n \u003cp\u003ehsa05100 \u0026nbsp; \u0026nbsp; \u0026nbsp;Bacterial invasion of epithelial cells\u003c/p\u003e\n \u003cp\u003ehsa05120 \u0026nbsp; \u0026nbsp; \u0026nbsp;Epithelial cell signaling in Helicobacter pylori infection\u003c/p\u003e\n \u003cp\u003ehsa05144 \u0026nbsp; \u0026nbsp; \u0026nbsp;Malaria\u003c/p\u003e\n \u003cp\u003ehsa05200 \u0026nbsp; \u0026nbsp; \u0026nbsp;Pathways in cancer\u003c/p\u003e\n \u003cp\u003ehsa05202 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Transcriptional misregulation in cancer\u003c/p\u003e\n \u003cp\u003ehsa05205 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Proteoglycans in cancer\u003c/p\u003e\n \u003cp\u003ehsa05206 \u0026nbsp; \u0026nbsp; \u0026nbsp;MicroRNAs in cancer\u003c/p\u003e\n \u003cp\u003ehsa05208 \u0026nbsp; \u0026nbsp; \u0026nbsp;Chemical carcinogenesis - reactive oxygen species\u003c/p\u003e\n \u003cp\u003ehsa05211 \u0026nbsp; \u0026nbsp; \u0026nbsp;Renal cell carcinoma\u003c/p\u003e\n \u003cp\u003ehsa05218 \u0026nbsp; \u0026nbsp; \u0026nbsp;Melanoma\u003c/p\u003e\n \u003cp\u003ehsa05223 \u0026nbsp; \u0026nbsp; \u0026nbsp;Non-small cell lung cancer\u003c/p\u003e\n \u003cp\u003ehsa05225 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hepatocellular carcinoma\u003c/p\u003e\n \u003cp\u003ehsa05226 \u0026nbsp; \u0026nbsp; \u0026nbsp;Gastric cancer\u003c/p\u003e\n \u003cp\u003ehsa05230 \u0026nbsp; \u0026nbsp; \u0026nbsp;Central carbon metabolism in cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.213552361396303%\" valign=\"top\" style=\"width: 7.8605%;\"\u003e\n \u003cp\u003ePLAU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.731006160164272%\" valign=\"top\" style=\"width: 10.8599%;\"\u003e\n \u003cp\u003eATF, u-PA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"null; width: 12.5000%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.369609856262834%\" valign=\"top\" style=\"width: 12.1011%;\"\u003e\n \u003cp\u003eUrokinase-type plasminogen activator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936344969199178%\" valign=\"top\" style=\"width: 10.9634%;\"\u003e\n \u003cp\u003eSecreted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.016427104722794%\" valign=\"top\" style=\"width: 13.2388%;\"\u003e\n \u003cp\u003eExpressed in the prostate gland and prostate cancers.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.811088295687885%\" valign=\"top\" style=\"width: 13.4456%;\"\u003e\n \u003cp\u003eSpecifically cleaves the zymogen plasminogen to form the active enzyme plasmin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.921971252566735%\" valign=\"top\" style=\"width: 19.0307%;\"\u003e\n \u003cp\u003emap04064 \u0026nbsp; \u0026nbsp; NF-kappa B signaling pathway\u003c/p\u003e\n \u003cp\u003emap04610 \u0026nbsp; \u0026nbsp; Complement and coagulation cascades\u003c/p\u003e\n \u003cp\u003emap05202 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Transcriptional misregulation in cancer\u003c/p\u003e\n \u003cp\u003emap05205 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Proteoglycans in cancer\u003c/p\u003e\n \u003cp\u003emap05206 \u0026nbsp; \u0026nbsp; MicroRNAs in cancer\u003c/p\u003e\n \u003cp\u003emap05215 \u0026nbsp; \u0026nbsp; Prostate cancer\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\u003cp\u003eTable 5 \u0026nbsp; Cell cluster according to model phenotypes\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"973\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" rowspan=\"2\"\u003e\n \u003cp\u003elndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" rowspan=\"2\"\u003e\n \u003cp\u003ePopuations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" rowspan=\"2\"\u003e\n \u003cp\u003eModel phenotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"32\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"31\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocytes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3 T cells + B cells +NK cells + plasmablasts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"29\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3 T cells\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD8 T cells + CD4 T cells + \u0026gamma; T cells + MAIT/NKT cells\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD8 T cells\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3+CD66b-CD19-CD8+CD4-CD14-CD161-TCRgd-CD123-CD11c-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD8 na\u0026iuml;ve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD8 T cells + CD45RA+CCR7+CD27+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD8 central memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD8 T cells + CD45RA-CCR7+CD27+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD8 effector memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD8 T cells + CCR7-CD27+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD8 terminal effector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD8 T cells + CCR7-CD27-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD4 T cells\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD66b-CD3+ CD8-CD4+ CD14-TCRgd-CD11c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"29\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4 naive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4 T cells + CD45RA+CCR7+CD27+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4 central memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4 T cells + CD45RA-CCR7+CD27+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4 effector memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4 T cells + CD45RA-CCR7-CD27+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4 terminal effector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4 T cells + CD45RA-CCR7-CD27-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eTregs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4 T cells + CD25+CD127-CCR4+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eTh1-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4 T cells + CXCR3+CCR6-CXCR5-CCR4-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eTh2-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4 T cells + CXCR3-CCR6-CXCR5-CCR4+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eTh17-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4 T cells + CXCR3-CCR6+ CXCR5-CCR4+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gamma; T cells\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD66b-CD3+CD8dm-CD4-CD14-TCRgd dm,+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"29\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAIT/NKT cells\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD66b-CD3+CD4-CD14-CD161+TCRgd-CD28+CD16-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"29\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eB cells\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3-CD14-CD56-CD16 dim-CD19+CD20+HLA-DR dim,+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"29\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eB naive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eB cells + CD27-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eB memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eB cells + CD27+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePasmablasts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3-CD14-CD16-dim CD66b-CD20-CD19+CD56-CD38++CD27+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"29\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNK cells\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD14-CD3-CD123-CD66b-CD45RA+CD56 dim+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"29\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eNKearly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eNK cells + CD57-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eNK late\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eNK cells + CD57+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonocytes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3-CD19-CD56-CD66b-HLA-DR+CD11c+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"29\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eMonocytes classical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eMonocytes + CD14+CD38+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eMonocytes transitional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eMonocytes + CD14dm CD38 dm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003eMonocytes non-dassical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003eMonocytes + CD14-CD38-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDCs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003epDCs + mDCs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"29\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003epDCs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3-CD19-CD14-CD20-CD66b-HLA-DR dim+ cD11c-CD123+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003emDCs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3-CD19-CD14-CD20-HLA-DR dim+CD11cdim+CD123-CD16dim.-CD38dim+CD294-HLA-D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGranulocytes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophils + basophils + cosinophils + CD66b- neutrophils\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"29\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophils\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD66b dm+cD16+ HL4-DR-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasophils\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eHLA-DR-CD66b-CD123 dim,+ cD38+CD294+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eEosinophils\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD14-CD3-CD19-HLA-DR-CD294+ CD66b dim+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.558067831449126%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29393627954779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD66b- neutrophils\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.14799588900308%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3-CD19-CD66b-CD56-HLA-DR-CD123-CD45-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Papillary thyroid cancer, DPP4, Immune infiltration, T cell exhaustion","lastPublishedDoi":"10.21203/rs.3.rs-4421908/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4421908/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePapillary thyroid cancer (PTC) is one of the most prevalent endocrine malignancy with a rapidly increasing incidence worldwide, a special immune microenvironment of which is not well characterized. Thus, the aim of this study was to identify the key biomarkers that regulate immune cells for the development and recurrence of PTC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe expression of immune-associated differentially expressed genes (DEGs) in human PTC was examined by bioinformatics analysis of TCGA and GEO datasets. The CIBERSORT and TIMER tool was used to analyze the distribution of tumor[1]infiltrating immune cells in PTC. Furthermore, DEG expression and function for the infiltration of CD8+ T cells were explored using human PTC specimens.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In this study, we identified DPP4 as a key gene in PTC by differential expression analysis among four GEO datasets and TCGA dataset and validated its overexpression profile by data from the TCGA, HPA databases, WB and PCR analysis. DPP4 upregulation significantly correlated with advanced grades, stages, and poor progression-free survival.Based on TIMER and CIBERSORT analysis, DPP4 expression tightly correlated with the infiltration of diverse immune cell types, especially CD8+ T cell subtypes. Compared with benign thyroid tumor, the proportion of CD3+CD8+ T cells in peripheral blood of PTC patients was significantly decreased, while the CD3+CD8+DPP4+ T cells of PTC patients was increased. The relative expression of PD-L1 and CTLA-4 in the CD8+DPP4+ T cells of PTC patients was higher than that in the CD8+DPP4- T cells. In addition, CD8+DPP4+ T cells of PTC patients showed the lower expression of IFN-γ and increased expression of IL-13 than that in benign thyroid tumor. The relative expression of IFN-γ, TNF-α, IL-4, IL-5, and IL-13 in CD8+DPP4+ T cells were both lower than that in CD8+DPP4- T cells among PTC and benign thyroid tumor patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our work suggests that the immune-associated DEG DPP4 is upregulated in PTC tissues and is tightly correlated with clinical stages and outcomes and regulates immune infiltration, but in particular involves in CD8+ T cell evasion and exhaustion. These findings may offer a new prospect for targeting CD8+ T cell exhaustion therapies for the treatment of PTC.\u003c/p\u003e","manuscriptTitle":"DPP4 Promotes Papillary Thyroid Cancer Progression by Regulating the Infiltration and Exhaustion of CD8+ T cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-30 19:41:57","doi":"10.21203/rs.3.rs-4421908/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":"32321007-5534-43c6-9736-c550ad6f0264","owner":[],"postedDate":"May 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-29T20:14:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-30 19:41:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4421908","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4421908","identity":"rs-4421908","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}