A new immune-prognostic signature of 6 differentially expressed cytokine/cytokine receptor pathway-related genes in clear cell renal cell carcinoma

A new immune-prognostic signature of 6 differentially expressed cytokine/cytokine receptor pathway-related genes in clear cell renal cell carcinoma | 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 A new immune-prognostic signature of 6 differentially expressed cytokine/cytokine receptor pathway-related genes in clear cell renal cell carcinoma Yating Zhan, Yan Jin, Kai Zhu, Menglu Bao, Yeping Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4417033/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: To explore the hub genes related with prognostic pathway based on tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC), a prognostic risk signature was identified to evaluate ccRCC patients’ prognosis. Methods: The immune scores of ccRCC patients were calculated via “ESTIMATE” package. The hub genes of the key pathway were selected using univariate cox and Lasso regression analysis. Cluster analysis and risk signature construction were performed in accordance with the expression levels and lasso coefficient of the hub genes. Results: Cytokine/cytokine receptor intersection pathway was considered as a key prognostic pathway in ccRCC. 6 differentially expressed cytokine/cytokine receptor pathway-related genes (DECCRGs) (CCR10, CXCL5, IL20RB, INHBE, KDR and RELT) were subsequently selected. Results of the cluster analysis revealed that the overall survival (OS) of the patients in cluster1 was better. Then, a 6-DECCRG immune-prognostic risk signature was established and used to evaluate the OS of ccRCC patients. This risk signature exhibited a good prognostic prediction ability in TCGA training cohort, which was further confirmed in TCGA testing cohort, whole cohort, GSE22541 cohort and a local cohort. Notably, the cluster groups and risk scores had a close connection to immune infiltration levels, respectively. CCR10, one of 6 DECCRGs, was further validated in renal cancer cells. Interestingly, reduced CCR10resulted in the inhibition of proliferation and migration in renal cancer cells. Conclusion: Collectively, a novel 6-DECCRG immune-prognostic risk signature contributes to the accurate prediction of ccRCC prognosis. Clear Cell Renal Cell Carcinoma Tumor Microenvironment Cluster Analysis Risk Signature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Clear cell renal cell carcinoma (ccRCC), a major type of the renal cell carcinoma (RCC), is accounting for 60–85% of RCC patients ( 1 , 2 ). Currently, radiotherapy as well as chemotherapy is not effective in most ccRCC cases, and surgery is the optimal treatment ( 3 ). In spite of taking early surgical treatment, metastasis and recurrence will still occur at a relatively high risk ( 4 ). Therefore, it is urgent to identify prognostic biomarkers for improving ccRCC therapy. Tumor microenvironment (TME) is crucial for the initiation and maintenance of tumorigenesis ( 5 ). TME represents the environment where cancer cells derive and growth, consisting of various cell types (cancer cells, stromal cells, immune cells etc.) and extracellular elements (chemokine, cytokines etc.) ( 6 , 7 ). Increasing studies have suggested that TME could serve as a key prognostic factor in the primary tumors ( 8 ). Unfortunately, the significance of TME in ccRCC prognosis is still unclear. Whether the effective indicators of TME could be novel prognostic biomarkers in ccRCC is largely unknown. Evaluating the immune and stromal elements of ccRCC environment may offer novel insights in tumor biology and contribute to the establishment of credible prognostic signatures. In our research, the immune and stromal scores of ccRCC patients were assessed. The top 10 signaling pathways were selected via the Gene Set Enrichment Analysis (GSEA) between low- and high-immune score groups. Only 1 immune-related pathway was chosen for the next studies via GSVA and survival analysis, of which 78 common differentially expressed cytokine/cytokine receptor pathway-related genes (DECCRGs) were obtained. Finally, we generated a novel 6-DECCRG immune-prognostic risk signature of in ccRCC. Materials and methods Data collection The mRNA data as well as clinical information of ccRCC were obtained from the Cancer Genome Atlas (TCGA) database (Table 1 ). GSE53757 data, which includes 72 ccRCC tissues and their paired para-tumor tissues, was sourced from the Gene Expression Omnibus (GEO) database. DECCRGs were picked out from TCGA and GSE53757 with adjust p 1. Table 1 The clinical characteristics and associated cohorts of 530 KIRC patients. Clinical parameters Variable Whole set(n = 530) Training set(n = 371) Testing set(n = 159) Status Alive 357 257 100 Death 173 114 59 Age 65 182 126 56 Gender Female 186 129 57 Male 344 242 102 Grade G1 14 11 3 G2 227 162 65 G3 206 146 60 G4 75 48 27 Unknown 8 4 4 Stage Stage I 265 190 75 Stage II 57 41 16 Stage III 123 84 39 Stage IV 82 54 28 Unknown 3 2 1 T T1 271 192 79 T2 69 47 22 T3 179 122 57 T4 11 10 1 M M0 420 298 122 M1 78 50 28 Unknown 32 23 9 N N0 239 172 67 N1 16 9 7 Unknown 275 190 85 Fig. Legends ESTIMATE algorithm and Differential Expression Analysis The immune, stromal and ESTIMATE scores of ccRCC patients were calculated via “ESTIMATE” package. Based on the median value of immune score, the levels of differentially expressed immune-related genes (DEIRGs) between the low- and high-score groups were analyzed via “limma” package ( 9 ). The DEIRGs were screened according to the conditions (|log 2 FC| > 1 and adjusted p < 0.05). Functional enrichment analysis The DEIRGs were included into Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. GSEA was performed to find immune-related key pathways. The GSVA value of each signaling pathway from GSEA was calculated by “GSVA” package ( 10 ). Cytokine/cytokine receptor interaction pathway was confirmed to be related with ccRCC patients’ overall survival (OS). Next, 78 common DECCRGs between TCGA and GSE53757 were determined for the cluster analysis and risk signature construction. Cluster analysis The unsupervised hierarchical cluster analysis for the 6 DECCRGs (consensus clustering matrix for k = 2) was performed using “ConsensusClusterPlus” package ( 11 ). Subsequently, all the ccRCC patients were divided into 2 clusters (cluster1/2). Construction and evaluation of risk signature A 6-DECCRG immune-prognostic risk signature was constructed with the results of Lasso regression analysis in the TCGA training cohort. The formula was shown as Risk score = ∑Coef gene * Expression of gene. According to the median risk scores, the whole patients were assigned into the low- and high-risk groups. Additionally, the specificity and accuracy of the 6-DECCRG immune-prognostic risk signature were estimated via Kaplan-Meier curve and Receiver Operating Characteristic (ROC) curve using “survival” and “survivalROC” packages. To validate the signature internally and externally, the testing cohort (n = 159), whole cohort (n = 530), GSE22541 cohort (n = 24) and First Affiliated Hospital of Wenzhou Medical University cohort (FAHWMU cohort, n = 50) were utilized to estimate the predictive capability and adaptability of the 6-DECCRG immune-prognostic risk signature for ccRCC. Quantitative real-time PCR (qRT-PCR) From the FAHWMU, 50 kidney tissue samples from ccRCC patients were acquired. The Ethics Committee of the FAHWMU approved the application of these samples. Written informed consents were signed by participants. The mRNA expressions were examined via qRT-PCR. TRIzol was used to obtain total RNA. Then, cDNA was acquired from the reverse transcription of mRNA by TOROIVD qRT-PCR Master Mix. GAPDH served as an internal reference control. Real-time PCR was applied by SYBR Green master mix in the 7500 rapid quantitative PCR system (Applied Biosystems, USA). The relative expression levels of mRNAs were performed via the 2 −ΔCt method. Cell culture and transfection ACHN, a human ccRCC cell line, was obtained from ATCC. ACHN cells were cultured in DMEM medium with 10% fetal bovine serum (FBS) at 37°C and 5% CO 2 . The culture of ACHN cell was done at a destiny of 8 × 10 3 cells/well in a 6 well palate. When the cell density was close to 50%, transfection of cells was followed with lipo2000 packaged si-NC, si-CCR10#1 and si-CCR10#2 for 6 h. The medium was changed and replaced with fresh one. After successful transfection, cells were harvested to carry out following exploration. Cell Counting Kit-8 (CCK8) assay Cells were inoculated into a 96-well plate at a density of 5 × 10 3 per well. Then, cells were treated with 10 µl CCK8 solution. The absorbance of each well was determined. Cell migration assay Cell migration assay was performed to evaluate the migration ability of cell. Firstly, transwell filter membrane chambers were used for the culturation of 3 × 10 4 ACHN cells. Lower transwell chambers were contained in fresh medium with 20%FBS. After the culturation for nearly 36 h, the transwell chambers was fixed and stained to measure the migrated cell number. Colony formation assay An assessment of the ability to form colonies was conducted by means of a colony formation assay. Briefly, the transfected cells were inoculated in a 6-well plate with 200 cells per well. After the culturation for nearly 2 weeks, the colony could be observed. Then, the cell was fixed and stained to measure the colony number. Statistical Analysis All statistical calculations and plotting were done using R software. The Kaplan-Meier estimator was applied to construct survival charts. The "pROC" R package was used to measure the area under curve (AUC) value. The Wilcoxon test was employed to evaluate the distinctions between the two groups. It was deemed significant at p < 0.05. Results Immune scores are associated with OS and tumor stage As depicted in Fig. 1 , this study was represented in the form of a flow diagram. Increasing studies have demonstrated that TME could serve as a key prognostic factor in the primary tumors ( 8 ). Therefore, we assessed the value of TME in ccRCC prognosis. The immune, stromal and ESTIMATE scores in ccRCC patients were evaluated via ESTIMATE algorithm. All ccRCC patients were divided into the “high” and “low” groups according to the median of their immune, stromal and ESTIMATE scores. Based on the median of their immune, stromal and ESTIMATE scores, all ccRCC patients were categorized into high and low groups. As shown in the Kaplan-Meier curves, patients with low-immune score had a longer OS than patients with high-immune score (p = 0.033, Fig. 2 a). There was no significant survival difference in stromal and ESTIMATE scores (Fig. 2 b, c). Moreover, the relations between immune score and clinicopathologic characteristics indicated that immune score was highly associated with gender, grade, stage, T, M except age and N ( Fig. S1 ). These results suggest that immune scores are associated with OS and tumor stage. Functional enrichment analysis of DEIRGs Based on the median value of immune scores, differential expression analysis was performed to obtain the matrix of DEIRGs. The heatmap of the top 30 up- and down-regulatory DEIRGs was displayed in Fig. 2 d. To further examine the potential functions of DEIRGs, GO, KEGG and GSEA functional analysis were performed. It was found that immune cells-related biological process and molecular function were associated with ccRCC progression (Fig. 2 e). In KEGG enrichment analysis, DEIRGs were shown to be enriched in several immune-related pathways, specifically such as cytokine/cytokine receptor interaction pathway with the smallest p-value (Fig. 2 f). Then, the top 10 GSEA pathways were shown in Fig. 2 g, including antigen processing and presentation, cell adhesion molecules, chemokine signaling pathway, cytokine/cytokine receptor interaction, hematopoietic cell lineage, leishmania infection, natural killer cell mediated cytotoxicity, T cell receptor signaling pathway, Toll like receptor signaling pathway and Viral myocarditis. Cytokine/cytokine receptor interaction pathway is an immune-related prognostic pathway To further evaluate the enrichment degree of the top 10 GSEA pathways, the GSVA value of each pathway was calculated. The correlation among the top 10 GSEA pathways with GSVA values were shown in Fig. S2 . In addition, all GSEA pathways were positively correlated with immune score, suggesting that these GSEA pathways may be immune-related pathways ( Fig. S3 ). Next, survival analysis showed better prognosis in the patients with low GSVA in the cytokine/cytokine receptor interaction pathway (Fig. 3 d). No significant OS difference was found in other pathways (Fig. 3 ). Our results suggest that cytokine/cytokine receptor interaction pathway is a key immune-related prognostic pathway. Identification of 78 DECCRGs To further investigate the prognostic value of DECCRGs, we extracted 78 common DECCRGs between TCGA and GSE53757 (Fig. 4 a). Employing univariate cox regression analysis, 24 prognostic DECCRGs were identified in the training cohort for next studies. As indicated by the forest plot, 20 DECCRGs were considered as dangerous factors and 4 DECCRGs were considered as protect factors (Fig. 4 b). Then, the expressions of 24 prognostic DECCRGs were displayed in the heatmap (Fig. 4 c). Moreover, the potential protein interaction networks among 24 prognostic DECCRGs were analyzed by the STRING database (Fig. 4 d). The correlations among these prognostic DECCRGs were also displayed in Fig. 4 e. Cluster analysis with 6 hub DECCRGs Lasso regression analysis was used to decrease the overfitted genes in the TCGA training cohort and 6 hub DECCRGs (CCR10, CXCL5, IL20RB, INHBE, KDR and RELT) were selected (Fig. 5 a, b). Then, 530 patients were clustered into cluster1 (n = 370) and cluster2 (n = 160) (Fig. 5 c). A better OS was found in patients in cluster1 in comparison with cluster2 (p < 0.001, Fig. 5 d). As shown in Fig. 5 e, the expressions of IL20RB and INHBE were higher in cluster2. Additionally, there was a significant difference in the most clinicopathological features, such as gender, grade, stage, T, M and N, between cluster1 and cluster2. Subsequently, the relationships between 22 immune cell types and the clusters were analyzed. Higher infiltration levels of B native cells, monocytes, macrophages M1, dendritic cells resting and mast cells resting were found in cluster1 in comparison with cluster2. By contrast, cluster2 was characterized by a higher infiltration of plasma cells, regulatory T cells (Tregs) and macrophages M0 (Fig. 5 f). Meanwhile, the correlation between tumor microenvironment and cluster subgroups showed that cluster2 was correlated with higher immune, stromal and ESTIMATE scores (Fig. 5 g). Construction and validation of the 6-DECCRG immune-prognostic risk signature In accordance with the lasso coefficients of 6 hub DECCRGs, a risk score formula was established to evaluate ccRCC prognosis: Risk score = (0.0808 * CCR10) + (0.0598 * CXCL5) + (0.0166 * IL20RB) + (0.0043 * INHBE) + (− 0.0930 * KDR) + (0.4824* RELT). Subsequently, we calculated the risk scores of ccRCC patients in the training cohort. Patients were classified into low- and high-risk groups with training median risk score according to the cutoff value. The distribution of risk score, survival status and heatmap of the 6-DECCRG immune-prognostic risk signature in the TCGA training cohort were shown in Fig. 6 a. Notably, patients with the low-risk had the longer OS (p < 0.0001, Fig. 6 b). As indicated by the ROC curves, the AUC values for this risk signature were 0.791, 0.729 and 0.715 for 1-, 2- and 3-year, separately (Fig. 6 c). Additionally, we further explore the predictive value of risk signature in the TCGA testing cohort (n = 159), whole TCGA cohort (n = 530), GSE22541 cohort (n = 24) and FAHWMU cohort (n = 50). It was found that patients in high-risk group showed a markedly worse prognosis in all cohorts (Fig. 6 d, f, h, j). The AUC values predicting the 1-year OS in TCGA testing cohort, whole TCGA cohort, GSE22541 cohort and FAHWMU cohort were 0.714, 0.762, 0.758 and 0.677, respectively (Fig. 6 e, g, i, k). All these data suggest that this risk signature exhibits a good applicability to predict ccRCC prognosis. Construction of nomogram To explore the underlying prognostic value of this risk signature, univariate and multivariate Cox regression analysis were performed with risk score and clinicopathologic characteristics. Both risk score and age were considered as independent prognostic factors ( Fig. S4a, b ). Then, a nomogram was established with age and risk score (Fig. 7 a). The calibration curves of the nomogram exhibited a good consistency in ccRCC prognosis (Fig. 7 b-d). Subsequently, the relationships between risk score and clinicopathologic characteristics were further analyzed. No correlation was found between age and risk score (Fig. 7 e). In comparison with the female, there was a higher risk score in the male (Fig. 7 f). A higher risk score was observed in patients with higher grade, stage and immune score (Fig. 7 g-i). Notably, ccRCC patients in cluster2, associated with poor prognosis, had a higher risk score compared with cluster1 (Fig. 7 j). The stratified survival analysis revealed that patients with low risk score exhibited a better prognosis in all the clinical subgroups ( Fig. S4c-f ). 6-DECCRG immune-prognostic risk signature is correlated with immune cell infiltration Whether the risk signature is associated with the infiltration levels of 22 immune cell types was further investigated. A negative association was observed between risk score and fractions of macrophages M1 as well as mast cells resting (Fig. 8 a, b). The risk score had a positive relation with the fractions of macrophages M0, plasma cells, CD4 T memory activated cells and Tregs (Fig. 8 c-f). Moreover, higher levels of PD-1, LAG3, and B7-H3 were found in patients with high-risk. In addition, patients in the high-risk group presented higher CD8 + T cell depletion ( Fig. 8 g). Overall, these results suggest that this risk signature is correlated with ccRCC immune microenvironment. Roles of CCR10 in ccRCC The immune-prognostic risk biomarkers in our 6-DECCRG signature are including CCR10, CXCL5, IL20RB, INHBE, KDR, and RELT. Besides for CCR10, the functions of CXCL5, IL20RB, INHBE, KDR, and RELT have been engaged in the development and prognosis of RCC. Therefore, we further explored the roles of CCR10 in RCC. Firstly, CCR10 was suppressed by CCR10 knockdown in ACHN cells (Fig. 9 A). Furthermore, loss of CCR10 reduced the proliferation of ACHN cells (Fig. 9 B). Interestingly, cell migration was inhibited by loss of CCR10 (Fig. 9 C). Likewise, analysis of colony formation showed that less colonies were formed in cells with CCR10 knockdown compared with the control (Fig. 9 D). Taken together, CCR10 may serve as a oncogene in ccRCC progression. Discussion This study aimed to explore the relationship between TME and ccRCC prognosis, and identify the potential biomarkers for ccRCC prognosis. Cytokine/cytokine receptor pathway was identified as a key immune-related prognostic pathway in ccRCC progression. Next, an immune-prognostic risk signature was generated with 6 hub DECCRGs (CCR10, CXCL5, IL20RB, INHBE, KDR and RELT). Results of cluster analysis confirmed that 6 hub DECCRGs participated in ccRCC prognosis. Our risk signature contributes to better prediction of prognosis in ccRCC patients and provides potential therapeutic targets for ccRCC. Cytokines, important mediators of the innate and adaptive immune systems, are generated by various kinds of cells such as lymphocytes, macrophages, NK cells ( 12 ). Cytokines are interactive with cytokine receptors to trigger molecular events and regulation gene transcription ( 13 ). Disorder of cytokines as well as cytokine receptors have been reported in multiple tumors. Zeng et al. found that down-regulation of VEGFA induces the suppression of migration and invasion in ccRCC ( 14 ). CXCR1/CXCR2 inhibitors have been found to stimulate tumor apoptosis and restrain proangiogenic factors in ccRCC ( 15 ). Unfortunately, the comprehensive understanding of cytokine/cytokine receptor pathway in ccRCC prognosis still remains largely unknown. Notably, the pathway of cytokine/cytokine receptor interaction was identified to be related with prognosis in ccRCC patients in this study. The 6-DECCRG immune-prognostic risk signature exhibited a good prediction for ccRCC prognosis. Overall, DECCRGs are potential predictors in ccRCC prognosis. CCR10, CXCL5, IL20RB, INHBE, KDR and RELT were included in the 6-DECCRG immune-prognostic risk signature. Lin et al. found that CCR10 promotes breast cancer progression via regulation of ERK1/2 and MMP-7 ( 16 ). Dysregulation of CXCL5 has been found in ccRCC ( 17 ). Interestingly, patients with increased IL20RB had a worse OS in papillary renal cell carcinoma ( 18 ). INHBA has been reported to induce a reduction in VCAN and accelerate the development of colon cancer cells ( 19 ). It is known that KDR level is related to the disorder of focal adhesion in ccRCC ( 20 ). RELT has been found as an oncogene in esophageal squamous cell carcinoma through stimulating the NF-κB pathway ( 21 ). Moreover, Hua et al. constructed an immune-related signature in ccRCC, which also included CXCL5 and KDR ( 22 ). However, there is little known about the biological roles of these DECCRGs in ccRCC. Therefore, the biological mechanisms of these DECCRGs in the progression of ccRCC should be further explored in the future. Numerous studies have indicated that immune infiltration is associated with ccRCC progression ( 23 , 24 ). Moreover, cytokines or cytokine receptors have been demonstrated to influence the immune infiltration in ccRCC ( 25 ). For instance, infiltrating CD4 + T cells can regulate RCC proliferation by regulating TGFβ1/YBX1/HIF2α signals ( 26 ). Tregs have been found to secrete immunosuppressive cytokines to promote tumor progression in TME ( 27 ). Santagata et al. reported that CXCR4 is related with the suppressive activity of Tregs in renal cancer ( 28 ). Tumor-associated macrophages, an important immune composition in the TME, are related with the promotion or inhibition of tumor ( 29 , 30 ). Previously, Ikemoto et al. showed that macrophage activation induces RCC cells proliferation by generating macrophage-derived cytokines like IL-6, TNF-α and IL-1β ( 31 ). In this study, the risk scores were highly associated with the infiltration levels of the macrophages M1, macrophages M0, CD4 + T memory activated cells and Tregs. Our data suggest that this 6-DECCRG immune-prognostic risk signature is associated with immune cell infiltration. Recently, many signatures have been built based on TME in ccRCC. For example, a 6-gene prognostic signature characterized by the TME showed a good predictive ability of OS in ccRCC patients ( 32 ). Luo et al. comprehensively analyzed the TME and identified a 12-gene signature in ccRCC ( 33 ). Compared with the previous prognosis signatures in ccRCC, there are several advantages in our study. Firstly, cytokine/cytokine receptor pathway is identified as a key immune-related prognostic pathway in the progression of ccRCC. Secondly, a novel 6-DECCRG immune-prognostic risk signature has been established in ccRCC, which exhibits good survival prediction in ccRCC. Finally, the biology roles of CCR10 are validated via cell experiments. Conclusion This study reveals a 6-DECCRG immune-prognostic risk signature for ccRCC, which contributes to accurate prognosis prediction for ccRCC patients. Our results also provide new prospects for promising biomarkers and therapeutic targets in ccRCC. Declarations Acknowledgements The investigators are grateful to all participants for their cooperation in the study. Moreover, we sincerely appreciate the TCGA and GEO databases that are freely available to us. Ethics approval The studies involving human participants were approved by the Human Research Ethics Committee in the First Affiliated Hospital of Wenzhou Medical University. Written informed consent of all participants were obtained. All methods of this study were carried out in accordance with the Declaration of Helsinki. Availability of data and materials This study used publicly available datasets from the TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/). From the First Affiliated Hospital of Wenzhou Medical University cohort, the mRNA expressions of 6 differentially expressed cytokine/cytokine receptor pathway-related genes were obtained via quantitative real-time PCR experiment. Consent for publication Not applicable. Competing interests The authors declare that the research was conducted without any commercial or financial conflict of interest. Funding This work was supported by the special fund for clinical research of Wu Jieping Medical Foundation (No. 320.6750.2021-04-45). Authors’ contributions ZY designed the project and analyzed the data. YJ, YZ and KZ participated in writing the manuscript. DH participated in formal analysis and language editing. All authors contributed to the article and approved the submitted version. References Cancer Genome Atlas Research N. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 2013;499(7456):43-9. Hakimi AA, Pham CG, Hsieh JJ. A clear picture of renal cell carcinoma. Nat Genet. 2013;45(8):849-50. Makhov P, Joshi S, Ghatalia P, Kutikov A, Uzzo RG, Kolenko VM. Resistance to Systemic Therapies in Clear Cell Renal Cell Carcinoma: Mechanisms and Management Strategies. Mol Cancer Ther. 2018;17(7):1355-64. 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In Vivo Imaging of Pro- and Antitumoral Cellular Components of the Tumor Microenvironment. J Nucl Med. 2018;59(2):183-8. Santagata S, Napolitano M, D'Alterio C, Desicato S, Maro SD, Marinelli L, et al. Targeting CXCR4 reverts the suppressive activity of T-regulatory cells in renal cancer. Oncotarget. 2017;8(44):77110-20. Kitamura T, Qian BZ, Pollard JW. Immune cell promotion of metastasis. Nat Rev Immunol. 2015;15(2):73-86. Sica A, Larghi P, Mancino A, Rubino L, Porta C, Totaro MG, et al. Macrophage polarization in tumour progression. Semin Cancer Biol. 2008;18(5):349-55. Ikemoto S, Yoshida N, Narita K, Wada S, Kishimoto T, Sugimura K, et al. Role of tumor-associated macrophages in renal cell carcinoma. Oncol Rep. 2003;10(6):1843-9. Zhang L, Li J, Zhang M, Wang L, Yang T, Shao Q, et al. Identification of a Six-Gene Prognostic Signature Characterized by Tumor Microenvironment Immune Profiles in Clear Cell Renal Cell Carcinoma. Front Genet. 2021;12:722421. Luo J, Xie Y, Zheng Y, Wang C, Qi F, Hu J, et al. Comprehensive insights on pivotal prognostic signature involved in clear cell renal cell carcinoma microenvironment using the ESTIMATE algorithm. Cancer Med. 2020;9(12):4310-23. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.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-4417033","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322330678,"identity":"0c1a758e-fbdc-4927-ab4b-1f2fe4d64b91","order_by":0,"name":"Yating Zhan","email":"","orcid":"","institution":"The First Affiliated Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Yating","middleName":"","lastName":"Zhan","suffix":""},{"id":322330679,"identity":"0d632f56-bbd5-4f15-89c8-78b201990940","order_by":1,"name":"Yan Jin","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Jin","suffix":""},{"id":322330680,"identity":"ea51840c-ddfd-4cd8-bf54-6cfa03e13ab1","order_by":2,"name":"Kai Zhu","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhu","suffix":""},{"id":322330681,"identity":"0cb8a773-8d9d-4458-96a5-2a7a8de30b6e","order_by":3,"name":"Menglu Bao","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Menglu","middleName":"","lastName":"Bao","suffix":""},{"id":322330682,"identity":"3b902c2d-c481-4284-a024-61c563a44dd7","order_by":4,"name":"Yeping Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYBACPmYwZcPDz95ApBY2iJY0GcmeA8RqgVCHbQxuOBCrhZ3H7DHPn/M8DDcYGD98zCHKYTzmxjw8t3kYZzcwS87cRpwWM+kcids8zDIH2Jh5iddicI6HTSKBJC0JB3h4SNDCVm7850AyjwTPwWbi/MLPf3jbwxl/7Oztjzcf/PCRGC0MDBxmUAZjA1HqgYD9GbEqR8EoGAWjYKQCAHDEKieH7U2VAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yeping","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-05-14 06:55:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4417033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4417033/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60444745,"identity":"9ee968b2-4609-49eb-b882-bdca03e24506","added_by":"auto","created_at":"2024-07-16 20:37:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1038732,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow diagram of this study.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4417033/v1/d2cf44c4f289b9de75741c99.jpg"},{"id":60443497,"identity":"cd93db7b-7325-468e-8b9a-c8e94ca3076d","added_by":"auto","created_at":"2024-07-16 20:21:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2130756,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and enrichment analysis of DEIRGs. \u003cstrong\u003e(a-c)\u003c/strong\u003e Kaplan-Meier curves based on the median of immune, stromal and ESTIMATE scores. \u003cstrong\u003e(d)\u003c/strong\u003e Heatmap of top 30 DEIRGs between low- and high-immune score groups in TCGA. \u003cstrong\u003e(e)\u003c/strong\u003e GO enrichment analysis of DEIRGs. \u003cstrong\u003e(f)\u003c/strong\u003eKEGG enrichment analysis of DEIRGs. \u003cstrong\u003e(g)\u003c/strong\u003e GSEA enrichment analysis between low- and high-immune score groups to find the top 10 signaling pathways.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4417033/v1/9dbfed3906c497fc3f9d758f.jpg"},{"id":60443493,"identity":"c1b9a001-83d8-4e2b-a0bd-67bfbd929bdb","added_by":"auto","created_at":"2024-07-16 20:21:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":517981,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of the top 10 GSEA pathways. Antigen processing and presentation \u003cstrong\u003e(a)\u003c/strong\u003e, cell adhesion molecules \u003cstrong\u003e(b)\u003c/strong\u003e, chemokine signaling pathway \u003cstrong\u003e(c)\u003c/strong\u003e, cytokine/cytokine receptor interaction \u003cstrong\u003e(d)\u003c/strong\u003e, hematopoietic cell lineage \u003cstrong\u003e(e)\u003c/strong\u003e, leishmania infection \u003cstrong\u003e(f)\u003c/strong\u003e, natural killer cell mediated cytotoxicity \u003cstrong\u003e(g)\u003c/strong\u003e, T cell receptor signaling pathway \u003cstrong\u003e(h)\u003c/strong\u003e, Toll like receptor signaling pathway \u003cstrong\u003e(i)\u003c/strong\u003e and viral myocarditis \u003cstrong\u003e(j)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4417033/v1/098cd9daa5973d5a5cca4b19.jpg"},{"id":60444341,"identity":"b2dfe800-9514-49ec-93c9-7dd8baedac70","added_by":"auto","created_at":"2024-07-16 20:29:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1627511,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of 78 DECCRGs \u003cstrong\u003e(a)\u003c/strong\u003e Venn for common DECCRGs between TCGA and GSE53757. \u003cstrong\u003e(b)\u003c/strong\u003e Result of the univariate cox regression analysis of DECCRGs in the TCGA training cohort. \u003cstrong\u003e(c)\u003c/strong\u003eHeatmap of 24 prognostic DECCRGs. \u003cstrong\u003e(d)\u003c/strong\u003e Protein-protein interaction (PPI) network of prognostic DECCRGs. \u003cstrong\u003e(e)\u003c/strong\u003e Correlation network of prognostic DECCRGs, among which different colors represented different degrees of the correlation coefficient.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4417033/v1/6a84cbdb278eabb4eb6a75ff.jpg"},{"id":60443494,"identity":"3160fc48-52f4-4ad3-bc14-a478198a150d","added_by":"auto","created_at":"2024-07-16 20:21:36","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1202198,"visible":true,"origin":"","legend":"\u003cp\u003eClustering analyses of 6 hub DECCRGs. \u003cstrong\u003e(a, b)\u003c/strong\u003e Lasso regression was performed on the 24 prognostic DECCRGs and 6 hub DECCRGs were screened out. \u003cstrong\u003e(c)\u003c/strong\u003e Unsupervised hierarchical cluster analysis. \u003cstrong\u003e(d)\u003c/strong\u003e Kaplan-Meier curves of OS. \u003cstrong\u003e(e)\u003c/strong\u003e Heatmap and clinicopathologic characteristics.\u003cstrong\u003e (f)\u003c/strong\u003e Violin plot of the infiltration levels of 22 immune cell types. \u003cstrong\u003e(g)\u003c/strong\u003e Boxplot of the immune score, stromal score and ESTIMATE score.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4417033/v1/350e9dce540e9fac2f1125b4.jpg"},{"id":60443500,"identity":"36d2c30a-9ded-4d58-9277-847082cb9f3d","added_by":"auto","created_at":"2024-07-16 20:21:36","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1092587,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and validation of the 6-DECCRG immune-prognostic risk signature. \u003cstrong\u003e(a)\u003c/strong\u003e Distribution of risk score and survival time in the TCGA training cohort. \u003cstrong\u003e(b)\u003c/strong\u003e Kaplan-Meier curve in the TCGA training cohort. \u003cstrong\u003e(c)\u003c/strong\u003e ROC curve in the TCGA training cohort. \u003cstrong\u003e(d)\u003c/strong\u003eKaplan-Meier curve in the TCGA testing cohort. \u003cstrong\u003e(e)\u003c/strong\u003e ROC curve in the TCGA testing cohort. \u003cstrong\u003e(f)\u003c/strong\u003e Kaplan-Meier curve in the whole TCGA cohort. \u003cstrong\u003e(g)\u003c/strong\u003eROC curve in the whole TCGA cohort. \u003cstrong\u003e(h)\u003c/strong\u003e Kaplan-Meier curve in the GSE22541 cohort. \u003cstrong\u003e(i)\u003c/strong\u003e ROC curve in the GSE22541 cohort. \u003cstrong\u003e(j)\u003c/strong\u003eKaplan-Meier curve in the FAHWMU cohort. \u003cstrong\u003e(k)\u003c/strong\u003e ROC curve in the FAHWMU cohort.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4417033/v1/3f73a5b6d673feb68c7d841b.jpg"},{"id":60444343,"identity":"f03dbf45-61e7-480e-8291-f3fd4ee2c23c","added_by":"auto","created_at":"2024-07-16 20:29:36","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1173171,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of nomogram. \u003cstrong\u003e(a)\u003c/strong\u003e A nomogram. \u003cstrong\u003e(b-d)\u003c/strong\u003e The calibration plot of nomogram within 1-, 2- and 3-year, respectively. \u003cstrong\u003e(e-j)\u003c/strong\u003e The correlation between risk scores and clinicopathologic characteristics including age, gender, grade, stage, immune score and cluster.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4417033/v1/cf2e0f1666c5c1a73d1732f4.jpg"},{"id":60443502,"identity":"587b4f0e-f47a-487a-bff0-649ead23b5ba","added_by":"auto","created_at":"2024-07-16 20:21:37","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1084662,"visible":true,"origin":"","legend":"\u003cp\u003e6-DECCRG immune-prognostic risk signature is correlated with immune cell infiltration. \u003cstrong\u003e(a-f)\u003c/strong\u003e Scatter plot of association between risk sore and immune cells: Macrophages M1, Mast cells resting, Macrophages M0, Plasma cells, T cells CD4 memory activated and Tregs. \u003cstrong\u003e(g)\u003c/strong\u003eImmune checkpoint analysis.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4417033/v1/488da18975ac557d23d7e215.jpg"},{"id":60443501,"identity":"6d33a57f-4630-4a1c-85a9-c529b246f277","added_by":"auto","created_at":"2024-07-16 20:21:36","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2600472,"visible":true,"origin":"","legend":"\u003cp\u003eThe roles of CCR10 in ccRCC. \u003cstrong\u003e(a) \u003c/strong\u003eKnockdown of CCR10 in ACHN. \u003cstrong\u003e(b) \u003c/strong\u003eCCK8 assay.\u003cstrong\u003e (c) \u003c/strong\u003eTranswell assay. \u003cstrong\u003e(d)\u003c/strong\u003eColony assay.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4417033/v1/a608ba7e07b5db9e710f2ed6.jpg"},{"id":76176111,"identity":"92d8ed32-eb8f-4250-8046-b6a2bb7a162b","added_by":"auto","created_at":"2025-02-13 06:32:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13579665,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4417033/v1/910189a2-f1d5-4e1f-8cb3-784584c947e0.pdf"},{"id":60443499,"identity":"36231354-63ae-48aa-8a3f-ef870ac73af2","added_by":"auto","created_at":"2024-07-16 20:21:36","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":974543,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4417033/v1/19102782bc9c8403cb1618ba.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A new immune-prognostic signature of 6 differentially expressed cytokine/cytokine receptor pathway-related genes in clear cell renal cell carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClear cell renal cell carcinoma (ccRCC), a major type of the renal cell carcinoma (RCC), is accounting for 60\u0026ndash;85% of RCC patients (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Currently, radiotherapy as well as chemotherapy is not effective in most ccRCC cases, and surgery is the optimal treatment (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In spite of taking early surgical treatment, metastasis and recurrence will still occur at a relatively high risk (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Therefore, it is urgent to identify prognostic biomarkers for improving ccRCC therapy.\u003c/p\u003e \u003cp\u003eTumor microenvironment (TME) is crucial for the initiation and maintenance of tumorigenesis (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). TME represents the environment where cancer cells derive and growth, consisting of various cell types (cancer cells, stromal cells, immune cells etc.) and extracellular elements (chemokine, cytokines etc.) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Increasing studies have suggested that TME could serve as a key prognostic factor in the primary tumors (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Unfortunately, the significance of TME in ccRCC prognosis is still unclear. Whether the effective indicators of TME could be novel prognostic biomarkers in ccRCC is largely unknown. Evaluating the immune and stromal elements of ccRCC environment may offer novel insights in tumor biology and contribute to the establishment of credible prognostic signatures.\u003c/p\u003e \u003cp\u003eIn our research, the immune and stromal scores of ccRCC patients were assessed. The top 10 signaling pathways were selected via the Gene Set Enrichment Analysis (GSEA) between low- and high-immune score groups. Only 1 immune-related pathway was chosen for the next studies via GSVA and survival analysis, of which 78 common differentially expressed cytokine/cytokine receptor pathway-related genes (DECCRGs) were obtained. Finally, we generated a novel 6-DECCRG immune-prognostic risk signature of in ccRCC.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe mRNA data as well as clinical information of ccRCC were obtained from the Cancer Genome Atlas (TCGA) database (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). GSE53757 data, which includes 72 ccRCC tissues and their paired para-tumor tissues, was sourced from the Gene Expression Omnibus (GEO) database. DECCRGs were picked out from TCGA and GSE53757 with adjust p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e FC| \u0026gt;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe clinical characteristics and associated cohorts of 530 KIRC patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhole set(n\u0026thinsp;=\u0026thinsp;530)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTraining set(n\u0026thinsp;=\u0026thinsp;371)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTesting set(n\u0026thinsp;=\u0026thinsp;159)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eFig. Legends\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eESTIMATE algorithm and Differential Expression Analysis\u003c/h2\u003e \u003cp\u003eThe immune, stromal and ESTIMATE scores of ccRCC patients were calculated via \u0026ldquo;ESTIMATE\u0026rdquo; package. Based on the median value of immune score, the levels of differentially expressed immune-related genes (DEIRGs) between the low- and high-score groups were analyzed via \u0026ldquo;limma\u0026rdquo; package (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The DEIRGs were screened according to the conditions (|log\u003csub\u003e2\u003c/sub\u003e FC| \u0026gt; 1 and adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eThe DEIRGs were included into Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. GSEA was performed to find immune-related key pathways. The GSVA value of each signaling pathway from GSEA was calculated by \u0026ldquo;GSVA\u0026rdquo; package (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Cytokine/cytokine receptor interaction pathway was confirmed to be related with ccRCC patients\u0026rsquo; overall survival (OS). Next, 78 common DECCRGs between TCGA and GSE53757 were determined for the cluster analysis and risk signature construction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCluster analysis\u003c/h2\u003e \u003cp\u003eThe unsupervised hierarchical cluster analysis for the 6 DECCRGs (consensus clustering matrix for k\u0026thinsp;=\u0026thinsp;2) was performed using \u0026ldquo;ConsensusClusterPlus\u0026rdquo; package (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Subsequently, all the ccRCC patients were divided into 2 clusters (cluster1/2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and evaluation of risk signature\u003c/h2\u003e \u003cp\u003eA 6-DECCRG immune-prognostic risk signature was constructed with the results of Lasso regression analysis in the TCGA training cohort. The formula was shown as Risk score = \u0026sum;Coef gene * Expression of gene. According to the median risk scores, the whole patients were assigned into the low- and high-risk groups. Additionally, the specificity and accuracy of the 6-DECCRG immune-prognostic risk signature were estimated via Kaplan-Meier curve and Receiver Operating Characteristic (ROC) curve using \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survivalROC\u0026rdquo; packages. To validate the signature internally and externally, the testing cohort (n\u0026thinsp;=\u0026thinsp;159), whole cohort (n\u0026thinsp;=\u0026thinsp;530), GSE22541 cohort (n\u0026thinsp;=\u0026thinsp;24) and First Affiliated Hospital of Wenzhou Medical University cohort (FAHWMU cohort, n\u0026thinsp;=\u0026thinsp;50) were utilized to estimate the predictive capability and adaptability of the 6-DECCRG immune-prognostic risk signature for ccRCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative real-time PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eFrom the FAHWMU, 50 kidney tissue samples from ccRCC patients were acquired. The Ethics Committee of the FAHWMU approved the application of these samples. Written informed consents were signed by participants. The mRNA expressions were examined via qRT-PCR. TRIzol was used to obtain total RNA. Then, cDNA was acquired from the reverse transcription of mRNA by TOROIVD qRT-PCR Master Mix. GAPDH served as an internal reference control. Real-time PCR was applied by SYBR Green master mix in the 7500 rapid quantitative PCR system (Applied Biosystems, USA). The relative expression levels of mRNAs were performed via the 2\u003csup\u003e\u0026minus;ΔCt\u003c/sup\u003e method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and transfection\u003c/h2\u003e \u003cp\u003eACHN, a human ccRCC cell line, was obtained from ATCC. ACHN cells were cultured in DMEM medium with 10% fetal bovine serum (FBS) at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e. The culture of ACHN cell was done at a destiny of 8 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells/well in a 6 well palate. When the cell density was close to 50%, transfection of cells was followed with lipo2000 packaged si-NC, si-CCR10#1 and si-CCR10#2 for 6 h. The medium was changed and replaced with fresh one. After successful transfection, cells were harvested to carry out following exploration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell Counting Kit-8 (CCK8) assay\u003c/h2\u003e \u003cp\u003eCells were inoculated into a 96-well plate at a density of 5 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e per well. Then, cells were treated with 10 \u0026micro;l CCK8 solution. The absorbance of each well was determined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell migration assay\u003c/h2\u003e \u003cp\u003eCell migration assay was performed to evaluate the migration ability of cell. Firstly, transwell filter membrane chambers were used for the culturation of 3 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e ACHN cells. Lower transwell chambers were contained in fresh medium with 20%FBS. After the culturation for nearly 36 h, the transwell chambers was fixed and stained to measure the migrated cell number.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eColony formation assay\u003c/h2\u003e \u003cp\u003eAn assessment of the ability to form colonies was conducted by means of a colony formation assay. Briefly, the transfected cells were inoculated in a 6-well plate with 200 cells per well. After the culturation for nearly 2 weeks, the colony could be observed. Then, the cell was fixed and stained to measure the colony number.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical calculations and plotting were done using R software. The Kaplan-Meier estimator was applied to construct survival charts. The \"pROC\" R package was used to measure the area under curve (AUC) value. The Wilcoxon test was employed to evaluate the distinctions between the two groups. It was deemed significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImmune scores are associated with OS and tumor stage\u003c/h2\u003e \u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, this study was represented in the form of a flow diagram. Increasing studies have demonstrated that TME could serve as a key prognostic factor in the primary tumors (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Therefore, we assessed the value of TME in ccRCC prognosis. The immune, stromal and ESTIMATE scores in ccRCC patients were evaluated via ESTIMATE algorithm. All ccRCC patients were divided into the \u0026ldquo;high\u0026rdquo; and \u0026ldquo;low\u0026rdquo; groups according to the median of their immune, stromal and ESTIMATE scores. Based on the median of their immune, stromal and ESTIMATE scores, all ccRCC patients were categorized into high and low groups. As shown in the Kaplan-Meier curves, patients with low-immune score had a longer OS than patients with high-immune score (p\u0026thinsp;=\u0026thinsp;0.033, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). There was no significant survival difference in stromal and ESTIMATE scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c). Moreover, the relations between immune score and clinicopathologic characteristics indicated that immune score was highly associated with gender, grade, stage, T, M except age and N (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). These results suggest that immune scores are associated with OS and tumor stage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of DEIRGs\u003c/h2\u003e \u003cp\u003eBased on the median value of immune scores, differential expression analysis was performed to obtain the matrix of DEIRGs. The heatmap of the top 30 up- and down-regulatory DEIRGs was displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed. To further examine the potential functions of DEIRGs, GO, KEGG and GSEA functional analysis were performed. It was found that immune cells-related biological process and molecular function were associated with ccRCC progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). In KEGG enrichment analysis, DEIRGs were shown to be enriched in several immune-related pathways, specifically such as cytokine/cytokine receptor interaction pathway with the smallest p-value (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Then, the top 10 GSEA pathways were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg, including antigen processing and presentation, cell adhesion molecules, chemokine signaling pathway, cytokine/cytokine receptor interaction, hematopoietic cell lineage, leishmania infection, natural killer cell mediated cytotoxicity, T cell receptor signaling pathway, Toll like receptor signaling pathway and Viral myocarditis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCytokine/cytokine receptor interaction pathway is an immune-related prognostic pathway\u003c/h2\u003e \u003cp\u003eTo further evaluate the enrichment degree of the top 10 GSEA pathways, the GSVA value of each pathway was calculated. The correlation among the top 10 GSEA pathways with GSVA values were shown in \u003cb\u003eFig. S2\u003c/b\u003e. In addition, all GSEA pathways were positively correlated with immune score, suggesting that these GSEA pathways may be immune-related pathways (\u003cb\u003eFig. S3\u003c/b\u003e). Next, survival analysis showed better prognosis in the patients with low GSVA in the cytokine/cytokine receptor interaction pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). No significant OS difference was found in other pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Our results suggest that cytokine/cytokine receptor interaction pathway is a key immune-related prognostic pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of 78 DECCRGs\u003c/h2\u003e \u003cp\u003eTo further investigate the prognostic value of DECCRGs, we extracted 78 common DECCRGs between TCGA and GSE53757 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Employing univariate cox regression analysis, 24 prognostic DECCRGs were identified in the training cohort for next studies. As indicated by the forest plot, 20 DECCRGs were considered as dangerous factors and 4 DECCRGs were considered as protect factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Then, the expressions of 24 prognostic DECCRGs were displayed in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Moreover, the potential protein interaction networks among 24 prognostic DECCRGs were analyzed by the STRING database (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). The correlations among these prognostic DECCRGs were also displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCluster analysis with 6 hub DECCRGs\u003c/h2\u003e \u003cp\u003eLasso regression analysis was used to decrease the overfitted genes in the TCGA training cohort and 6 hub DECCRGs (CCR10, CXCL5, IL20RB, INHBE, KDR and RELT) were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b). Then, 530 patients were clustered into cluster1 (n\u0026thinsp;=\u0026thinsp;370) and cluster2 (n\u0026thinsp;=\u0026thinsp;160) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). A better OS was found in patients in cluster1 in comparison with cluster2 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee, the expressions of IL20RB and INHBE were higher in cluster2. Additionally, there was a significant difference in the most clinicopathological features, such as gender, grade, stage, T, M and N, between cluster1 and cluster2. Subsequently, the relationships between 22 immune cell types and the clusters were analyzed. Higher infiltration levels of B native cells, monocytes, macrophages M1, dendritic cells resting and mast cells resting were found in cluster1 in comparison with cluster2. By contrast, cluster2 was characterized by a higher infiltration of plasma cells, regulatory T cells (Tregs) and macrophages M0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). Meanwhile, the correlation between tumor microenvironment and cluster subgroups showed that cluster2 was correlated with higher immune, stromal and ESTIMATE scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of the 6-DECCRG immune-prognostic risk signature\u003c/h2\u003e \u003cp\u003eIn accordance with the lasso coefficients of 6 hub DECCRGs, a risk score formula was established to evaluate ccRCC prognosis: Risk score = (0.0808 * CCR10) + (0.0598 * CXCL5) + (0.0166 * IL20RB) + (0.0043 * INHBE) + (\u0026minus;\u0026thinsp;0.0930 * KDR) + (0.4824* RELT).\u003c/p\u003e \u003cp\u003eSubsequently, we calculated the risk scores of ccRCC patients in the training cohort. Patients were classified into low- and high-risk groups with training median risk score according to the cutoff value. The distribution of risk score, survival status and heatmap of the 6-DECCRG immune-prognostic risk signature in the TCGA training cohort were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea. Notably, patients with the low-risk had the longer OS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). As indicated by the ROC curves, the AUC values for this risk signature were 0.791, 0.729 and 0.715 for 1-, 2- and 3-year, separately (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Additionally, we further explore the predictive value of risk signature in the TCGA testing cohort (n\u0026thinsp;=\u0026thinsp;159), whole TCGA cohort (n\u0026thinsp;=\u0026thinsp;530), GSE22541 cohort (n\u0026thinsp;=\u0026thinsp;24) and FAHWMU cohort (n\u0026thinsp;=\u0026thinsp;50). It was found that patients in high-risk group showed a markedly worse prognosis in all cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed, f, h, j). The AUC values predicting the 1-year OS in TCGA testing cohort, whole TCGA cohort, GSE22541 cohort and FAHWMU cohort were 0.714, 0.762, 0.758 and 0.677, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee, g, i, k). All these data suggest that this risk signature exhibits a good applicability to predict ccRCC prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of nomogram\u003c/h2\u003e \u003cp\u003eTo explore the underlying prognostic value of this risk signature, univariate and multivariate Cox regression analysis were performed with risk score and clinicopathologic characteristics. Both risk score and age were considered as independent prognostic factors (\u003cb\u003eFig. S4a, b\u003c/b\u003e). Then, a nomogram was established with age and risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). The calibration curves of the nomogram exhibited a good consistency in ccRCC prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb-d). Subsequently, the relationships between risk score and clinicopathologic characteristics were further analyzed. No correlation was found between age and risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee). In comparison with the female, there was a higher risk score in the male (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef). A higher risk score was observed in patients with higher grade, stage and immune score (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eg-i). Notably, ccRCC patients in cluster2, associated with poor prognosis, had a higher risk score compared with cluster1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ej). The stratified survival analysis revealed that patients with low risk score exhibited a better prognosis in all the clinical subgroups (\u003cb\u003eFig. S4c-f\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e6-DECCRG immune-prognostic risk signature is correlated with immune cell infiltration\u003c/h2\u003e \u003cp\u003eWhether the risk signature is associated with the infiltration levels of 22 immune cell types was further investigated. A negative association was observed between risk score and fractions of macrophages M1 as well as mast cells resting (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea, b). The risk score had a positive relation with the fractions of macrophages M0, plasma cells, CD4 T memory activated cells and Tregs (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec-f). Moreover, higher levels of PD-1, LAG3, and B7-H3 were found in patients with high-risk. In addition, patients in the high-risk group presented higher CD8\u0026thinsp;+\u0026thinsp;T cell depletion \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg). Overall, these results suggest that this risk signature is correlated with ccRCC immune microenvironment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eRoles of CCR10 in ccRCC\u003c/h2\u003e \u003cp\u003eThe immune-prognostic risk biomarkers in our 6-DECCRG signature are including CCR10, CXCL5, IL20RB, INHBE, KDR, and RELT. Besides for CCR10, the functions of CXCL5, IL20RB, INHBE, KDR, and RELT have been engaged in the development and prognosis of RCC. Therefore, we further explored the roles of CCR10 in RCC. Firstly, CCR10 was suppressed by CCR10 knockdown in ACHN cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Furthermore, loss of CCR10 reduced the proliferation of ACHN cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Interestingly, cell migration was inhibited by loss of CCR10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Likewise, analysis of colony formation showed that less colonies were formed in cells with CCR10 knockdown compared with the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). Taken together, CCR10 may serve as a oncogene in ccRCC progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to explore the relationship between TME and ccRCC prognosis, and identify the potential biomarkers for ccRCC prognosis. Cytokine/cytokine receptor pathway was identified as a key immune-related prognostic pathway in ccRCC progression. Next, an immune-prognostic risk signature was generated with 6 hub DECCRGs (CCR10, CXCL5, IL20RB, INHBE, KDR and RELT). Results of cluster analysis confirmed that 6 hub DECCRGs participated in ccRCC prognosis. Our risk signature contributes to better prediction of prognosis in ccRCC patients and provides potential therapeutic targets for ccRCC.\u003c/p\u003e \u003cp\u003eCytokines, important mediators of the innate and adaptive immune systems, are generated by various kinds of cells such as lymphocytes, macrophages, NK cells (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Cytokines are interactive with cytokine receptors to trigger molecular events and regulation gene transcription (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Disorder of cytokines as well as cytokine receptors have been reported in multiple tumors. Zeng et al. found that down-regulation of VEGFA induces the suppression of migration and invasion in ccRCC (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). CXCR1/CXCR2 inhibitors have been found to stimulate tumor apoptosis and restrain proangiogenic factors in ccRCC (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Unfortunately, the comprehensive understanding of cytokine/cytokine receptor pathway in ccRCC prognosis still remains largely unknown. Notably, the pathway of cytokine/cytokine receptor interaction was identified to be related with prognosis in ccRCC patients in this study. The 6-DECCRG immune-prognostic risk signature exhibited a good prediction for ccRCC prognosis. Overall, DECCRGs are potential predictors in ccRCC prognosis.\u003c/p\u003e \u003cp\u003eCCR10, CXCL5, IL20RB, INHBE, KDR and RELT were included in the 6-DECCRG immune-prognostic risk signature. Lin et al. found that CCR10 promotes breast cancer progression via regulation of ERK1/2 and MMP-7 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Dysregulation of CXCL5 has been found in ccRCC (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Interestingly, patients with increased IL20RB had a worse OS in papillary renal cell carcinoma (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). INHBA has been reported to induce a reduction in VCAN and accelerate the development of colon cancer cells (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). It is known that KDR level is related to the disorder of focal adhesion in ccRCC (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). RELT has been found as an oncogene in esophageal squamous cell carcinoma through stimulating the NF-κB pathway (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Moreover, Hua et al. constructed an immune-related signature in ccRCC, which also included CXCL5 and KDR (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). However, there is little known about the biological roles of these DECCRGs in ccRCC. Therefore, the biological mechanisms of these DECCRGs in the progression of ccRCC should be further explored in the future.\u003c/p\u003e \u003cp\u003eNumerous studies have indicated that immune infiltration is associated with ccRCC progression (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Moreover, cytokines or cytokine receptors have been demonstrated to influence the immune infiltration in ccRCC (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). For instance, infiltrating CD4\u0026thinsp;+\u0026thinsp;T cells can regulate RCC proliferation by regulating TGFβ1/YBX1/HIF2α signals (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Tregs have been found to secrete immunosuppressive cytokines to promote tumor progression in TME (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Santagata et al. reported that CXCR4 is related with the suppressive activity of Tregs in renal cancer (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Tumor-associated macrophages, an important immune composition in the TME, are related with the promotion or inhibition of tumor (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Previously, Ikemoto et al. showed that macrophage activation induces RCC cells proliferation by generating macrophage-derived cytokines like IL-6, TNF-α and IL-1β (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In this study, the risk scores were highly associated with the infiltration levels of the macrophages M1, macrophages M0, CD4\u0026thinsp;+\u0026thinsp;T memory activated cells and Tregs. Our data suggest that this 6-DECCRG immune-prognostic risk signature is associated with immune cell infiltration.\u003c/p\u003e \u003cp\u003eRecently, many signatures have been built based on TME in ccRCC. For example, a 6-gene prognostic signature characterized by the TME showed a good predictive ability of OS in ccRCC patients (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Luo et al. comprehensively analyzed the TME and identified a 12-gene signature in ccRCC (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Compared with the previous prognosis signatures in ccRCC, there are several advantages in our study. Firstly, cytokine/cytokine receptor pathway is identified as a key immune-related prognostic pathway in the progression of ccRCC. Secondly, a novel 6-DECCRG immune-prognostic risk signature has been established in ccRCC, which exhibits good survival prediction in ccRCC. Finally, the biology roles of CCR10 are validated via cell experiments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals a 6-DECCRG immune-prognostic risk signature for ccRCC, which contributes to accurate prognosis prediction for ccRCC patients. Our results also provide new prospects for promising biomarkers and therapeutic targets in ccRCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe investigators are grateful to all participants for their cooperation in the study. Moreover, we sincerely appreciate the TCGA and GEO databases that are freely available to us.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were approved by the Human Research Ethics Committee in the First Affiliated Hospital of Wenzhou Medical University. Written informed consent of all participants were obtained. All methods of this study were carried out in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available datasets from the TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/). From the First Affiliated Hospital of Wenzhou Medical University cohort, the mRNA expressions of 6 differentially expressed cytokine/cytokine receptor pathway-related genes were obtained via quantitative real-time PCR experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted without any commercial or financial conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the special fund for clinical research of Wu Jieping Medical Foundation (No. 320.6750.2021-04-45).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZY designed the project and analyzed the data. YJ, YZ and KZ participated in writing the manuscript. DH participated in formal analysis and language editing. All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCancer Genome Atlas Research N. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 2013;499(7456):43-9.\u003c/li\u003e\n\u003cli\u003eHakimi AA, Pham CG, Hsieh JJ. A clear picture of renal cell carcinoma. Nat Genet. 2013;45(8):849-50.\u003c/li\u003e\n\u003cli\u003eMakhov P, Joshi S, Ghatalia P, Kutikov A, Uzzo RG, Kolenko VM. Resistance to Systemic Therapies in Clear Cell Renal Cell Carcinoma: Mechanisms and Management Strategies. Mol Cancer Ther. 2018;17(7):1355-64.\u003c/li\u003e\n\u003cli\u003eHsieh JJ, Purdue MP, Signoretti S, Swanton C, Albiges L, Schmidinger M, et al. Renal cell carcinoma. Nat Rev Dis Primers. 2017;3:17009.\u003c/li\u003e\n\u003cli\u003eArneth B. 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Comprehensive insights on pivotal prognostic signature involved in clear cell renal cell carcinoma microenvironment using the ESTIMATE algorithm. Cancer Med. 2020;9(12):4310-23.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Clear Cell Renal Cell Carcinoma, Tumor Microenvironment, Cluster Analysis, Risk Signature","lastPublishedDoi":"10.21203/rs.3.rs-4417033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4417033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e To explore the hub genes related with prognostic pathway based on tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC), a prognostic risk signature was identified to evaluate ccRCC patients’ prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The immune scores of ccRCC patients were calculated via “ESTIMATE” package. The hub genes of the key pathway were selected using univariate cox and Lasso regression analysis. Cluster analysis and risk signature construction were performed in accordance with the expression levels and lasso coefficient of the hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eCytokine/cytokine receptor intersection pathway was considered as a key prognostic pathway in ccRCC. 6 differentially expressed cytokine/cytokine receptor pathway-related genes (DECCRGs) (CCR10, CXCL5, IL20RB, INHBE, KDR and RELT) were subsequently selected. Results of the cluster analysis revealed that the overall survival (OS) of the patients in cluster1 was better. Then, a 6-DECCRG immune-prognostic risk signature was established and used to evaluate the OS of ccRCC patients. This risk signature exhibited a good prognostic prediction ability in TCGA training cohort, which was further confirmed in TCGA testing cohort, whole cohort, GSE22541 cohort and a local cohort. Notably, the cluster groups and risk scores had a close connection to immune infiltration levels, respectively. CCR10, one of 6 DECCRGs, was further validated in renal cancer cells. Interestingly, reduced CCR10resulted in the inhibition of proliferation and migration in renal cancer cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Collectively, a novel 6-DECCRG immune-prognostic risk signature contributes to the accurate prediction of ccRCC prognosis.\u003c/p\u003e","manuscriptTitle":"A new immune-prognostic signature of 6 differentially expressed cytokine/cytokine receptor pathway-related genes in clear cell renal cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-16 20:21:31","doi":"10.21203/rs.3.rs-4417033/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":"3ed75f5f-04f3-40d6-8cac-a3e3cdb4ab86","owner":[],"postedDate":"July 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-13T06:24:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-16 20:21:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4417033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4417033","identity":"rs-4417033","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}