The Construction and Exploration of the CeRNA Network and Patterns of Tumor-infiltrating Immune Cells in Kidney Renal Clear Cell Carcinoma

The Construction and Exploration of the CeRNA Network and Patterns of Tumor-infiltrating Immune Cells in Kidney Renal Clear 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Construction and Exploration of the CeRNA Network and Patterns of Tumor-infiltrating Immune Cells in Kidney Renal Clear Cell Carcinoma Weiwei Jia, Pengjia Li, Mingxia Ma, Xiaochen Niu, Lina Bai, Haihu Hao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-113464/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract KIRC is the malignant tumor with the highest incidence and poor prognosis in renal cell carcinoma. We want to explore the possible mechanisms of KIRC and effective prognostic-related biomarkers. The sequencing information of 3 types of RNA (mRNA, lncRNA and miRNA) in 539 cases of KIRC tissues and 72 cases of normal tissues is obtained from the TCGA database. Methods such as univariate Cox regression analysis, lasso regression screening, and multivariate Cox regression analysis were used to construct a prognostic model based on the CeRNA network. There are 3074 mRNAs, 359 lncRNAs and 132 miRNAs differentially expressed that have been identified through differential analysis. A complete mRNA-miRNA-lncRNA (SIX1-hsa-miR-200b-3p-MALAT1) network was obtained based on the CeRNA network. The CIBERSORT algorithm was used to analyze the degree of infiltration of 22 kinds of immune cells from each sample of KIRC. Construction of a prognostic model based on tumor-infiltrating immune cells, 2 immune cells (Mast cells resting, T cells follicular helper) were identified by constructing a prognostic model. There was a negative correlation between lncRNA MALAT1 and Mast cells resting (R= -0.27, P < 0.001); while there was a positive correlation between lncRNA MALAT1 and T cells follicular helper (R = 0.23, P < 0.001). General Biochemistry Immunology KIRC CeRNA network Tumor-infiltrating immune cells prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Renal cell carcinoma (RCC) is one of the top ten malignant tumors in the world [ 1 ]. Compared with other subtypes of RCC, kidney renal clear cell carcinoma (KIRC) is the most common histopathological type and has a poor prognosis [ 2 ]. Many patients have progressed to the advanced stage when they are diagnosed, and the 5-year survival rate (FSR) has also decreased significantly, because the early clinical symptoms and signs of KIRC are relatively insidious and non-specific [ 3 ]. In view of the limited diagnostic strategies and poor prognosis of KIRC, we urgently need to search for biological markers which are helpful for diagnosis, targeted therapy and prognosis prediction. Messenger RNA (mRNA) is a protein coding gene, which participates in the formation and development of KIRC. Recently, non coding RNA had also drawn remarkable attention, particularly in terms of microRNA (miRNA) and long non coding RNA (lncRNA), which is indispensable for the regulation of cellular processes [ 4 ]. The competitive endogenous RNA (CeRNA) hypothesis is that lncRNA could compete for the miRNA recognizing elements (MRE) to influences the mRNA regulation, and which is put forward by Salmena et al. Recent studies have shown that CeRNA networks control biological behaviors, which via regulating oncogenes and tumor suppressor genes, such as tumor invasion and metastasis [ 5 ]. However, there is no definition of CeRNA network to predict the prognosis of KIRC. Therefore, in this study, we constructed a CeRNA network about KIRC to identify the potential interactions involved in the differentially expressed lncRNA, miRNA and mRNA in KIRC, which to determine the molecular targets of prognostic evaluation. Surgical treatment is the only effective treatment method for localised KIRC, but patients with metastatic must be treated with systemic therapy [ 6 ]. However, due to the long-term use of systemic therapy and the development of tumors, the occurrence of tumor immune escape has been promoted [ 2 ]. Some immunosuppressive factors may be hijacked by cancer cells to avoid the attack of immune system [ 7 ]. It is essential to maintain the host's immune homeostasis and eliminate cancer cells by balancing immune stimulus factors and inhibitory factors [ 8 ]. Recent studies have shown that immunotherapy has become an important method to improve the survival rate of patients with KIRC [ 9 ]. Recently, a number of evidences show that miRNA can regulate cancer immune surveillance and promote immune escape [ 10 ]. It is not clear how miRNA (as the regulatory hub of CeRNA) changes the CeRNA network and how CeRNA changes the immune cells of KIRC, and the correlation between the two changes. We use bioinformatics analysis to construct a complete gene interaction CeRNA network. By screening the differentially expressed lncRNA, miRNA and mRNA in KIRC, in order to explore the key genes involved in the development of KIRC. We conducted co-expression analysis based on the risk genes related to prognosis and the differentially expressed immune cells in KIRC to further explore the potential molecular mechanism of KIRC. Not only that, we further explored the relationship between CeRNA and immune infiltration, and finally obtained the connection between CeRNA network and immune cells. Our researchs provide new insights into the occurrence and development of KIRC, as well as survival prediction biomarkers and potential therapeutic targets. Methods 1.1 Data download and differential gene expression analysis There are 539 KIRC and 72 normal tissues of 3 types of RNA information (including mRNA, lncRNA and miRNA) and the clinical data were downloaded by using the GDC RNA toolkit package of the R language (R 4.0.2) in the cancer genome atlas database[43]. Firstly, We used the log(fold change，FC)≥1 or ≤1 and false discovery rate (FDR) adjusted P value<0.05 as criteria to identify significant differential genes. The heat maps were drawn by the "pheatmap" R package, and the volcano maps were drawn by the "ggplot2" R package. Then, hypergeometric testing and correlation analysis were used to select significantly different and related genes, and use Cytoscape v.3.7.2 software to visualize the CeRNA network. It does not require relevant ethical review and approval, due to the data used in this study came from the TCGA database. 1.2 Construction of prognosis model related to CeRNA network The expression levels of nodes in CeRNA network were combined with clinical data by using perl (perl 5.26.3) language. The network nodes related to survival prognosis were selected by Kaplan-Meier survival analysis using the R "survival" package according to the filter condition: P <0.05. The construction of the prognostic model based on the CeRNA network was completed by single factor Cox regression analysis, lasso regression and multivariate Cox regression analysis. The formula of prognosis model: risk score= expression of risk genes 1 *coef 1 +expression of risk genes 2 *coef 2 +...＋expression of risk genes n * coef n . The survival curve of the model and the time-dependent receiver operating characteristic curve (ROC) were drawn, in order to estimate the accuracy and resolution of the model. 1.3 CIBERSORT algorithm Evaluation The CIBERSORT algorithm was used to estimate the degree of infiltration of 22 immune cell types in each KIRC sample, with a CIBERSORT output of p <0.05, indicated that the Example of these cells were significant, which was worthy of further study. We evaluated the difference between immune infiltrating cells in tumor tissue and normal tissue with Wilcoxon rank-sum test. Finally, histogram and differential heat map of tumor-infiltrating immune cells were drawn. 1.4 Construction of immune infiltration model The degree of tumor-infiltrating immune cells in each tumor sample was combined with clinical information to evaluate the prognostic value of immune cell infiltration in tumor tissues, and then the survival curve was drawn by Kaplan-Meier survival analysis using the survival package (P<0.05). The prognosis model based on the degree of tumor-infiltrating immune cells in each sample was combined with clinical information to evaluate the prognostic value of immune cell infiltration in tumor tissues, and then the survival curve was drawn by Kaplan-Meier survival analysis using the survival package was constructed by single factor Cox regression analysis, lasso regression and multivariate Cox regression analysis. The survival curve of the model and the ROC curve of the model were drawn. 1.5 Co-expression analysis In order to evaluate the co-expression of the key tumor-infiltrating immune cells and key nodes in the CeRNA network, and to explore the correlation between tumor-infiltrating immune cells and CeRNA network expression, the "Corrplot" R package was used to perform co-expression analysis on the key nodes based on the CeRNA network and the key cells based on the tumor-infiltrating immune cells model, and output significant related pairs. According to the correlation analysis, the corrected P value was 0.001. Results 2.1 The Flow chart of the Analytical Process The flow chart of the analysis process is shown in Figure 1. 2.2 Identification of Differentially expressed Genes The RNA-sequencing information was downloaded in 539 tumor tissues and 72 normal tissue samples from TCGA-KIRC database. There were 3074 differentially expressed mRNAs (1055 upregulated and 2019 downregulated, Figure 2A, D), and 359 differentially expressed lncRNAs (71 upregulated and 280 downregulated, Figure 2B, E) and 132 differentially expressed miRNAs (70 upregulated and 62 downregulated, Figure 2C, F). 2.3 Construction of CeRNA Network 20 mRNAs, 7 lncRNAs and 14 miRNAs with significant differences were obtained through the analysis of hypergeometric distribution test and correlation test (P<0.001) . In this study, we constructed a CeRNA network using these significantly different RNAs (Figure 3). 2.4 Construction of Risk Prognosis Model 9 risk genes were obtained through lasso regression analysis (Figure 4A, 4B) and multi-factor COX regression analysis (Figure 4E)，including 5 mRNAs (RELT, MYO9B, KCNN, SIX1, OTOGL), 1 lncRNA (MALAT1) and 3 miRNAs (hsa-miR-130b-3p, hsa-miR-200b-3p, hsa-miR-21-5p). And the risk score (RS) of the risk prognosis model according to this formula (RS=RELT*0.228-MYO9B*0.546+KCNN4*0.265+SIX1*0.135-OTOGL*0.142+MALAT1*0.223+hsa-miR-130b-3p*0.321-hsa-miR-200b-3p*0.091+hsa-miR-21-5p*0.254) was calculated. After matched the risk genes to the CeRNA network, we obtained a complete mRNA-miRNA-lncRNA (SIE1-hsa-miR-200b-3p-MALAT1) relationship chain. The survival curve of the model was drawn by the Kaplan-Meier method. The results showed that the survival rate of patients in the high-risk group was significantly lower than that of the low-risk group (Figure 4c). In order to estimate the accuracy of the model, we have further drawn the ROC curve (1-year AUC: 0.769; 3-year AUC: 0.750; 5-year AUC: 0,771, Figure 4D). 2.5 Evaluation of CIBERSORT algorithm and Construction of Immune Infiltration Model The proportions of 22 immune cells in different samples were provided by CIBERSORT algorithm analysis (Figures 5A, 5B). 2 tumor-infiltratings immune cells (Mast cells resting, T cells follicular helper) were identified by lasso regression analysis (Figure 6A, 6B) and multivariate Cox regression analysis (Figure 6E). The formula for the constructed prognosis model related to immune infiltration was: risk score=T cells follicular helper*10.132-Mast cells resting*10.401. The survival curve of the model was drawn by the Kaplan–Meier method. The results showed that the survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (Figure 6C). We further drawn the ROC curve to estimate the accuracy of the model (1-year AUC: 0.589; 3-year AUC: 0.633; 5-year AUC: 0,610, Figure 6D). 2.6 The Correlation Analysis of Risk Immune Cells and Clinical Firstly, the R language was used to draw a heat map of the differential expression of risk immune cells in the low-risk group and the high-risk group. The results showed that as the risk score increased, the cell content of T cells follicular helper gradually increased , while the cell content of Mast cells resting gradually decreased (Figure 7A). Then we also analyzed the proportions of two different subtypes of immune cells in different clinical staging using non-parameter test and Kaplan-Meier method. The results demonstrated that the expression quantitative of T cells follicular helper in stage IV patients was significantly higher than in stage I patients (Figure 7B), and the expression quantitative of Mast cells resting decreased significantly as the stage level increased (Figure 7c). 2.7 Co-expression Analysis The co-expression relationship graph was obtained by analyzing the correlation between the tumor-infiltrating immune cells model and the CeRNA network nodes by using Pearson correlation analysis (Figure 8A, B). There was a negative correlation between Mast cells resting and T cells follicular helper (R= -0.26, see Figure 8A, B), lncRNA MALAT1 and Mast cells resting was negative correlation. (R= -0.27, P<0.001, Figure 8C), however there was a positive association of lncRNA MALAT1 with T cells follicular helper (R=0.23, P<0.001, Figure 8D).. Discussion Renal cell carcinoma is one of the most deadly cancers in the urinary system, and its incidence is increasing year by year [ 11 ]. KIRC accounts for about 80% of all RCC histological subtypes and had the high mortality, invasiveness and metastasis rate [ 12 ]. Characteristic changes at the molecular level play a crucial role in the occurrence and metastasis of tumors, and had been considered as prognostic factors [ 13 ]. We found that the genes and tumor-infiltrating immune cells are differentially expressed between KIRC tumor tissue and normal tissue. In response to this finding, we constructed risk prediction models based on CeRNA network and tumor-infiltrating immune cells. Among them, we analyzed the correlation between CeRNA network and tumor-infiltrating immune cells, we inferred a potential mechanism of KIRC recurrence and that was lncRNA MALTA1 regulating Mast cells resting (MCs) and T cells follicular helper (TFH). Our findings could help clinical oncologists assess the prognosis and risk of recurrence of KIRC. CeRNA network is that lncRNA compete for the miRNA recognition elements (MRE) to influences the mRNA regulation, thus, we speculate that this type of analysis could uncover molecular interactions and gene regulatory networks that have been missed by proteomic and conventional genomic methods [ 5 ]. Studies shown that 3’UTRs from coding genes may act as endogenous decoys, which binds to the MRE recognition sequence of miRNA through the principle of complementary base pairing and participates in post-transcriptional regulation of oncogenes and tumor suppressors [ 14 ], and lncRNA can also competitively bind the MRE sequence of miRNA to regulate the expression level of mRNA in tumor cells [ 15 ]. More importantly, lncRNA modulated mitochondrial membrane potential and enhanced the release of cytochrome C, indicating that apoptosis of KIRC induced by lncRNA may belong to mitochondria-mediated apoptosis [ 16 ], and lncRNA can also participate in the proliferation of cancer cells by promoting the expression level of KIRC distant metastasis related genes[ 17 ]. In this study, we obtain 3074 differentially expressed mRNA (1055 upregulated and 2019 downregulated), 359 differentially expressed lncRNA (71 upregulated and 280 downregulated), and 132 differentially expressed miRNA(70 upregulated and 62 downregulated) through bioinformatics analysis the RNA sequencing (RNA-seq) data of KIRC. We have constructed a CeRNA network that can prepare the prognosis of KIRC through the analysis of hypergeometric distribution test and correlation test, which contains 20 mRNAs, 7 lncRNAs and 14 miRNAs. And then ,we use the single factor COX analysis, lasso regression and multivariate Cox regression analysis to obtain 9 genes (RELT, MYO9B, KCNN4, SIX1, OTOGL, MALAT1, HSA-Mir-130b-3P, HSA-Mir-200b-3p, HSA-Mir-21-5p) in CeRNA that were significantly correlated with the overall survival rate of KIRC. We constructed a prognostic model using these risk genes and got the AUC values (for 1-year survival rates: 0.794, 3-year survival rates: 0.762, and 5-year survival rates: 0.782). After matching the 9 risk genes and the CeRNA network, it was found that hsa-miR-200-3p (miRNA), SIX1 (mRNA) and MALAT1 (lncRNA) were significantly correlated (P = 4.30E-6). SIX1 is a key transcription factor involved in the occurrence and development of a lot of tumor and its biological activity is regulated by miRNA [ 18 , 19 ]. SIX1 may contribute to ovarian epithelial carcinogenesis by simultaneously increasing proliferation and decreasing apoptosis and imply that SIX1 may be an important target of ovarian cancer therapy response [ 19 ]. SIX1 can regulate the mitochondrial membrane potential by regulating the expression of the anti-apoptotic protein Bcl-2, and affect mitochondrial apoptosis via caspase-7, which suggests that SIX1 can be used as an effective target for the prognosis and treatment of gastric cancer [ 20 ]. However, there is few study had clearly shown the relationship between SIX1 and KIRC. Our results show that the decreased expression of miR-200 in KIRC may be involved in the formation and development of cancer by promoting the increased expression of SIX1. Many previous studies have shown that the miR-200 family members of KIRC were significantly downregulated compared with normal kidney tissue. It is speculated that miR-200b as tumor biomarkers in renal tumor biopsies is feasible [ 21 , 22 ]. Rola Saleeb et al. found that miR-200b was down-regulated in primary KIRC and further decreased in metastatic foci. More importantly, Kaplan-Meier survival curves indicate that miR-200b and miR-200c positive patients have significantly longer disease-free survival [ 23 ]. Therefore, the expression of miR-200 is negatively correlated with the progression of KIRC. In this study, we found that the HR value of miR-200 was less than 1 through single-factor COX, lasso regression and multi-factor COX analysis, indicating that miR-200 is a gene that protects renal clear cell carcinoma. This is consistent with the results of miR-200 family members mentioned in previous studies that inhibit the proliferation, migration, and invasion of cancer cells [ 24 ]. There is another important RNA in the significantly related network of hsa-miR-200-3p(miRNA)-SIX1(mRNA)-MALAT1(lncRNA), this is lncRNA MALAT1. Metastasis associated with lung adenocarcinoma transcript-1 (MALAT1) is one of the long non-coding RNA (lncRNA) associated with tumors which is composed of more than 8000 nucleotides and located on chromosome 11q13 [ 25 ]. MALAT1 was initially considered to be an effective prognostic factor for non-small cell lung cancer (NSCLC), tumors with high MALAT1 expression have a nearly five-fold increase in the risk of metastasis compared with tumors with low MALAT-1 expression [ 26 ]. Feng et al. proved that the direct binding between MALAT1 and miR-200a can promote the proliferation of lung cancer cells and lead to the development of gefitinib resistance [ 27 ]. In addition to NSCLC, MALAT1 is also present in other malignant tumors. In endometrial cancer, the expression of MALAT1 is negatively correlated with the expression of miR-200c, and its knockdown can promote the expression of miR-200c in cancer cells [ 28 ]. Studies have found that MALAT1 can also adjust the expression of miR-140 in prostate cancer cells to change the mRNA and protein expression levels of apoptosis inhibitor protein to promote the occurrence and development of tumors [ 29 ]. More importantly, some studies have shown that MALAT1 in KIRC promotes the growth of cancer cells by inhibiting cell apoptosis and accelerating the expression of specific proteins of epithelial-mesenchymal transition (EMT) [ 30 ]. In this study, we found that MALAT1 is a high-risk factor in KIRC through bioinformatics analysis, and its expression level is positively correlated with tumor formation and development, which is consistent with the role of MALAT1 in other tumors. Therefore, we speculate that hsa-miR-200-3p (miRNA) - SIX1 (mRNA) - MALAT1 (lncRNA) plays an important role in the development of renal clear cell carcinoma. However, how these genes affect the occurrence and development of KIRC is still unclear. In the past few decades, more and more evidences show that the tumor-cell phenotype is determined by the intrinsic activity of cancer cells and the interaction of cells (especially tumor-infiltrating immune cells, TIICs) in the tumor microenvironment [ 31 , 32 ]. Complex interactions between the immune cells in this microenvironment may promote cancer progression by inducing immune dysfunction in RCC patients [ 33 ]. Interestingly, lncRNA MALAT1 is an important inflammation regulator. In patients with systemic lupus erythematosus (SLE), lncRNA MALAT1 can be used as a CeRNA to interfere with the inhibitory effect of miRNA on the inflammatory factor IL-21, so the expression of MALAT1 in SLE patients is significantly increased [ 34 ]. During an asthma attack, MALAT1 activates airway inflammation and airway hyperresponsiveness of T cells by inducing the expression of miR-155 in T cells [ 35 , 36 ]. However, it is not clear whether lncRNA MALAT1 is involved in the process of immune invasion, immune cell killing and immune escape in the tumor microenvironment of RCC. In this study, we analyzed the correlation between gene expression and tumor-infiltrating immune cells to prove that MALAT1 and MCs, (R = -0.27, P = 8.95E-5), MALAT1 and TFH (R = 0.23, P = 9.85E-4) were significantly correlated. Therefore, Therefor, we speculate that MALAT1, the core molecule of the CeRNA network, participates in the occurrence of KIRC by regulating the content of tumor-infiltrating immune cells, such as TFH and MCs. We first analyzed by using the CIBERSORT algorithm and found that the expression levels of resting MCs and TFH between KIRC and normal tissues are significantly different. Compared with other algorithms, CIBERSORT is the best algorithm to distinguish between resting and IgE-activated MCs. Our results suggested that most of the MCs in KIRC are in resting state. Studies have shown that MCs activated by IgE can prevent the occurrence of cancer [ 37 ]. However, there are high concentrations of polyamines in malignant tumor cells [ 38 ]. Polyamines oxidized by polyamine oxidase can prevent the activation of MCs by IgE, thereby inhibiting the tumor suppressor effect of resting MCs [ 39 ]. The resting MCs in KIRC can promote the immune escape of tumor cells, which is beneficial to tumors growth [ 40 ]. Therefore, when the content of resting MC is higher, it may promote the occurrence of tumors. We observed that the expression of MALAT1 increased in KIRC tumor tissues, and the content of resting MCs decreased observably. There is a negative correlation between MALAT1 and MCs. Above all, we speculate that MALAT1 may affect the generation of KIRC by the content of resting MCs. TFH is an immune cell located in the peritumoral tertiary lymphoid structures (TLS), which belongs to a subset of CD4 + T cells [ 41 ]. TFH provides signals for the proliferation and activation of B cells by secretes a large number of chemokines in B cell [ 42 ]. Our results proved that the expression of lncRNA MALAT1 in the CeRNA network is positively correlated with the contents of TFH. The expression of MALAT1 in KIRC cells is increased, which suggested that the content of TFH in KIRC cells is higher than that in normal tissues. By using Multi-factor COX analysis, we found that the content of TFH in KIRC cells is ascended distinctly, which is consistent with our speculation. Therefore, we believed that TFH may be one of the maker cells of immune infiltration in KIRC. In summary, this study constructed a KIRC related CeRNA network through the TCGA database, and selected risk genes through single factor COX analysis, lasso regression and multivariate Cox regression analysis to predict the survival rate of patients. We obtained a significant correlation network of hsa-miR-200-3p(miRNA)-SIX1(mRNA)-MALAT1(lncRNA) through correlation analysis. In order to explore how the genes in this network affect the formation of KIRC, this study also constructed another risk model based on tumor-infiltrating immune cells, and screened out risky tumor-infiltrating immune cells related to the occurrence and development of KIRC. We speculated that hsa-miR-200-3p and MALAT1 affect the internal mechanism of KIRC by regulating the expression of SIX1, and the relationship between MALAT1 and immune infiltration, so as to promote the self-management of KIRC patients. Our research is a correlation study from multiple dimensions rather than a biological mechanism study. Based on the results of this research, we will verify our conclusions through biological experiments. In the future, we will combine more data and more experiments to explore the impact of KIRC molecular biology on CeRNA, as well as the relationship between tumor cells, TFH and resting MCs. Conclusion Based on the bioinformatics analysis of the CeRNA network and tumor-infiltrating immune cells, our research inferred that hsa-miR-200-3p, MALAT1 and SIX1 are involved in the internal mechanism of KIRC, and the relationship between MALAT1 and tumor-infiltrating immune cells (MCs, TFH). Abbreviations RCC Renal Cell Carcinoma KIRC Kidney Renal Clear Cell Carcinoma CeRNA Competing Endogenous RNA MCs Mast Cells resting TFH T cells Follicular Helper mRNA Messenger RNA lncRNA Long Non-Coding RNA miRNA MicroRNA MRE MiRNA Recognition Elements NSCLC Non-Small Cell Lung Cancer RNA-seq RNA Sequencing TIICs Especially Tumor-Infiltrating Immune Cells, SLE Systemic Lupus Erythematosus TLS Tertiary Lymphoid Structures FSR 5-year Survival Rate FC Fold Change FDR False Discovery Rate ROC Receiver Operating Characteristic Declarations Authors’ contributions WW Jia analyzed and explained the RNA-seq and clinical data of TCGA-KIRC, and was the main contributor to the manuscript. L Wang directed the writing of the manuscript. XH Wang directed the writing of the manuscript. The final manuscript read and approved by all authors. Funding This work was sponsored by the Fund for Shanxi “1331 Project” Key Subjects Construction. Ethics approval and consent to participate Not applicable. Consent for publication All authors reviewed and approved the final version for submission. Availability of data and material The data set used in this study is a publicly available data set. This data can be found here: TCGA (https://tcga-data.nci.nih.gov/tcga/). Competing interests The authors declare that they have no competing interests. Acknowledgements Not applicable References Siegel RL, Miller KD, Jemal A: Cancer statistics, 2018 . CA Cancer J Clin 2018, 68 (1):7-30. Xiao GF, Yan X, Chen Z, Zhang RJ, Liu TZ, Hu WL: Identification of a Novel Immune-Related Prognostic Biomarker and Small-Molecule Drugs in Clear Cell Renal Cell Carcinoma (ccRCC) by a Merged Microarray-Acquired Dataset and TCGA Database . Front Genet 2020, 11 :810. Gupta K, Miller JD, Li JZ, Russell MW, Charbonneau C: Epidemiologic and socioeconomic burden of metastatic renal cell carcinoma (mRCC): a literature review . Cancer Treat Rev 2008, 34 (3):193-205. 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Arch Bronconeumol 2019, 55 (12):627-633. Chen G, Zhang M, Liang Z, Chen S, Chen F, Zhu J, Zhao M, He J, Hua W, Duan P: Association of polymorphisms in MALAT1 with the risk of endometrial cancer in Southern Chinese women . J Clin Lab Anal 2020, 34 (4):e23146. Hao T, Wang Z, Yang J, Zhang Y, Shang Y, Sun J: MALAT1 knockdown inhibits prostate cancer progression by regulating miR-140/BIRC6 axis . Biomed Pharmacother 2020, 123 :109666. Ye Y, Zhang F, Chen Q, Huang Z, Li M: LncRNA MALAT1 modified progression of clear cell kidney carcinoma (KIRC) by regulation of miR-194-5p/ACVR2B signaling . Mol Carcinog 2019, 58 (2):279-292. Candido J, Hagemann T: Cancer-related inflammation . J Clin Immunol 2013, 33 Suppl 1 :S79-84. Swann JB, Smyth MJ: Immune surveillance of tumors . J Clin Invest 2007, 117 (5):1137-1146. Thompson RH, Dong H, Lohse CM, Leibovich BC, Blute ML, Cheville JC, Kwon ED: PD-1 is expressed by tumor-infiltrating immune cells and is associated with poor outcome for patients with renal cell carcinoma . Clin Cancer Res 2007, 13 (6):1757-1761. Yang H, Liang N, Wang M, Fei Y, Sun J, Li Z, Xu Y, Guo C, Cao Z, Li S et al : Long noncoding RNA MALAT-1 is a novel inflammatory regulator in human systemic lupus erythematosus . Oncotarget 2017, 8 (44):77400-77406. Cao S, Wang Y, Li J, Lv M, Niu H, Tian Y: Tumor-suppressive function of long noncoding RNA MALAT1 in glioma cells by suppressing miR-155 expression and activating FBXW7 function . Am J Cancer Res 2016, 6 (11):2561-2574. Haasch D, Chen YW, Reilly RM, Chiou XG, Koterski S, Smith ML, Kroeger P, McWeeny K, Halbert DN, Mollison KW et al : T cell activation induces a noncoding RNA transcript sensitive to inhibition by immunosuppressant drugs and encoded by the proto-oncogene, BIC . Cell Immunol 2002, 217 (1-2):78-86. Singer J, Jensen-Jarolim E: IgE-based Immunotherapy of Cancer -A Comparative Oncology Approach . J Carcinog Mutagen 2014, 5 (3):1000176. Casero RA, Jr., Marton LJ: Targeting polyamine metabolism and function in cancer and other hyperproliferative diseases . Nat Rev Drug Discov 2007, 6 (5):373-390. Vliagoftis H, Boucher WS, Mak LL, Theoharides TC: Inhibition of mast cell secretion by oxidation products of natural polyamines . Biochem Pharmacol 1992, 43 (10):2237-2245. Rohr-Udilova N, Klinglmuller F, Schulte-Hermann R, Stift J, Herac M, Salzmann M, Finotello F, Timelthaler G, Oberhuber G, Pinter M et al : Deviations of the immune cell landscape between healthy liver and hepatocellular carcinoma . Sci Rep 2018, 8 (1):6220. Gu-Trantien C, Willard-Gallo K: Tumor-infiltrating follicular helper T cells: The new kids on the block . Oncoimmunology 2013, 2 (10):e26066. Hosseini H, Obradovic MMS, Hoffmann M, Harper KL, Sosa MS, Werner-Klein M, Nanduri LK, Werno C, Ehrl C, Maneck M et al : Early dissemination seeds metastasis in breast cancer . Nature 2016, 540 (7634):552-558. Additional Declarations No competing interests reported. Supplementary Files sup1.xlsx sup2.xlsx sup3.xlsx sup4.xlsx sup5.xlsx Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-113464","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":53200237,"identity":"5f2a62f4-6c4b-40c9-8b6c-58a8a8dc299e","order_by":0,"name":"Weiwei Jia","email":"","orcid":"","institution":"Shanxi Bethune Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Jia","suffix":""},{"id":53200238,"identity":"65b0c534-aa2e-4d74-b261-2d34d12bfc17","order_by":1,"name":"Pengjia Li","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pengjia","middleName":"","lastName":"Li","suffix":""},{"id":53200239,"identity":"206b8d60-4f50-45f4-bdf0-c14fb66042f0","order_by":2,"name":"Mingxia Ma","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingxia","middleName":"","lastName":"Ma","suffix":""},{"id":53200240,"identity":"cd644e4c-a5f1-4fca-9213-95af200a4c40","order_by":3,"name":"Xiaochen Niu","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaochen","middleName":"","lastName":"Niu","suffix":""},{"id":53200241,"identity":"4ad81b98-66b4-48cb-b870-90c01eccb67d","order_by":4,"name":"Lina Bai","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Bai","suffix":""},{"id":53200242,"identity":"8cf9ffb0-8192-4bc3-97ff-908007de00ec","order_by":5,"name":"Haihu Hao","email":"","orcid":"","institution":"Shanxi Bethune Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haihu","middleName":"","lastName":"Hao","suffix":""},{"id":53200243,"identity":"052420c9-e847-4d56-bba3-88796fefbf50","order_by":6,"name":"Xiaohui Wang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Wang","suffix":""},{"id":53200244,"identity":"87bd0f1b-3b0c-4780-a83e-f7c182c3c396","order_by":7,"name":"li wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYDACZjApwcMm//jAgQ8VxGuxkeFjSEs8OOMM8Xal2cgx5Bgf5m0hQq1uO++zDx/3HOZhYzjz4QBvA4M8v9gB/FrMDrMbz5zxDKiFsXfDAckdDIYzZycQ0sLGzMxzAKiFmXfDAcMzDAkGt4nWwsbz4EBiG/Fa0njYeHgYDhwkVgvjjAM2PGwSbAYHG85IEOGX88eYGT4ckLCXn8H8+POfCht5fmkCWtCBBGnKR8EoGAWjYBRgBwCz/T/brddo5QAAAABJRU5ErkJggg==","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"li","middleName":"","lastName":"wang","suffix":""}],"badges":[],"createdAt":"2020-11-21 21:19:25","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-113464/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-113464/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13869667,"identity":"dada9944-4361-4b98-b338-b7814191b61d","added_by":"auto","created_at":"2021-09-22 15:06:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":374616,"visible":true,"origin":"","legend":"The Flowchart of the Analytical Process\nTCGA: The Cancer Genome Atlas.","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/2d14acd6a85f60afab5b9ddb.png"},{"id":13869669,"identity":"65abd18a-6a2a-4053-bcd6-a5e9aed042e0","added_by":"auto","created_at":"2021-09-22 15:06:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1244922,"visible":true,"origin":"","legend":"Identification of Differentially expressed Genes\na: Heat map of differentially expressed mRNA. B: Heat map of differentially expressed lncRNA. c: Heat map of differentially expressed miRNA. d: Volcano map of differentially expressed mRNA. e: Volcano map of differentially expressed lncRNA. f: Volcano map of differentially expressed miRNA","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/8cd0d985743864bcf848e870.png"},{"id":13869668,"identity":"140be5e7-5aa1-4826-808c-2afd2840030a","added_by":"auto","created_at":"2021-09-22 15:06:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":566612,"visible":true,"origin":"","legend":"Construction of CeRNA Network\nThe larger the width of each gene icon, the more nodes connected to the gene. Green: mRNA. Blue: lncRNA. Yellow: miRNA.","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/739bf59723b1871b0f18b6c8.png"},{"id":13869674,"identity":"55872e5c-10da-4474-92a1-74da4447b171","added_by":"auto","created_at":"2021-09-22 15:06:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":477505,"visible":true,"origin":"","legend":"Construction of Risk Prognosis Model\na, b: results of lasso regression analysis. c: model survival curve drawn by Kaplan-Meier method, Blue: low-risk Group, Red: high-risk group, P\u003c0.001. d: ROC curve (the value of AUC represents the area under the curve). e: Multivariate Cox regression analysis, Hazard ratio\u003e1 represents positive correlation, Hazard ratio\u003c1 represents negative correlation.","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/43b2560a907710a62e9889ee.png"},{"id":13869889,"identity":"37adb9bd-d2a3-4d4d-b079-3afe9e5455d7","added_by":"auto","created_at":"2021-09-22 15:09:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1217821,"visible":true,"origin":"","legend":"Identification of Differentially expressed Immune Cells\na: The heat map of differential immune cells b: The histogram of differential immune cells.","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/0de1cf41866bd52bdb47a1d1.png"},{"id":13869670,"identity":"2bb618f8-c2c1-4bc8-93e2-99a4aa5ec797","added_by":"auto","created_at":"2021-09-22 15:06:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":338234,"visible":true,"origin":"","legend":"Construction of Risk Prognosis Model\na, b: Results of lasso regression analysis. c: Survival curve drawn by Kaplan-Meier method, Blue: low-risk group, Red: high-risk group, P=0.01. d: ROC curve (the value of AUC represents the area under the curve). e: Multivariate Cox regression analysis, Hazard ratio\u003e1 represents positive correlation, Hazard ratio\u003c1 represents negative correlation.","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/1233664957115a9399829198.png"},{"id":13869671,"identity":"2870ba30-cac1-4745-84c9-8a56b754b481","added_by":"auto","created_at":"2021-09-22 15:06:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":351614,"visible":true,"origin":"","legend":"Expression Quantitative of Immune Cells in Different Stages of Clinical\na: Heat map of the expression of risk immune cells in the low-risk group and the high-risk group. Pink: low-risk group, Green: high-risk group, Blue: low expression, Red: high expression. b: The expression quantitative of T cells follicular helper in different stages. c: The expression quantitative of Mast cells resting in different stages.","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/e503e221f3ddd7e3926e49eb.png"},{"id":13869672,"identity":"613ad3f4-9cbc-4ccf-82cb-0ca8a0cf6fa4","added_by":"auto","created_at":"2021-09-22 15:06:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1162972,"visible":true,"origin":"","legend":"Co-expression Analysis\na: Co-expression relationship diagram between immune cells. Red: positive correlation, Blue: negative correlation. b: Co-expression relationship diagram between immune cells and key genes in the CeRNA network. Red: positive correlation, Blue: Negative correlation. c: Correlation between lncRNA MALAT1 and Mast cells resting, P\u003c0.001. d: Correlation between lncRNA MALAT1 and T cells follicular helper, P\u003c0.001.","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/3a31cda85204ddb7f1212918.png"},{"id":13869915,"identity":"9a2e5c7c-9702-4edc-84c1-a396ada04f1e","added_by":"auto","created_at":"2021-09-22 15:09:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3934473,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/28f0b6e1-0aa9-460c-82f5-c8474d1608ad.pdf"},{"id":13869890,"identity":"a8ddac12-397c-483f-a1ff-75310aff952a","added_by":"auto","created_at":"2021-09-22 15:09:14","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":37981712,"visible":true,"origin":"","legend":"","description":"","filename":"sup1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/0d1a4a673b40530f12205376.xlsx"},{"id":13869678,"identity":"37be575a-0004-43ef-9514-b990649d3189","added_by":"auto","created_at":"2021-09-22 15:06:15","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":38086284,"visible":true,"origin":"","legend":"","description":"","filename":"sup2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/46b0f1ac0313a13fbe4a131b.xlsx"},{"id":13869677,"identity":"bb736564-06c6-4847-8fba-16ca831f1b89","added_by":"auto","created_at":"2021-09-22 15:06:14","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":38279048,"visible":true,"origin":"","legend":"","description":"","filename":"sup3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/a84a9bb95af7381a8503a28b.xlsx"},{"id":13869676,"identity":"240c3fd3-b2fb-4ec7-b8c0-c5965f96b1ad","added_by":"auto","created_at":"2021-09-22 15:06:14","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":41250232,"visible":true,"origin":"","legend":"","description":"","filename":"sup4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/42ee3875e0085233f18f33cd.xlsx"},{"id":13869680,"identity":"f16f4d89-8c23-425a-b6e4-7b1e6bb7b939","added_by":"auto","created_at":"2021-09-22 15:06:15","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":43407892,"visible":true,"origin":"","legend":"","description":"","filename":"sup5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-113464/v2/1796727fe007919479315a16.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Construction and Exploration of the CeRNA Network and Patterns of Tumor-infiltrating Immune Cells in Kidney Renal Clear Cell Carcinoma\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eRenal cell carcinoma (RCC) is one of the top ten malignant tumors in the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Compared with other subtypes of RCC, kidney renal clear cell carcinoma (KIRC) is the most common histopathological type and has a poor prognosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Many patients have progressed to the advanced stage when they are diagnosed, and the 5-year survival rate (FSR) has also decreased significantly, because the early clinical symptoms and signs of KIRC are relatively insidious and non-specific [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In view of the limited diagnostic strategies and poor prognosis of KIRC, we urgently need to search for biological markers which are helpful for diagnosis, targeted therapy and prognosis prediction.\u003c/p\u003e \u003cp\u003eMessenger RNA (mRNA) is a protein coding gene, which participates in the formation and development of KIRC. Recently, non coding RNA had also drawn remarkable attention, particularly in terms of microRNA (miRNA) and long non coding RNA (lncRNA), which is indispensable for the regulation of cellular processes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The competitive endogenous RNA (CeRNA) hypothesis is that lncRNA could compete for the miRNA recognizing elements (MRE) to influences the mRNA regulation, and which is put forward by Salmena et al. Recent studies have shown that CeRNA networks control biological behaviors, which via regulating oncogenes and tumor suppressor genes, such as tumor invasion and metastasis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, there is no definition of CeRNA network to predict the prognosis of KIRC. Therefore, in this study, we constructed a CeRNA network about KIRC to identify the potential interactions involved in the differentially expressed lncRNA, miRNA and mRNA in KIRC, which to determine the molecular targets of prognostic evaluation.\u003c/p\u003e \u003cp\u003eSurgical treatment is the only effective treatment method for localised KIRC, but patients with metastatic must be treated with systemic therapy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, due to the long-term use of systemic therapy and the development of tumors, the occurrence of tumor immune escape has been promoted [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Some immunosuppressive factors may be hijacked by cancer cells to avoid the attack of immune system [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It is essential to maintain the host's immune homeostasis and eliminate cancer cells by balancing immune stimulus factors and inhibitory factors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent studies have shown that immunotherapy has become an important method to improve the survival rate of patients with KIRC [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Recently, a number of evidences show that miRNA can regulate cancer immune surveillance and promote immune escape [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is not clear how miRNA (as the regulatory hub of CeRNA) changes the CeRNA network and how CeRNA changes the immune cells of KIRC, and the correlation between the two changes.\u003c/p\u003e \u003cp\u003eWe use bioinformatics analysis to construct a complete gene interaction CeRNA network. By screening the differentially expressed lncRNA, miRNA and mRNA in KIRC, in order to explore the key genes involved in the development of KIRC. We conducted co-expression analysis based on the risk genes related to prognosis and the differentially expressed immune cells in KIRC to further explore the potential molecular mechanism of KIRC. Not only that, we further explored the relationship between CeRNA and immune infiltration, and finally obtained the connection between CeRNA network and immune cells. Our researchs provide new insights into the occurrence and development of KIRC, as well as survival prediction biomarkers and potential therapeutic targets.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1 Data download and differential gene expression analysis \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are 539 KIRC and 72 normal tissues of 3 types of RNA information (including mRNA, lncRNA and miRNA) and the clinical data were downloaded by using the GDC RNA toolkit package of the R language (R 4.0.2) in the cancer genome atlas database[43]. Firstly, We used the log(fold change，FC)\u0026ge;1 or \u0026le;1 and false discovery rate (FDR) adjusted P value\u0026lt;0.05 as criteria to identify significant differential genes. The heat maps were drawn by the \"pheatmap\" R package, and the volcano maps were drawn by the \"ggplot2\" R package. Then, hypergeometric testing and correlation analysis were used to select significantly different and related genes, and use Cytoscape v.3.7.2 software to visualize the CeRNA network. It does not require relevant ethical review and approval, due to the data used in this study came from the TCGA database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Construction of prognosis model related to CeRNA network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression levels of nodes in CeRNA network were combined with clinical data by using perl (perl 5.26.3) language. The network nodes related to survival prognosis were selected by Kaplan-Meier survival analysis using the R \"survival\" package according to the filter condition: P \u0026lt;0.05. The construction of the prognostic model based on the CeRNA network was completed by single factor Cox regression analysis, lasso regression and multivariate Cox regression analysis. The formula of prognosis model: risk score= expression of risk genes\u003csub\u003e1\u003c/sub\u003e*coef\u003csub\u003e1\u003c/sub\u003e+expression of risk genes\u003csub\u003e2\u003c/sub\u003e*coef\u003csub\u003e2\u003c/sub\u003e+...＋expression of risk genes\u003csub\u003en \u003c/sub\u003e* coef\u003csub\u003en\u003c/sub\u003e. The survival curve of the model and the time-dependent receiver operating characteristic curve (ROC) were drawn, in order to estimate the accuracy and resolution of the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 CIBERSORT algorithm Evaluation \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CIBERSORT algorithm was used to estimate the degree of infiltration of 22 immune cell types in each KIRC sample, with a CIBERSORT output of p \u0026lt;0.05, indicated that the Example of these cells were significant, which was worthy of further study. We evaluated the difference between immune infiltrating cells in tumor tissue and normal tissue with Wilcoxon rank-sum test. Finally, histogram and differential heat map of tumor-infiltrating immune cells were drawn.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Construction of immune infiltration model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe degree of tumor-infiltrating immune cells in each tumor sample was combined with clinical information to evaluate the prognostic value of immune cell infiltration in tumor tissues, and then the survival curve was drawn by Kaplan-Meier survival analysis using the survival package (P\u0026lt;0.05). The prognosis model based on the degree of tumor-infiltrating immune cells in each sample was combined with clinical information to evaluate the prognostic value of immune cell infiltration in tumor tissues, and then the survival curve was drawn by Kaplan-Meier survival analysis using the survival package was constructed by single factor Cox regression analysis, lasso regression and multivariate Cox regression analysis. The survival curve of the model and the ROC curve of the model were drawn.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Co-expression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to evaluate the co-expression of the key tumor-infiltrating immune cells and key nodes in the CeRNA network, and to explore the correlation between tumor-infiltrating immune cells and CeRNA network expression, the \"Corrplot\" R package was used to perform co-expression analysis on the key nodes based on the CeRNA network and the key cells based on the tumor-infiltrating immune cells model, and output significant related pairs. According to the correlation analysis, the corrected P value was 0.001.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 The Flow chart of the Analytical Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe flow chart of the analysis process is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Identification of Differentially expressed Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA-sequencing information was downloaded in 539 tumor tissues and 72 normal tissue samples from TCGA-KIRC database. There were 3074 differentially expressed mRNAs (1055 upregulated and 2019 downregulated, Figure 2A, D), and 359 differentially expressed lncRNAs (71 upregulated and 280 downregulated, Figure 2B, E) and 132 differentially expressed miRNAs (70 upregulated and 62 downregulated, Figure 2C, F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Construction of CeRNA Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e20 mRNAs, 7 lncRNAs and 14 miRNAs with significant differences were obtained through the analysis of hypergeometric distribution test and correlation test (P\u0026lt;0.001) .\u0026nbsp; In this study, we constructed a CeRNA network using these significantly different RNAs (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Construction of Risk Prognosis Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e9 risk genes were obtained through lasso regression analysis (Figure 4A, 4B) and multi-factor COX regression analysis (Figure 4E)，including 5 mRNAs (RELT, MYO9B, KCNN, SIX1, OTOGL), 1 lncRNA (MALAT1) and 3 miRNAs (hsa-miR-130b-3p, hsa-miR-200b-3p, hsa-miR-21-5p). And the risk score (RS) of the risk prognosis model according to this formula (RS=RELT*0.228-MYO9B*0.546+KCNN4*0.265+SIX1*0.135-OTOGL*0.142+MALAT1*0.223+hsa-miR-130b-3p*0.321-hsa-miR-200b-3p*0.091+hsa-miR-21-5p*0.254) was calculated. After matched the risk genes to the CeRNA network, we obtained a complete mRNA-miRNA-lncRNA (SIE1-hsa-miR-200b-3p-MALAT1) relationship chain. The survival curve of the model was drawn by the Kaplan-Meier method. The results showed that the survival rate of patients in the high-risk group was significantly lower than that of the low-risk group (Figure 4c). In order to estimate the accuracy of the model, we have further drawn the ROC curve (1-year AUC: 0.769; 3-year AUC: 0.750; 5-year AUC: 0,771, Figure 4D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Evaluation of CIBERSORT algorithm and Construction of Immune Infiltration Model \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportions of 22 immune cells in different samples were provided by CIBERSORT algorithm analysis (Figures 5A, 5B). 2 tumor-infiltratings immune cells (Mast cells resting, T cells follicular helper) were identified by lasso regression analysis (Figure 6A, 6B) and multivariate Cox regression analysis (Figure 6E). The formula for the constructed prognosis model related to immune infiltration was: risk score=T cells follicular helper*10.132-Mast cells resting*10.401. The survival curve of the model was drawn by the Kaplan\u0026ndash;Meier method. The results showed that the survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (Figure 6C). We further drawn the ROC curve to estimate the accuracy of the model (1-year AUC: 0.589; 3-year AUC: 0.633; 5-year AUC: 0,610, Figure 6D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 The Correlation Analysis of Risk Immune Cells and Clinical\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, the R language was used to draw a heat map of the differential expression of risk immune cells in the low-risk group and the high-risk group. The results showed that as the risk score increased, the cell content of T cells follicular helper gradually increased , while the cell content of Mast cells resting gradually decreased (Figure 7A). Then we also analyzed the proportions of two different subtypes of immune cells in different clinical staging using non-parameter test and Kaplan-Meier method. The results demonstrated that the expression quantitative of T cells follicular helper in stage IV patients was significantly higher than in stage I patients (Figure 7B), and the expression quantitative of Mast cells resting decreased significantly as the stage level increased (Figure 7c).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Co-expression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe co-expression relationship graph was obtained by analyzing the correlation between the tumor-infiltrating immune cells model and the CeRNA network nodes by using Pearson correlation analysis (Figure 8A, B). There was a negative correlation between Mast cells resting and T cells follicular helper (R= -0.26, see Figure 8A, B), lncRNA MALAT1 and Mast cells resting was negative correlation. (R= -0.27, P\u0026lt;0.001, Figure 8C), however there was a positive association of lncRNA MALAT1 with T cells follicular helper (R=0.23, P\u0026lt;0.001, Figure 8D)..\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRenal cell carcinoma is one of the most deadly cancers in the urinary system, and its incidence is increasing year by year [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. KIRC accounts for about 80% of all RCC histological subtypes and had the high mortality, invasiveness and metastasis rate [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Characteristic changes at the molecular level play a crucial role in the occurrence and metastasis of tumors, and had been considered as prognostic factors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We found that the genes and tumor-infiltrating immune cells are differentially expressed between KIRC tumor tissue and normal tissue. In response to this finding, we constructed risk prediction models based on CeRNA network and tumor-infiltrating immune cells. Among them, we analyzed the correlation between CeRNA network and tumor-infiltrating immune cells, we inferred a potential mechanism of KIRC recurrence and that was lncRNA MALTA1 regulating Mast cells resting (MCs) and T cells follicular helper (TFH). Our findings could help clinical oncologists assess the prognosis and risk of recurrence of KIRC.\u003c/p\u003e \u003cp\u003eCeRNA network is that lncRNA compete for the miRNA recognition elements (MRE) to influences the mRNA regulation, thus, we speculate that this type of analysis could uncover molecular interactions and gene regulatory networks that have been missed by proteomic and conventional genomic methods [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Studies shown that 3\u0026rsquo;UTRs from coding genes may act as endogenous decoys, which binds to the MRE recognition sequence of miRNA through the principle of complementary base pairing and participates in post-transcriptional regulation of oncogenes and tumor suppressors [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and lncRNA can also competitively bind the MRE sequence of miRNA to regulate the expression level of mRNA in tumor cells [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. More importantly, lncRNA modulated mitochondrial membrane potential and enhanced the release of cytochrome C, indicating that apoptosis of KIRC induced by lncRNA may belong to mitochondria-mediated apoptosis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and lncRNA can also participate in the proliferation of cancer cells by promoting the expression level of KIRC distant metastasis related genes[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this study, we obtain 3074 differentially expressed mRNA (1055 upregulated and 2019 downregulated), 359 differentially expressed lncRNA (71 upregulated and 280 downregulated), and 132 differentially expressed miRNA(70 upregulated and 62 downregulated) through bioinformatics analysis the RNA sequencing (RNA-seq) data of KIRC. We have constructed a CeRNA network that can prepare the prognosis of KIRC through the analysis of hypergeometric distribution test and correlation test, which contains 20 mRNAs, 7 lncRNAs and 14 miRNAs. And then ,we use the single factor COX analysis, lasso regression and multivariate Cox regression analysis to obtain 9 genes (RELT, MYO9B, KCNN4, SIX1, OTOGL, MALAT1, HSA-Mir-130b-3P, HSA-Mir-200b-3p, HSA-Mir-21-5p) in CeRNA that were significantly correlated with the overall survival rate of KIRC. We constructed a prognostic model using these risk genes and got the AUC values (for 1-year survival rates: 0.794, 3-year survival rates: 0.762, and 5-year survival rates: 0.782). After matching the 9 risk genes and the CeRNA network, it was found that hsa-miR-200-3p (miRNA), SIX1 (mRNA) and MALAT1 (lncRNA) were significantly correlated (P\u0026thinsp;=\u0026thinsp;4.30E-6). SIX1 is a key transcription factor involved in the occurrence and development of a lot of tumor and its biological activity is regulated by miRNA [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. SIX1 may contribute to ovarian epithelial carcinogenesis by simultaneously increasing proliferation and decreasing apoptosis and imply that SIX1 may be an important target of ovarian cancer therapy response [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. SIX1 can regulate the mitochondrial membrane potential by regulating the expression of the anti-apoptotic protein Bcl-2, and affect mitochondrial apoptosis via caspase-7, which suggests that SIX1 can be used as an effective target for the prognosis and treatment of gastric cancer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, there is few study had clearly shown the relationship between SIX1 and KIRC. Our results show that the decreased expression of miR-200 in KIRC may be involved in the formation and development of cancer by promoting the increased expression of SIX1.\u003c/p\u003e \u003cp\u003eMany previous studies have shown that the miR-200 family members of KIRC were significantly downregulated compared with normal kidney tissue. It is speculated that miR-200b as tumor biomarkers in renal tumor biopsies is feasible [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Rola Saleeb et al. found that miR-200b was down-regulated in primary KIRC and further decreased in metastatic foci. More importantly, Kaplan-Meier survival curves indicate that miR-200b and miR-200c positive patients have significantly longer disease-free survival [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, the expression of miR-200 is negatively correlated with the progression of KIRC. In this study, we found that the HR value of miR-200 was less than 1 through single-factor COX, lasso regression and multi-factor COX analysis, indicating that miR-200 is a gene that protects renal clear cell carcinoma. This is consistent with the results of miR-200 family members mentioned in previous studies that inhibit the proliferation, migration, and invasion of cancer cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is another important RNA in the significantly related network of hsa-miR-200-3p(miRNA)-SIX1(mRNA)-MALAT1(lncRNA), this is lncRNA MALAT1. Metastasis associated with lung adenocarcinoma transcript-1 (MALAT1) is one of the long non-coding RNA (lncRNA) associated with tumors which is composed of more than 8000 nucleotides and located on chromosome 11q13 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. MALAT1 was initially considered to be an effective prognostic factor for non-small cell lung cancer (NSCLC), tumors with high MALAT1 expression have a nearly five-fold increase in the risk of metastasis compared with tumors with low MALAT-1 expression [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Feng et al. proved that the direct binding between MALAT1 and miR-200a can promote the proliferation of lung cancer cells and lead to the development of gefitinib resistance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition to NSCLC, MALAT1 is also present in other malignant tumors. In endometrial cancer, the expression of MALAT1 is negatively correlated with the expression of miR-200c, and its knockdown can promote the expression of miR-200c in cancer cells [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Studies have found that MALAT1 can also adjust the expression of miR-140 in prostate cancer cells to change the mRNA and protein expression levels of apoptosis inhibitor protein to promote the occurrence and development of tumors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. More importantly, some studies have shown that MALAT1 in KIRC promotes the growth of cancer cells by inhibiting cell apoptosis and accelerating the expression of specific proteins of epithelial-mesenchymal transition (EMT) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, we found that MALAT1 is a high-risk factor in KIRC through bioinformatics analysis, and its expression level is positively correlated with tumor formation and development, which is consistent with the role of MALAT1 in other tumors. Therefore, we speculate that hsa-miR-200-3p (miRNA) - SIX1 (mRNA) - MALAT1 (lncRNA) plays an important role in the development of renal clear cell carcinoma. However, how these genes affect the occurrence and development of KIRC is still unclear.\u003c/p\u003e \u003cp\u003eIn the past few decades, more and more evidences show that the tumor-cell phenotype is determined by the intrinsic activity of cancer cells and the interaction of cells (especially tumor-infiltrating immune cells, TIICs) in the tumor microenvironment [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Complex interactions between the immune cells in this microenvironment may promote cancer progression by inducing immune dysfunction in RCC patients [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Interestingly, lncRNA MALAT1 is an important inflammation regulator. In patients with systemic lupus erythematosus (SLE), lncRNA MALAT1 can be used as a CeRNA to interfere with the inhibitory effect of miRNA on the inflammatory factor IL-21, so the expression of MALAT1 in SLE patients is significantly increased [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. During an asthma attack, MALAT1 activates airway inflammation and airway hyperresponsiveness of T cells by inducing the expression of miR-155 in T cells [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, it is not clear whether lncRNA MALAT1 is involved in the process of immune invasion, immune cell killing and immune escape in the tumor microenvironment of RCC.\u003c/p\u003e \u003cp\u003eIn this study, we analyzed the correlation between gene expression and tumor-infiltrating immune cells to prove that MALAT1 and MCs, (R = -0.27, P\u0026thinsp;=\u0026thinsp;8.95E-5), MALAT1 and TFH (R\u0026thinsp;=\u0026thinsp;0.23, P\u0026thinsp;=\u0026thinsp;9.85E-4) were significantly correlated. Therefore, Therefor, we speculate that MALAT1, the core molecule of the CeRNA network, participates in the occurrence of KIRC by regulating the content of tumor-infiltrating immune cells, such as TFH and MCs.\u003c/p\u003e \u003cp\u003eWe first analyzed by using the CIBERSORT algorithm and found that the expression levels of resting MCs and TFH between KIRC and normal tissues are significantly different. Compared with other algorithms, CIBERSORT is the best algorithm to distinguish between resting and IgE-activated MCs. Our results suggested that most of the MCs in KIRC are in resting state. Studies have shown that MCs activated by IgE can prevent the occurrence of cancer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, there are high concentrations of polyamines in malignant tumor cells [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Polyamines oxidized by polyamine oxidase can prevent the activation of MCs by IgE, thereby inhibiting the tumor suppressor effect of resting MCs [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The resting MCs in KIRC can promote the immune escape of tumor cells, which is beneficial to tumors growth [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore, when the content of resting MC is higher, it may promote the occurrence of tumors. We observed that the expression of MALAT1 increased in KIRC tumor tissues, and the content of resting MCs decreased observably. There is a negative correlation between MALAT1 and MCs. Above all, we speculate that MALAT1 may affect the generation of KIRC by the content of resting MCs.\u003c/p\u003e \u003cp\u003eTFH is an immune cell located in the peritumoral tertiary lymphoid structures (TLS), which belongs to a subset of CD4\u003csup\u003e+\u003c/sup\u003e T cells [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. TFH provides signals for the proliferation and activation of B cells by secretes a large number of chemokines in B cell [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Our results proved that the expression of lncRNA MALAT1 in the CeRNA network is positively correlated with the contents of TFH. The expression of MALAT1 in KIRC cells is increased, which suggested that the content of TFH in KIRC cells is higher than that in normal tissues. By using Multi-factor COX analysis, we found that the content of TFH in KIRC cells is ascended distinctly, which is consistent with our speculation. Therefore, we believed that TFH may be one of the maker cells of immune infiltration in KIRC.\u003c/p\u003e \u003cp\u003eIn summary, this study constructed a KIRC related CeRNA network through the TCGA database, and selected risk genes through single factor COX analysis, lasso regression and multivariate Cox regression analysis to predict the survival rate of patients. We obtained a significant correlation network of hsa-miR-200-3p(miRNA)-SIX1(mRNA)-MALAT1(lncRNA) through correlation analysis. In order to explore how the genes in this network affect the formation of KIRC, this study also constructed another risk model based on tumor-infiltrating immune cells, and screened out risky tumor-infiltrating immune cells related to the occurrence and development of KIRC. We speculated that hsa-miR-200-3p and MALAT1 affect the internal mechanism of KIRC by regulating the expression of SIX1, and the relationship between MALAT1 and immune infiltration, so as to promote the self-management of KIRC patients.\u003c/p\u003e \u003cp\u003eOur research is a correlation study from multiple dimensions rather than a biological mechanism study. Based on the results of this research, we will verify our conclusions through biological experiments. In the future, we will combine more data and more experiments to explore the impact of KIRC molecular biology on CeRNA, as well as the relationship between tumor cells, TFH and resting MCs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on the bioinformatics analysis of the CeRNA network and tumor-infiltrating immune cells, our research inferred that hsa-miR-200-3p, MALAT1 and SIX1 are involved in the internal mechanism of KIRC, and the relationship between MALAT1 and tumor-infiltrating immune cells (MCs, TFH).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRenal Cell Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKIRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney Renal Clear Cell Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCeRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCompeting Endogenous RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMast Cells resting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTFH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT cells Follicular Helper\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMessenger RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003elncRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong Non-Coding RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emiRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicroRNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMiRNA Recognition Elements\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-Small Cell Lung Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA-seq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRNA Sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEspecially Tumor-Infiltrating Immune Cells,\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSLE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystemic Lupus Erythematosus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTertiary Lymphoid Structures\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFSR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e5-year Survival Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFold Change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse Discovery Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWW Jia analyzed and explained the RNA-seq and clinical data of TCGA-KIRC, and was the main contributor to the manuscript. L Wang directed the writing of the manuscript. XH Wang directed the writing of the manuscript. The final manuscript read and approved by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was sponsored by the Fund for Shanxi \u0026ldquo;1331 Project\u0026rdquo; Key Subjects Construction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data set used in this study is a publicly available data set. This data can be found here: TCGA (https://tcga-data.nci.nih.gov/tcga/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Jemal A: \u003cstrong\u003eCancer statistics, 2018\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2018, \u003cstrong\u003e68\u003c/strong\u003e(1):7-30.\u003c/li\u003e\n\u003cli\u003eXiao GF, Yan X, Chen Z, Zhang RJ, Liu TZ, Hu WL: \u003cstrong\u003eIdentification of a Novel Immune-Related Prognostic Biomarker and Small-Molecule Drugs in Clear Cell Renal Cell Carcinoma (ccRCC) by a Merged Microarray-Acquired Dataset and TCGA Database\u003c/strong\u003e. \u003cem\u003eFront Genet \u003c/em\u003e2020, 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al\u003c/em\u003e: \u003cstrong\u003eEarly dissemination seeds metastasis in breast cancer\u003c/strong\u003e. \u003cem\u003eNature \u003c/em\u003e2016, \u003cstrong\u003e540\u003c/strong\u003e(7634):552-558.\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":"KIRC, CeRNA network, Tumor-infiltrating immune cells, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-113464/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-113464/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eKIRC is the malignant tumor with the highest incidence and poor prognosis in renal cell carcinoma. We want to explore the possible mechanisms of KIRC and effective prognostic-related biomarkers. The sequencing information of 3 types of RNA (mRNA, lncRNA and miRNA) in 539 cases of KIRC tissues and 72 cases of normal tissues is obtained from the TCGA database. Methods such as univariate Cox regression analysis, lasso regression screening, and multivariate Cox regression analysis were used to construct a prognostic model based on the CeRNA network. There are 3074 mRNAs, 359 lncRNAs and 132 miRNAs differentially expressed that have been identified through differential analysis. A complete mRNA-miRNA-lncRNA (SIX1-hsa-miR-200b-3p-MALAT1) network was obtained based on the CeRNA network. The CIBERSORT algorithm was used to analyze the degree of infiltration of 22 kinds of immune cells from each sample of KIRC. Construction of a prognostic model based on tumor-infiltrating immune cells, 2 immune cells (Mast cells resting, T cells follicular helper) were identified by constructing a prognostic model. There was a negative correlation between lncRNA MALAT1 and Mast cells resting (R= -0.27, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); while there was a positive correlation between lncRNA MALAT1 and T cells follicular helper (R\u0026thinsp;=\u0026thinsp;0.23, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e","manuscriptTitle":"The Construction and Exploration of the CeRNA Network and Patterns of Tumor-infiltrating Immune Cells in Kidney Renal Clear Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2021-09-22 15:06:11","doi":"10.21203/rs.3.rs-113464/v2","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}},{"code":1,"date":"2020-11-25 19:12:51","doi":"10.21203/rs.3.rs-113464/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":"b423e41e-918b-4c18-ba6f-c03d96725614","owner":[],"postedDate":"September 22nd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":7349218,"name":"General Biochemistry"},{"id":7349219,"name":"Immunology"}],"tags":[],"updatedAt":"2021-09-22T15:06:12+00:00","versionOfRecord":[],"versionCreatedAt":"2021-09-22 15:06:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-113464","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-113464","identity":"rs-113464","version":["v2"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}