Integrative analysis of immune infiltration and microenvironment characteristics in renal clear cell carcinoma induced by cell senescence

Integrative analysis of immune infiltration and microenvironment characteristics in renal clear cell carcinoma induced by cell senescence | 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 Integrative analysis of immune infiltration and microenvironment characteristics in renal clear cell carcinoma induced by cell senescence Xiangxiang Zhang, Xiaoping Wang, Hengping Li, Xiangrong Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2492545/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 Our study aims to investigate the characteristics of the tumor microenvironment as well as to study the immunological infiltration in renal clear cell carcinoma that results from cell senescence. Methods Firstly, based on information from the Cancer Genome Atlas (TCGA) database, we collected ccRCC's mRNA, clinical data, and mutation data. From the comprehensive gene expression database (GEO), we acquired individuals gene expression profiles and relevant clinical data with ccRCC. We obtained senescence genes from the Aging Atlas database, extracted the expression of senescence genes from TCGA and GEO databases, and subsequently analyzed the differences. After which, the Kaplan Meier (KM) survival rate was utilised to determine survival-related prognostic genes; Cross genes were obtained from the intersection of differential genes and prognostic genes. By utilising the least absolute shrinkage and selection operator (lasso) regression and cross-validation, the genes included in the construction of the prognostic model were identified. The risk score was detected based on the signature, and the sample was then categorized into high-risk and low-risk groups. GSEA enrichment analysis, immune checkpoint analysis and the expression degree analysis of each model gene in immune cells were conducted among high-risk group and low-risk group respectively. The model we built was validated using the IMvigor210 database. Finally, we screened drugs that can inhibit the expression of high-risk genes from the Connectivity Map (CMAP) database by using risk differential genes. Results We obtained 37 cross genes and identified 17 genes that could be used to construct prediction model. We found that the tumor mutation load was higher in the high-risk groups. Even though high-risk patients were more likely to evade immunotherapy, there was no significant difference between the two groups when treated with PD-1, CTLA-4, or PD-1, combined with CTLA-4 immunotherapy. The verification results of IMvigor210 database were compatible with the study outcomes. Finally, we screened 6 drugs that can inhibit the expression of high-risk genes from the CMAP database by using risk differential genes. Conclusion The tumor microenvironment of ccRCC induced by cell senescence may have an immune escape or resistance when receiving immunotherapy. These findings may have some guiding significance for clinical individualized immunotherapy. cell senescence renal clear cell carcinoma immune infiltration immune checkpoint inhibitors immunotherapy Figures Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Renal cell carcinoma (RCC) is one of the most prevalent cancers of the urinary system. The incidence rate of RCC accounts for about 5% of all newly diagnosed cases of cancer in males and 3% of total cases in females[ 1 ]. CcRCC is the most prevalent (75–80%) subtype of RCC and the most studied subtype of RCC[ 2 ]. Surgical resection may be curative for localized (organ localized) diseases. Unfortunately, 25–30% of individuals are diagnosed with distant metastasis[ 3 ], and approximately 40% of patients have recurrence after surgical resection[ 4 ]. All RCC subtypes are largely unresponsive to conventional chemotherapy or radiotherapy[ 5 , 6 ]. In recent years, the treatment protocol for metastatic clear cell renal cell carcinoma (mccRCC) has significantly changed due to the emergence of ICI anti-PD-1 and anti-PD-L1, which is used as monotherapy and in combination with anti-CTLA-4 or antiangiogenic agents[ 7 ]. Although the overall response rate (ORR) of the prognosis of mccRCC has been greatly improved by ICI combined therapy, there are still many patients with poor effect of immunotherapy due to drug resistance. There are numerous factors that contribute to primary or acquired medication resistance, including the internal factors of patients, tumor cells, and microenvironments[ 7 ]. We aim to investigate the function of tumor microenvironment components in disease progression and ICI resistance, and explore effective personalized treatment regimens. Despite substantial research into tumorigenesis and development, the etiology of ccRCC and method of carcinogenesis remains unknown[ 8 ]. Cell senescence may have a significant role in the occurrence, progression, and immune regulation of ccRCC. Cell senescence is a process that occurs in diploid cells and can induce stable growth arrest with considerable phenotypic changes, including chromatin remodelling, metabolic reprogramming, enhanced autophagy, and the implementation of complex pro-inflammatory secretory groups[ 9 – 11 ]. Cellular senescence is a cell condition related to a variety of physiological processes and age-related illnesses[ 12 ]. Cell senescence has always been regarded as a tumor inhibition mechanism to prevent the abnormal proliferation of damaged cells in benign and precancerous tumors[ 13 , 14 ]. The tumor suppressive pathways of p53/p21 and p16 INK4a/Rb are responsible for inhibiting growth[ 15 ]. Studies have confirmed that the double knockout of p16INK4a and p21WAF1/CIP1 genes increases the speed of cancer development[ 16 ]. Also, cellular senescence has been shown to be an essential tumor suppressive mechanism in vivo, and that stimulants that induce genotoxicity, carcinogenesis, oxidation and replication stress will trigger cellular senescence[ 17 , 18 ]. Many studies in the last decade have depicted that senescence cells produce a variety of proteins, such as inflammatory cytokines, chemokines, growth factors, and matrix metalloproteinase (MMP), which are termed senescence-associated secretory phenotype (SASP) —fostering cellular senescence via autocrine and paracrine signalling[ 19 – 21 ]. During the process of ageing, senescent cells accumulate due to the failure of senescence surveillance caused by the decline of immune function[ 22 ]. Therefore, the long-term secretion of SASP factor may promote the development of cancer[ 23 – 25 ]. The senescent of the immune system is also the reason for the damage of tumor immune surveillance and the increased risk of tumor occurrence[ 9 ]. Senescent cells only have several nonexclusive markers, rather than universal or specific biomarkers. Histochemical staining for senescence-associated β-galactosidase (SA-β-GAL) is the most prevalent marker of cell senescence[ 26 ]. P16INK4 is a tumor suppressor protein, which is another marker regularly used to identify senescent cells in cultures and tissues. Other senescence markers related to p53 trans activated targets, include up-regulated expression of tumor suppressor proteins DEC1 and DcR2. Senescent cells also significantly downregulated lamin B1 expression[ 27 – 29 ]. These marks are rarely used, because their effects are not yet widely validated. Although there are numerous prognostic markers, the variety and complexity of the tumor microenvironment increase the difficulties of immunotherapy and diminish its efficacy. Therefore, an accurate understanding of cellular senescence heterogeneity helps manage individualised therapy. We aim to fully investigate the heterogeneous immune molecular phenotype and tumor microenvironment characteristics of renal cell carcinoma caused by cell senescence. Using high-throughput sequencing data and clinical information of ccRCC samples collected from the public database, we identified 37 prognosis-related senescence genes. Using lasso regression analysis, we screened 17 model construction genes and grouped them according to risk scores. The grouping results demonstrated substantial differences in tumor mutation and immune checkpoint analysis among high-risk and low-risk groups, but there were no significant difference when patients received immunotherapy. Imvigor210 database was utilised to verify the results, which were consistent with the study. The results of this study suggest that cancer caused by cell senescence may have the possibility of immune escape when receiving immunotherapy. In the future, it is necessary to choose other immune checkpoints for further exploration. Materials And Methods Data Extraction and Processing The GDC-client tool was used to obtain 541 ccRCC patients' and 72 normal tissue mRNA-seq data (counting format), single nucleotide variation (SNV) data, and clinical data from the TCGA database ( https://portal.gdc.cancer.gov/ )[ 30 ]. We obtained transcriptome data and clinical data of 28 normal renal tissues in GTEx database ( https://commonfund.nih.gov/GTEx/ ). The log2-transformed mRNA-seq gene expression value can be used for further investigation. GSE29609 and GSE40912, which includes 71 ccRCC samples and high-throughput sequencing data, was also retrieved from the GEO database for external validation. ( https://www.ncbi.nlm.nih.gov/gds/ )[ 31 ]. The Ageing Atlas database identified 279 age-related genes ( https://ngdc.cncb.ac.cn/aging/index).(We utilised a CIBERSORT algorithm tool to evaluate the composition of immune cells based on the gene expression patterns of various organs to evaluate the immune infiltration in samples.[ 32 ] LM22 gene signature and CIBERSORT source code were obtained from the CIBERSORT website ( https://cibersort.stanford.edu/download.php ). Moreover, the National Center for Biotechnology website ( https://www.immport.org/resources ) was utilised to acquire 47 immune checkpoint genes. The scoring files for immune escape and immunotherapy can be collected from tumor immune dysfunction and exclusion (TIDE)( http://tide.dfci.harvard.edu/ ). The scoring file for immunotherapy analysis was obtained from The Cancer Immunome Atlas (TCIA)( https://tcia.at/home ). Relevant verification files of the IMvigor210 database are downloaded from the IMvigor210corebiologies package using the R software. Screening of intersection genes of differential genes and prognostic genes In TCGA and GEO databases, the expression of senescence genes that have been retrieved was extracted. There were 541 ccRCC samples and 100 para carcinoma samples evaluated using the edgeR programme, with the log2FC| >1.5 and p less than 0.05 being used as the criterion for differentially expressed genes (DEGs). The edger package was utilized to identify the genes significantly related to the prognosis from TCGA database, and the cutoff value was set to coxPfilter = 0.05. Veen algorithm was applied to DEGs and prognostic genes obtained from TCGA database, and cross genes were obtained from ccRCC. Construction of prognosis model and Grouping The prognostic model was built using data from the TCGA database as a training set, and data from the GEO database was utilised as a testing group to ensure the model of prognosis accuracy. In the prognostic model, each of the gene's expression was used to calculate a risk score for each sample. They were then divided into two categories: those with a high-risk score and those with a low-risk score. In the GEO database, the median value in the TCGA database is also used to divide modeling gene into high and low-risk groups. First Kaplan-Meier survival curve can be used to compare the overall survival rates of high and low-risk groups. The model's accuracy in predicting patient survival was then evaluated using receiver operating characteristic (ROC) curves. A multivariate/univariate Cox regression analyses were also performed to assess if the risk score was affected by other clinical prognostic factors such as age, gender, grade, or stage. Lastly, we used an independent data set (GSE29609 and GSE40912) to test whether the prognostic characteristics of cross genes have a strong ability to predict patient survival. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) GO analysis was executed to evaluate cross gene function. We conducted GSEA analysis to evaluate important functional phenotypes between groups at high and low risk. considering the enrichment score (ES) as the evaluation index; an ES > 0 indicates that this pathway or function is active in the high-risk group. On the contrary, this pathway or function is active in the low-risk group. Lastly, the gridExtra package is used to visualise the results of GSEA. Analysis of tumor mutation burden and survival SNV data from the TCGA database was downloaded for patients in the high and low-risk categories, and we utilised the ggpubr software package to examine the tumor mutation burden of these patients. First, the optimal cutoff value was obtained by analysis, and Tumors with high and low mutation loads were separated. Then the tumor mutation load group and risk group were combined, and the survival and survminer packages were utilized to evaluate the survival of patients with tumor mutation load combined with risk. Variance analysis, immune cell correlation analysis, immune checkpoint difference and survival analysis To clarify the correlation between immune cells and patient risk scores, we downloaded the pan-cancer immune cell infiltration file and used R-package analysis to obtain the heat map of the correlation. Similarly, to investigate the differences in terms of genes associated to immune tests in high-risk and low-risk groups, we downloaded 47 immune checkpoint genes, and obtained the correlation analysis of checkpoint genes through the ggpubr package analysis. Survival and survminer packages were utilized to analyze the survival of high and low gene expression and high and low risk-groups. Analysis of immune escape and immunotherapy To detect the effect of immunotherapy and the potential of immune escape in patients with high and low-risk groups. We first entered the TCGA risk file into the tide database to obtain the tide risk score. Then the ggpubr package was used to analyze the difference in tide scores in high and low-risk groups. The higher the tide score, the greater the possibility of immune escape when receiving immunotherapy. At the same time, we downloaded the scoring file of immunotherapy from the TCIA database, and used the ggpubr package analysis to obtain the scores of PD-1 and CTLA-4 immunotherapy between high-risk and low-risk groups. The higher the score, the better the effect of immunotherapy. Model validation and immunotherapy analysis validation of IMvigor210 We used the IMvigor210 cohort to further validate the research findings, as well as the results of the model and immunotherapy analysis. The data files of imvigor210 regarding expression, clinical and survival rates, were downloaded the by imvigor210corebiologies package. The survival difference of patients in the high-risk and low-risk groups was obtained by survival and survminer package analysis. Similar methods were utilized to investigate the survival difference and the effect of immunotherapy at various immune checkpoints in high and low risk groups. Drug screening and drug structure analysis We used risk difference genes to screen small molecule drugs in the CMAP database, sorted them according to the connectivity score value and FDR, and screened 6 small molecule drugs that can reduce the gene expression of high-risk groups. Then, we further queried the PubChem database( https://pubchem.ncbi.nlm.nih.gov/ ) to obtain the secondary and tertiary structures of candidate drugs. Results Identification of intersection genes There were a total of 541 individuals diagnosed with ccRCC who were included in the TCGA database, whereas 71 patients were included in the GEO database. The distribution of 58 senescence-related differential genes in ccRCC is depicted (Figs. 1 a and b). In total, 152 genes significantly related to prognosis were screened, and the genes intersecting with differential genes are displayed (Fig. 1 c). Veen calculation results showed 37 intersection genes (Fig. 1 d). The visual circle diagram of the co-expression of intersection genes is displayed in Fig. 1 e. Grouping and verification based on clinical model construction The least absolute shrinkage and selection operator (Lasso) Cox regression analysis demonstrates that 17 out of the 37 cross genes are good candidates for constructing prognostic features (Fig. 2 a-b). The Cox regression analysis results of 17 cross genes used to construct the model are displayed (Fig. 2 c). Using the patients risk score, they were categorized into high-risk and low-risk groups. In the TCGA and GEO databases, we noticed significant OS disparities between high-risk and low-risk groups of patients, as demonstrated (Figs. 2 d-e). Therefore, the area under the ROC curve demonstrates that the constructed model accurately predicts the survival time of patients. The areas under the 1,3, and 5-year ROC curves in TCGA and GEO models are displayed (Figs. 2 f-g). According to the characteristics of the genes constructed by the model, the respective risk curves of TCGA and GEO are obtained, including risk score distribution, survival status and heat map (Figures. 3a-b). In addition, age, tumor grade, and tumor stage were substantially related with the prognosis of patients and may be employed as independent prognostic factors, as shown by univariate and multivariate independent prognostic analyses (p < 0.05) (Figures. 3c-d). Finally, we verified the clinical grouping model to verify whether the constructed model applies to early or later-stage patients. In both the early and late stages, the low-risk group had greater overall survival than the high-risk group (p < 0.05) (Figures. 3e-f). Go analysis and GSEA analysis Go analysis demonstrated that the relationship between cross genes and each go entry (Fig. 4 a). Different colours represent various GO; logFC indicates the degree of gene expression. The darker the colour, the higher the enrichment. High and low-risk groups' active pathways and functions were identified (Fig. 4 b). The abscissa indicates sorted genes, and the ordinate shows enrichment scores. Curves with various colors represent different pathways. The peak of the curve appears at the top left of the abscissa, indicating that these pathways are active in the high-risk group. If the peak appears at the bottom right of the abscissa, this indicates that these pathways are active in the low-risk group. Mutation load and survival analysis of tumor The analysis of the results of the tumor mutation load showed that the high-risk group had a greater amount of tumor mutations than the low-risk group (Figure. 5a). The tumor mutation correlation analysis showed that the relation among tumor mutation frequency and patient risk score was positive. (Figure. 5b). The Kaplan-Meier curve shows that the low-risk group has a greater OS than the high-risk group (Fig. 5 c). The combined analysis of tumor mutation load and risk group demonstrated significant differences in OS among the four groups (Figure. 5d). Variance analysis, immune cell correlation analysis, immune checkpoint difference and survival analysis Through difference analysis, we can appreciate which genes vary among groups with high and low risk groups, and further clarify the differential expression of genes involved in chemokines, growth factors, regulatory factors, proteases and regulators, soluble or abscission receptors or ligands, as well as interleukin (Figure. 6a). The immune cell correlation analysis results show which immune cells are related to the patient's risk score and helps to generate the relevant heatmap (Figure. 6b). The difference in the analysis results of immune checkpoints demonstrated that the genes that correlated to immune checkpoints were different in high and low-risk groups (Figure. 6c). By analyzing the immune checkpoints survival, we obtained the survival curve of immune checkpoint genes that were significantly related to survival. The patients were categorised into four groups according to the target gene expression and risk. The results demonstrated significant differences in OS among the four groups. This study lists the survival curves of PD-1, CTLA-4 and BTLA at common immune checkpoints. (Figures. 6d-f). Analysis of immune escape and immunotherapy The study indicated that the high-risk group had a higher tide score than the low-risk group, showing that the high-risk group is more likely to evade immune therapy (Figure. 7a). There was not a significant difference in the efficacy among the high-risk and low-risk groups when they received immunotherapy (p > 0.05), regardless of whether they received PD-1 or CTLA-4 or PD-1 combined with CTLA-4 (Figures. 7b-e). Model validation and immunotherapy analysis validation of IMvigor210 Each sample in the IMvigor210 database was assigned a risk score using the model's formula. The patients were categorised into high-risk and low-risk groups using their risk score. A comparison of the two groups' rates of survival was carried out. The results demonstrated that the low-risk group had a much greater survival rate than the other groups (Figure. 8a). In imvigor210 database, four groups were divided based on the expression of immune checkpoint genes and patients' risk. Comparisons were made between the four groups' rates of survival. The findings showed that there were substantial disparities in the rates of survival among the four groups (Figures. 8b-c). There was no significant difference in the immune risk score among the reactive and non-reactive groups based on the risk grouping of the imvigor210 database model (p = 0.46) (Figure. 8d). Drug screening and drug structure analysis We screened 6 drugs that could significantly reduce gene expression in high-risk groups according to the drug's connectivity score and FDR value (Table 1 ). Then we searched PubChem to obtain the secondary structure and tertiary structure of 6 the drugs (Fig. 9 – 10 ). Table 1 Six drugs that can reduce gene expression of high-risk groups rank cmap name enrichment fdr 1 enzastaurin -0.598 15.6536 2 voreloxin -0.5879 15.3525 3 BIIB-021 -0.577 15.3525 4 ZM-447439 -0.577 15.3525 5 lovastatin -0.5754 15.3525 6 treprostinil -0.5722 15.3525 Discussion The 5-year survival rate for individuals diagnosed with localised renal carcinoma was approximately 93% between 2008 and 2014. However, for individuals with mRCC, the survival rate decreased to 12%[ 33 ]. The emergence of targeted therapy has raised the survival rate of RCC patients, although the 5-year survival rate of patients with mRCC remains quite low, especially for individuals with poor prognostic factors[ 34 ]. Interleukin-2 (IL-2) and interferon α (IFN- α) Cytokine therapy, represented by, has displayed some benefits in a small number of patients with advanced RCC (aRCC), but it has only proved effective in a limited proportion of patients[ 35 ]. In addition, cytokine therapy is associated with high toxicity levels, limiting its general use[ 36 ]. Now the immunotherapy of mRCC has developed from cytokine to checkpoint inhibitor. It targets immunosuppressive checkpoints, containing programmed cell death-1 (PD-1) receptor, programmed cell death ligand-1 (PD-L1), and cytotoxic T lymphocyte associated protein 4 (CTLA-4)[ 37 ]. There were just a few people who had an objective response to checkpoint inhibitors; others had delayed response; and many people saw no theraputic benefits[ 38 , 39 ]. There are a number of concepts that attempt to explain why checkpoint inhibitor medication may not be effective in some patients. Its expression may be related to the pathogenesis and mechanism of renal tumors. This is because in renal tumors, there are a variety of etiologies, the gene and cellular composition of the microenvironment surrounding the tumor have an effect on the number, function, and localization of immune effector cells, thus it is possible that this has a significant bearing on the body's response to checkpoint inhibitors[ 40 ]. Cell senescence represents one of the most crucial risk factors for cancer patients. Although the close relationship between cell senescence and the tumor development has become evident, the changes related to cell ageing in the renal tumor microenvironment still remain elusive[ 41 ]. The progress of sequencing technology and bioinformatics tools makes it possible to describe the changes related to cell senescence and renal tumor microenvironment. In this study, we comprehensively evaluated the characteristics of immune infiltration and the microenvironment of ccRCC induced by cell senescence, and analyzed and verified the efficacy of immunotherapy in ccRCC induced by cell senescence. This study screened 37 ageing genes significantly related to prognosis in ccRCC. Also, lasso regression analysis depicted that 17 of them were more suitable for constructing the prognosis model. Furthermore, the KM survival analysis of the model's high-risk and low-risk groups reveals that TCGA and GEO data samples have significant differences in OS, and the ROC curve results further show that the model provides accurate and dependable results. Furthermore, age was found to be an independent prognostic factor for ccRCC by univariate and multivariate Cox regression analyses, suggesting a close link among ageing and cancer progression, which is consistent with previous research results[ 41 , 42 ]. To further evaluate whether the constructed model applies to patients of various clinical stages, we analysed the survival rate of the early and late high-risk groups, which revealed results consistent with the our expectations. Gene enrichment analysis demonstrated that adipocytokines, cytokines and receptors, ErbB, hematopoietic cell lineage and immunity were active in high-risk group. Recent research has demonstrated that certain hormones derived from adipose tissue may have a major impact on the growth and proliferation of tumor stroma and internal malignant cells[ 43 ]. All immunological responses involve cytokines in multiple intricate ways. Cytokine interactions are comprised of intricate and interrelated positive and negative feedback systems that provide homeostasis and immune regulation[ 44 ]. Age-related immune response alters in the hematopoietic system due to decreased hematopoietic stem cell function, which ultimately contribute to increased susceptibility to infection, autoimmunity, anemia, and myeloproliferative diseases[ 45 ]. ErbB receptors are overexpressed or mutated in many cancers; and its overexpression and over-activation are associated with poor prognosis, drug resistance, cancer metastasis and low survival[ 46 ]. The human immune system is responsible for identifying self and non-self to protect the body from exogenous and endogenous diseases. In addition, the immune system recognizes many threats and eliminates them in order to maintain homeostasis[ 47 ]. The primary immunodeficiency and tight junction pathway were significant in the low-risk group. Congenital genetic defects or dysfunction of one or other immune system components may disturb the complex physiological balance and functional bodily homeostasis, thus reducing its preventive ability and even actively promoting the formation of tumor diseases[ 48 ]. Disturbance of the expression, function, or disruption of tight junction protein integrity are associated with various diseases, including skin, intestinal and lung diseases, as well as various forms of cancers[ 49 ]. However, greater number of studies are required to elucidate the mechanistic links between cancer and cell senescence. This study concluded that the tumor mutation load of the high-risk group was higher, while the survival rate of the group with high tumor mutation was lower. Because chemokines, growth factors, regulatory factors, proteases and regulators, soluble or exfoliative receptors or ligands, as well as Interleukins are crucial for the regulation of cancer cells and immune cells, they are able to enhance cytotoxicity and play a wide range of anti-tumor activities. Therefore, this study investigated the differential gene expression of high- and low-risk groups in these aspects, which can be used for further immunotherapy studies. Cellular immunotherapy is a novel form of tumor therapy that has a remarkable curative effect. It is a novel type of anti-cancer autoimmune therapy. Therefore, this study evaluated the association between immune cells and patient risk scores, and analyzed the immune checkpoints on immune cells. The results also demonstrated the differential expression of model genes at different immune checkpoints in high and low-risk groups. These results have a strong guiding significance for future research. Immunotherapy was found to have no significant effect on high-risk or low-risk groups despite significant differences in tumor mutation load, immune cell infiltration expression, and the differential expression of immune checkpoints. IMvigor database verification results were consistent with this study's results. Therefore, despite the advantages of immune checkpoints, the overall effect of immunity will be affected by the different genetic composition of tumors and the cellular composition of tumor microenvironment in different etiological types[ 33 ]. The mechanism of ICI drug resistance can be primary or congenital, or secondary or acquired[ 50 ]. They include the abnormal expression of MHC class I molecules and the expression of immunosuppressive cytokines. In addition to the above two possible mechanisms, regulatory T cells (Tregs), regulatory B cells (Bregs), myelogenous suppressor cells (MDSCs), and tumor-associated macrophages (TAMs) are all capable of inducing an immunosuppressive response throughout the process of tumor immune escape. However, the immune escape pathways mediated by Tregs, Bregs, MDSCs and TAMs have not been thoroughly studied. related research focuses mostly on the substances provided by immunosuppressive cells that can cause immunological escape and perform an immunosuppressive effect, as well as their signal transduction pathways and the interaction between immunosuppressive cells and other immune cells[ 51 ]. Our study reveals that through enhancing sugar chains and glycosylation, tumor cells may be able to evade anti-tumor immunity and even contribute to regulating immune cell activity to enhance tumor growth[ 52 ]. This will become a new field in the near future. Finally, based on drug screening analysis of the differences of risk pertaining to the genes, this study identified 6 drugs that can significantly reduce gene expression in high-risk groups. Enzastaurin is a selective inhibitor of protein kinase C β Active new oral anti-tumor drugs, used to treat solid cancer and blood cancer, participate in the active AKT and MAPK signalling pathways in many cancers[ 53 ]. Voreoxin, an anticancer quinolone derivative, has shown strong activity in tumor models in vivo and in vitro[ 54 ]. BIIB-021 is a synthetic HSP90 inhibitor, and the overexpression of HSP90 is a factor in tumor development. Monotherapy with HSP90 inhibitors has achieved some success[ 55 ]. ZM447,439 (ZM) is an auroral selective ATP competitive inhibitor, which interferes with spindle integrity checkpoint and chromosome separation. Studies have confirmed that laser kinase is a potential molecular target of ZM for cancer treatment[ 56 ]. Lovastatin has a significant inhibitory effect on the activity of cancer cells in a variety of cancers (such as breast cancer, liver cancer, cervical cancer, lung cancer and colon cancer). At the same time, Lovastatin has been shown to also increase the sensitivity of some types of cancer cells to chemotherapy drugs and enhance their therapeutic effect[ 57 ]. Tryprostinil is an EP2 receptor agonist; EP2 receptor activation engages β-arrestin in a G-protein-independent pathway that promotes tumor cell growth and migration[ 58 ]. Notably, this study has several limitations. First, these survival related cross genes were identified from the TCGA database, where patients were mainly white or American. Considering the genetic heterogeneity, these cross genes need to be verified in more databases. Secondly, this study lacks in vivo and in vitro experiments to further verify the risk difference of genes and selected drugs. Finally, this study did not further study the possible mechanism of immune escape. Conclusion This study confirmed that immunotherapy is not ideal for treating ccRCC induced by cell senescence. Although the specific immune escape mechanism remains clear, it provides a basis and reference for clinical immunotherapy. Of course, further research and exploration of corresponding clinical trials are required in fruitier follow-up research. Abbreviations ccRCC, clear cell renal cell carcinoma; aRCC, advanced RCC; mccRCC, metastatic clear cell renal cell carcinoma; KM, Kaplan-Meier; SASP, secretory phenotype; ICI, immune checkpoint inhibitors; ORR, overall response rates; lasso, least absolute contraction and selection operator; TAMs, tumor-associated macrophages. Declarations Ethics approval and consent to participate Not applicable. All data sets used in this work are obtained from public databases and are provided free of charge. This work does not include any experiments on humans or animals. Patients involved in the public database have been registered after ethical approval. In addition, this study was reviewed by the Ethics Committee (Ethics Committee of Gansu Provincial People's Hospital) and was deemed to be exempt from ethical approval. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This study was supported by a grant from Gansu Provincial Hospital (17GSSY3-4). Author Contributions The research concept and design were carried out by XW and HL. XZ, MZ, YL, XW and XL carried out material preparation, data collection and analysis. The first draft was written by XW, HL and XZ. All authors commented on the previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements The information of this study comes from TCGA, GEO, CMAP and other databases. We thank them for providing the data sources used in our research. We thank Charles worth for the language editing assistance provided in this manuscript. We also thank the state-owned student of bioinformatics (WeChat official account). Availability of data and material Publicly available datasets were analyzed in this study. The results published here are mainly based on the generated data provided by TCGA(https://portal.gdc.cancer.gov/)、GEO(https://www.ncbi.nlm.nih.gov/gds/)、Ageing Atlas database(https://ngdc.cncb.ac.cn/aging/index)、CIBERSORT website(https://cibersort.stanford.edu/download.php)、National Center for Biotechnology Information website(https://www.immport.org/resources)、TIDE(http://tide.dfci.harvard.edu/)、TCIA(https://tcia.at/home)、PubChem database(https://pubchem.ncbi.nlm.nih.gov/). References Siegel RL, Miller KD, Jemal A: Cancer statistics, 2019 . CA-CANCER J CLIN 2019, 69 (1):7-34. 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GENE DEV 2014, 28 (2):99-114. Gorgoulis V, Adams PD, Alimonti A, Bennett DC, Bischof O, Bishop C, Campisi J, Collado M, Evangelou K, Ferbeyre G et al : Cellular Senescence: Defining a Path Forward . CELL 2019, 179 (4):813-827. Loo TM, Miyata K, Tanaka Y, Takahashi A: Cellular senescence and senescence-associated secretory phenotype via the cGAS-STING signaling pathway in cancer . CANCER SCI 2020, 111 (2):304-311. Campisi J: Aging, cellular senescence, and cancer . ANNU REV PHYSIOL 2013, 75 :685-705. McHugh D, Gil J: Senescence and aging: Causes, consequences, and therapeutic avenues . J CELL BIOL 2018, 217 (1):65-77. Takeuchi S, Takahashi A, Motoi N, Yoshimoto S, Tajima T, Yamakoshi K, Hirao A, Yanagi S, Fukami K, Ishikawa Y et al : Intrinsic cooperation between p16INK4a and p21Waf1/Cip1 in the onset of cellular senescence and tumor suppression in vivo . CANCER RES 2010, 70 (22):9381-9390. Muñoz-Espín D, Serrano M: Cellular senescence: from physiology to pathology . NAT REV MOL CELL BIO 2014, 15 (7):482-496. Bernadotte A, Mikhelson VM, Spivak IM: Markers of cellular senescence. Telomere shortening as a marker of cellular senescence . Aging (Albany NY) 2016, 8 (1):3-11. Wajapeyee N, Serra RW, Zhu X, Mahalingam M, Green MR: Oncogenic BRAF induces senescence and apoptosis through pathways mediated by the secreted protein IGFBP7 . CELL 2008, 132 (3):363-374. Acosta JC, O'Loghlen A, Banito A, Guijarro MV, Augert A, Raguz S, Fumagalli M, Da CM, Brown C, Popov N et al : Chemokine signaling via the CXCR2 receptor reinforces senescence . CELL 2008, 133 (6):1006-1018. Kuilman T, Michaloglou C, Vredeveld LC, Douma S, van Doorn R, Desmet CJ, Aarden LA, Mooi WJ, Peeper DS: Oncogene-induced senescence relayed by an interleukin-dependent inflammatory network . CELL 2008, 133 (6):1019-1031. Ovadya Y, Landsberger T, Leins H, Vadai E, Gal H, Biran A, Yosef R, Sagiv A, Agrawal A, Shapira A et al : Impaired immune surveillance accelerates accumulation of senescent cells and aging . NAT COMMUN 2018, 9 (1):5435. Freund A, Orjalo AV, Desprez PY, Campisi J: Inflammatory networks during cellular senescence: causes and consequences . TRENDS MOL MED 2010, 16 (5):238-246. Yoshimoto S, Loo TM, Atarashi K, Kanda H, Sato S, Oyadomari S, Iwakura Y, Oshima K, Morita H, Hattori M et al : Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome . NATURE 2013, 499 (7456):97-101. Faget DV, Ren Q, Stewart SA: Unmasking senescence: context-dependent effects of SASP in cancer . NAT REV CANCER 2019, 19 (8):439-453. Dimri GP, Lee X, Basile G, Acosta M, Scott G, Roskelley C, Medrano EE, Linskens M, Rubelj I, Pereira-Smith O et al : A biomarker that identifies senescent human cells in culture and in aging skin in vivo . P NATL ACAD SCI USA 1995, 92 (20):9363-9367. Collado M, Gil J, Efeyan A, Guerra C, Schuhmacher AJ, Barradas M, Benguría A, Zaballos A, Flores JM, Barbacid M et al : Tumour biology: senescence in premalignant tumours . NATURE 2005, 436 (7051):642. Shimi T, Butin-Israeli V, Adam SA, Hamanaka RB, Goldman AE, Lucas CA, Shumaker DK, Kosak ST, Chandel NS, Goldman RD: The role of nuclear lamin B1 in cell proliferation and senescence . GENE DEV 2011, 25 (24):2579-2593. Freund A, Laberge RM, Demaria M, Campisi J: Lamin B1 loss is a senescence-associated biomarker . MOL BIOL CELL 2012, 23 (11):2066-2075. Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM: The Cancer Genome Atlas Pan-Cancer analysis project . NAT GENET 2013, 45 (10):1113-1120. Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository . NUCLEIC ACIDS RES 2002, 30 (1):207-210. Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA, Steiner D et al : Determining cell type abundance and expression from bulk tissues with digital cytometry . NAT BIOTECHNOL 2019, 37 (7):773-782. Bergerot P, Lamb P, Wang E, Pal SK: Cabozantinib in Combination with Immunotherapy for Advanced Renal Cell Carcinoma and Urothelial Carcinoma: Rationale and Clinical Evidence . MOL CANCER THER 2019, 18 (12):2185-2193. Pal SK, Ghate SR, Li N, Swallow E, Peeples M, Zichlin ML, Perez JR, Agarwal N, Vogelzang NJ: Real-World Survival Outcomes and Prognostic Factors Among Patients Receiving First Targeted Therapy for Advanced Renal Cell Carcinoma: A SEER-Medicare Database Analysis . CLIN GENITOURIN CANC 2017, 15 (4):e573-e582. McDermott DF: Immunotherapy of metastatic renal cell carcinoma . CANCER-AM CANCER SOC 2009, 115 (10 Suppl):2298-2305. Rosenberg SA: Interleukin 2 for patients with renal cancer . Nat Clin Pract Oncol 2007, 4 (9):497. McKay RR, Bossé D, Choueiri TK: Evolving Systemic Treatment Landscape for Patients With Advanced Renal Cell Carcinoma . J CLIN ONCOL 2018:O2018790253. Bellmunt J, de Wit R, Vaughn DJ, Fradet Y, Lee JL, Fong L, Vogelzang NJ, Climent MA, Petrylak DP, Choueiri TK et al : Pembrolizumab as Second-Line Therapy for Advanced Urothelial Carcinoma . NEW ENGL J MED 2017, 376 (11):1015-1026. Motzer RJ, Escudier B, McDermott DF, George S, Hammers HJ, Srinivas S, Tykodi SS, Sosman JA, Procopio G, Plimack ER et al : Nivolumab versus Everolimus in Advanced Renal-Cell Carcinoma . NEW ENGL J MED 2015, 373 (19):1803-1813. Chen DS, Mellman I: Elements of cancer immunity and the cancer-immune set point . NATURE 2017, 541 (7637):321-330. Cao J, Li J, Yang X, Li P, Yao Z, Han D, Ying L, Wang L, Tian J: Transcriptomics analysis for the identification of potential age-related genes and cells associated with three major urogenital cancers . SCI REP-UK 2021, 11 (1):641. Kendal WS: Dying with cancer: the influence of age, comorbidity, and cancer site . CANCER-AM CANCER SOC 2008, 112 (6):1354-1362. Housa D, Housova J, Vernerova Z, Haluzik M: Adipocytokines and cancer . PHYSIOL RES 2006, 55 (3):233-244. Lippitz BE: Cytokine patterns in patients with cancer: a systematic review . LANCET ONCOL 2013, 14 (6):e218-e228. Pinho S, Frenette PS: Haematopoietic stem cell activity and interactions with the niche . NAT REV MOL CELL BIO 2019, 20 (5):303-320. Wang Z: ErbB Receptors and Cancer . Methods Mol Biol 2017, 1652 :3-35. Abbott M, Ustoyev Y: Cancer and the Immune System: The History and Background of Immunotherapy . SEMIN ONCOL NURS 2019, 35 (5):150923. Haas OA: Primary Immunodeficiency and Cancer Predisposition Revisited: Embedding Two Closely Related Concepts Into an Integrative Conceptual Framework . FRONT IMMUNOL 2018, 9 :3136. Zeisel MB, Dhawan P, Baumert TF: Tight junction proteins in gastrointestinal and liver disease . GUT 2019, 68 (3):547-561. Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A: Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy . CELL 2017, 168 (4):707-723. Jian Y, Yang K, Sun X, Zhao J, Huang K, Aldanakh A, Xu Z, Wu H, Xu Q, Zhang L et al : Current Advance of Immune Evasion Mechanisms and Emerging Immunotherapies in Renal Cell Carcinoma . FRONT IMMUNOL 2021, 12 :639636. Bull C, den Brok MH, Adema GJ: Sweet escape: sialic acids in tumor immune evasion . Biochim Biophys Acta 2014, 1846 (1):238-246. Bourhill T, Narendran A, Johnston RN: Enzastaurin: A lesson in drug development . CRIT REV ONCOL HEMAT 2017, 112 :72-79. Hawtin RE, Stockett DE, Byl JA, McDowell RS, Nguyen T, Arkin MR, Conroy A, Yang W, Osheroff N, Fox JA: Voreloxin is an anticancer quinolone derivative that intercalates DNA and poisons topoisomerase II . PLOS ONE 2010, 5 (4):e10186. Birbo B, Madu EE, Madu CO, Jain A, Lu Y: Role of HSP90 in Cancer . INT J MOL SCI 2021, 22 (19). Long ZJ, Xu J, Yan M, Zhang JG, Guan Z, Xu DZ, Wang XR, Yao J, Zheng FM, Chu GL et al : ZM 447439 inhibition of aurora kinase induces Hep2 cancer cell apoptosis in three-dimensional culture . CELL CYCLE 2008, 7 (10):1473-1479. Xie L, Zhu G, Shang J, Chen X, Zhang C, Ji X, Zhang Q, Wei Y: An overview on the biological activity and anti-cancer mechanism of lovastatin . CELL SIGNAL 2021, 87 :110122. Jiang J, Dingledine R: Prostaglandin receptor EP2 in the crosshairs of anti-inflammation, anti-cancer, and neuroprotection . TRENDS PHARMACOL SCI 2013, 34 (7):413-423. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2492545","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":173008313,"identity":"3e24e173-4f51-43c5-84c6-f00669cd055a","order_by":0,"name":"Xiangxiang Zhang","email":"","orcid":"","institution":"Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiangxiang","middleName":"","lastName":"Zhang","suffix":""},{"id":173008314,"identity":"e8f6ac71-4747-49a6-b294-0a4d2bfc61e6","order_by":1,"name":"Xiaoping Wang","email":"","orcid":"","institution":"Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Wang","suffix":""},{"id":173008315,"identity":"f50fffc3-3cbe-4eff-abf3-e6c0da561c59","order_by":2,"name":"Hengping Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACefnDBw58+GFjZ3+8+QBxWgxnsCU+nNmTlsxw5lgCkdbc4DE25mE7zNhwI8eAOB2Ms9vSJGfwMDMzNuR8vPGGwU5Ot4GAFnaZw8ckPliw8TEznN1sOYch2djsACFbGtJAtvAwszH2bpPmYTiQuI2QFoYDOWbSPGwSjD3MPM+I1HIjB+R9A8YZbDxsxGkx7DkGCuSEZAMeNmPLOQZE+EWevRkUlf/tDOQfP7zxpsJOjqAWFCDBQ2TUIGshVccoGAWjYBSMCAAACsxDiSBOLlcAAAAASUVORK5CYII=","orcid":"","institution":"Gansu Provincial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hengping","middleName":"","lastName":"Li","suffix":""},{"id":173008316,"identity":"6a5788e7-dd99-4e40-a482-5e11d0f23f1c","order_by":3,"name":"Xiangrong Wang","email":"","orcid":"","institution":"Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiangrong","middleName":"","lastName":"Wang","suffix":""},{"id":173008317,"identity":"f6d7b62d-6244-4072-be10-d6598f7a5d9c","order_by":4,"name":"Mao Zhang","email":"","orcid":"","institution":"Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mao","middleName":"","lastName":"Zhang","suffix":""},{"id":173008318,"identity":"b37a4084-780e-4dc6-b663-d2d5e250ce01","order_by":5,"name":"Yang Liu","email":"","orcid":"","institution":"Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Liu","suffix":""},{"id":173008322,"identity":"d45b9bee-206c-4067-8249-6e5a33010d69","order_by":6,"name":"Xuanpeng Li","email":"","orcid":"","institution":"Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuanpeng","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-01-18 16:29:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2492545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2492545/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":32534629,"identity":"d268313c-7d5a-452d-8f37-f8d2daca99e4","added_by":"auto","created_at":"2023-02-06 14:58:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":942531,"visible":true,"origin":"","legend":"\u003cp\u003eGrouping and verification based on clinical model construction. (a–b) Lasso Cox regression analysis showed 17 good candidate genes for constructing prognostic characteristics of the model. (c) Univariate Cox regression analysis of 17 cross genes were used to construct prediction model. (d–e) KM survival analysis of high and low-risk groups in TCGA and GEO. (f–g) ROC curve evaluates the 1-, 3-, and 5-year prediction efficiencies of the prediction model (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-2492545/v1/83aded35fd63533cc2552f73.png"},{"id":32535895,"identity":"d26c9271-f2e4-408f-a9ac-69f7b84d80eb","added_by":"auto","created_at":"2023-02-06 15:06:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":929941,"visible":true,"origin":"","legend":"\u003cp\u003e(a–b) Risk curves of TCGA and GEO , including risk score distribution, survival status and heat map.(c) Univariate Cox regression analysis showed that age, tumor grade and stage were significantly correlated with overall survival.(d) Multivariate Cox regression analysis showed that age, tumor grade and stage were independent prognostic factors for OS.(e–f) The low-risk group had a longer overall survival than the high-risk group in the early and late stages (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-2492545/v1/892705c2a5e3e93b22d55ade.png"},{"id":32536947,"identity":"1dff3e02-64d4-4994-917d-29c365e9d1dd","added_by":"auto","created_at":"2023-02-06 15:22:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1061556,"visible":true,"origin":"","legend":"\u003cp\u003eGo and GSEA analyses. (a) This figure shows the relationship between the intersection gene and each GO. The left half circle represents the gene name, and the right half circle represents the name of each GO. Different colours represent different GO. LogFC represents the degree of gene expression. The darker the colour, the higher the degree of enrichment. (b) Identifying the active pathways or functions of high and low-risk groups.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-2492545/v1/2091a5455270132cb8d06ac1.png"},{"id":32536346,"identity":"81e95f54-d693-465d-bc39-dbb2713cbadd","added_by":"auto","created_at":"2023-02-06 15:14:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":415953,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of tumor mutation burden and survival. (a) The high-risk group had a higher tumor mutation load than the low-risk group. (b) The tumor mutation correlation analysis showed that the tumor mutation frequency was positively correlated with the patient risk score. (c) Kaplan Meier curve demonstrates that the low-risk group has a higher OS than the high-risk group. (d) The combined analysis of tumor mutation load and risk group showed significant differences in OS among the four groups (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-2492545/v1/d74af226850fe6c2c457f8fe.png"},{"id":32535901,"identity":"3a3a457d-c437-4968-8284-d13fc001eeee","added_by":"auto","created_at":"2023-02-06 15:06:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1173748,"visible":true,"origin":"","legend":"\u003cp\u003eVariance analysis,immune cell correlation analysis, immune checkpoint difference and survival analysis\u003cstrong\u003e \u003c/strong\u003e(a)\u003cstrong\u003e \u003c/strong\u003eThe differential analysis identifies differentially expressed genes in the high-risk and low-risk groups, and further identifies differentially expressed genes involved in the following: chemokines, growth factors (and its regulatory factors), proteases (and its regulatory factors), soluble or abscission receptors or ligands, and interleukins. (b) Correlation analysis between immune cells and patient risk scores: the abscissa represents the immune cells, and the ordinate represents the risk scores of model genes. Red represents positive correlation and blue represents negative correlation.(c) Immune-checkpoint related genes differed between high and low-risk groups. The abscissa represents genes related to immune checkpoints, and the ordinate represents gene expression.(d–f) Survival analysis curve of common immune checkpoints PD-1, CTLA-4 and BTLA (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-2492545/v1/6522dc53de0ffdf76df505cc.png"},{"id":32535898,"identity":"dc06e418-4bf5-46f4-905f-3627db718b33","added_by":"auto","created_at":"2023-02-06 15:06:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":578727,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of immune escape and immunotherapy. (a) The tide score of the high-risk group was higher than that of the low-risk group. The abscissa is the risk group, and the ordinate is the tide score. (b-e) There was no significant difference in immune scores between high and low-risk groups when receiving PD-1 or CTLA-4 or PD-1 combined with CTLA-4. The abscissa is the group, and the ordinate is the immune score (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-2492545/v1/7b861bceba164cd2d9f4d714.png"},{"id":32537448,"identity":"00c8259a-fef8-4caa-bf5f-4ba813f0dfcf","added_by":"auto","created_at":"2023-02-06 15:30:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":519475,"visible":true,"origin":"","legend":"\u003cp\u003eModel validation and immunotherapy analysis validation of IMvigor210. (a) In imvigor210 database, the survival rate of the low-risk group is higher than that of the high-risk group. (b) There were significant differences in survival rates between groups when receiving PD-1 treatment. (c) There were significant differences in survival rates between groups when receiving CTLA-4 treatment. (d) There was no significant difference in the immune risk score between the reactive group and the non-reactive group (p=0.46).\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-2492545/v1/59460d5b3731e556a5cd7318.png"},{"id":32534637,"identity":"434a94f2-50c3-4f43-aa46-a8a2cd2e221f","added_by":"auto","created_at":"2023-02-06 14:58:15","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":358824,"visible":true,"origin":"","legend":"\u003cp\u003eDrug secondary structure.(a. enzastaurin b. voreloxin c. BIIB-021 d. ZM-447439 e. lovastatin f. treprostinil).\u003c/p\u003e","description":"","filename":"fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-2492545/v1/03dfe0f77ffb498d35f9c136.png"},{"id":32534630,"identity":"012ae877-75bd-4bae-9203-62cfffaf0ac3","added_by":"auto","created_at":"2023-02-06 14:58:15","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":399919,"visible":true,"origin":"","legend":"\u003cp\u003eTertiary structure of drug. (a. enzastaurin b. voreloxin c. BIIB-021 d. ZM-447439 e. lovastatin f. treprostinil).\u003c/p\u003e","description":"","filename":"fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-2492545/v1/9f624ce33e218213cc5932c3.png"},{"id":34567590,"identity":"70659c79-dc0b-48fd-bf7f-a339cbf790be","added_by":"auto","created_at":"2023-03-21 07:59:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3666521,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2492545/v1/542dc4d7-b40b-4a4c-89b0-5803c5d0c46c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative analysis of immune infiltration and microenvironment characteristics in renal clear cell carcinoma induced by cell senescence","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRenal cell carcinoma (RCC) is one of the most prevalent cancers of the urinary system. The incidence rate of RCC accounts for about 5% of all newly diagnosed cases of cancer in males and 3% of total cases in females[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. CcRCC is the most prevalent (75\u0026ndash;80%) subtype of RCC and the most studied subtype of RCC[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Surgical resection may be curative for localized (organ localized) diseases. Unfortunately, 25\u0026ndash;30% of individuals are diagnosed with distant metastasis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and approximately 40% of patients have recurrence after surgical resection[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. All RCC subtypes are largely unresponsive to conventional chemotherapy or radiotherapy[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In recent years, the treatment protocol for metastatic clear cell renal cell carcinoma (mccRCC) has significantly changed due to the emergence of ICI anti-PD-1 and anti-PD-L1, which is used as monotherapy and in combination with anti-CTLA-4 or antiangiogenic agents[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although the overall response rate (ORR) of the prognosis of mccRCC has been greatly improved by ICI combined therapy, there are still many patients with poor effect of immunotherapy due to drug resistance. There are numerous factors that contribute to primary or acquired medication resistance, including the internal factors of patients, tumor cells, and microenvironments[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. We aim to investigate the function of tumor microenvironment components in disease progression and ICI resistance, and explore effective personalized treatment regimens. Despite substantial research into tumorigenesis and development, the etiology of ccRCC and method of carcinogenesis remains unknown[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Cell senescence may have a significant role in the occurrence, progression, and immune regulation of ccRCC.\u003c/p\u003e \u003cp\u003eCell senescence is a process that occurs in diploid cells and can induce stable growth arrest with considerable phenotypic changes, including chromatin remodelling, metabolic reprogramming, enhanced autophagy, and the implementation of complex pro-inflammatory secretory groups[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Cellular senescence is a cell condition related to a variety of physiological processes and age-related illnesses[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Cell senescence has always been regarded as a tumor inhibition mechanism to prevent the abnormal proliferation of damaged cells in benign and precancerous tumors[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The tumor suppressive pathways of p53/p21 and p16 INK4a/Rb are responsible for inhibiting growth[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies have confirmed that the double knockout of p16INK4a and p21WAF1/CIP1 genes increases the speed of cancer development[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Also, cellular senescence has been shown to be an essential tumor suppressive mechanism in vivo, and that stimulants that induce genotoxicity, carcinogenesis, oxidation and replication stress will trigger cellular senescence[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Many studies in the last decade have depicted that senescence cells produce a variety of proteins, such as inflammatory cytokines, chemokines, growth factors, and matrix metalloproteinase (MMP), which are termed senescence-associated secretory phenotype (SASP) \u0026mdash;fostering cellular senescence via autocrine and paracrine signalling[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. During the process of ageing, senescent cells accumulate due to the failure of senescence surveillance caused by the decline of immune function[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, the long-term secretion of SASP factor may promote the development of cancer[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The senescent of the immune system is also the reason for the damage of tumor immune surveillance and the increased risk of tumor occurrence[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Senescent cells only have several nonexclusive markers, rather than universal or specific biomarkers.\u003c/p\u003e \u003cp\u003eHistochemical staining for senescence-associated β-galactosidase (SA-β-GAL) is the most prevalent marker of cell senescence[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. P16INK4 is a tumor suppressor protein, which is another marker regularly used to identify senescent cells in cultures and tissues. Other senescence markers related to p53 trans activated targets, include up-regulated expression of tumor suppressor proteins DEC1 and DcR2. Senescent cells also significantly downregulated lamin B1 expression[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These marks are rarely used, because their effects are not yet widely validated. Although there are numerous prognostic markers, the variety and complexity of the tumor microenvironment increase the difficulties of immunotherapy and diminish its efficacy. Therefore, an accurate understanding of cellular senescence heterogeneity helps manage individualised therapy.\u003c/p\u003e \u003cp\u003eWe aim to fully investigate the heterogeneous immune molecular phenotype and tumor microenvironment characteristics of renal cell carcinoma caused by cell senescence. Using high-throughput sequencing data and clinical information of ccRCC samples collected from the public database, we identified 37 prognosis-related senescence genes. Using lasso regression analysis, we screened 17 model construction genes and grouped them according to risk scores. The grouping results demonstrated substantial differences in tumor mutation and immune checkpoint analysis among high-risk and low-risk groups, but there were no significant difference when patients received immunotherapy. Imvigor210 database was utilised to verify the results, which were consistent with the study. The results of this study suggest that cancer caused by cell senescence may have the possibility of immune escape when receiving immunotherapy. In the future, it is necessary to choose other immune checkpoints for further exploration.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Extraction and Processing\u003c/h2\u003e \u003cp\u003eThe GDC-client tool was used to obtain 541 ccRCC patients' and 72 normal tissue mRNA-seq data (counting format), single nucleotide variation (SNV) data, and clinical data from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We obtained transcriptome data and clinical data of 28 normal renal tissues in GTEx database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://commonfund.nih.gov/GTEx/\u003c/span\u003e\u003cspan address=\"https://commonfund.nih.gov/GTEx/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The log2-transformed mRNA-seq gene expression value can be used for further investigation. GSE29609 and GSE40912, which includes 71 ccRCC samples and high-throughput sequencing data, was also retrieved from the GEO database for external validation. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The Ageing Atlas database identified 279 age-related genes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/aging/index).(We\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/aging/index).(We\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e utilised a CIBERSORT algorithm tool to evaluate the composition of immune cells based on the gene expression patterns of various organs to evaluate the immune infiltration in samples.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] LM22 gene signature and CIBERSORT source code were obtained from the CIBERSORT website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersort.stanford.edu/download.php\u003c/span\u003e\u003cspan address=\"https://cibersort.stanford.edu/download.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Moreover, the National Center for Biotechnology website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.immport.org/resources\u003c/span\u003e\u003cspan address=\"https://www.immport.org/resources\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilised to acquire 47 immune checkpoint genes. The scoring files for immune escape and immunotherapy can be collected from tumor immune dysfunction and exclusion (TIDE)(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The scoring file for immunotherapy analysis was obtained from The Cancer Immunome Atlas (TCIA)(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcia.at/home\u003c/span\u003e\u003cspan address=\"https://tcia.at/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Relevant verification files of the IMvigor210 database are downloaded from the IMvigor210corebiologies package using the R software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eScreening of intersection genes of differential genes and prognostic genes\u003c/h2\u003e \u003cp\u003eIn TCGA and GEO databases, the expression of senescence genes that have been retrieved was extracted. There were 541 ccRCC samples and 100 para carcinoma samples evaluated using the edgeR programme, with the log2FC| \u0026gt;1.5 and p less than 0.05 being used as the criterion for differentially expressed genes (DEGs). The edger package was utilized to identify the genes significantly related to the prognosis from TCGA database, and the cutoff value was set to coxPfilter\u0026thinsp;=\u0026thinsp;0.05. Veen algorithm was applied to DEGs and prognostic genes obtained from TCGA database, and cross genes were obtained from ccRCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of prognosis model and Grouping\u003c/h2\u003e \u003cp\u003eThe prognostic model was built using data from the TCGA database as a training set, and data from the GEO database was utilised as a testing group to ensure the model of prognosis accuracy. In the prognostic model, each of the gene's expression was used to calculate a risk score for each sample. They were then divided into two categories: those with a high-risk score and those with a low-risk score. In the GEO database, the median value in the TCGA database is also used to divide modeling gene into high and low-risk groups. First Kaplan-Meier survival curve can be used to compare the overall survival rates of high and low-risk groups. The model's accuracy in predicting patient survival was then evaluated using receiver operating characteristic (ROC) curves. A multivariate/univariate Cox regression analyses were also performed to assess if the risk score was affected by other clinical prognostic factors such as age, gender, grade, or stage. Lastly, we used an independent data set (GSE29609 and GSE40912) to test whether the prognostic characteristics of cross genes have a strong ability to predict patient survival.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGene Ontology (GO) and Gene Set Enrichment Analysis (GSEA)\u003c/h2\u003e \u003cp\u003eGO analysis was executed to evaluate cross gene function. We conducted GSEA analysis to evaluate important functional phenotypes between groups at high and low risk. considering the enrichment score (ES) as the evaluation index; an ES\u0026thinsp;\u0026gt;\u0026thinsp;0 indicates that this pathway or function is active in the high-risk group. On the contrary, this pathway or function is active in the low-risk group. Lastly, the gridExtra package is used to visualise the results of GSEA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of tumor mutation burden and survival\u003c/h2\u003e \u003cp\u003eSNV data from the TCGA database was downloaded for patients in the high and low-risk categories, and we utilised the ggpubr software package to examine the tumor mutation burden of these patients. First, the optimal cutoff value was obtained by analysis, and Tumors with high and low mutation loads were separated. Then the tumor mutation load group and risk group were combined, and the survival and survminer packages were utilized to evaluate the survival of patients with tumor mutation load combined with risk.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVariance analysis, immune cell correlation analysis, immune checkpoint difference and survival analysis\u003c/h2\u003e \u003cp\u003eTo clarify the correlation between immune cells and patient risk scores, we downloaded the pan-cancer immune cell infiltration file and used R-package analysis to obtain the heat map of the correlation. Similarly, to investigate the differences in terms of genes associated to immune tests in high-risk and low-risk groups, we downloaded 47 immune checkpoint genes, and obtained the correlation analysis of checkpoint genes through the ggpubr package analysis. Survival and survminer packages were utilized to analyze the survival of high and low gene expression and high and low risk-groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of immune escape and immunotherapy\u003c/h2\u003e \u003cp\u003eTo detect the effect of immunotherapy and the potential of immune escape in patients with high and low-risk groups. We first entered the TCGA risk file into the tide database to obtain the tide risk score. Then the ggpubr package was used to analyze the difference in tide scores in high and low-risk groups. The higher the tide score, the greater the possibility of immune escape when receiving immunotherapy. At the same time, we downloaded the scoring file of immunotherapy from the TCIA database, and used the ggpubr package analysis to obtain the scores of PD-1 and CTLA-4 immunotherapy between high-risk and low-risk groups. The higher the score, the better the effect of immunotherapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eModel validation and immunotherapy analysis validation of IMvigor210\u003c/h2\u003e \u003cp\u003eWe used the IMvigor210 cohort to further validate the research findings, as well as the results of the model and immunotherapy analysis. The data files of imvigor210 regarding expression, clinical and survival rates, were downloaded the by imvigor210corebiologies package. The survival difference of patients in the high-risk and low-risk groups was obtained by survival and survminer package analysis. Similar methods were utilized to investigate the survival difference and the effect of immunotherapy at various immune checkpoints in high and low risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDrug screening and drug structure analysis\u003c/h2\u003e \u003cp\u003eWe used risk difference genes to screen small molecule drugs in the CMAP database, sorted them according to the connectivity score value and FDR, and screened 6 small molecule drugs that can reduce the gene expression of high-risk groups. Then, we further queried the PubChem database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to obtain the secondary and tertiary structures of candidate drugs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of intersection genes\u003c/h2\u003e \u003cp\u003eThere were a total of 541 individuals diagnosed with ccRCC who were included in the TCGA database, whereas 71 patients were included in the GEO database. The distribution of 58 senescence-related differential genes in ccRCC is depicted (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and b). In total, 152 genes significantly related to prognosis were screened, and the genes intersecting with differential genes are displayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Veen calculation results showed 37 intersection genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The visual circle diagram of the co-expression of intersection genes is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGrouping and verification based on clinical model construction\u003c/h2\u003e \u003cp\u003eThe least absolute shrinkage and selection operator (Lasso) Cox regression analysis demonstrates that 17 out of the 37 cross genes are good candidates for constructing prognostic features (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-b). The Cox regression analysis results of 17 cross genes used to construct the model are displayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Using the patients risk score, they were categorized into high-risk and low-risk groups. In the TCGA and GEO databases, we noticed significant OS disparities between high-risk and low-risk groups of patients, as demonstrated (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed-e). Therefore, the area under the ROC curve demonstrates that the constructed model accurately predicts the survival time of patients. The areas under the 1,3, and 5-year ROC curves in TCGA and GEO models are displayed (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef-g).\u003c/p\u003e \u003cp\u003eAccording to the characteristics of the genes constructed by the model, the respective risk curves of TCGA and GEO are obtained, including risk score distribution, survival status and heat map (Figures. 3a-b). In addition, age, tumor grade, and tumor stage were substantially related with the prognosis of patients and may be employed as independent prognostic factors, as shown by univariate and multivariate independent prognostic analyses (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figures. 3c-d). Finally, we verified the clinical grouping model to verify whether the constructed model applies to early or later-stage patients. In both the early and late stages, the low-risk group had greater overall survival than the high-risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figures. 3e-f).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGo analysis and GSEA analysis\u003c/h2\u003e \u003cp\u003eGo analysis demonstrated that the relationship between cross genes and each go entry (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Different colours represent various GO; logFC indicates the degree of gene expression. The darker the colour, the higher the enrichment. High and low-risk groups' active pathways and functions were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The abscissa indicates sorted genes, and the ordinate shows enrichment scores. Curves with various colors represent different pathways. The peak of the curve appears at the top left of the abscissa, indicating that these pathways are active in the high-risk group. If the peak appears at the bottom right of the abscissa, this indicates that these pathways are active in the low-risk group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMutation load and survival analysis of tumor\u003c/h2\u003e \u003cp\u003eThe analysis of the results of the tumor mutation load showed that the high-risk group had a greater amount of tumor mutations than the low-risk group (Figure. 5a). The tumor mutation correlation analysis showed that the relation among tumor mutation frequency and patient risk score was positive. (Figure. 5b). The Kaplan-Meier curve shows that the low-risk group has a greater OS than the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). The combined analysis of tumor mutation load and risk group demonstrated significant differences in OS among the four groups (Figure. 5d).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eVariance analysis, immune cell correlation analysis, immune checkpoint difference and survival analysis\u003c/h2\u003e \u003cp\u003eThrough difference analysis, we can appreciate which genes vary among groups with high and low risk groups, and further clarify the differential expression of genes involved in chemokines, growth factors, regulatory factors, proteases and regulators, soluble or abscission receptors or ligands, as well as interleukin (Figure. 6a). The immune cell correlation analysis results show which immune cells are related to the patient's risk score and helps to generate the relevant heatmap (Figure. 6b). The difference in the analysis results of immune checkpoints demonstrated that the genes that correlated to immune checkpoints were different in high and low-risk groups (Figure. 6c). By analyzing the immune checkpoints survival, we obtained the survival curve of immune checkpoint genes that were significantly related to survival. The patients were categorised into four groups according to the target gene expression and risk. The results demonstrated significant differences in OS among the four groups. This study lists the survival curves of PD-1, CTLA-4 and BTLA at common immune checkpoints. (Figures. 6d-f).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of immune escape and immunotherapy\u003c/h2\u003e \u003cp\u003eThe study indicated that the high-risk group had a higher tide score than the low-risk group, showing that the high-risk group is more likely to evade immune therapy (Figure. 7a). There was not a significant difference in the efficacy among the high-risk and low-risk groups when they received immunotherapy (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), regardless of whether they received PD-1 or CTLA-4 or PD-1 combined with CTLA-4 (Figures. 7b-e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eModel validation and immunotherapy analysis validation of IMvigor210\u003c/h2\u003e \u003cp\u003eEach sample in the IMvigor210 database was assigned a risk score using the model's formula. The patients were categorised into high-risk and low-risk groups using their risk score. A comparison of the two groups' rates of survival was carried out. The results demonstrated that the low-risk group had a much greater survival rate than the other groups (Figure. 8a). In imvigor210 database, four groups were divided based on the expression of immune checkpoint genes and patients' risk. Comparisons were made between the four groups' rates of survival. The findings showed that there were substantial disparities in the rates of survival among the four groups (Figures. 8b-c). There was no significant difference in the immune risk score among the reactive and non-reactive groups based on the risk grouping of the imvigor210 database model (p\u0026thinsp;=\u0026thinsp;0.46) (Figure. 8d).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDrug screening and drug structure analysis\u003c/h2\u003e \u003cp\u003eWe screened 6 drugs that could significantly reduce gene expression in high-risk groups according to the drug's connectivity score and FDR value (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Then we searched PubChem to obtain the secondary structure and tertiary structure of 6 the drugs (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\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\u003eSix drugs that can reduce gene expression of high-risk groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003erank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecmap name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eenrichment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003efdr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eenzastaurin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.6536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003evoreloxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.5879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.3525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIIB-021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.3525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZM-447439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.3525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elovastatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.5754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.3525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etreprostinil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.5722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.3525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe 5-year survival rate for individuals diagnosed with localised renal carcinoma was approximately 93% between 2008 and 2014. However, for individuals with mRCC, the survival rate decreased to 12%[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The emergence of targeted therapy has raised the survival rate of RCC patients, although the 5-year survival rate of patients with mRCC remains quite low, especially for individuals with poor prognostic factors[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Interleukin-2 (IL-2) and interferon α (IFN- α) Cytokine therapy, represented by, has displayed some benefits in a small number of patients with advanced RCC (aRCC), but it has only proved effective in a limited proportion of patients[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, cytokine therapy is associated with high toxicity levels, limiting its general use[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Now the immunotherapy of mRCC has developed from cytokine to checkpoint inhibitor. It targets immunosuppressive checkpoints, containing programmed cell death-1 (PD-1) receptor, programmed cell death ligand-1 (PD-L1), and cytotoxic T lymphocyte associated protein 4 (CTLA-4)[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. There were just a few people who had an objective response to checkpoint inhibitors; others had delayed response; and many people saw no theraputic benefits[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. There are a number of concepts that attempt to explain why checkpoint inhibitor medication may not be effective in some patients. Its expression may be related to the pathogenesis and mechanism of renal tumors. This is because in renal tumors, there are a variety of etiologies, the gene and cellular composition of the microenvironment surrounding the tumor have an effect on the number, function, and localization of immune effector cells, thus it is possible that this has a significant bearing on the body's response to checkpoint inhibitors[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCell senescence represents one of the most crucial risk factors for cancer patients. Although the close relationship between cell senescence and the tumor development has become evident, the changes related to cell ageing in the renal tumor microenvironment still remain elusive[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The progress of sequencing technology and bioinformatics tools makes it possible to describe the changes related to cell senescence and renal tumor microenvironment. In this study, we comprehensively evaluated the characteristics of immune infiltration and the microenvironment of ccRCC induced by cell senescence, and analyzed and verified the efficacy of immunotherapy in ccRCC induced by cell senescence.\u003c/p\u003e \u003cp\u003eThis study screened 37 ageing genes significantly related to prognosis in ccRCC. Also, lasso regression analysis depicted that 17 of them were more suitable for constructing the prognosis model. Furthermore, the KM survival analysis of the model's high-risk and low-risk groups reveals that TCGA and GEO data samples have significant differences in OS, and the ROC curve results further show that the model provides accurate and dependable results. Furthermore, age was found to be an independent prognostic factor for ccRCC by univariate and multivariate Cox regression analyses, suggesting a close link among ageing and cancer progression, which is consistent with previous research results[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. To further evaluate whether the constructed model applies to patients of various clinical stages, we analysed the survival rate of the early and late high-risk groups, which revealed results consistent with the our expectations.\u003c/p\u003e \u003cp\u003eGene enrichment analysis demonstrated that adipocytokines, cytokines and receptors, ErbB, hematopoietic cell lineage and immunity were active in high-risk group. Recent research has demonstrated that certain hormones derived from adipose tissue may have a major impact on the growth and proliferation of tumor stroma and internal malignant cells[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. All immunological responses involve cytokines in multiple intricate ways. Cytokine interactions are comprised of intricate and interrelated positive and negative feedback systems that provide homeostasis and immune regulation[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Age-related immune response alters in the hematopoietic system due to decreased hematopoietic stem cell function, which ultimately contribute to increased susceptibility to infection, autoimmunity, anemia, and myeloproliferative diseases[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. ErbB receptors are overexpressed or mutated in many cancers; and its overexpression and over-activation are associated with poor prognosis, drug resistance, cancer metastasis and low survival[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The human immune system is responsible for identifying self and non-self to protect the body from exogenous and endogenous diseases. In addition, the immune system recognizes many threats and eliminates them in order to maintain homeostasis[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The primary immunodeficiency and tight junction pathway were significant in the low-risk group. Congenital genetic defects or dysfunction of one or other immune system components may disturb the complex physiological balance and functional bodily homeostasis, thus reducing its preventive ability and even actively promoting the formation of tumor diseases[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Disturbance of the expression, function, or disruption of tight junction protein integrity are associated with various diseases, including skin, intestinal and lung diseases, as well as various forms of cancers[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, greater number of studies are required to elucidate the mechanistic links between cancer and cell senescence.\u003c/p\u003e \u003cp\u003eThis study concluded that the tumor mutation load of the high-risk group was higher, while the survival rate of the group with high tumor mutation was lower. Because chemokines, growth factors, regulatory factors, proteases and regulators, soluble or exfoliative receptors or ligands, as well as Interleukins are crucial for the regulation of cancer cells and immune cells, they are able to enhance cytotoxicity and play a wide range of anti-tumor activities. Therefore, this study investigated the differential gene expression of high- and low-risk groups in these aspects, which can be used for further immunotherapy studies. Cellular immunotherapy is a novel form of tumor therapy that has a remarkable curative effect. It is a novel type of anti-cancer autoimmune therapy. Therefore, this study evaluated the association between immune cells and patient risk scores, and analyzed the immune checkpoints on immune cells. The results also demonstrated the differential expression of model genes at different immune checkpoints in high and low-risk groups. These results have a strong guiding significance for future research.\u003c/p\u003e \u003cp\u003eImmunotherapy was found to have no significant effect on high-risk or low-risk groups despite significant differences in tumor mutation load, immune cell infiltration expression, and the differential expression of immune checkpoints. IMvigor database verification results were consistent with this study's results. Therefore, despite the advantages of immune checkpoints, the overall effect of immunity will be affected by the different genetic composition of tumors and the cellular composition of tumor microenvironment in different etiological types[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The mechanism of ICI drug resistance can be primary or congenital, or secondary or acquired[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. They include the abnormal expression of MHC class I molecules and the expression of immunosuppressive cytokines. In addition to the above two possible mechanisms, regulatory T cells (Tregs), regulatory B cells (Bregs), myelogenous suppressor cells (MDSCs), and tumor-associated macrophages (TAMs) are all capable of inducing an immunosuppressive response throughout the process of tumor immune escape. However, the immune escape pathways mediated by Tregs, Bregs, MDSCs and TAMs have not been thoroughly studied. related research focuses mostly on the substances provided by immunosuppressive cells that can cause immunological escape and perform an immunosuppressive effect, as well as their signal transduction pathways and the interaction between immunosuppressive cells and other immune cells[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Our study reveals that through enhancing sugar chains and glycosylation, tumor cells may be able to evade anti-tumor immunity and even contribute to regulating immune cell activity to enhance tumor growth[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This will become a new field in the near future.\u003c/p\u003e \u003cp\u003eFinally, based on drug screening analysis of the differences of risk pertaining to the genes, this study identified 6 drugs that can significantly reduce gene expression in high-risk groups. Enzastaurin is a selective inhibitor of protein kinase C β Active new oral anti-tumor drugs, used to treat solid cancer and blood cancer, participate in the active AKT and MAPK signalling pathways in many cancers[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Voreoxin, an anticancer quinolone derivative, has shown strong activity in tumor models in vivo and in vitro[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. BIIB-021 is a synthetic HSP90 inhibitor, and the overexpression of HSP90 is a factor in tumor development. Monotherapy with HSP90 inhibitors has achieved some success[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. ZM447,439 (ZM) is an auroral selective ATP competitive inhibitor, which interferes with spindle integrity checkpoint and chromosome separation. Studies have confirmed that laser kinase is a potential molecular target of ZM for cancer treatment[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Lovastatin has a significant inhibitory effect on the activity of cancer cells in a variety of cancers (such as breast cancer, liver cancer, cervical cancer, lung cancer and colon cancer). At the same time, Lovastatin has been shown to also increase the sensitivity of some types of cancer cells to chemotherapy drugs and enhance their therapeutic effect[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Tryprostinil is an EP2 receptor agonist; EP2 receptor activation engages β-arrestin in a G-protein-independent pathway that promotes tumor cell growth and migration[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, this study has several limitations. First, these survival related cross genes were identified from the TCGA database, where patients were mainly white or American. Considering the genetic heterogeneity, these cross genes need to be verified in more databases. Secondly, this study lacks in vivo and in vitro experiments to further verify the risk difference of genes and selected drugs. Finally, this study did not further study the possible mechanism of immune escape.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study confirmed that immunotherapy is not ideal for treating ccRCC induced by cell senescence. Although the specific immune escape mechanism remains clear, it provides a basis and reference for clinical immunotherapy. Of course, further research and exploration of corresponding clinical trials are required in fruitier follow-up research.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eccRCC, clear cell renal cell carcinoma; aRCC, advanced RCC; mccRCC, metastatic clear cell renal cell carcinoma; KM, Kaplan-Meier; SASP, secretory phenotype; ICI, immune checkpoint inhibitors; ORR, overall response rates; lasso, least absolute contraction and selection operator; TAMs, tumor-associated macrophages.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. All data sets used in this work are obtained from public databases and are provided free of charge. This work does not include any experiments on humans or animals. Patients involved in the public database have been registered after ethical approval. In addition, this study was reviewed by the Ethics Committee (Ethics Committee of Gansu Provincial People's Hospital) and was deemed to be exempt from ethical approval.\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\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a grant from Gansu Provincial Hospital (17GSSY3-4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research concept and design were carried out by XW and HL. XZ, MZ, YL, XW and XL carried out material preparation, data collection and analysis. The first draft was written by XW, HL and XZ. All authors commented on the previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe information of this study comes from TCGA, GEO, CMAP and other databases. We thank them for providing the data sources used in our research. We thank Charles worth for the language editing assistance provided in this manuscript. We also thank the state-owned student of bioinformatics (WeChat official account).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. The results published here are mainly based on the generated data provided by TCGA(https://portal.gdc.cancer.gov/)、GEO(https://www.ncbi.nlm.nih.gov/gds/)、Ageing Atlas database(https://ngdc.cncb.ac.cn/aging/index)、CIBERSORT website(https://cibersort.stanford.edu/download.php)、National Center for Biotechnology Information website(https://www.immport.org/resources)、TIDE(http://tide.dfci.harvard.edu/)、TCIA(https://tcia.at/home)、PubChem database(https://pubchem.ncbi.nlm.nih.gov/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSiegel RL, Miller KD, Jemal A: \u003cstrong\u003eCancer statistics, 2019\u003c/strong\u003e. \u003cem\u003eCA-CANCER J CLIN\u003c/em\u003e 2019, \u003cstrong\u003e69\u003c/strong\u003e(1):7-34.\u003c/li\u003e\n \u003cli\u003eShuch B, Amin A, Armstrong AJ, Eble JN, Ficarra V, Lopez-Beltran A, Martignoni G, Rini BI, Kutikov A: \u003cstrong\u003eUnderstanding pathologic variants of renal cell carcinoma: distilling therapeutic opportunities from biologic complexity\u003c/strong\u003e. \u003cem\u003eEUR UROL\u003c/em\u003e 2015, \u003cstrong\u003e67\u003c/strong\u003e(1):85-97.\u003c/li\u003e\n \u003cli\u003eGupta K, Miller JD, Li JZ, Russell MW, Charbonneau C: \u003cstrong\u003eEpidemiologic and socioeconomic burden of metastatic renal cell carcinoma (mRCC): a literature review\u003c/strong\u003e. \u003cem\u003eCANCER TREAT REV\u003c/em\u003e 2008, \u003cstrong\u003e34\u003c/strong\u003e(3):193-205.\u003c/li\u003e\n \u003cli\u003eKim SH, Park B, Hwang EC, Hong SH, Jeong CW, Kwak C, Byun SS, Chung J: \u003cstrong\u003eRetrospective Multicenter Long-Term Follow-up Analysis of Prognostic Risk Factors for Recurrence-Free, Metastasis-Free, Cancer-Specific, and Overall Survival After Curative Nephrectomy in Non-metastatic Renal Cell Carcinoma\u003c/strong\u003e. \u003cem\u003eFRONT ONCOL\u003c/em\u003e 2019, \u003cstrong\u003e9\u003c/strong\u003e:859.\u003c/li\u003e\n \u003cli\u003eGoyal R, Gersbach E, Yang XJ, 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\u003cstrong\u003e34\u003c/strong\u003e(7):413-423.\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":"cell senescence, renal clear cell carcinoma, immune infiltration, immune checkpoint inhibitors, immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-2492545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2492545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOur study aims to investigate the characteristics of the tumor microenvironment as well as to study the immunological infiltration in renal clear cell carcinoma that results from cell senescence.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFirstly, based on information from the Cancer Genome Atlas (TCGA) database, we collected ccRCC's mRNA, clinical data, and mutation data. From the comprehensive gene expression database (GEO), we acquired individuals gene expression profiles and relevant clinical data with ccRCC. We obtained senescence genes from the Aging Atlas database, extracted the expression of senescence genes from TCGA and GEO databases, and subsequently analyzed the differences. After which, the Kaplan Meier (KM) survival rate was utilised to determine survival-related prognostic genes; Cross genes were obtained from the intersection of differential genes and prognostic genes. By utilising the least absolute shrinkage and selection operator (lasso) regression and cross-validation, the genes included in the construction of the prognostic model were identified. The risk score was detected based on the signature, and the sample was then categorized into high-risk and low-risk groups. GSEA enrichment analysis, immune checkpoint analysis and the expression degree analysis of each model gene in immune cells were conducted among high-risk group and low-risk group respectively. The model we built was validated using the IMvigor210 database. Finally, we screened drugs that can inhibit the expression of high-risk genes from the Connectivity Map (CMAP) database by using risk differential genes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe obtained 37 cross genes and identified 17 genes that could be used to construct prediction model. We found that the tumor mutation load was higher in the high-risk groups. Even though high-risk patients were more likely to evade immunotherapy, there was no significant difference between the two groups when treated with PD-1, CTLA-4, or PD-1, combined with CTLA-4 immunotherapy. The verification results of IMvigor210 database were compatible with the study outcomes. Finally, we screened 6 drugs that can inhibit the expression of high-risk genes from the CMAP database by using risk differential genes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe tumor microenvironment of ccRCC induced by cell senescence may have an immune escape or resistance when receiving immunotherapy. These findings may have some guiding significance for clinical individualized immunotherapy.\u003c/p\u003e","manuscriptTitle":"Integrative analysis of immune infiltration and microenvironment characteristics in renal clear cell carcinoma induced by cell senescence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-06 14:58:10","doi":"10.21203/rs.3.rs-2492545/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":"c9ea5653-8bad-40ab-885c-d6326344bfad","owner":[],"postedDate":"February 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-21T07:59:43+00:00","versionOfRecord":[],"versionCreatedAt":"2023-02-06 14:58:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2492545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2492545","identity":"rs-2492545","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}