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

doi:10.21203/rs.3.rs-2492545/v1

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

https://doi.org/10.21203/rs.3.rs-2492545/v1

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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

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.

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.

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. 1a and b). In total, 152 genes significantly related to prognosis were screened, and the genes intersecting with differential genes are displayed (Fig. 1c). Veen calculation results showed 37 intersection genes (Fig. 1d). The visual circle diagram of the co-expression of intersection genes is displayed in Fig. 1e.

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. 2a-b). The Cox regression analysis results of 17 cross genes used to construct the model are displayed (Fig. 2c). 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. 2d-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. 2f-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. 4a). 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. 4b). 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. 5c). 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

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.

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.

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.

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/).

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

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials And Methods

Data Extraction and Processing

Screening of intersection genes of differential genes and prognostic genes

Construction of prognosis model and Grouping

Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA)

Analysis of tumor mutation burden and survival

Variance analysis, immune cell correlation analysis, immune checkpoint difference and survival analysis

Analysis of immune escape and immunotherapy

Model validation and immunotherapy analysis validation of IMvigor210

Drug screening and drug structure analysis

Results

Identification of intersection genes

Grouping and verification based on clinical model construction

Go analysis and GSEA analysis

Mutation load and survival analysis of tumor

Variance analysis, immune cell correlation analysis, immune checkpoint difference and survival analysis

Analysis of immune escape and immunotherapy

Model validation and immunotherapy analysis validation of IMvigor210

Drug screening and drug structure analysis

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

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