Mendelian Randomization and Single-Cell Analyses Identify the Links Between IL6 and Pan B Cells in Clear Cell Renal Cell Carcinoma

Mendelian Randomization and Single-Cell Analyses Identify the Links Between IL6 and Pan B Cells in Clear Cell Renal Cell Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mendelian Randomization and Single-Cell Analyses Identify the Links Between IL6 and Pan B Cells in Clear Cell Renal Cell Carcinoma Dengqiang Lin, Jinglai Lin, Qi Sun, Xiaoyi Hu, Shuopeng Ye, Fan Chao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6891410/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 Although several risk factors associated with renal cell carcinoma (RCC) have been identified, the etiology of the disease remains unclear. While certain cytokines have been observed in RCC patients compared to healthy individuals, the role of cytokines in promoting RCC development and progression remains uncertain. Methods We conducted a two-sample bidirectional Mendelian randomization (MR) analysis to explore the causal effects of cytokines on clear cell RCC (ccRCC). Integrated bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) analyses were employed to unveil potential mechanisms, which were further corroborated by immunohistochemical staining and plasma cytokine detection using ELISA. Additionally, we developed a diagnostic model using logistic regression analysis. Finally, sensitivity to immunotherapy and targeted therapy was estimated using the R package. Results We found bidirectional causal effects of interleukin (IL) 6 in ccRCC, indicating a complementary and mutually reinforcing relationship. Although no statistical differences were observed in IL6 expression between ccRCC and normal tissues, plasma IL6 levels in ccRCC patients were significantly higher than in control cases, positively correlating with T stage. To mitigate potential bias from RNA-seq, we conducted scRNA-seq analysis, confirming IL6 expression in both tumor and normal tissues, consistent with RNA-seq results. Moreover, IL6 expression was found to be unevenly distributed in the B cell cluster, predominantly in the Pan B cell. Trajectory and pseudotime analyses suggested that the malignant progression of cells may be driven by interactions between IL6 and Pan B cells. Subsequently, we identified 13 Pan B cells-specific oncogenes. Using these genes, we constructed a diagnostic model with an area under the curve of 0.988, identifying ZFAS1 (Zinc finger antisense RNA 1) and BCL2A1 (BCL2 related protein A1) as independent risk factors. Finally, we demonstrated that IL6 not only influences immunotherapy response but also affects targeted therapy response. Conclusion Our analysis confirms a causal correlation between IL6 and ccRCC, suggesting that IL6 may serve as a potential target for diagnostic, therapeutic, and prognostic interventions in ccRCC. Renal cell carcinoma Clear cell renal cell carcinoma Cytokine interleukin 6 Mendelian randomization study Single-cell RNA sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Renal cell carcinoma (RCC) ranks among the 10 most common cancers and accounts for nearly 90% of adult kidney malignancies worldwide[ 1 , 2 ]. Clear cell RCC (ccRCC) is the predominant histopathological subtype and the leading cause of death related to kidney cancer[ 3 , 4 ].The 5-year survival rate drops from 53% in stage III to just 8% in stage IV disease[ 5 ]. Notably, 17–30% of patients present with advanced stage at their initial diagnosis, and 20–40% of those initially diagnosed at an early stage eventually progress, even after curative treatment[ 6 – 9 ]. Therefore, these statistics underscore the importance of identifying risk factors to aid in prevention and improve clinical outcomes in RCC. Cytokines play an essential role in immune responses and are involved in inflammation and immune-related diseases, including cancers, diabetes and viral infections [ 10 ]. In numerous solid tumors, including RCC, tumor cells secrete various cytokines to reshape the microenvironment and promote progression [ 11 ]. For instance, elevated granulocyte macrophage colony-stimulating factor in tumor tissues is significantly associated with lymph node metastasis, advanced TNM stage, high Fuhrman grade and tumor necrosis[ 12 ]. Fitzgerald et al[ 13 ] demonstrated that the incubation of interleukin (IL) 6, IL8, or both can increase the migration of RCC 786-O cells in vitro. A meta-analysis of 22 studies revealed higher IL6 levels in RCC patients, particularly in advanced cases, and identified it as an adverse prognostic factor [ 14 ]. Furthermore, plasma cytokine levels can influence responses to therapies, including past IL2 treatment, current tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) [ 14 , 15 ]. However, inconsistencies persist regarding the relationship between the levels of certain cytokines in tissues or plasma and RCC risk. Moreover, there is considerable variation in cytokine levels, especially in plasma, across different studies. Additionally, traditional observational study designs face challenges in controlling for potential confounders, contributing to bias in conclusions. Mendelian randomization (MR) is a genetic epidemiological method that utilizes genetic variants as instrumental variables (IVs) to infer causal associations between exposures and diseases [ 16 ]. Based on the principle of random allele allocation before birth, MR mimics a randomized controlled trial, effectively minimizing potential confounding factors and offering robust evidence for causality. Following the publication of a genome-wide association study (GWAS) meta-analysis of 41 cytokines[ 17 ], we conducted a two-sample bidirectional MR analysis to assess causal inferences between ccRCC and various cytokines (Fig. 1 ). While MR analysis offers valuable insights, understanding the potential mechanisms remains essential. Recent advances in single-cell RNA sequencing (scRNA-seq) allow for higher resolution analysis of gene expression and genomic aberrations in tumor and peritumoral cells than bulk RNA sequencing (RNA-seq). In our study, we integrated scRNA-seq and RNA-seq analysis to uncover the potential mechanisms connecting ccRCC and cytokines (see Fig. 1 ). The implications of our findings may provide deeper insights of cytokine-driven tumorigenesis, thereby guiding the diagnosis, treatment, and prognosis of ccRCC. 2 Methods 2.1 Data Source We extracted summary data for genetic variants in 41 plasma chemokines from a recent GWAS on the human plasma proteome[ 17 ].GWAS summary data for ccRCC were obtained from the FinnGen consortium R9 release, comprising 287,137 controls and 901 cases[ 18 ]. Additionally, we retrieved a scRNA-seq dataset (GSE178481) and two gene expression profiles (GSE213324 (20 normal tissues vs 21 RCCs) and GSE105261 (including 9 normal tissues, 9 primary ccRCCs and 26 metastasis ccRCCs)) of RCC from the Gene Expression Omnibus (GEO) database. We also acquired gene expression profiles and clinical data from the Cancer Genome Atlas (TCGA) database. A total of 803 human oncogenes were obtained from the oncogene database ( http://ongene.bioinfo-minzhao.org/ ). The study was conducted in accordance with the Declaration of Helsinki. And blood samples and clinical information from 37 patients were collected between August 2023 and December 2023 from the Department of Urology at Zhongshan Hospital affiliated with Fudan University in China (B2016-030). The inclusion criteria were as follows: (1) receiving nephrectomy or biopsy, (2) RCC verified histopathologically, (3) RCC irrespective of pathological type, (4) availability of complete clinical data and peripheral blood samples. The exclusion criteria were as follows: (1) non-ccRCC pathological types (e.g. renal hamartoma, lymphoma and renal pelvic carcinoma), (2) incomplete clinical or sample information. In addition, 46 patients without tumors were included as controls for comparison. 2.2 Genetic variants associated with cytokines We identified single nucleotide polymorphisms (SNPs) associated with cytokines as candidate IVs using a statistically significant threshold (p < 5×10 − 6) and stringent linkage disequilibrium (LD) criteria (LD r2 10,000 kb)[ 19 ]. Candidate IVs were further screened based on the following criteria: (1) SNPs with a minor allele frequency (MAF) ≤ 0.01 were eliminated.(2) SNPs with an F-statistic ≤ 10 were excluded. The F-statistic was calculated using the formula by R software: F = R2×(N-1-K)/((1-R2)×K), where R2 represents the proportion of variance in the exposure explained by the IVs, N is the sample size, and K is the number of IVs [ 20 , 21 ]. (3) SNPs associated with the outcome at a significance level of p < 5×10 − 6 were removed. (4) Palindromic SNPs were excluded. 2.3 Quality control The expression profiling data obtained from GSE213324 and TCGA were initially processed using the Transcripts Per Million (TPM) method and subsequently transformed using log2(TPM + 1) for normalization. Similarly, the scRNA-seq data underwent quality control to filter out low-quality or dying cells, applying criteria such as a gene number range of 300 to 2500 and mitochondrial gene content of less than 5%, as well as genes expressed in more than 5 cells. Subsequently, the global-scaling normalization method 'LogNormalize' was employed to normalize the scRNA-seq data. Finally, the results of the quality control for the scRNA-seq data are presented in Fig. S1 a. 2.4 Dimension reduction, cell clustering and cluster annotation Principal component analysis (PCA) was conducted to visualize the data distribution (Fig. S1 b and S1 c), followed by the identification of the top 2000 variable genes using the FindVariableFeatures function of the Seurat R package (Fig. S1 d). Subsequently, the FindNeighbors and FindClusters functions were utilized for cell clustering. Finally, dimensionality reduction was achieved using the uniform manifold approximation and projection (UMAP) method through the RunUMAP function. To annotate cell clusters and identify different cell types, the scMayoMap R package, in combination with manual annotation[ 22 ], was employed. 2.5 Assessment of the Immune-microenvironment and Tumor-infiltrating Immune Cells The stromal and immune score and tumor purity was inferred using ESTIMATE (Estimation of stromal and immune cells in malignant tumors using expression data) analysis based on all expressed genes[ 23 ]. Additionally, all expressed genes were also inputted into the CIBERSORT (Cell-type identification by estimating relative subsets of RNA transcripts) web portal ( https://cibersort.stanford.edu/ ) to estimate the infiltration rates of 22 types of immunocytes in the tumor microenvironment through 1,000 iterations, utilizing the LM22 gene signature[ 24 ].Furthermore, the MCPCOUNTER [ 25 ] and EPIC (Estimating the proportions of immune and cancer cells) methods were employed to assess the relative proportions of tumor-infiltrating immune and tumor cells[ 26 ]. 2.6 Cell trajectory and CellChat analysis The Monocle R package was employed to analyze cell trajectory and pseudo-time for identified cell types. Additionally, the iTALK R package was utilized to further explore intercellular interactions and visualize networks. Default parameters were applied for both analyses. 2.7 Enzyme-linked immunosorbent assay (ELISA) and Flow Cytometry Cytokines (IL1B, IL2R, IL6, IL8, IL10 and tumor necrosis factor (TNF)) in the supernatant were detected using an ELISA kit following the manufacturer's instructions (Siemens, Germany). For flow cytometry analysis, peripheral blood was firstly collected using ethylenediaminetetraacetic acid dipotassium salt as an anticoagulant. An automated cell counter (Becton, Dickinson and Company (BD) Trucount) was applied to count the cells and adjust the cell concentration to 1×10⁶ cells/mL. After washing twice with phosphate buffered saline, the lymphocyte subset detection reagents (BD Biosciences, USA) were added, including: FITC-conjugated anti-CD3 monoclonal antibody, APC-Cy7-conjugated anti-CD8 monoclonal antibody, PE-Cy7-conjugated anti-CD4 monoclonal antibody, PE-conjugated anti-CD16 monoclonal antibody and anti-CD56 monoclonal antibody, APC-conjugated anti-CD19 monoclonal antibody, and PerCP-Cy5.5-conjugated anti-CD45 monoclonal antibody. Then the cell suspension was thoroughly mixing and incubated for 15 minutes in the dark at room temperature. Finally, flow cytometry was then employed to identify and characterize the cell populations (BD Biosciences, USA). 2.8 Establishing a predictive model We initially identified genes associated with ccRCC using univariate logistic regression (LR) analysis. Following this, a predictive model based on the selected genes was established for diagnosing ccRCC through multivariate LR analysis. Receiver operating characteristic curve (ROC) analysis was performed, and the area under the curve (AUC) was computed to assess the predictive efficiency of the model. Additionally, calibration curves were generated after 1000 bootstrap resampling to evaluate the consistency between predicted probabilities and actual probabilities. 2.9 Immune phenotype score (IPS) and drug susceptibility In addition, IPS of ccRCC patients were obtained from The Cancer Immunome Atlas database[ 27 ] to predict immunotherapy sensitivity. Furthermore, the oncopredict R package was utilized to predict the response to commonly utilized chemotherapy and molecular targeted drugs in different IL6 expression groups[ 28 ]. 2.10 Statistical analysis Statistical analyses were conducted using SPSS v 29.0.1.0 software or R v4.2.0. MR analyses were performed using Two-SampleMR v0.5.6 and the MRPRESSO package v1.0 in R v4.20. Several approaches were employed to detect the causal association between cytokines and ccRCC, including inverse variance weighted (IVW), weighted median, MR-Egger (MR Egger regression), and MR-PRESSO (MR pleiotropy residual sum and outlier). The IVW method utilized a meta-analysis approach to combine the Wald ratio estimates for each SNP to obtain the causal effect[ 29 ].Additional MR analysis approaches included the weighted median (WM) method and MR-Egger regression[ 29 , 30 ].MR-PRESSO and MR-Egger regression intercept analysis were used to assess potential horizontal pleiotropy[ 31 ]. Cochrane’s Q test was performed to assess heterogeneity. SNPs with a p-value < 0.05 were considered to indicate horizontal pleiotropy or heterogeneity. Continuous variables were expressed as mean and standard deviation, while categorical variables were expressed as percentage/frequency. Comparisons between two groups for continuous variables were conducted using the t-test, while the chi-square test or Fisher’s exact test was used for categorical variables. A p-value < 0.05 was considered statistically significant. 3 Results 3.1 Causal relationships between IL6 and ccRCC A schematic chart of our research design is displayed in Fig. 1 . Figure 2 illustrate that five cytokines-beta nerve growth factor (bNGF), fibroblast growth factor-basic (FGFB), IL5, IL6, and IL17-were found to have a causal effect on ccRCC. According to IVW estimates, bNGF (odds ratio (OR): 1.26, 95% confidence interval (CI): 1.00-1.60, P < 0.05), FGFB (OR: 1.30, 95% CI: 1.04–1.63, P < 0.05), IL5 (OR: 1.67, 95% CI: 1.35–2.07, P < 0.05), and IL6 (OR: 1.51, 95% CI: 1.01–2.25, P < 0.05) exhibited positive effects on ccRCC risk. Among them, genetically predicted higher IL6 level was associated with a 51% increase in the odds of developing of ccRCC, compared to lower IL6 levels. Conversely, IL17 showed an adverse effect (OR: 0.75, 95% CI: 0.61–0.94, P 10, indicating no evidence of weak instrument bias. These IVs explained 2.03%, 1.38%, 1.56%, 1.50%, and 2.92% of the variability in bNGF, FGFB, IL5, IL6, and IL17, respectively. Cochran’s IVW Q test revealed no heterogeneity for any IV among the 41 cytokines, except for TNF-a (Table S2). Similarly, MR-Egger regression intercept analysis detected no horizontal pleiotropy for any IV in ccRCC, suggesting plausible causal associations (Table S2). Except for IL8 (outlier: rs116383510, P 0.05) by MR-PRESSO test (Table S3). After excluding outlier rs116383510, IL8 still did not show a causal effect in ccRCC. Leave-one-out analysis results (Fig. S2 ) indicated that causal associations between cytokines and ccRCC were not substantially affected by any particular IV. We further assessed reverse causation effects through reverse MR analysis, treating ccRCC as an exposure and cytokines as outcomes. Bidirectional causal effects between ccRCC and IL6 were identified (Fig. 1 ), with ccRCC showing a significant positive effect on IL6 (OR: 1.05, 95% CI: 1.02–1.09, P < 0.05). Additionally, ccRCC exhibited significant positive effects on IL9 (OR: 1.07, 95% CI: 1.00-1.13, P < 0.05), IL10 (OR: 1.07, 95% CI: 1.03–1.11, P < 0.05), IL12p70 (OR: 1.17, 95% CI: 1.00-1.38, P < 0.05), cutaneous T-cell-attracting chemokine (CTACK, OR: 1.06, 95% CI: 1.04–1.09, P < 0.05), and macrophage inflammatory protein-1β (MIP1b, OR: 1.06, 95% CI: 1.03–1.10, P < 0.05). Conversely, negative causal associations were found for interferon gamma-induced protein 10 (IP10, OR: 0.96, 95% CI: 0.94–0.98, P < 0.05) and monokine induced by gamma interferon (MIG, OR: 0.94, 95% CI: 0.90–0.97, P < 0.05) via IVW (Fig. 2 ). No heterogeneity or horizontal pleiotropy was observed via Cochran’s IVW Q test and MR-Egger regression analysis, respectively, except for IL8 (Table S2). No potential outliers were found among the IVs except for RANTES (Regulated on activation, normal T cell expressed and secreted, Table S3). 3.2 IL6 level related with clinicopathological features in ccRCC To assess IL6 levels in RCC, we analyzed data from TCGA. Lower levels of IL6 were significantly found in renal papillary cell carcinoma and renal chromophobe cell carcinoma, but not in ccRCC compared to normal tissues (Fig. 3 a). Pan-cancer analysis revealed that IL6 was almost significantly down-regulated in these tumors, as shown in Fig. 3 a, except for colon adenocarcinoma. Surprisingly, IL6 levels in ccRCC exhibited a similar downtrend compared to normal tissue in TCGA and GSE105261 (Fig. 3 a and Fig. 3 b, P > 0.05). However, in GSE213324, a significant difference was observed between normal and RCC, but not in ccRCC (Fig. 3 c). Immunohistochemical staining results from the Human Protein Atlas database illustrated that IL6 exhibited low expression levels in both normal tissues and ccRCC (Fig. 3 d).As shown in Fig. 3 e, IL6 was primarily expressed in the cytoplasm of renal tubular cells in normal tissues, with positivity observed in 13 out of 16 samples (81%).In contrast, the positivity frequency was 10 out of 16 samples (63%) in ccRCC patients from our clinical cohort (P > 0.05).To further explore whether IL6 could affect ccRCC, we found that a higher level of IL6 was associated with worse prognosis through Kaplan-Meier analysis (Fig. 3 f). Although there was no statistical difference between ccRCC and normal tissues, the level of plasma IL6 from patients with ccRCC, but not nccRCC, was higher than that of patients without any tumor (7.60 ± 2.00 vs 4.21 ± 0.32 pg/ml, P < 0.05, Fig. 3 g).Moreover, the level of plasma IL6 was also positively correlated with T stage (correlation coefficient (CC): 0.72, Fig. 3 h and Table S5).Additionally, IL6 showed a positive correlation with status (CC: 0.24), platelet level (CC: 0.18), calcium level (CC: 0.12), histologic grade (CC: 0.25), T stage (CC: 0.21), and stage (CC: 0.19) (Fig. 3 i and Table S5).Among these, platelet level and calcium level were both items of the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC), known to be associated with prognosis[ 32 ]. In our cohort, 46 control cases and 37 tumor cases were included (detailed in Table S5), of which 27 (72.97%) were ccRCC, 6 (16.22%) were papillary RCC, 3 (8.11%) were chromophobe RCC, and 1 (2.70%) was FH-deficient RCC, confirmed histopathologically with surgery or biopsy. Among RCCs, 24 patients were male and 13 were female, with a mean age of 58.73 ± 12.45 years. The maximum diameter of the primary tumor ranged from 1.9 cm to 12.5 cm. Three patients developed metastasis at the initial diagnosis, including lung, liver, and lymph node metastasis. There were no significant differences between patients without any tumor and those with ccRCC regarding the levels of plasma other cytokines (e.g., TNF, IL1B, IL2R, IL8, and IL10, Fig.s 3 ). 3.3 IL6 related with Pan B cell types The analysis of RNA-seq data from TCGA may result in deviation. Therefore, scRNA-seq data (GSE178481) was further utilized for analysis. After quality control, 105,904 cells were screened and clustered into 31 clusters using the k-Nearest Neighbor (KNN) clustering algorithm (Fig. 4 a). The distribution of sample types and IL6 expression plot are shown in Fig. 4 b and Fig. 4 c, respectively. Similarly to the results from TCGA, we observed IL6 expression in both tumor and normal tissues. Based on molecular characteristics, 14 cell types were annotated, including T cells, NK cells, and B cells (Fig. 4 d).Although IL6 was mainly expressed in endothelial cells (27.5%) and dendritic cells (24.3%), followed by B cells (16.5%, Fig. 4 i), surprisingly, IL6 expression was unevenly distributed within the B cell cluster (Fig. 4 d).To explore which B cell subtype was associated with IL6, the B cell cluster was re-analyzed and then re-clustered into 14 clusters (Fig. 4 e).Through Fig. 4 f and Fig. 4 g, we similarly found mixed expression of IL6 in tumor and normal tissues. After manual annotation [ 22 ], 4 B cell subtypes were identified: memory B cells, Pan B cells, plasmablasts, and plasma cells (Fig. 4 h). As illustrated in Fig. 4 i, IL6 was predominantly expressed in the Pan B cell (92.1%) rather than memory B cells (4.6%) or plasmablasts (3.3%). Given that IL6 was related to Pan B cells, immune score and tumor purity were subsequently assessed using the ESTIMATE method. Compared with the IL6-high group (≥ mean value (1.26)), the immune score and ESTIMATE score were lower (immune score: 1827 ± 52.44 vs.1493 ± 36.7, P < 0.05; ESTIMATE score: 2739 ± 81.97 vs.2092 ± 57.19, P < 0.05), but tumor purity score was higher in the low-risk group (0.53 ± 0.01 vs 0.60 ± 0.01, P < 0.05, Fig. 4 j), consistent with the results shown in Fig.s 4 b and 4 f, and suggesting that IL6 expression in ccRCC was correlated with immune infiltration. The CIBERSORT method using the LM22 model matrix was employed to evaluate the diversity in the infiltration of 22 types of immune cells between the IL6-high and IL6-low groups. We found that infiltration rates of 12 types of immune cells (i.e., naive B cells, plasma cells, etc.) differed significantly between the two groups (Fig. 4 k). Compared with the IL6-low group, the rates of naive B cells and plasma cells were higher in the IL6-high group. Additionally, results from MCPCOUNTER and EPIC methods also indicated that B cell infiltration was closely related to a high level of IL6 (Fig. 4 k). However, in our cohort, we did not find a significant difference between patients without any tumor and those with ccRCC for immune cells from serum samples, except for CD8 + suppressor T cells (Fig.s 4 ). 3.4 Interaction between IL6 and Pan B cells A trajectory analysis was conducted to assess the relationship between IL6 and Pan B cells.As illustrated in Fig. 5 a, cells were categorized into three different stages.An overlapping distribution between Pan B cells and high levels of IL6 was observed, primarily concentrated in stage 3, where a mixture of cells, including normal and tumor cells, were situated. Therefore, we speculated that cell malignant progression might be driven by interactions between IL6 and Pan B cells. Pseudotime analysis results validated this speculation (Fig. 5 b). Along the pseudotime trajectory, we observed that IL6 level was gradually downregulated and Pan B cells gradually transformed into other B cell subtype while normal cells exhibited a gradual transition into cancerous states. Simultaneously, CellChat analysis revealed that Pan B cells exhibited higher outgoing interaction strength in the C-type lectin-like receptors (CLEC) signaling pathway, while incoming interactions were prominent in the MIF and APP signaling pathways (Fig. 5 c). In the CLEC signaling pathway, Pan B cells acted as signal senders primarily interacting with plasmablasts (Fig. 5 d). In the macrophage migration inhibitory factor (MIF) and amyloid precursor protein (APP) signaling pathways, Pan B cells acted as signal receivers interacting with plasmablasts and plasma cells, respectively (Fig. 5 e and 5 f). Subsequently, differentially expressed genes (DEGs) were identified for each B cell subtype, and the top 10 DEGs were displayed among the four subtypes using a heatmap (Fig. 5 g). To validate whether cell malignant progression might be driven by interactions between IL6 and Pan B cells, 13 exclusively differential expressed oncogenes (DEOs) for Pan B cells were first screened using a Venn diagram. These included NCOA3 (Nuclear receptor coactivator 3), ZFAS1 (Zinc finger antisense RNA 1), BLK (B lymphoid tyrosine kinase), BCL2A1 (BCL2 related protein A1), GLO1 (Glyoxalase 1), ADAM28 (ADAM metallopeptidase domain 28), EIF3E (Eukaryotic translation initiation factor 3 subunit e), SWAP70 (Swap switching b-cell complex 70kda subunit), HSPA4 (Heat shock protein family a (Hsp70) member 4), EEF1A1 (Eukaryotic translation elongation factor 1 alpha 1), RPL23 (Ribosomal protein L23), FOXP1 (Forkhead box P1), and MDM4 (MDM4 regulator of p53) (Fig. 5 h). 3.5 DEOs helping to diagnose ccRCC Among all DEOs, 6 DEOs (FOXP1, ZFAS1, BLK, BCL2A1, ADAM28, and RPL23) were positively correlated with IL6, while 3 DEOs (GLO1, HSPA4, and EEF1A1) showed a negative correlation in the TCGA dataset (Fig. 6 a). In GSE213324, 10 DEOs (MDM4, FOXP1, NCOA3, ZFAS1, BLK, BCL2A1, ADAM28, EIF3E, SWAP70, and RPL23) were positively associated with IL6, while none of the DEOs showed a negative correlation (Fig. 6 b).These results indicated a significant correlation between most DEOs and IL6. Subsequently, patients from the TCGA dataset were divided into a training set and a testing set in a ratio of 7: 3. Univariate LR analysis identified a total of 9 DEOs associated with ccRCC at P < 0.05 in the training set (Table S4). Among them, three genes with the greatest predictive value were ZFAS1 (OR: 88.35, 95% CI: 28.06-278.23), BCL2A1 (OR: 35.04, 95% CI: 13.91–88.25), and RPL23 (OR: 15.72, 95% CI: 6.91–35.75). Subsequently, multivariate LR analysis was performed to construct a diagnostic model based on these DEOs. After adjustment for other DEOs, ZFAS1 (OR: 84.15, 95% CI: 12.32-1051.56, P < 0.05) and BCL2A1 (OR: 6.17, 95% CI: 1.42–36.74, P < 0.05) were identified as independent risk factors (Fig. 6 c and Table s4).The detailed scoring system is illustrated in Fig. 6 d. ROC curve analysis (Fig. 6 e, 6 f, and 6 g) demonstrated AUC values for ccRCC diagnosis of 0.988, 0.984, and 0.912 in the TCGA training set, TCGA testing set, and GSE213324 testing set, respectively. After 1000 bootstrap resamplings, the diagnostic model exhibited excellent consistency between predicted ccRCC probability and actual ccRCC probability, as shown by the calibration curve in the TCGA training set, TCGA testing set, and GSE213324 testing set, respectively (Fig. 6 h, 6 i, and 6 j).These findings not only suggest that our model demonstrates good diagnostic potential for ccRCC but also indirectly indicate the existence of a relationship between IL6 and ccRCC. 3.6 Relationship between IL6 level and immunotherapy/TKIs response Considering the relationship between IL6 and immune cells, IPS was further investigated to predict immunotherapy sensitivity in ccRCC patients with different levels of IL6. Regardless of whether immune checkpoints (Programmed cell death protein 1 (PD1) or cytotoxic T-lymphocyte associated protein 4 (CTLA4)) were expressed or not, patients in the IL6-low group had a higher IPS, suggesting a better immunotherapy response (Fig. 7 a). Additionally, TKIs were also recommended as a treatment option for ccRCC. Therefore, the 50% inhibitory concentration (IC50) of some TKIs was investigated (Fig. 7 b). It was observed that the different levels of IL6 could affect the therapeutic response of TKIs. In other words, some TKIs showed a better curative effect in the IL6-low group, while others did not. This suggests that it is necessary to detect IL6 levels before initiating TKIs therapy. 4 Discussion Although known risk factors such as age, gender, race, location, smoking, hypertension, and obesity [ 33 – 37 ], the etiology of ccRCC remains poorly understood. In this study, we conducted a two-sample MR analysis to infer causal relationships between ccRCC and circulating cytokine levels, which mitigates confounding and reverse causation issues inherent in observational studies. This approach provides relatively robust evidence using genetic variants from non-experimental settings. Our findings identified bNGF, FGFB, IL5, and IL6 as risk factors of ccRCC, while IL17 exhibited a protective effect. Furthermore, reverse MR analysis revealed that ccRCC might significantly influence cytokine levels, particularly IL6, IL9, IL10, IL12p70, CTACK, MIP1b, IP10 and MIG. Of particular significance, we identified bidirectional causal effects between ccRCC and IL6, suggesting a feedback loop that may reinforce disease progression. IL6 is a pleiotropic proinflammatory cytokine secreted by various cells, such as endothelial, immune and tumor cells. In RCC, IL6 is spontaneously secreted by normal renal cortical and tumor cells [ 38 ]. As an one of the most studied cytokines, IL6 plays a significant role in the development and progression of RCC [ 39 , 40 ]. Elevated IL6 levels have been observed in RCC tissue samples[ 41 ] and patient plasma [ 42 ], and associated with unfavorable prognosis and resistance to multiple treatment, including TKIs (e.g., sorafenib, sunitinib, and pazopanib)[ 43 – 45 ], ICIs[ 14 ], doxorubicin[ 46 ], IL2[ 15 ], interferon-alpha (IFN-a)[ 43 ], advanced stage[ 47 ], and reduced survival[ 48 , 49 ]. Our findings align with Chen et al.[ 46 ], who reported that increased plasma IL6, but not other cytokines in ccRCC patients. Additionally, increased IL6 expression positively correlated with the histologic grade, T stage, stage and worse overall survival in our study, further underscoring the close relationship between IL6 and RCC. Autocrine and paracrine production of IL-6 could lead to STAT3 (Signal transducer and activator of transcription 3) phosphorylation, mediating tumorigenesis and development [ 50 ]. It not only mediates IL6-associated proliferation [ 51 ] but also promotes metastasis in RCC [ 52 ]. Additionally, STAT3 could also induce IL6-associated treatment resistance. For example, IL6 and IL6-associated molecules/pathways markedly upregulated following TKI treatment (sorafenib, sunitinib, and pazopanib), such as STAT3, AKT (Protein kinase b), mTOR (Mechanistic target of rapamycin), 4EBP1 (Eukaryotic translation initiation factor 4e binding protein 1), S6RP (ribosomal protein s6), p70S6 (Ribosomal protein s6 kinase beta-1), and NFκB (Nuclear factor kappa-light-chain-enhancer of activated b cells)[ 43 ]. But combining TKIs with IL6R blockade (e.g. Tocilizumab) suppressed this activation, improved TKIs response and delayed treatment resistance[ 43 ]. In our research, we also found IL6 may influence responses to immune or TKI treatment. For example, patients with low IL6 levels exhibited a better immunotherapy response, even without high expression of PD1 or CTLA4. Therefore, IL6 assessment could optimize treatment choices in clinical practice. In addition, IL6 may also affect RCC via additional molecules/pathways, such as SOCS3 (Suppressor of cytokine signaling 3), PI3K (Phosphoinositide 3-kinase)/AKT/mTOR, and RAS (Rat sarcoma viral oncogene homolog)/RAF (Rapidly accelerated fibrosarcoma)/MEK(MAPK/ERK kinase)/ERK (Extracellular signal-regulated kinase) pathways[ 43 , 53 – 55 ]. So, we could explore potential mechanisms in those directions in future. Given that immune cell infiltration plays a significant role in cancer initiation and progression, we analyzed the relationship between IL6 and immune cells from different perspectives. We found IL6 distribution was related to Pan B cells using scRNA-seq. Results from cell trajectory and pseudotime analysis further suggested that cell malignant progression may be driven by the interaction between IL6 and Pan B cells. Subsequently, we identified 13 Pan B cell-specific oncogenes, from which nine were used to establish a diagnostic model. ZFAS1 and BCL2A1 emerged as independent risk factors. And this model exhibited excellent diagnostic performance, with AUCs of 0.988, 0.984, and 0.912 in the TCGA training, TCGA testing, and GSE213324 dataset, respectively. Our model not only confirmed the link between ccRCC and Pan B cells, but also demonstrated potential utility in the differential diagnosis of RCC. Furthermore, it also provided valuable insights for future researchers on RCC diagnosis, treatment and prognosis. In recent years, tumor-infiltrating B cells (TIBs) have gained more and more attention in in tumor immunosurveillance. Their effects can be either tumor-promoting or tumor-suppressing, depending on TME’s composition, B cell phenotypes, and the antibodies they produce. Higher TIB levels have been associated with favorable outcomes in cancers such as melanoma, sarcoma, breast, esophageal, non–small cell lung, colon, and biliary tract cancers [ 56 ]. However, TIBs have also been linked to worse prognosis in bladder cancer [ 57 ] and increased prostate cancer recurrence following prostatectomy [ 58 ]. In line with our findings, Iglesia M.D. et al. [ 59 ] reported that elevated B cell and plasma cell gene signatures were related to poor prognosis in RCC, and enriched in tumors of patients responding to ICI therapy [ 60 ]. The functional diversity of B cells makes their classification in the TME difficult, as traditional surface markers may not fully reflect their heterogeneity [ 61 ]. Certainly, our research has some limitations. We didn’t explore the detailed mechanisms about link between IL6, Pan B cells and ccRCC progression. Additionally, due to limited follow-up data in our cohort, we couldn’t assess prognostic value of serum IL6. Finally, external validation of our diagnostic model are still required. Nevertheless, by identifying IL6 as a potential therapeutic target, our study provides novel insights that may guide future research and aid in the development of improved diagnostic and therapeutic strategies for ccRCC patients. Abbreviations RCC: renal cell carcinoma; ccRCC: clear cell renal cell carcinoma; TKI: tyrosine kinase inhibitor; ICI: immune checkpoint inhibitor; MR: Mendelian randomization; IV: instrumental variable; GWAS: genome-wide association study; scRNA-seq: single-cell RNA sequencing; RNA-seq: bulk RNA sequencing; GEO: Gene Expression Omnibus; TCGA: the Cancer Genome Atlas; SNP: single-nucleotide polymorphism; LD: linkage disequilibrium; MAF: minor allele frequency; TPM: the Transcripts Per Million; PCA: Principal component analysis; UMAP: the uniform manifold approximation and projection; LR: Logistic regression; ROC: receiver operating characteristic curve; AUC: the area under the curve; IPS: immune phenotype score; MR-PRESSO: MR pleiotropy residual sum and outlier; MR-Egger: Mendelian Randomization Egger regression; IVW: inverse variance weighted; WM: the weighted median; OR: odds ratio; CI: confidence interval; CC: correlation coefficient; IMDC: the International Metastatic Renal Cell Carcinoma Database Consortium; KNN: the k-Nearest Neighbor; DEGs: differentially expressed genes; DEOs: differentially expressed oncogenes; IC50: the 50% inhibitory concentration; IL-1b: Interleukin 1-beta; IL-1ra: Interleukin 1 receptor antagonist; IL-2: Interleukin 2; IL-2ra: Interleukin 2 receptor, alpha subunit; IL-4: Interleukin 4; IL-5: Interleukin 5; IL-6: Interleukin 6; IL-7: Interleukin 7; IL-8: Interleukin 8; IL-9: Interleukin 9; IL-10: Interleukin 10; IL-12p70: Interleukin 12p70; IL-13: Interleukin 13; IL-16: Interleukin 16; IL-17: Interleukin 17; IL-18: Interleukin 18; CTACK: Cutaneous T-cell attracting (CCL27) ; GRO-a: Growth regulated oncogene-a (CXCL1); IP-10: Interferon gamma-induced protein 10 (CXCL10); MCP-1: Monocyte chemotactic protein-1; MCP-3: Monocyte specific chemokine 3 (CCL7); MIG: Monokine induced by interferon-gamma; MIP-1a: Macrophage inflammatory protein-1a (CCL3); MIP-1b: Macrophage inflammatory protein-1 beta; RANTES: Regulated on activation, normal T-cell expressed and secreted (CCL5); SDF-1a: Stromal cell-derived factor-1 alpha; FGFB: fibroblast growth factor-basic; bNGF: Beta nerve growth factor; G-CSF: Granulocyte colony-stimulating factor; HGF: Hepatocyte growth factor; M-CSF: Macrophage colony-stimulating factor; PDGF-bb: Platelet-derived growth factor BB; SCF: Stem cell factor; SCGFb: Stem cell growth factor beta; VEGF: Vascular endothelial growth factor; IFNg: Interferon-gamma; MIF: Macrophage migration inhibitory factor; TNFa: Tumor necrosis factor alpha; TNFb: Tumor necrosis factor beta; TRAIL: the tumour necrosis factor-related apoptosis-inducing ligand. Declarations Funding None Ethics approval All studies involving human tissues were conducted in strict accordance with the Declaration of Helsinki and were approved by the Ethics Committee of Zhongshan Hospital Affiliated to Fudan University (B2016-030, 17 March 2016). Consent to participate All participants were fully informed about the purpose of sample collection, the intended research, and potential publication, and each signed an informed consent form for the donation of biological samples. Competing interests The authors declare no competing interests. Consent to publish Not applicable Author Contribution Conceptualization, Methodology, Data curation and Formal Analysis: Dengqiang Lin, Xiaoxia Li, Jinglai Lin and Qi Sun; Supervision: Shuopeng Ye and Fan Chao; Writing –original draft:Dengqiang Lin; Writing–review and editing: Xiaoyi Hu, Zhibing Xu and Jianming Guo. Acknowledgements The authors acknowledge the high-quality GWAS resources made available by the FinnGen study ( https://r9.finngen.fi/ ), the GWAS catalog ( http://www.ebi.ac.uk/gwas ), GEO ( https://www.ncbi.nlm.nih.gov/geo/ ) and TCGA ( https://portal.gdc.cancer.gov/ ) database. Data Availability The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17–48. 10.3322/caac.21763 . Padala SA, Barsouk A, Thandra KC, Saginala K, Mohammed A, Vakiti A, Rawla P. Barsouk A. Epidemiology of Renal Cell Carcinoma. World J Oncol. 2020;11:79–87. 10.14740/wjon1279 . Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71:209–49. 10.3322/caac.21660 . 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Chao","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Chao","suffix":""},{"id":496025996,"identity":"fb19ecaa-0ea8-4ae4-944a-cf7cf8d511e6","order_by":6,"name":"Xiaoxia Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIie2QsWrDMBCGTxiURVTrZckzCLx08MNIBDrFWbJkyBAoOEOpszZTX8FTZxmBJ5uuBS+GQKcOKYWQLqEKlDqEyDRbofpAxyH0cfcLwOP5g1wFtkh7KAQavnuw3bVToa1CZatoQLfStkz8KNCp9IBiM4tU2is/3j+TpzHnVW42MwS+uJOOxQIhi5uQsjgLWVlP+g9jqXWBgGWVORTSSGoGFOJsCNNaZS9MaE0RBMYuBbTcG0b5W5PvhFWeS6vsOxXSqMROwRGZs8MUPRI6T7oUcitUarPgaxjYLGp1yFKlyFxZODdFf7eN1ONyuLY/Vqslr8xmuo0GfHF/VgEg89MbdlR/yUWPPR6P5x/wBestYcfy0AujAAAAAElFTkSuQmCC","orcid":"","institution":"Fudan 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02:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6891410/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6891410/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88756375,"identity":"1f836c7c-6038-4970-a9d7-dd40dd6435d5","added_by":"auto","created_at":"2025-08-11 07:17:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":497565,"visible":true,"origin":"","legend":"\u003cp\u003eA schematic chart of our research design.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891410/v1/ebf63105c0d77e27b5bad02a.jpg"},{"id":88754883,"identity":"fc49ec70-d48d-4eb6-982d-8ed6afb9ee46","added_by":"auto","created_at":"2025-08-11 07:09:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":366568,"visible":true,"origin":"","legend":"\u003cp\u003eCausal relationships between cytokines and clear cell renal cell carcinoma through mendelian randomization analysis.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891410/v1/e231e4e35d0c8c8fab3e48cf.jpg"},{"id":88754886,"identity":"d70528e4-228f-483d-912d-cfa17088069a","added_by":"auto","created_at":"2025-08-11 07:09:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1019694,"visible":true,"origin":"","legend":"\u003cp\u003ea.Pan-cancer analysis.Red, tumor group; Blue, normal group, Purple, metastatic group.b.Expression level of IL6 in GSE105261.c.Expression level of IL6 in GSE213324.d.Immunohistochemical results from the Human Protein Atlas database.e.Immunohistochemical results from our cohort.1 and 3: Negative; 2 and 4: Positive.f.Correlation between IL6 and ccRCC prognosis.g.Level of blood IL6 in our cohort; h.Correlation between IL6 and clinicopathological features in TCGA; I.Correlation between IL6 and clinicopathological features in our cohort.N, normal group; RCC, renal cell carcinoma group; ccRCC/cc, clear cell RCC group; ncc, non-ccRCC group; Pri, primary ccRCC group; met, metastatic ccRCC group.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891410/v1/03c1ed21c0c965fe9076a830.jpg"},{"id":88754889,"identity":"ae13e3fd-9aa9-4f29-8c98-0803373dd0c7","added_by":"auto","created_at":"2025-08-11 07:09:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":815146,"visible":true,"origin":"","legend":"\u003cp\u003ea.Dimension reduction and cluster analysis.b.UMAP plot in benign adjacent normal and tumor samples.c.The distribution plot of IL6 expression.d.Annotation of a total of 14 cell types.e.Re-dimension reduction and re-cluster analysis of B cells group.f and g.The distribution plot of sample types (f) and IL6 expression (g) in B cells group.h.Annotation of a total of four B cell subtypes.i.The proportion of IL6 expression in different cells.j.Immune score using ESTIMATE analysis.k.Analysis of tumor-infiltrating immune cells using CIBERSORT, MCPCOUNTER, and EPIC methods.Red indicates statistically significant results.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891410/v1/96a5c713fd179fc8b6998916.jpg"},{"id":88757396,"identity":"c5a6ec93-77f1-4a1b-bf34-29a088f61061","added_by":"auto","created_at":"2025-08-11 07:25:24","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":494662,"visible":true,"origin":"","legend":"\u003cp\u003ea.Cell trajectory analysis.b.Pseudotime analysis.c.Outgoing and incoming signaling pattern between four B cell subtypes.d, e and f.Cell communication in CLEC (d), MIF (e) and APP (f) signaling network.g.Differentially expressed genes visualized using a heatmap.h.Differentially expressed oncogenes visualized using a Venn plot.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891410/v1/c502bdc3546cdcb0a4f967a5.jpg"},{"id":88756380,"identity":"87635713-e1ea-4464-b124-52a436b2e15b","added_by":"auto","created_at":"2025-08-11 07:17:24","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2160844,"visible":true,"origin":"","legend":"\u003cp\u003ea and b.Correlation between IL6 and 13 exclusive differentially expressed oncogenes of Pan B cells in TCGA (a) and GSE213324 (b).c.Forest plot displaying odds ratio.d.Nomogram based on nine genes.e, f and g.Receiver operating characteristic curve in TCGA training set (e), TCGA testing set (f) and GSE213324 testing set (g).h, i and j.Calibration curve in TCGA training set (h), TCGA testing set (i) and GSE213324 testing set (j).\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891410/v1/5085f13d1837934cbb747bd6.jpg"},{"id":88754895,"identity":"e778d29d-55fb-4bb3-8d90-bcc7dbcaf7c6","added_by":"auto","created_at":"2025-08-11 07:09:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":226353,"visible":true,"origin":"","legend":"\u003cp\u003ea.Estimating Immunotherapy response.b.Estimating tyrosine kinase inhibitor response.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891410/v1/f5458405a05a8c836307cdf2.jpg"},{"id":90967086,"identity":"b7e7c26d-48bd-4a44-baf2-fc7572520ace","added_by":"auto","created_at":"2025-09-10 06:47:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6500780,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6891410/v1/b03e5a77-f01a-4e4c-8c5d-b0ddd342624b.pdf"},{"id":88756377,"identity":"7a4117c8-fd86-4724-a248-4b4ea2ac3e30","added_by":"auto","created_at":"2025-08-11 07:17:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2330261,"visible":true,"origin":"","legend":"","description":"","filename":"TablesandfigsSup.docx","url":"https://assets-eu.researchsquare.com/files/rs-6891410/v1/fbd596f3489d091aa7abb566.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mendelian Randomization and Single-Cell Analyses Identify the Links Between IL6 and Pan B Cells in Clear Cell Renal Cell Carcinoma","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eRenal cell carcinoma (RCC) ranks among the 10 most common cancers and accounts for nearly 90% of adult kidney malignancies worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Clear cell RCC (ccRCC) is the predominant histopathological subtype and the leading cause of death related to kidney cancer[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].The 5-year survival rate drops from 53% in stage III to just 8% in stage IV disease[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notably, 17\u0026ndash;30% of patients present with advanced stage at their initial diagnosis, and 20\u0026ndash;40% of those initially diagnosed at an early stage eventually progress, even after curative treatment[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, these statistics underscore the importance of identifying risk factors to aid in prevention and improve clinical outcomes in RCC.\u003c/p\u003e\u003cp\u003eCytokines play an essential role in immune responses and are involved in inflammation and immune-related diseases, including cancers, diabetes and viral infections [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In numerous solid tumors, including RCC, tumor cells secrete various cytokines to reshape the microenvironment and promote progression [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For instance, elevated granulocyte macrophage colony-stimulating factor in tumor tissues is significantly associated with lymph node metastasis, advanced TNM stage, high Fuhrman grade and tumor necrosis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Fitzgerald et al[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] demonstrated that the incubation of interleukin (IL) 6, IL8, or both can increase the migration of RCC 786-O cells in vitro. A meta-analysis of 22 studies revealed higher IL6 levels in RCC patients, particularly in advanced cases, and identified it as an adverse prognostic factor [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, plasma cytokine levels can influence responses to therapies, including past IL2 treatment, current tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, inconsistencies persist regarding the relationship between the levels of certain cytokines in tissues or plasma and RCC risk. Moreover, there is considerable variation in cytokine levels, especially in plasma, across different studies. Additionally, traditional observational study designs face challenges in controlling for potential confounders, contributing to bias in conclusions.\u003c/p\u003e\u003cp\u003eMendelian randomization (MR) is a genetic epidemiological method that utilizes genetic variants as instrumental variables (IVs) to infer causal associations between exposures and diseases [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Based on the principle of random allele allocation before birth, MR mimics a randomized controlled trial, effectively minimizing potential confounding factors and offering robust evidence for causality. Following the publication of a genome-wide association study (GWAS) meta-analysis of 41 cytokines[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], we conducted a two-sample bidirectional MR analysis to assess causal inferences between ccRCC and various cytokines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhile MR analysis offers valuable insights, understanding the potential mechanisms remains essential. Recent advances in single-cell RNA sequencing (scRNA-seq) allow for higher resolution analysis of gene expression and genomic aberrations in tumor and peritumoral cells than bulk RNA sequencing (RNA-seq). In our study, we integrated scRNA-seq and RNA-seq analysis to uncover the potential mechanisms connecting ccRCC and cytokines (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The implications of our findings may provide deeper insights of cytokine-driven tumorigenesis, thereby guiding the diagnosis, treatment, and prognosis of ccRCC.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Source\u003c/h2\u003e\u003cp\u003eWe extracted summary data for genetic variants in 41 plasma chemokines from a recent GWAS on the human plasma proteome[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].GWAS summary data for ccRCC were obtained from the FinnGen consortium R9 release, comprising 287,137 controls and 901 cases[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, we retrieved a scRNA-seq dataset (GSE178481) and two gene expression profiles (GSE213324 (20 normal tissues vs 21 RCCs) and GSE105261 (including 9 normal tissues, 9 primary ccRCCs and 26 metastasis ccRCCs)) of RCC from the Gene Expression Omnibus (GEO) database. We also acquired gene expression profiles and clinical data from the Cancer Genome Atlas (TCGA) database. A total of 803 human oncogenes were obtained from the oncogene database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ongene.bioinfo-minzhao.org/\u003c/span\u003e\u003cspan address=\"http://ongene.bioinfo-minzhao.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. And blood samples and clinical information from 37 patients were collected between August 2023 and December 2023 from the Department of Urology at Zhongshan Hospital affiliated with Fudan University in China (B2016-030). The inclusion criteria were as follows: (1) receiving nephrectomy or biopsy, (2) RCC verified histopathologically, (3) RCC irrespective of pathological type, (4) availability of complete clinical data and peripheral blood samples. The exclusion criteria were as follows: (1) non-ccRCC pathological types (e.g. renal hamartoma, lymphoma and renal pelvic carcinoma), (2) incomplete clinical or sample information. In addition, 46 patients without tumors were included as controls for comparison.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Genetic variants associated with cytokines\u003c/h2\u003e\u003cp\u003eWe identified single nucleotide polymorphisms (SNPs) associated with cytokines as candidate IVs using a statistically significant threshold (p\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;6) and stringent linkage disequilibrium (LD) criteria (LD r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001, LD distance\u0026thinsp;\u0026gt;\u0026thinsp;10,000 kb)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Candidate IVs were further screened based on the following criteria: (1) SNPs with a minor allele frequency (MAF)\u0026thinsp;\u0026le;\u0026thinsp;0.01 were eliminated.(2) SNPs with an F-statistic\u0026thinsp;\u0026le;\u0026thinsp;10 were excluded. The F-statistic was calculated using the formula by R software: F\u0026thinsp;=\u0026thinsp;R2\u0026times;(N-1-K)/((1-R2)\u0026times;K), where R2 represents the proportion of variance in the exposure explained by the IVs, N is the sample size, and K is the number of IVs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. (3) SNPs associated with the outcome at a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;6 were removed. (4) Palindromic SNPs were excluded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Quality control\u003c/h2\u003e\u003cp\u003eThe expression profiling data obtained from GSE213324 and TCGA were initially processed using the Transcripts Per Million (TPM) method and subsequently transformed using log2(TPM\u0026thinsp;+\u0026thinsp;1) for normalization. Similarly, the scRNA-seq data underwent quality control to filter out low-quality or dying cells, applying criteria such as a gene number range of 300 to 2500 and mitochondrial gene content of less than 5%, as well as genes expressed in more than 5 cells. Subsequently, the global-scaling normalization method 'LogNormalize' was employed to normalize the scRNA-seq data. Finally, the results of the quality control for the scRNA-seq data are presented in Fig.\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Dimension reduction, cell clustering and cluster annotation\u003c/h2\u003e\u003cp\u003ePrincipal component analysis (PCA) was conducted to visualize the data distribution (Fig.\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec), followed by the identification of the top 2000 variable genes using the FindVariableFeatures function of the Seurat R package (Fig.\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003eS1\u003c/span\u003ed). Subsequently, the FindNeighbors and FindClusters functions were utilized for cell clustering. Finally, dimensionality reduction was achieved using the uniform manifold approximation and projection (UMAP) method through the RunUMAP function. To annotate cell clusters and identify different cell types, the scMayoMap R package, in combination with manual annotation[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], was employed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Assessment of the Immune-microenvironment and Tumor-infiltrating Immune Cells\u003c/h2\u003e\u003cp\u003eThe stromal and immune score and tumor purity was inferred using ESTIMATE (Estimation of stromal and immune cells in malignant tumors using expression data) analysis based on all expressed genes[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, all expressed genes were also inputted into the CIBERSORT (Cell-type identification by estimating relative subsets of RNA transcripts) web portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersort.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersort.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to estimate the infiltration rates of 22 types of immunocytes in the tumor microenvironment through 1,000 iterations, utilizing the LM22 gene signature[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].Furthermore, the MCPCOUNTER [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and EPIC (Estimating the proportions of immune and cancer cells) methods were employed to assess the relative proportions of tumor-infiltrating immune and tumor cells[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Cell trajectory and CellChat analysis\u003c/h2\u003e\u003cp\u003eThe Monocle R package was employed to analyze cell trajectory and pseudo-time for identified cell types. Additionally, the iTALK R package was utilized to further explore intercellular interactions and visualize networks. Default parameters were applied for both analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Enzyme-linked immunosorbent assay (ELISA) and Flow Cytometry\u003c/h2\u003e\u003cp\u003eCytokines (IL1B, IL2R, IL6, IL8, IL10 and tumor necrosis factor (TNF)) in the supernatant were detected using an ELISA kit following the manufacturer's instructions (Siemens, Germany). For flow cytometry analysis, peripheral blood was firstly collected using ethylenediaminetetraacetic acid dipotassium salt as an anticoagulant. An automated cell counter (Becton, Dickinson and Company (BD) Trucount) was applied to count the cells and adjust the cell concentration to 1\u0026times;10⁶ cells/mL. After washing twice with phosphate buffered saline, the lymphocyte subset detection reagents (BD Biosciences, USA) were added, including: FITC-conjugated anti-CD3 monoclonal antibody, APC-Cy7-conjugated anti-CD8 monoclonal antibody, PE-Cy7-conjugated anti-CD4 monoclonal antibody, PE-conjugated anti-CD16 monoclonal antibody and anti-CD56 monoclonal antibody, APC-conjugated anti-CD19 monoclonal antibody, and PerCP-Cy5.5-conjugated anti-CD45 monoclonal antibody. Then the cell suspension was thoroughly mixing and incubated for 15 minutes in the dark at room temperature. Finally, flow cytometry was then employed to identify and characterize the cell populations (BD Biosciences, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Establishing a predictive model\u003c/h2\u003e\u003cp\u003eWe initially identified genes associated with ccRCC using univariate logistic regression (LR) analysis. Following this, a predictive model based on the selected genes was established for diagnosing ccRCC through multivariate LR analysis. Receiver operating characteristic curve (ROC) analysis was performed, and the area under the curve (AUC) was computed to assess the predictive efficiency of the model. Additionally, calibration curves were generated after 1000 bootstrap resampling to evaluate the consistency between predicted probabilities and actual probabilities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Immune phenotype score (IPS) and drug susceptibility\u003c/h2\u003e\u003cp\u003eIn addition, IPS of ccRCC patients were obtained from The Cancer Immunome Atlas database[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] to predict immunotherapy sensitivity. Furthermore, the oncopredict R package was utilized to predict the response to commonly utilized chemotherapy and molecular targeted drugs in different IL6 expression groups[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using SPSS v 29.0.1.0 software or R v4.2.0. MR analyses were performed using Two-SampleMR v0.5.6 and the MRPRESSO package v1.0 in R v4.20. Several approaches were employed to detect the causal association between cytokines and ccRCC, including inverse variance weighted (IVW), weighted median, MR-Egger (MR Egger regression), and MR-PRESSO (MR pleiotropy residual sum and outlier).\u003c/p\u003e\u003cp\u003eThe IVW method utilized a meta-analysis approach to combine the Wald ratio estimates for each SNP to obtain the causal effect[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].Additional MR analysis approaches included the weighted median (WM) method and MR-Egger regression[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].MR-PRESSO and MR-Egger regression intercept analysis were used to assess potential horizontal pleiotropy[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCochrane\u0026rsquo;s Q test was performed to assess heterogeneity. SNPs with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to indicate horizontal pleiotropy or heterogeneity. Continuous variables were expressed as mean and standard deviation, while categorical variables were expressed as percentage/frequency.\u003c/p\u003e\u003cp\u003eComparisons between two groups for continuous variables were conducted using the t-test, while the chi-square test or Fisher\u0026rsquo;s exact test was used for categorical variables. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Causal relationships between IL6 and ccRCC\u003c/h2\u003e\u003cp\u003eA schematic chart of our research design is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrate that five cytokines-beta nerve growth factor (bNGF), fibroblast growth factor-basic (FGFB), IL5, IL6, and IL17-were found to have a causal effect on ccRCC. According to IVW estimates, bNGF (odds ratio (OR): 1.26, 95% confidence interval (CI): 1.00-1.60, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), FGFB (OR: 1.30, 95% CI: 1.04\u0026ndash;1.63, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), IL5 (OR: 1.67, 95% CI: 1.35\u0026ndash;2.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and IL6 (OR: 1.51, 95% CI: 1.01\u0026ndash;2.25, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) exhibited positive effects on ccRCC risk. Among them, genetically predicted higher IL6 level was associated with a 51% increase in the odds of developing of ccRCC, compared to lower IL6 levels. Conversely, IL17 showed an adverse effect (OR: 0.75, 95% CI: 0.61\u0026ndash;0.94, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The remaining cytokines did not display significant causal associations with ccRCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn Table S1, the F-statistic values for all SNPs selected as potential IVs were \u0026gt;\u0026thinsp;10, indicating no evidence of weak instrument bias. These IVs explained 2.03%, 1.38%, 1.56%, 1.50%, and 2.92% of the variability in bNGF, FGFB, IL5, IL6, and IL17, respectively. Cochran\u0026rsquo;s IVW Q test revealed no heterogeneity for any IV among the 41 cytokines, except for TNF-a (Table S2). Similarly, MR-Egger regression intercept analysis detected no horizontal pleiotropy for any IV in ccRCC, suggesting plausible causal associations (Table S2). Except for IL8 (outlier: rs116383510, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), no potential outliers were found among the IVs (global test P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) by MR-PRESSO test (Table S3). After excluding outlier rs116383510, IL8 still did not show a causal effect in ccRCC. Leave-one-out analysis results (Fig.\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) indicated that causal associations between cytokines and ccRCC were not substantially affected by any particular IV.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe further assessed reverse causation effects through reverse MR analysis, treating ccRCC as an exposure and cytokines as outcomes. Bidirectional causal effects between ccRCC and IL6 were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with ccRCC showing a significant positive effect on IL6 (OR: 1.05, 95% CI: 1.02\u0026ndash;1.09, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, ccRCC exhibited significant positive effects on IL9 (OR: 1.07, 95% CI: 1.00-1.13, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), IL10 (OR: 1.07, 95% CI: 1.03\u0026ndash;1.11, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), IL12p70 (OR: 1.17, 95% CI: 1.00-1.38, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), cutaneous T-cell-attracting chemokine (CTACK, OR: 1.06, 95% CI: 1.04\u0026ndash;1.09, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and macrophage inflammatory protein-1β (MIP1b, OR: 1.06, 95% CI: 1.03\u0026ndash;1.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, negative causal associations were found for interferon gamma-induced protein 10 (IP10, OR: 0.96, 95% CI: 0.94\u0026ndash;0.98, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and monokine induced by gamma interferon (MIG, OR: 0.94, 95% CI: 0.90\u0026ndash;0.97, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) via IVW (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No heterogeneity or horizontal pleiotropy was observed via Cochran\u0026rsquo;s IVW Q test and MR-Egger regression analysis, respectively, except for IL8 (Table S2). No potential outliers were found among the IVs except for RANTES (Regulated on activation, normal T cell expressed and secreted, Table S3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2 IL6 level related with clinicopathological features in ccRCC\u003c/h2\u003e\u003cp\u003eTo assess IL6 levels in RCC, we analyzed data from TCGA. Lower levels of IL6 were significantly found in renal papillary cell carcinoma and renal chromophobe cell carcinoma, but not in ccRCC compared to normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Pan-cancer analysis revealed that IL6 was almost significantly down-regulated in these tumors, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, except for colon adenocarcinoma. Surprisingly, IL6 levels in ccRCC exhibited a similar downtrend compared to normal tissue in TCGA and GSE105261 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, in GSE213324, a significant difference was observed between normal and RCC, but not in ccRCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Immunohistochemical staining results from the Human Protein Atlas database illustrated that IL6 exhibited low expression levels in both normal tissues and ccRCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, IL6 was primarily expressed in the cytoplasm of renal tubular cells in normal tissues, with positivity observed in 13 out of 16 samples (81%).In contrast, the positivity frequency was 10 out of 16 samples (63%) in ccRCC patients from our clinical cohort (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).To further explore whether IL6 could affect ccRCC, we found that a higher level of IL6 was associated with worse prognosis through Kaplan-Meier analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Although there was no statistical difference between ccRCC and normal tissues, the level of plasma IL6 from patients with ccRCC, but not nccRCC, was higher than that of patients without any tumor (7.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00 vs 4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 pg/ml, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eg).Moreover, the level of plasma IL6 was also positively correlated with T stage (correlation coefficient (CC): 0.72, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eh and Table S5).Additionally, IL6 showed a positive correlation with status (CC: 0.24), platelet level (CC: 0.18), calcium level (CC: 0.12), histologic grade (CC: 0.25), T stage (CC: 0.21), and stage (CC: 0.19) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ei and Table S5).Among these, platelet level and calcium level were both items of the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC), known to be associated with prognosis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn our cohort, 46 control cases and 37 tumor cases were included (detailed in Table S5), of which 27 (72.97%) were ccRCC, 6 (16.22%) were papillary RCC, 3 (8.11%) were chromophobe RCC, and 1 (2.70%) was FH-deficient RCC, confirmed histopathologically with surgery or biopsy. Among RCCs, 24 patients were male and 13 were female, with a mean age of 58.73\u0026thinsp;\u0026plusmn;\u0026thinsp;12.45 years. The maximum diameter of the primary tumor ranged from 1.9 cm to 12.5 cm. Three patients developed metastasis at the initial diagnosis, including lung, liver, and lymph node metastasis. There were no significant differences between patients without any tumor and those with ccRCC regarding the levels of plasma other cytokines (e.g., TNF, IL1B, IL2R, IL8, and IL10, Fig.s\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3 IL6 related with Pan B cell types\u003c/h2\u003e\u003cp\u003eThe analysis of RNA-seq data from TCGA may result in deviation. Therefore, scRNA-seq data (GSE178481) was further utilized for analysis. After quality control, 105,904 cells were screened and clustered into 31 clusters using the k-Nearest Neighbor (KNN) clustering algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The distribution of sample types and IL6 expression plot are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eb and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, respectively. Similarly to the results from TCGA, we observed IL6 expression in both tumor and normal tissues.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on molecular characteristics, 14 cell types were annotated, including T cells, NK cells, and B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).Although IL6 was mainly expressed in endothelial cells (27.5%) and dendritic cells (24.3%), followed by B cells (16.5%, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ei), surprisingly, IL6 expression was unevenly distributed within the B cell cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).To explore which B cell subtype was associated with IL6, the B cell cluster was re-analyzed and then re-clustered into 14 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ee).Through Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ef and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, we similarly found mixed expression of IL6 in tumor and normal tissues. After manual annotation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], 4 B cell subtypes were identified: memory B cells, Pan B cells, plasmablasts, and plasma cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eh). As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ei, IL6 was predominantly expressed in the Pan B cell (92.1%) rather than memory B cells (4.6%) or plasmablasts (3.3%).\u003c/p\u003e\u003cp\u003eGiven that IL6 was related to Pan B cells, immune score and tumor purity were subsequently assessed using the ESTIMATE method. Compared with the IL6-high group (\u0026ge;\u0026thinsp;mean value (1.26)), the immune score and ESTIMATE score were lower (immune score: 1827\u0026thinsp;\u0026plusmn;\u0026thinsp;52.44 vs.1493\u0026thinsp;\u0026plusmn;\u0026thinsp;36.7, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ESTIMATE score: 2739\u0026thinsp;\u0026plusmn;\u0026thinsp;81.97 vs.2092\u0026thinsp;\u0026plusmn;\u0026thinsp;57.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but tumor purity score was higher in the low-risk group (0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 vs 0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ej), consistent with the results shown in Fig.s\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eb and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ef, and suggesting that IL6 expression in ccRCC was correlated with immune infiltration.\u003c/p\u003e\u003cp\u003eThe CIBERSORT method using the LM22 model matrix was employed to evaluate the diversity in the infiltration of 22 types of immune cells between the IL6-high and IL6-low groups. We found that infiltration rates of 12 types of immune cells (i.e., naive B cells, plasma cells, etc.) differed significantly between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ek). Compared with the IL6-low group, the rates of naive B cells and plasma cells were higher in the IL6-high group. Additionally, results from MCPCOUNTER and EPIC methods also indicated that B cell infiltration was closely related to a high level of IL6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ek). However, in our cohort, we did not find a significant difference between patients without any tumor and those with ccRCC for immune cells from serum samples, except for CD8\u0026thinsp;+\u0026thinsp;suppressor T cells (Fig.s\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Interaction between IL6 and Pan B cells\u003c/h2\u003e\u003cp\u003eA trajectory analysis was conducted to assess the relationship between IL6 and Pan B cells.As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, cells were categorized into three different stages.An overlapping distribution between Pan B cells and high levels of IL6 was observed, primarily concentrated in stage 3, where a mixture of cells, including normal and tumor cells, were situated. Therefore, we speculated that cell malignant progression might be driven by interactions between IL6 and Pan B cells. Pseudotime analysis results validated this speculation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Along the pseudotime trajectory, we observed that IL6 level was gradually downregulated and Pan B cells gradually transformed into other B cell subtype while normal cells exhibited a gradual transition into cancerous states.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSimultaneously, CellChat analysis revealed that Pan B cells exhibited higher outgoing interaction strength in the C-type lectin-like receptors (CLEC) signaling pathway, while incoming interactions were prominent in the MIF and APP signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). In the CLEC signaling pathway, Pan B cells acted as signal senders primarily interacting with plasmablasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). In the macrophage migration inhibitory factor (MIF) and amyloid precursor protein (APP) signaling pathways, Pan B cells acted as signal receivers interacting with plasmablasts and plasma cells, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ee and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ef).\u003c/p\u003e\u003cp\u003eSubsequently, differentially expressed genes (DEGs) were identified for each B cell subtype, and the top 10 DEGs were displayed among the four subtypes using a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eg). To validate whether cell malignant progression might be driven by interactions between IL6 and Pan B cells, 13 exclusively differential expressed oncogenes (DEOs) for Pan B cells were first screened using a Venn diagram. These included NCOA3 (Nuclear receptor coactivator 3), ZFAS1 (Zinc finger antisense RNA 1), BLK (B lymphoid tyrosine kinase), BCL2A1 (BCL2 related protein A1), GLO1 (Glyoxalase 1), ADAM28 (ADAM metallopeptidase domain 28), EIF3E (Eukaryotic translation initiation factor 3 subunit e), SWAP70 (Swap switching b-cell complex 70kda subunit), HSPA4 (Heat shock protein family a (Hsp70) member 4), EEF1A1 (Eukaryotic translation elongation factor 1 alpha 1), RPL23 (Ribosomal protein L23), FOXP1 (Forkhead box P1), and MDM4 (MDM4 regulator of p53) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eh).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.5 DEOs helping to diagnose ccRCC\u003c/h2\u003e\u003cp\u003eAmong all DEOs, 6 DEOs (FOXP1, ZFAS1, BLK, BCL2A1, ADAM28, and RPL23) were positively correlated with IL6, while 3 DEOs (GLO1, HSPA4, and EEF1A1) showed a negative correlation in the TCGA dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). In GSE213324, 10 DEOs (MDM4, FOXP1, NCOA3, ZFAS1, BLK, BCL2A1, ADAM28, EIF3E, SWAP70, and RPL23) were positively associated with IL6, while none of the DEOs showed a negative correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).These results indicated a significant correlation between most DEOs and IL6.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, patients from the TCGA dataset were divided into a training set and a testing set in a ratio of 7: 3. Univariate LR analysis identified a total of 9 DEOs associated with ccRCC at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the training set (Table S4). Among them, three genes with the greatest predictive value were ZFAS1 (OR: 88.35, 95% CI: 28.06-278.23), BCL2A1 (OR: 35.04, 95% CI: 13.91\u0026ndash;88.25), and RPL23 (OR: 15.72, 95% CI: 6.91\u0026ndash;35.75). Subsequently, multivariate LR analysis was performed to construct a diagnostic model based on these DEOs. After adjustment for other DEOs, ZFAS1 (OR: 84.15, 95% CI: 12.32-1051.56, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and BCL2A1 (OR: 6.17, 95% CI: 1.42\u0026ndash;36.74, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were identified as independent risk factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ec and Table s4).The detailed scoring system is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ed.\u003c/p\u003e\u003cp\u003eROC curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ee, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ef, and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eg) demonstrated AUC values for ccRCC diagnosis of 0.988, 0.984, and 0.912 in the TCGA training set, TCGA testing set, and GSE213324 testing set, respectively. After 1000 bootstrap resamplings, the diagnostic model exhibited excellent consistency between predicted ccRCC probability and actual ccRCC probability, as shown by the calibration curve in the TCGA training set, TCGA testing set, and GSE213324 testing set, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eh, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ei, and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ej).These findings not only suggest that our model demonstrates good diagnostic potential for ccRCC but also indirectly indicate the existence of a relationship between IL6 and ccRCC.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Relationship between IL6 level and immunotherapy/TKIs response\u003c/h2\u003e\u003cp\u003eConsidering the relationship between IL6 and immune cells, IPS was further investigated to predict immunotherapy sensitivity in ccRCC patients with different levels of IL6. Regardless of whether immune checkpoints (Programmed cell death protein 1 (PD1) or cytotoxic T-lymphocyte associated protein 4 (CTLA4)) were expressed or not, patients in the IL6-low group had a higher IPS, suggesting a better immunotherapy response (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). Additionally, TKIs were also recommended as a treatment option for ccRCC. Therefore, the 50% inhibitory concentration (IC50) of some TKIs was investigated (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). It was observed that the different levels of IL6 could affect the therapeutic response of TKIs. In other words, some TKIs showed a better curative effect in the IL6-low group, while others did not. This suggests that it is necessary to detect IL6 levels before initiating TKIs therapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eAlthough known risk factors such as age, gender, race, location, smoking, hypertension, and obesity [\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], the etiology of ccRCC remains poorly understood. In this study, we conducted a two-sample MR analysis to infer causal relationships between ccRCC and circulating cytokine levels, which mitigates confounding and reverse causation issues inherent in observational studies. This approach provides relatively robust evidence using genetic variants from non-experimental settings. Our findings identified bNGF, FGFB, IL5, and IL6 as risk factors of ccRCC, while IL17 exhibited a protective effect. Furthermore, reverse MR analysis revealed that ccRCC might significantly influence cytokine levels, particularly IL6, IL9, IL10, IL12p70, CTACK, MIP1b, IP10 and MIG. Of particular significance, we identified bidirectional causal effects between ccRCC and IL6, suggesting a feedback loop that may reinforce disease progression.\u003c/p\u003e\u003cp\u003eIL6 is a pleiotropic proinflammatory cytokine secreted by various cells, such as endothelial, immune and tumor cells. In RCC, IL6 is spontaneously secreted by normal renal cortical and tumor cells [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. As an one of the most studied cytokines, IL6 plays a significant role in the development and progression of RCC [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Elevated IL6 levels have been observed in RCC tissue samples[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and patient plasma [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and associated with unfavorable prognosis and resistance to multiple treatment, including TKIs (e.g., sorafenib, sunitinib, and pazopanib)[\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], ICIs[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], doxorubicin[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], IL2[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], interferon-alpha (IFN-a)[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], advanced stage[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and reduced survival[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Our findings align with Chen et al.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], who reported that increased plasma IL6, but not other cytokines in ccRCC patients. Additionally, increased IL6 expression positively correlated with the histologic grade, T stage, stage and worse overall survival in our study, further underscoring the close relationship between IL6 and RCC.\u003c/p\u003e\u003cp\u003eAutocrine and paracrine production of IL-6 could lead to STAT3 (Signal transducer and activator of transcription 3) phosphorylation, mediating tumorigenesis and development [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. It not only mediates IL6-associated proliferation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] but also promotes metastasis in RCC [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Additionally, STAT3 could also induce IL6-associated treatment resistance. For example, IL6 and IL6-associated molecules/pathways markedly upregulated following TKI treatment (sorafenib, sunitinib, and pazopanib), such as STAT3, AKT (Protein kinase b), mTOR (Mechanistic target of rapamycin), 4EBP1 (Eukaryotic translation initiation factor 4e binding protein 1), S6RP (ribosomal protein s6), p70S6 (Ribosomal protein s6 kinase beta-1), and NFκB (Nuclear factor kappa-light-chain-enhancer of activated b cells)[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. But combining TKIs with IL6R blockade (e.g. Tocilizumab) suppressed this activation, improved TKIs response and delayed treatment resistance[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In our research, we also found IL6 may influence responses to immune or TKI treatment. For example, patients with low IL6 levels exhibited a better immunotherapy response, even without high expression of PD1 or CTLA4. Therefore, IL6 assessment could optimize treatment choices in clinical practice. In addition, IL6 may also affect RCC via additional molecules/pathways, such as SOCS3 (Suppressor of cytokine signaling 3), PI3K (Phosphoinositide 3-kinase)/AKT/mTOR, and RAS (Rat sarcoma viral oncogene homolog)/RAF (Rapidly accelerated fibrosarcoma)/MEK(MAPK/ERK kinase)/ERK (Extracellular signal-regulated kinase) pathways[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. So, we could explore potential mechanisms in those directions in future.\u003c/p\u003e\u003cp\u003eGiven that immune cell infiltration plays a significant role in cancer initiation and progression, we analyzed the relationship between IL6 and immune cells from different perspectives. We found IL6 distribution was related to Pan B cells using scRNA-seq.\u0026nbsp;Results from cell trajectory and pseudotime analysis further suggested that cell malignant progression may be driven by the interaction between IL6 and Pan B cells. Subsequently, we identified 13 Pan B cell-specific oncogenes, from which nine were used to establish a diagnostic model. ZFAS1 and BCL2A1 emerged as independent risk factors. And this model exhibited excellent diagnostic performance, with AUCs of 0.988, 0.984, and 0.912 in the TCGA training, TCGA testing, and GSE213324 dataset, respectively. Our model not only confirmed the link between ccRCC and Pan B cells, but also demonstrated potential utility in the differential diagnosis of RCC. Furthermore, it also provided valuable insights for future researchers on RCC diagnosis, treatment and prognosis. In recent years, tumor-infiltrating B cells (TIBs) have gained more and more attention in in tumor immunosurveillance. Their effects can be either tumor-promoting or tumor-suppressing, depending on TME\u0026rsquo;s composition, B cell phenotypes, and the antibodies they produce. Higher TIB levels have been associated with favorable outcomes in cancers such as melanoma, sarcoma, breast, esophageal, non\u0026ndash;small cell lung, colon, and biliary tract cancers [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. However, TIBs have also been linked to worse prognosis in bladder cancer [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and increased prostate cancer recurrence following prostatectomy [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In line with our findings, Iglesia M.D. et al. [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] reported that elevated B cell and plasma cell gene signatures were related to poor prognosis in RCC, and enriched in tumors of patients responding to ICI therapy [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The functional diversity of B cells makes their classification in the TME difficult, as traditional surface markers may not fully reflect their heterogeneity [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCertainly, our research has some limitations. We didn\u0026rsquo;t explore the detailed mechanisms about link between IL6, Pan B cells and ccRCC progression. Additionally, due to limited follow-up data in our cohort, we couldn\u0026rsquo;t assess prognostic value of serum IL6. Finally, external validation of our diagnostic model are still required. Nevertheless, by identifying IL6 as a potential therapeutic target, our study provides novel insights that may guide future research and aid in the development of improved diagnostic and therapeutic strategies for ccRCC patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRCC: renal cell carcinoma; ccRCC: clear cell renal cell carcinoma; TKI: tyrosine kinase inhibitor; ICI: immune checkpoint inhibitor; MR: Mendelian randomization; IV: instrumental variable; GWAS: genome-wide association study; scRNA-seq: single-cell RNA sequencing; RNA-seq: bulk RNA sequencing; GEO: Gene Expression Omnibus; TCGA: the Cancer Genome Atlas; SNP: single-nucleotide polymorphism; LD: linkage disequilibrium; MAF: minor allele frequency; TPM: the Transcripts Per Million; PCA: Principal component analysis; UMAP: the uniform manifold approximation and projection; LR: Logistic regression; ROC: receiver operating characteristic curve; AUC: the area under the curve; IPS: immune phenotype score; MR-PRESSO: MR pleiotropy residual sum and outlier; MR-Egger: Mendelian Randomization Egger regression; IVW: inverse variance weighted; WM: the weighted median; OR: odds ratio; CI: confidence interval; CC: correlation coefficient; IMDC: the International Metastatic Renal Cell Carcinoma Database Consortium; KNN: the k-Nearest Neighbor; DEGs: differentially expressed genes; DEOs: differentially expressed oncogenes; IC50: the 50% inhibitory concentration; IL-1b: Interleukin 1-beta; IL-1ra: Interleukin 1 receptor antagonist; IL-2: Interleukin 2; IL-2ra: Interleukin 2 receptor, alpha subunit; IL-4: Interleukin 4; IL-5: Interleukin 5; IL-6: Interleukin 6; IL-7: Interleukin 7; IL-8: Interleukin 8; IL-9: Interleukin 9; IL-10: Interleukin 10; IL-12p70: Interleukin 12p70; IL-13: Interleukin 13; IL-16: Interleukin 16; IL-17: Interleukin 17; IL-18: Interleukin 18; CTACK: Cutaneous T-cell attracting (CCL27) ; GRO-a: Growth regulated oncogene-a (CXCL1); IP-10: Interferon gamma-induced protein 10 (CXCL10); MCP-1: Monocyte chemotactic protein-1; MCP-3: Monocyte specific chemokine 3 (CCL7); MIG: Monokine induced by interferon-gamma; MIP-1a: Macrophage inflammatory protein-1a (CCL3); MIP-1b: Macrophage inflammatory protein-1 beta; RANTES: Regulated on activation, normal T-cell expressed and secreted (CCL5); SDF-1a: Stromal cell-derived factor-1 alpha; FGFB: fibroblast growth factor-basic; bNGF: Beta nerve growth factor; G-CSF: Granulocyte colony-stimulating factor; \u0026nbsp;HGF: Hepatocyte growth factor; M-CSF: Macrophage colony-stimulating factor; PDGF-bb: Platelet-derived growth factor BB; SCF: Stem cell factor; SCGFb: Stem cell growth factor beta; VEGF: Vascular endothelial growth factor; IFNg: Interferon-gamma; MIF: Macrophage migration inhibitory factor; TNFa: Tumor necrosis factor alpha; TNFb: Tumor necrosis factor beta; TRAIL: the tumour necrosis factor-related apoptosis-inducing ligand.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003eAll studies involving human tissues were conducted in strict accordance with the Declaration of Helsinki and were approved by the Ethics Committee of Zhongshan Hospital Affiliated to Fudan University (B2016-030, 17 March 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003eAll participants were fully informed about the purpose of sample collection, the intended research, and potential publication, and each signed an informed consent form for the donation of biological samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e Not applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Methodology, Data curation and Formal Analysis: Dengqiang Lin, Xiaoxia Li, Jinglai Lin and Qi Sun; Supervision: Shuopeng Ye and Fan Chao; Writing \u0026ndash;original draft:Dengqiang Lin; Writing\u0026ndash;review and editing: Xiaoyi Hu, Zhibing Xu and Jianming Guo.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors acknowledge the high-quality GWAS resources made available by the FinnGen study (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r9.finngen.fi/\u003c/span\u003e\u003cspan address=\"https://r9.finngen.fi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the GWAS catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ebi.ac.uk/gwas\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/gwas\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and TCGA (\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) database.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. 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Nature. 2020;577:549\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDowns-Canner SM, Meier J, Vincent B, G,Serody JS. B Cell Function in the Tumor Microenvironment. Annu Rev Immunol. 2022;40:169\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-immunol-101220-015603\u003c/span\u003e\u003cspan address=\"10.1146/annurev-immunol-101220-015603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"Renal cell carcinoma, Clear cell renal cell carcinoma, Cytokine, interleukin 6, Mendelian randomization study, Single-cell RNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-6891410/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6891410/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAlthough several risk factors associated with renal cell carcinoma (RCC) have been identified, the etiology of the disease remains unclear. While certain cytokines have been observed in RCC patients compared to healthy individuals, the role of cytokines in promoting RCC development and progression remains uncertain.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a two-sample bidirectional Mendelian randomization (MR) analysis to explore the causal effects of cytokines on clear cell RCC (ccRCC). Integrated bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) analyses were employed to unveil potential mechanisms, which were further corroborated by immunohistochemical staining and plasma cytokine detection using ELISA. Additionally, we developed a diagnostic model using logistic regression analysis. Finally, sensitivity to immunotherapy and targeted therapy was estimated using the R package.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe found bidirectional causal effects of interleukin (IL) 6 in ccRCC, indicating a complementary and mutually reinforcing relationship. Although no statistical differences were observed in IL6 expression between ccRCC and normal tissues, plasma IL6 levels in ccRCC patients were significantly higher than in control cases, positively correlating with T stage. To mitigate potential bias from RNA-seq, we conducted scRNA-seq analysis, confirming IL6 expression in both tumor and normal tissues, consistent with RNA-seq results. Moreover, IL6 expression was found to be unevenly distributed in the B cell cluster, predominantly in the Pan B cell. Trajectory and pseudotime analyses suggested that the malignant progression of cells may be driven by interactions between IL6 and Pan B cells. Subsequently, we identified 13 Pan B cells-specific oncogenes. Using these genes, we constructed a diagnostic model with an area under the curve of 0.988, identifying ZFAS1 (Zinc finger antisense RNA 1) and BCL2A1 (BCL2 related protein A1) as independent risk factors. Finally, we demonstrated that IL6 not only influences immunotherapy response but also affects targeted therapy response.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur analysis confirms a causal correlation between IL6 and ccRCC, suggesting that IL6 may serve as a potential target for diagnostic, therapeutic, and prognostic interventions in ccRCC.\u003c/p\u003e","manuscriptTitle":"Mendelian Randomization and Single-Cell Analyses Identify the Links Between IL6 and Pan B Cells in Clear Cell Renal Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 07:09:19","doi":"10.21203/rs.3.rs-6891410/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":"d1d67692-b7e5-4634-802b-8cb644b49427","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T06:39:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-11 07:09:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6891410","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6891410","identity":"rs-6891410","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}