Constructing a Neutrophil Extracellular Trap Model Based on Machine Learning to Predict Clinical Outcomes and Immunotherapy Response in Renal Cell Carcinoma

Constructing a Neutrophil Extracellular Trap Model Based on Machine Learning to Predict Clinical Outcomes and Immunotherapy Response in 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 Article Constructing a Neutrophil Extracellular Trap Model Based on Machine Learning to Predict Clinical Outcomes and Immunotherapy Response in Renal Cell Carcinoma Yihao Zhu, Yajian Li, Xuwen Li, Yuan Yu, Can Chen, Mingshuai Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4700747/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 Neutrophil extracellular traps (NETs) represent a novel form of inflammatory cell death within neutrophils. Emerging research indicates that NETs promote cancer progression and metastasis in various ways. This study aims to provide prognostic NETs characteristics and therapeutic targets for patients with renal cell carcinoma (RCC). NMF analysis was conducted on 89 NET-related genes in the training cohort. Subsequently, WGCNA networks were utilized to study the subtype feature genes. Six machine learning algorithms were assessed for model training, and the optimal model was selected based on 1-year, 3-year, and 5-year AUC values. A NETs signature was then constructed to predict overall survival in RCC patients. Furthermore, multi-omics validation was performed based on NETs signature. Finally, stable knockout key gene RCC cell lines were established to verify the biological function of KCNN4 both in vitro and in vivo . This study highlights the emerging hot topic of NETs in RCC. We provide a prognostic NETs signature and identify multiple roles of KCNN4 in RCC. This work contributes to risk stratification and the identification of new therapeutic targets for RCC patients. Neutrophil Extracellular Traps Renal Cell Carcinoma KCNN4 Prognostic Model Non-negative Matrix Factorization Machine Learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Renal cell carcinoma (RCC) is the most common kidney tumor in adults and ranks among the top ten causes of cancer-related deaths worldwide [ 1 ]. The pathological types of RCC include clear cell renal cell carcinoma, papillary renal cell carcinoma, chromophobe renal cell carcinoma, collecting duct carcinoma, and unclassified renal cell carcinoma [ 2 ]. Clear cell renal cell carcinoma is the most common subtype, originating from the proximal renal tubules and typically exhibiting an aggressive phenotype [ 3 ]. Surgical treatment has been an effective option for primary RCC, but it shows limited efficacy in metastatic cases. Approximately one-third of RCC patients have metastases at the time of diagnosis [ 4 ]. Given the promising results of immune checkpoint inhibitors (ICI) in treating RCC, researchers have focused on immune cells and immune responses in the tumor microenvironment (TME) of RCC [ 5 ]. As important members of the TME, neutrophils are functionally linked to the progression of RCC [ 6 , 7 ]. Neutrophils in the TME are considered crucial pathogenic factors contributing to tumor progression [ 8 , 9 ], especially through the formation of neutrophil extracellular traps (NETs), which are recognized as novel pro-tumor mechanisms in various cancers [ 10 ]. NETs are reticular structures composed of DNA, histones, and granules released by activated neutrophils [ 11 ]. Previous studies have indicated that the levels of NETs in healthy individuals' peripheral blood are lower than those in cancer patients [ 12 , 13 ]. Additionally, in vitro studies have shown that co-culturing neutrophils with breast cancer and lung cancer cells can induce NETs formation [ 14 – 16 ]. Recent studies have revealed that NETs also play an anti-tumor role in the TME [ 17 ]. On one hand, NETs can repel cytotoxic cells and attract immunosuppressive cells. On the other hand, NETs can act as physical barriers to prevent antibody-dependent cell-mediated cytotoxicity (ADCC) [ 18 ]. Notably, NETs can express PD-L1, leading to the exhaustion of CD8 + T cells within the TME. Anti-NETs therapies may enhance immune cell-mediated tumor cell killing and synergize with immune checkpoint inhibitors (ICIs) [ 19 , 20 ]. Therefore, combining anti-NETs therapy with ICIs might reduce the incidence of ICI resistance, providing a novel therapeutic strategy for cancer treatment. Currently, a novel anti-cancer strategy involves inhibiting the formation of NETs in tumors or promoting their degradation [ 21 ]. Unfortunately, drugs specifically targeting NETs have yet to be developed, which limits improvements in cancer patient outcomes [ 22 ]. However, targeting drugs that modulate neutrophil recruitment and NET formation has shown promise in preclinical models, suggesting a new therapeutic strategy of targeting upstream mediators of NET formation in tumors [ 23 ]. Therefore, exploring the upstream mediators of NETs has become essential to targeted therapy. In this study, we developed a prognostic NETs signature for RCC patients. Notably, we demonstrated that KCNN4 is a crucial mediator of NETs formation and plays multiple roles in tumor migration and invasion, making it a potential oncogenic factor in RCC patients. Methods Data downloading and processing This study utilized publicly available data obtained from several online databases. Gene expression data and clinical data for RCC were downloaded from The Cancer Genome Atlas (TCGA) at https://portal.gdc.cancer.gov/ , comprising 613 RNA-seq samples and 537 clinical data. Gene expression data for RCC were obtained from the Gene Expression Omnibus (GEO) database at https://www.ncbi.nlm.nih.gov/geo/ , including GSE167573 (n = 77) [ 24 ] and GSE29609 (n = 39) [ 25 ]. Immunotherapy cohorts included CheckMate009/010 (n = 76) [ 26 ] and CheckMate025 (n = 207) [ 27 ]. The data obtained from GEO were processed using the R package "limma" (version 3.50.1) for normalization and log2 transformation. Batch effects between datasets were removed using the "ComBat" function, for the data obtained from TCGA, gene ID annotation was performed using the Ensembl database. In cases where multiple gene names existed for a single gene, only the gene with the highest expression level was retained. RNA-seq expression data downloaded from TCGA were provided as transcripts per million (TPM), which were normalized using log2(TPM + 1). Non-negative matrix factorization clustering based on NETs related genes In previous studies, a total of 89 NETs-related genes were identified [ 28 , 29 ]. Non-negative matrix factorization (NMF) was employed to perform clustering analysis on the TCGA cohort based on the expression levels of these NETs-related genes. In NMF, the number of subtypes (k) must be manually specified. The optimal k value can be determined by evaluating the product of the cophenetic correlation coefficient and dispersion coefficient provided by the NMF algorithm. To determine the optimal k value, we iterate over multiple k values (k = 3 ~ 8), running NMF for each k value (50 iterations), and selecting the k value that maximizes the product of the cophenetic correlation coefficient and dispersion coefficient. After determining the optimal number of subtypes, we run NMF with the optimal k value as a parameter (500 iterations) to identify subtypes within the TCGA cohort. The R package "nmf" is used throughout this process. Enrichment analysis and single sample gene set enrichment analysis R package "clusterprofiles" (version 4.2.2) was used for conducting Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to predict the biological functions of genes. Enrichment analysis results with a p-value < 0.05 were considered significant. Hallmark and immune cell infiltration gene sets were collected from the MSigDB database ( https://www.gsea-msigdb.org/gsea/index.jsp ). Single-sample Gene Set Enrichment Analysis (ssGSEA) was performed using the "gsva" function from the R package "GSVA" (version 1.44.3) to evaluate the normalized enrichment scores (NESs) of gene sets in each patient. The R package "estimate" was utilized to calculate StromalScore, ImmuneScore, ESTIMATEScore, and TumorPurity for each subtype. Construction of WGCNA network Weighted Gene Co-expression Network Analysis (WGCNA) is a systems biology approach that identifies modules of highly correlated genes based on their associations with phenotypes, aiming to find candidate biomarker genes and potential therapeutic targets. In this study, we utilized gene expression profiles from RCC in TCGA to construct a WGCNA network. First, samples and genes were filtered, and then the R package "WGCNA" (version 1.71) was used to calculate the Pearson correlations between all pairs of genes in the selected samples, constructing an adjacency matrix. To ensure a scale-free network, we chose β = 5 (R 2 = 0.80) as the soft-thresholding parameter. To further identify functional modules in the weighted gene co-expression network, we calculated the Topological Overlap Measure (TOM) based on the adjacency matrix. Using the TOM values, a dynamic tree-cutting approach was employed to define gene modules and select module eigengenes (MEs). MEs are considered representative of the gene expression profiles within each module. Building prognostic NETs signatures using machine learning frameworks To establish a robust NETs signature, six machine learning algorithms were employed in the TCGA cohort, including CoxBoost, elastic network (Enet), lasso, random forest (RF), Ridge regression, and support vector machine (SVM). Patients were divided into training and validation sets in a 7:3 ratio, and 10-fold cross-validation was used to evaluate model performance. The 1 years, 3 years, and 5 years AUC values were computed and compared for each model to select the best NETs signature. Construction of nomogram model After selecting the best NETs signature, ssGSEA was used to calculate the NESs of the signature. Using the NESs of the NETs signature and other clinical features, a nomogram model was constructed to predict RCC patients' overall survival (OS) rates at 1 year, 3 years, and 5 years. Calibration curves were built to assess the consistency between actual survival and predicted survival. The R package "rms" was utilized to construct the nomogram model. NETs signature annotation of tumor microenvironment, signaling pathways, and immune-related features We collected 5 categories of immune modulators: antigen presentation, immune suppression, immune activation, chemokines, and receptors. Signal pathways related to targeted therapy and immunotherapy were gathered from the MSigDB database, and NESs were computed using ssGSEA. Five immune deconvolution methods assessed immune cell infiltration abundance in RCC patients, including QUANTISEQ, CIBERSORT, MCPCOUNTER, xCell, and EPIC. The Tumor Immune Phenotype (TIP) tracker ( http://biocchrbmu.edu.cn ) was used to evaluate anticancer immune activity [ 30 ]. The TIDE ( http://tide.dfci.harvard.edu/ ) database was used for immune-related feature analysis [ 31 ]. Cell lines and culture conditions Human kidney cell carcinoma cell lines (Caki-1, ACHN, OS-RC-2, 786-O, Caki-2, and 769-P) and normal renal cell lines (HK-2) were obtained from the ATCC (Manassas, VA, USA). The kidney cell carcinoma cell lines were grown in Ham’s F-12K medium (Jiangsu Kaiji Biotechnology Co, Ltd) or RPMI-1640 medium (Gibco Laboratories, Grand Island, NY). And HK-2 was cultivated in DMEM medium (Gibco Laboratories, Grand Island, NY). Western blotting assay and cell counting kit-8 (CCK-8) assay Western blot analysis was conducted to determine the expression levels of genes. The blots were incubated with an antibody overnight at 4°C. The following antibodies were used: KCNN4 (Abcam, ab215990), GAPDH (Abcam, ab8245), PCNA (Abcam, ab29), N-cadherin (Abcam, ab76011), E-cadherin (Abcam, ab314063), Vimentin (Abcam, ab92547). We used the CCK-8 assay (Beyotime, Shanghai, China) to measure cell proliferation according to a specific protocol. Wound healing assay As previously described [ 32 ], RCC cell lines were cultured in serum-free media. Monolayers were scratched using a pipette tip and washed with PBS. After 24 hours, wound healing was captured under a microscope (Beckman). Animal experiments All animal experiments were conducted in accordance with ethical standards and approved by the Ethics Committee of the Cancer Hospital, Chinese Academy of Medical Sciences. In vivo studies involved subcutaneous injection of 5×10 6 Caki-1 and 769-P cell lines in 100 µL PBS into the flanks of nude mice. Tumor volumes were monitored weekly using the formula volume = (length×width 2 )/2. Mice were euthanized on day 28 post-transplantation. Lung and subcutaneous tumor tissues were collected for further analysis. Lung samples were embedded in paraffin, and sections were stained with hematoxylin and eosin (H&E) to visualize the size of lung metastases. Immunohistochemistry The mouse antibodies against KCNN4 (Abcam, ab215990), N-cadherin (Abcam, ab76011), E-cadherin (Abcam, ab314063), Vimentin (Abcam, ab92547, 1:200) and Ki-67 (Abcam, ab15580) were purchased from Abcam. IHC experiments were conducted according to standard procedures. ImageJ's IHC Profiler macro was used to quantify the protein intensity expression in the samples. Statistical analysis We used the “survminer” package (version 0.4.9) in R to compute the optimal cutpoint using the “surv_cutpoint” function, which grouped patients accordingly. Subsequently, we utilized both the “survminer” and “survival” packages (version 3.3-1) to plot Kaplan-Meier survival curves for different patient groups and to perform log-rank tests to assess the significance of differences between groups. ROC curve analysis was conducted using the “pROC” package (version 1.18.0). Graphical visualization was performed using the “ggplot2” package (version 3.3.5). All statistical analyses in this study were conducted using the R programming language (version 4.2.0). Unless otherwise stated, all statistical tests were two-sided, and P < 0.05 was considered statistically significant. Results Development and validation of NETs related gene subtypes According to the expression levels of 89 NETs-related genes, we performed NMF analysis on the TCGA cohort. The correlation plot indicated that k = 4 was the optimal number of subtypes, thus RCC patients were divided into C1-C4 subtypes (Fig. 1 A-B). The clinical data summary of patients with different subtypes of RCC is shown in Table S1 . There were no significant differences in age, gender, and N stage distribution among the four subtypes, whereas UICC stage, T stage, M stage, grade, and tumor location showed significant differences in distribution. The Kaplan-Meier survival curve analysis revealed significant differences in OS among the four subtypes (Fig. 1 C). Specifically, patients with the C2 subtype had better prognosis, while patients with C1 and C3 subtypes had shorter survival times. Enrichment analysis using ssGSEA on hallmark pathways showed that the C1 subtype enriched in the Angiogenesis pathway, while oxidative phosphorylation was enriched in the C2 subtype (Fig. 1 D). The PI3K_AKT_mTOR_signaling pathway was enriched in the C3 subtype, and the C4 subtype was significantly enriched in Interferon_gamma_response and Interferon_alpha_response (Fig. 1 D). Deconvolution analysis revealed that the four subtypes enriched different immune cells (Fig. 1 E). Additionally, the tumor immune infiltration status was also significantly enriched in different subtypes; for example, the C2 subtype had higher levels of StromalScore, ImmuneScore, and ESTIMATEScore, while showing lower TumorPurity. In contrast, the C4 subtype had higher TumorPurity (Fig. 1 F-I). WGCNA network identifies subtype-related genes We set a β soft threshold of 5 and constructed the WGCNA network (R 2 = 0.8, Fig. 2 A-B). We identified the ME green module as the most significant module (Fig. 2 C), which contained 824 genes (correlation coefficient 0.63, p = 2.4e-92, Fig. 2 D). The univariate Cox regression analysis was performed on genes in the green module, filtering for 330 genes with p < 0.01 for further analysis (Table S2 ). Subsequently, we applied six machine learning algorithms to filter the 330 genes and select the optimal NETs signature. We computed the AUC values for each model at 1 year, 3 years, and 5 years to evaluate their prognostic performance for RCC patients. The results showed that the Ridge model selected genes with the highest AUC values (Fig. 2 E-G), identifying a total of 7 genes (Table S2 ). Next, we conducted GO enrichment analysis and KEGG pathway analysis on the genes identified by the Ridge model. GO enrichment analysis revealed that these signature genes were enriched in tumor necrosis factor production (BP, Fig. 2 H), tertiary granule (CC, Fig. 2 I), and MHC class I receptor activity (MF, Fig. 2 J). KEGG pathway analysis indicated enrichment of these genes in osteoclast differentiation (Fig. 2 K). Determination and verification of prognostic NETs signatures Based on ssGSEA, we calculated the NESs of the signature genes in RCC patients from the Ridge model and defined this as the NETs signature. Using the optimal cutoff value for the NETs signature, we divided patients into high and low groups. We first evaluated the clinical differences between the high and low NETs signature groups (Table S3 ). It is evident that the high NETs signature group has a higher proportion of advanced-stage patients (Stages III-IV), while the low NETs signature group has a higher proportion of early-stage patients (Stages I-II). Further analysis of their impact on RCC prognosis revealed that the low NETs signature group has significantly longer survival times than the high NETs signature group (Fig. 3 A). Additionally, ROC analysis demonstrated that the NETs signature has higher predictive efficacy in RCC patients for 1-year, 3-year, and 5-year prognosis (Fig. 3 B). To ensure the robustness of the analysis, we further validated the results using four validation cohorts. The results showed that the NETs signature performed well across all four validation cohorts (Fig. 3 C-F). To provide a quantitative method for clinicians to assess patient prognosis, we further explored the potential associations between the NETs signature and clinical-pathological characteristics in the TCGA cohort. We constructed a nomogram model incorporating the NETs signature and clinical-pathological characteristics (Fig. 3 G). Higher scores of the NETs signature in RCC patients were associated with poorer prognosis. Using the nomogram model's calibration curves, we predicted the survival probabilities of patients at 1 year, 3 years, and 5 years after diagnosis using the NETs signature. The calibration curves for 1-year, 3-year, and 5-year survival probabilities effectively predicted the actual survival probabilities at these time points (Fig. 3 H-J). These results indicate that the nomogram model based on the NETs signature has strong discrimination and calibration capabilities. Features of NETs signatures in tumor microenvironment In RCC patients, the NETs signature showed significant correlations with five categories of immune regulators (Fig. 4 A). Antigen presentation, immune suppression, immune activation, and receptor molecules were especially highly expressed in the high NETs signature group, whereas chemokines were highly expressed in the low NETs signature group. Various deconvolution algorithms were used to assess the abundance of immune cell infiltration between the two patient groups. In the high NETs signature group, RCC patients were enriched with immune-promoting cells, such as NK cells, CD8 + T cells, and CD4 + T cells. Conversely, in the low NETs signature group, RCC patients were enriched with immune-suppressive cells, such as myeloid-derived suppressor cells (MDSCs), neutrophils, mast cells, and fibroblasts (Fig. 4 B-C). We used online tools to calculate the TIP scores of RCC patients to explore the biological mechanisms associated with the NETs signature. The cancer immune cycle was more activated in the high NETs signature group, including tumor antigen release (Step 1), tumor antigen presentation (Step 2), immune cell activation (Step 3), recruitment of tumor-infiltrating immune cells (Step 4), and infiltration of immune cells (Step 5). In contrast, the killing of tumor cells (Step 7) was enriched in the low NETs signature group (Fig. 4 D). Additionally, the NETs signature was significantly positively correlated with PD-1 immunotherapy, tumor-related miRNAs, and mismatch repair (Fig. 4 E). We also observed significant positive correlations between the NETs signature and the HER2 and KIT signaling pathways, while significant negative correlations were observed with the RET and FLT3 signaling pathways (Fig. 4 F). NETs related immune characteristics We further explored the relationship between the NETs signature and various immunotherapy predictive factors. The TIDE score, Dysfunction score, IFNG levels, Merrck18, and CD8 molecules were higher in the high NETs signature group (Fig. 5 A). In contrast, the Exclusion score, MDSCs, cancer-associated fibroblasts, and tumor-associated M2 macrophages were higher in the low NETs signature group (Fig. 5 A). Additionally, we analyzed the relationship between the NETs signature and immunotherapy response rates. The results revealed that the low NETs signature group had a higher proportion of immunotherapy responders (Fig. 5 B). The NETs signature was further analyzed in multiple validation cohorts. It was evident that the low NETs signature group had a higher response rate to immunotherapy, while the high NETs signature group was relatively resistant (Fig. 5 C-F). Based on the TIDE algorithm, RCC patients with a low NETs signature had a better response to immunotherapy. Knockdown of KCNN4 inhibits mRNA and protein expression levels of KCNN4 Since KCNN4 shows higher expression levels and survival correlation in RCC patients, we selected it for validation in both in vivo and in vitro experiments (Fig. 6 A-B, Table S4 ). First, we analyzed the expression differences of KCNN4 in various RCC cell lines and normal renal cell lines. We found that KCNN4 has higher mRNA expression levels in RCC cell lines, especially in Caki-1 (Fig. 6 C). To further analyze the role of KCNN4 in RCC patients, we first knocked down KCNN4 in the Caki-1 cell lines. After knocking down KCNN4, both the mRNA and protein expression levels of KCNN4 were significantly lower than those in the control group (Fig. 6 D), and similar results were shown in the Western blotting experiment (Fig. 6 E). Next, KCNN4 was overexpressed in the 769-P cell lines, significantly increasing both mRNA and protein expression levels of KCNN4 (Fig. 6 F-G). Knockdown of KCNN4 inhibits tumor cell growth and EMT ability We further investigated KCNN4, first knocking it down (sh-KCNN4) in the Caki-1 cell lines. The CCK8 assay showed that sh-KCNN4 inhibited the proliferation of Caki-1 cells (Fig. 7 A). The colony formation assay indicated that sh-KCNN4 disrupted the colony formation of Caki-1 cells (Fig. 7 B-C). The wound healing assay results demonstrated that sh-KCNN4 suppressed the invasion of Caki-1 cells (Fig. 7 D-E). Western blotting assay of the EMT ability in Caki-1 cells revealed a slight increase in E-cadherin and a downregulation of N-cadherin and Vimentin when KCNN4 was inhibited (Fig. 7 F). Next, we overexpressed KCNN4 in the 769-P cell lines. The CCK8 assay showed that KCNN4 overexpression promoted the proliferation of 769-P cells (Fig. 7 G). The colony formation assay indicated that KCNN4 overexpression enhanced the colony formation of 769-P cells (Fig. 7 H-I). The wound healing assay results demonstrated that KCNN4 overexpression increased the invasion of 769-P cells (Fig. 7 J-K). Western blotting assay of the EMT ability in 769-P cells revealed a downregulation of E-cadherin and a slight increase in N-cadherin and Vimentin when KCNN4 was overexpressed (Fig. 7 L). Inhibiting KCNN4 can inhibit tumor growth and metastasis in vivo In the in vivo experiments, we established a subcutaneous xenograft tumor model in nude mice. Compared to the NC group, the sh-KCNN4 group exhibited significantly smaller and lighter tumors (Fig. 8 A-B). The tumor growth curve indicated that sh-KCNN4 inhibited tumor growth (Fig. 8 C). Furthermore, HE staining of lung tissues showed that sh-KCNN4 reduced the formation of lung metastatic nodules and tumor thrombi in vivo (Fig. 8 D). IHC results demonstrated that the expression of KCNN4 was downregulated in the sh-KCNN4 group, along with a reduction in Ki-67 expression and suppression of EMT-related proteins such as N-cadherin and Vimentin, while E-cadherin was increased (Fig. 8 E). In contrast, the tumors in the KCNN4 overexpression group were significantly larger and heavier than the NC group (Figure S1 A-B). The tumor growth curve indicated that KCNN4 overexpression promoted tumor growth (Figure S1 C). Additionally, HE staining of lung tissues showed that KCNN4 overexpression increased the formation of lung metastatic nodules and tumor thrombi in vivo (Figure S1 D). IHC results demonstrated that the expression of KCNN4 was upregulated in the KCNN4 overexpression group, along with an increase in Ki-67 expression and enhancement of EMT-related proteins such as N-cadherin and Vimentin, while E-cadherin was decreased (Figure S1 E). Discussion As key mediators of extracellular matrix formation, angiogenesis, and immune response, NETs play a crucial role in tumor progression and metastasis. NETs-related genes have been shown to be promising therapeutic targets in various cancers. Therefore, establishing a robust prognostic signature and exploring genes that mediate NETs formation may provide new therapeutic strategies for treating RCC. Based on previously identified NETs-related genes, this study classified RCC patients into four subtypes. The tumor staging differences among the four subtypes were statistically significant, with the C2 subtype having a better prognosis and the C1 subtype having a poorer prognosis. In our study, six machine learning methods were used to predict patient survival. The Ridge algorithm demonstrated the best performance and was used to establish the NETs signature. Prognostic analysis indicated that the NETs signature is a risk marker for OS in RCC patients. ROC analysis further revealed that the NETs signature has high accuracy in predicting 1 year, 3 years, and 5 years of OS in RCC patients. Patients in the high NETs signature group exhibited a large presence of anti-tumor immune cells in the TME, such as NK cells, CD8 + T cells, and CD4 + T cells. Conversely, the low NETs signature group of RCC patients was enriched with immunosuppressive cells, including MDSCs, neutrophils, mast cells, and fibroblasts. Additionally, various immune regulators, such as antigen presentation, immunosuppression, immune stimulation, chemokines, and receptors, were upregulated in the high NETs signature group, inhibiting tumor cell recurrence and metastasis. The cancer immunity cycle was also more activated in the high NETs signature group. These factors suggest that patients in the high NETs signature group should have better prognoses. However, in our study, patients in the low NETs signature group achieved better outcomes. We need to explore further the mechanisms underlying this contradiction in future studies. From the perspective of immunotherapy, the NETs signature can predict the response rate of RCC patients receiving anti-PD-1 or anti-PD-L1 treatment. Notably, patients in the high NETs signature group benefit less from immunotherapy. Several immunosuppressive markers are upregulated in the high NETs signature group, suggesting a potential association between the lower response rate and these immunosuppressive markers. Therefore, improving the expression levels of these immunosuppressive markers in the tumor microenvironment of the high NETs signature group should be a primary therapeutic focus. Few studies have addressed the role of KCNN4 in RCC. Here, we identified the biological functions of KCNN4 through both in vitro and in vivo experiments. Briefly, KCNN4 is a risk factor for the survival of RCC patients and is associated with advanced pathological stages. Notably, the knockdown of KCNN4 not only inhibited tumor cell growth but also suppressed EMT capabilities. In a subcutaneous xenograft tumor model, we demonstrated that KCNN4 knockdown could inhibit tumor growth and reduce lung metastasis. Moreover, in vivo experiments also showed that NETs formation-related proteins, including NE and Vimentin, were downregulated in the sh-KCNN4 group. KCNN4 can induce neutrophil infiltration in RCC and regulate the formation of NETs. There is some evidence supporting this hypothesis. KCNN4 plays a crucial role in type I IFN signaling activation. IFN-α/IFN-γ, as important stimuli, can induce NETs formation, suggesting that KCNN4 may be involved in regulating NETs. Additionally, research by Kantari et al. has shown that KCNN4 interacts with proteinase 3 (PR3) and inhibits macrophage clearance of apoptotic neutrophils. This process contributes to pro-inflammatory effects and NETs formation. This study has several limitations. Firstly, it is based on publicly available bulk data, which do not accurately reflect the cell-cell interaction effects of neutrophils and other immune cells. Moreover, due to the short lifespan of neutrophils, single-cell sequencing faces challenges in sample acquisition and relatively low sequencing depth. Additionally, although this study reveals the association between KCNN4 and NETs formation, the underlying mechanisms still need further validation at the pathway level. Conclusions As mentioned above, NETs play an irreplaceable role in the tumor progression of RCC. We performed NMF analysis on RCC patients and identified four subtypes associated with NETs. Using various machine learning algorithms, this study established and validated a robust NETs signature for RCC patients. Subsequently, KCNN4 was further screened as a key gene and validated through in vitro and in vivo experiments. We ultimately demonstrated that KCNN4 is detrimental to RCC tumor growth and is associated with EMT capability and NETs formation. Declarations Author contributions Yihao Zhu: data curation, writing–original draft. Yajian Li and Xuwen Li: methodology, editing, validation. Yuan Yu: visualization, software. Can Chen: editing. Mingshuai Wang: IHC assistant. Dong Chen: cell cultural guidance. Nianzeng Xing: conceptualization. Xiongjun Ye and Feiya Yang: conceptualization, writing review, editing, check, and approval. Funding This research was funded by the National Natural Science Foundation of China (No. 62076007) and the Beijing Natural Science Foundation (No. 7232132). Data availability statement Public datasets were analyzed in this study. TCGA RCC cohort was downloaded from the TCGA database (http://cancergenome.nih.gov/). GSE167573 and GSE29609 were downloaded from the GEO database(http://www.ncbi.nlm.nih.gov/geo/). CheckMate009/010 and CheckMate025 were obtained from Tumor Immunotherapy Gene Expression Resource databases (http://tiger.canceromics.org/). The original data and material were available when required from the corresponding author Xiongjun Ye. Conflict of interest The authors declare that they have no known competing financial interests in this paper. Ethics approval and consent to participate This study was approved by the ethics committee of the Cancer Hospital, Chinese Academy of Medical Sciences (NCC2024A284) and was in accordance with ARRIVE guidelines. Consent for publication All the authors and patients signed the consent for publication. References Capitanio U, Bensalah K, Bex A, Boorjian SA, Bray F, Coleman J, Gore JL, Sun M, Wood C, Russo P: Epidemiology of Renal Cell Carcinoma. Eur Urol 2019, 75(1):74-84. 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Cancers (Basel) 2020, 12(10). Park J, Wysocki RW, Amoozgar Z, Maiorino L, Fein MR, Jorns J, Schott AF, Kinugasa-Katayama Y, Lee Y, Won NH et al : Cancer cells induce metastasis-supporting neutrophil extracellular DNA traps. Sci Transl Med 2016, 8(361):361ra138. Han AX, Long BY, Li CY, Huang DD, Xiong EQ, Li FJ, Wu GL, Liu Q, Yang GB, Hu HY: Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma. Apoptosis 2024. Teijeira A, Garasa S, Ochoa MC, Villalba M, Olivera I, Cirella A, Eguren-Santamaria I, Berraondo P, Schalper KA, de Andrea CE et al : IL8, Neutrophils, and NETs in a Collusion against Cancer Immunity and Immunotherapy. Clin Cancer Res 2021, 27(9):2383-2393. Kaltenmeier C, Yazdani HO, Morder K, Geller DA, Simmons RL, Tohme S: Neutrophil Extracellular Traps Promote T Cell Exhaustion in the Tumor Microenvironment. Front Immunol 2021, 12:785222. Wang H, Zhang H, Wang Y, Brown ZJ, Xia Y, Huang Z, Shen C, Hu Z, Beane J, Ansa-Addo EA et al : Regulatory T-cell and neutrophil extracellular trap interaction contributes to carcinogenesis in non-alcoholic steatohepatitis. J Hepatol 2021, 75(6):1271-1283. Papayannopoulos V, Metzler KD, Hakkim A, Zychlinsky A: Neutrophil elastase and myeloperoxidase regulate the formation of neutrophil extracellular traps. J Cell Biol 2010, 191(3):677-691. Lewis HD, Liddle J, Coote JE, Atkinson SJ, Barker MD, Bax BD, Bicker KL, Bingham RP, Campbell M, Chen YH et al : Inhibition of PAD4 activity is sufficient to disrupt mouse and human NET formation. Nat Chem Biol 2015, 11(3):189-191. Tang F, Tie Y, Tu C, Wei X: Surgical trauma-induced immunosuppression in cancer: Recent advances and the potential therapies. Clin Transl Med 2020, 10(1):199-223. Sun G, Chen J, Liang J, Yin X, Zhang M, Yao J, He N, Armstrong CM, Zheng L, Zhang X et al : Integrated exome and RNA sequencing of TFE3-translocation renal cell carcinoma. Nat Commun 2021, 12(1):5262. Edeline J, Mottier S, Vigneau C, Jouan F, Perrin C, Zerrouki S, Fergelot P, Patard JJ, Rioux-Leclercq N: Description of 2 angiogenic phenotypes in clear cell renal cell carcinoma. Hum Pathol 2012, 43(11):1982-1990. Miao D, Margolis CA, Gao W, Voss MH, Li W, Martini DJ, Norton C, Bosse D, Wankowicz SM, Cullen D et al : Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma. Science 2018, 359(6377):801-806. Braun DA, Hou Y, Bakouny Z, Ficial M, Sant' Angelo M, Forman J, Ross-Macdonald P, Berger AC, Jegede OA, Elagina L et al : Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma. Nat Med 2020, 26(6):909-918. Wang J, Li Q, Yin Y, Zhang Y, Cao Y, Lin X, Huang L, Hoffmann D, Lu M, Qiu Y: Excessive Neutrophils and Neutrophil Extracellular Traps in COVID-19. Front Immunol 2020, 11:2063. Zhang Y, Guo L, Dai Q, Shang B, Xiao T, Di X, Zhang K, Feng L, Shou J, Wang Y: A signature for pan-cancer prognosis based on neutrophil extracellular traps. J Immunother Cancer 2022, 10(6). Xu L, Deng C, Pang B, Zhang X, Liu W, Liao G, Yuan H, Cheng P, Li F, Long Z et al : TIP: A Web Server for Resolving Tumor Immunophenotype Profiling. Cancer Res 2018, 78(23):6575-6580. Avila Cobos F, Alquicira-Hernandez J, Powell JE, Mestdagh P, De Preter K: Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commun 2020, 11(1):5650. Zheng S, Lin F, Zhang M, Fu J, Ge X, Mu N: AK001058 promotes the proliferation and migration of colorectal cancer cells by regulating methylation of ADAMTS12. Am J Transl Res 2019, 11(9):5869-5878. Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Figure S1: In vivo experiments of KCNN4 overexpression. A, Tumor images in null mice in NC group and KCNN4 overexpression group; B, Tumor weight in null mice in NC group and KCNN4 overexpression group; C, Tumor growth curves in null mice in NC group and KCNN4 overexpression group; D, HE staining of lung tissues in null mice in NC group and KCNN4 overexpression group; E, Immunohistochemical staining of EMT-related proteins in null mice in NC group and KCNN4 overexpression group. TableS1.docx TableS2.docx TableS3.docx TableS4.docx gelsandblots.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4700747","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":335355436,"identity":"9a9017b7-cdfe-4be1-b796-99953af800ba","order_by":0,"name":"Yihao Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACNmbG9h8fKmrk+NkbiNTCx858QHLGmWPGkj0HiNQix8+WIM3bxpxocCOBaIfxGBjwsLElMNx8vPEGQ41NNFFaEiR4ZPIYZ6cVWzAcS8ttIEbLAQMJtmJm6RwzCcaGw0RpMWxIMGBObJM8Q7QWtmSGAwnMiT0SPERrYT7G2HDgmLEED9AvCcT4Rb7/YBvz3381cvbHD2+88aHGhrAWZGAgkUCKcogWUnWMglEwCkbByAAAimU39jB4/I4AAAAASUVORK5CYII=","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Yihao","middleName":"","lastName":"Zhu","suffix":""},{"id":335355437,"identity":"3f4138fa-163b-4b68-98b2-773b1b13a24f","order_by":1,"name":"Yajian Li","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yajian","middleName":"","lastName":"Li","suffix":""},{"id":335355438,"identity":"093943be-2e40-4e15-832c-345b88711d49","order_by":2,"name":"Xuwen Li","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xuwen","middleName":"","lastName":"Li","suffix":""},{"id":335355439,"identity":"736dda46-75d6-4e10-926c-4aba3996ea43","order_by":3,"name":"Yuan Yu","email":"","orcid":"","institution":"Zhejiang Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Yu","suffix":""},{"id":335355440,"identity":"f29442a2-889f-4195-8883-9946ffd3a2de","order_by":4,"name":"Can Chen","email":"","orcid":"","institution":"Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Can","middleName":"","lastName":"Chen","suffix":""},{"id":335355441,"identity":"97451026-91fa-479c-bca7-f316f8b53db6","order_by":5,"name":"Mingshuai Wang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Mingshuai","middleName":"","lastName":"Wang","suffix":""},{"id":335355442,"identity":"28b0c520-9ba3-4d45-9851-b2cbc6967301","order_by":6,"name":"Dong Chen","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Chen","suffix":""},{"id":335355443,"identity":"e7f6bb78-15db-4727-80d7-caf5bb5684c4","order_by":7,"name":"Nianzeng Xing","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Nianzeng","middleName":"","lastName":"Xing","suffix":""},{"id":335355444,"identity":"d706411f-f7f4-4c51-95b0-a43e9cd9cc78","order_by":8,"name":"Feiya Yang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Feiya","middleName":"","lastName":"Yang","suffix":""},{"id":335355445,"identity":"3d08853f-ef1b-488f-8669-550feee2d57a","order_by":9,"name":"Xiongjun Ye","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiongjun","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2024-07-07 14:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4700747/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4700747/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62219687,"identity":"5b179239-1e94-49aa-883a-4de3e112f28d","added_by":"auto","created_at":"2024-08-11 12:15:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5261351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNMF analysis using NETs-related genes.\u003c/strong\u003e A, Relationship between NMF class coefficients and the number of subtypes; B, Heatmap illustrating the distribution of NETs expression in tumor samples across NETs subtypes; C, Kaplan-Meier curves showing OS of patients in different NETs subtypes; D, Differences in hallmark enrichment scores among NETs subtypes; E, Differences in CIBERSORT-related immune cell abundance among NETs subtypes; F, Differences in Stromal Score among NETs subtypes; G, Differences in Immune Score among NETs subtypes; H, Differences in ESTIMATE Score among NETs subtypes; I, Differences in Tumor Purity among NETs subtypes.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/90cb4dacb2b10f61add25f0c.png"},{"id":62219684,"identity":"295978ab-aa0c-4cb1-849f-15b461f7cc2b","added_by":"auto","created_at":"2024-08-11 12:15:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":532464,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of characteristic genes for NETs subtypes and construction of the NETs signature.\u003c/strong\u003e A, Scale-free fitting index of the network topology obtained using the soft-thresholding analysis method; B, Hierarchical clustering analysis to detect co-expression modules with corresponding color assignments, where each color represents a module in the gene co-expression network constructed by WGCNA; C, Correlation between genes in different modules and NETs subtypes; D, Analysis of gene significance and module membership in the green module; E-G, Comparison of AUC values for machine learning algorithms at 1 year, 3 years, and 5 years; H-K, Bubble plots showing enrichment analysis results of NETs signature-related genes in BP (biological process), CC (cellular component), MF (molecular function), and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/bfda9c3a712c720dd209bfcc.png"},{"id":62221597,"identity":"6b0cebbd-f5e6-4066-b770-2740d277a1c3","added_by":"auto","created_at":"2024-08-11 12:31:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":947857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstablishment of prognostic NETs signature for RCC.\u003c/strong\u003e A, Kaplan-Meier curves of overall survival (OS) for patients grouped by NETs signature; B, AUC values for the NETs signature predicting 1 year, 3 years, and 5 years survival in RCC patients; C-F, Kaplan-Meier curves of OS for patients grouped by NETs signature in four validation cohorts: GSE167573, GSE29609, CheckMate009/010, and CheckMate025; G, Nomogram model constructed with the NETs signature; H-J, Calibration plots of the nomogram model showing the consistency between predicted and actual survival rates at 1 year, 3 years, and 5 years.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/3b8ca625ed3c8ee2675f0a4c.png"},{"id":62220747,"identity":"5286fd34-87ff-42ae-a985-05a8e15817c4","added_by":"auto","created_at":"2024-08-11 12:23:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":825031,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune characteristics of the NETs signature.\u003c/strong\u003e A, Heatmap showing the correlation between the NETs signature and 5 classes of immune regulatory molecules; B, Heatmap illustrating differences between NETs signature and immune infiltrating cells using the Xcell algorithm; C, Heatmap demonstrating differences between NETs signature and immune infiltrating cells using QUANTISEQ, CIBERSORT, MCPCOUNTER, and EPIC algorithms; D, Bean plot showing differences between NETs signature and TIP score; E, Heatmap showing the correlation between the NETs signature in RCC and immune-related pathways; F, Heatmap showing the correlation between the NETs signature in RCC and targeted therapy-related pathways.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/f5c2431d7afa9f93c5da8fce.png"},{"id":62219692,"identity":"aa2d4fe3-ed8d-4ba3-bb86-3c7afc1c92cd","added_by":"auto","created_at":"2024-08-11 12:15:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1195194,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive value of the NETs signature for immunotherapy.\u003c/strong\u003e A, Violin plot showing TIDE score, Exclusion score, Dysfunction score, IFNG level, Merck18 score, CD274 score, CD8 score, MDSC score, CAF score, and tumor-associated M2 macrophage score between the two NETs signature groups; B, Bar graph showing the number of responders to immunotherapy in each NETs signature group; C-F, Bar graphs showing the number of responders to immunotherapy in four validation cohorts, namely GSE167573, GSE29609, CheckMate009/010, and CheckMate025 cohorts.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/1c87a77f9e1156f6847b5fed.png"},{"id":62219693,"identity":"b96ebfb8-f825-4651-9470-84f748b08e22","added_by":"auto","created_at":"2024-08-11 12:15:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":297376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSilencing KCNN4 inhibits its mRNA and protein expression levels.\u003c/strong\u003e A, Kaplan-Meier survival analysis of KCNN4 in the TCGA cohort; B, ROC curve analysis of KCNN4 in the TCGA cohort; C, qRT-PCR analysis detecting the expression of KCNN4 in normal kidney cell lines HK-2 and six RCC cell lines; D, qRT-PCR analysis detecting the knockdown efficiency of KCNN4 at the RNA level in the Caki-1 cell lines; E, Western blotting detecting the knockdown efficiency of KCNN4 at the protein level in the Caki-1 cell lines; F, qRT-PCR analysis detecting the overexpression efficiency of KCNN4 at the RNA level in the 769-P cell lines; G, Western blotting detecting the overexpression efficiency of KCNN4 at the protein level in the 769-P cell lines.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/c8f52dcd0e55fc2ca02af306.png"},{"id":62220750,"identity":"25b1cacc-bcc5-4e07-aa0b-95b61a23c271","added_by":"auto","created_at":"2024-08-11 12:23:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3364958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003esh-KCNN4 inhibits the growth of renal cell carcinoma.\u003c/strong\u003e A, CCK-8 assay to detect changes in proliferation levels of Caki-1 cell lines after KCNN4 knockdown; B, colony formation assay to detect the effect of KCNN4 knockdown on proliferation levels of Caki-1 cell lines; C, statistical graph of colony formation assay for Caki-1 cell lines; D, scratch assay to detect the effect of KCNN4 knockdown on the migration ability of Caki-1 cell lines; E, statistical graph of scratch assay for Caki-1 cell lines; F, Western blotting to detect the knockdown efficiency of KCNN4 on EMT-related proteins in Caki-1 cell lines; G, CCK-8 assay to detect changes in proliferation levels of 769-P cell lines after KCNN4 overexpression; H, colony formation assay to detect the effect of KCNN4 overexpression on proliferation levels of 769-P cell lines; I, statistical graph of colony formation assay for 769-P cell lines; J, scratch assay to detect the effect of KCNN4 overexpression on the migration ability of 769-P cell lines; K, statistical graph of scratch assay for 769-P cell lines; L, Western blotting to detect the overexpression efficiency of KCNN4 on EMT-related proteins in 769-P cell lines.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/75f3ee83afe7024d5d26f2db.png"},{"id":62220752,"identity":"dbb8b311-6167-47ac-9cdf-aadfbcd02e50","added_by":"auto","created_at":"2024-08-11 12:23:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3504068,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e experiments of sh-KCNN4.\u003c/strong\u003e A, Tumor growth in null mice in sh-NC group and sh-KCNN4 group; B, Tumor weight in null mice in sh-NC group and sh-KCNN4 group; C, Tumor growth curves in null mice in sh-NC group and sh-KCNN4 group; D, HE staining of lung tissues in null mice in sh-NC group and sh-KCNN4 group; E, Immunohistochemical staining of EMT-related proteins in null mice in sh-NC group and sh-KCNN4 group.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/ed692cf7c30dfd7b058a712b.png"},{"id":62890079,"identity":"410496a8-4b89-4724-8954-0a275cae6469","added_by":"auto","created_at":"2024-08-20 17:11:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16732746,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/5495e02e-8681-4e67-b947-cfb3597e0755.pdf"},{"id":62220749,"identity":"e1b1deb6-ca50-4a88-9e81-1ecd2cb154e5","added_by":"auto","created_at":"2024-08-11 12:23:49","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11183080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1: \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIn vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eexperiments of KCNN4 overexpression.\u003c/strong\u003e A, Tumor images in null mice in NC group and KCNN4 overexpression group; B, Tumor weight in null mice in NC group and KCNN4 overexpression group; C, Tumor growth curves in null mice in NC group and KCNN4 overexpression group; D, HE staining of lung tissues in null mice in NC group and KCNN4 overexpression group; E, Immunohistochemical staining of EMT-related proteins in null mice in NC group and KCNN4 overexpression group.\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/6b14671d5f5cd4ab3a93ccf5.tif"},{"id":62219682,"identity":"187eb83f-62fa-4706-85af-b0f793ffa217","added_by":"auto","created_at":"2024-08-11 12:15:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22468,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/f11a397ef2d3820f15752f08.docx"},{"id":62220746,"identity":"0af89b86-455b-45a0-94fc-f2c5e54bb0b5","added_by":"auto","created_at":"2024-08-11 12:23:48","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18996,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/cc98f3819ecd310a9a6ad1a5.docx"},{"id":62219688,"identity":"d9b02788-c6d3-46dd-8016-b823974e0886","added_by":"auto","created_at":"2024-08-11 12:15:49","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21638,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/bacec17a2d4f02c62c141807.docx"},{"id":62221598,"identity":"1f14e757-c5af-4e2d-9a62-4818afdfde6c","added_by":"auto","created_at":"2024-08-11 12:31:49","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15580,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/af259e53190656cf7508d458.docx"},{"id":62219695,"identity":"8b048a41-806b-484e-8b66-0d79f90b93dd","added_by":"auto","created_at":"2024-08-11 12:15:49","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":8251820,"visible":true,"origin":"","legend":"","description":"","filename":"gelsandblots.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4700747/v1/6367ca7e10a7a74e9fc5676e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Constructing a Neutrophil Extracellular Trap Model Based on Machine Learning to Predict Clinical Outcomes and Immunotherapy Response in Renal Cell Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRenal cell carcinoma (RCC) is the most common kidney tumor in adults and ranks among the top ten causes of cancer-related deaths worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The pathological types of RCC include clear cell renal cell carcinoma, papillary renal cell carcinoma, chromophobe renal cell carcinoma, collecting duct carcinoma, and unclassified renal cell carcinoma [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Clear cell renal cell carcinoma is the most common subtype, originating from the proximal renal tubules and typically exhibiting an aggressive phenotype [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Surgical treatment has been an effective option for primary RCC, but it shows limited efficacy in metastatic cases. Approximately one-third of RCC patients have metastases at the time of diagnosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Given the promising results of immune checkpoint inhibitors (ICI) in treating RCC, researchers have focused on immune cells and immune responses in the tumor microenvironment (TME) of RCC [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As important members of the TME, neutrophils are functionally linked to the progression of RCC [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNeutrophils in the TME are considered crucial pathogenic factors contributing to tumor progression [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], especially through the formation of neutrophil extracellular traps (NETs), which are recognized as novel pro-tumor mechanisms in various cancers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. NETs are reticular structures composed of DNA, histones, and granules released by activated neutrophils [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Previous studies have indicated that the levels of NETs in healthy individuals' peripheral blood are lower than those in cancer patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, \u003cem\u003ein vitro\u003c/em\u003e studies have shown that co-culturing neutrophils with breast cancer and lung cancer cells can induce NETs formation [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have revealed that NETs also play an anti-tumor role in the TME [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. On one hand, NETs can repel cytotoxic cells and attract immunosuppressive cells. On the other hand, NETs can act as physical barriers to prevent antibody-dependent cell-mediated cytotoxicity (ADCC) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Notably, NETs can express PD-L1, leading to the exhaustion of CD8\u0026thinsp;+\u0026thinsp;T cells within the TME. Anti-NETs therapies may enhance immune cell-mediated tumor cell killing and synergize with immune checkpoint inhibitors (ICIs) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, combining anti-NETs therapy with ICIs might reduce the incidence of ICI resistance, providing a novel therapeutic strategy for cancer treatment.\u003c/p\u003e \u003cp\u003eCurrently, a novel anti-cancer strategy involves inhibiting the formation of NETs in tumors or promoting their degradation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Unfortunately, drugs specifically targeting NETs have yet to be developed, which limits improvements in cancer patient outcomes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, targeting drugs that modulate neutrophil recruitment and NET formation has shown promise in preclinical models, suggesting a new therapeutic strategy of targeting upstream mediators of NET formation in tumors [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, exploring the upstream mediators of NETs has become essential to targeted therapy.\u003c/p\u003e \u003cp\u003eIn this study, we developed a prognostic NETs signature for RCC patients. Notably, we demonstrated that KCNN4 is a crucial mediator of NETs formation and plays multiple roles in tumor migration and invasion, making it a potential oncogenic factor in RCC patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData downloading and processing\u003c/h2\u003e \u003cp\u003eThis study utilized publicly available data obtained from several online databases. Gene expression data and clinical data for RCC were downloaded from The Cancer Genome Atlas (TCGA) at \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, comprising 613 RNA-seq samples and 537 clinical data. Gene expression data for RCC were obtained from the Gene Expression Omnibus (GEO) database at \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, including GSE167573 (n\u0026thinsp;=\u0026thinsp;77) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and GSE29609 (n\u0026thinsp;=\u0026thinsp;39) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Immunotherapy cohorts included CheckMate009/010 (n\u0026thinsp;=\u0026thinsp;76) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and CheckMate025 (n\u0026thinsp;=\u0026thinsp;207) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe data obtained from GEO were processed using the R package \"limma\" (version 3.50.1) for normalization and log2 transformation. Batch effects between datasets were removed using the \"ComBat\" function, for the data obtained from TCGA, gene ID annotation was performed using the Ensembl database. In cases where multiple gene names existed for a single gene, only the gene with the highest expression level was retained. RNA-seq expression data downloaded from TCGA were provided as transcripts per million (TPM), which were normalized using log2(TPM\u0026thinsp;+\u0026thinsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eNon-negative matrix factorization clustering based on NETs related genes\u003c/h2\u003e \u003cp\u003eIn previous studies, a total of 89 NETs-related genes were identified [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Non-negative matrix factorization (NMF) was employed to perform clustering analysis on the TCGA cohort based on the expression levels of these NETs-related genes. In NMF, the number of subtypes (k) must be manually specified. The optimal k value can be determined by evaluating the product of the cophenetic correlation coefficient and dispersion coefficient provided by the NMF algorithm. To determine the optimal k value, we iterate over multiple k values (k\u0026thinsp;=\u0026thinsp;3\u0026thinsp;~\u0026thinsp;8), running NMF for each k value (50 iterations), and selecting the k value that maximizes the product of the cophenetic correlation coefficient and dispersion coefficient. After determining the optimal number of subtypes, we run NMF with the optimal k value as a parameter (500 iterations) to identify subtypes within the TCGA cohort. The R package \"nmf\" is used throughout this process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment analysis and single sample gene set enrichment analysis\u003c/h2\u003e \u003cp\u003eR package \"clusterprofiles\" (version 4.2.2) was used for conducting Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to predict the biological functions of genes. Enrichment analysis results with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e \u003cp\u003eHallmark and immune cell infiltration gene sets were collected from the MSigDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Single-sample Gene Set Enrichment Analysis (ssGSEA) was performed using the \"gsva\" function from the R package \"GSVA\" (version 1.44.3) to evaluate the normalized enrichment scores (NESs) of gene sets in each patient. The R package \"estimate\" was utilized to calculate StromalScore, ImmuneScore, ESTIMATEScore, and TumorPurity for each subtype.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of WGCNA network\u003c/h2\u003e \u003cp\u003eWeighted Gene Co-expression Network Analysis (WGCNA) is a systems biology approach that identifies modules of highly correlated genes based on their associations with phenotypes, aiming to find candidate biomarker genes and potential therapeutic targets. In this study, we utilized gene expression profiles from RCC in TCGA to construct a WGCNA network. First, samples and genes were filtered, and then the R package \"WGCNA\" (version 1.71) was used to calculate the Pearson correlations between all pairs of genes in the selected samples, constructing an adjacency matrix. To ensure a scale-free network, we chose β\u0026thinsp;=\u0026thinsp;5 (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.80) as the soft-thresholding parameter. To further identify functional modules in the weighted gene co-expression network, we calculated the Topological Overlap Measure (TOM) based on the adjacency matrix. Using the TOM values, a dynamic tree-cutting approach was employed to define gene modules and select module eigengenes (MEs). MEs are considered representative of the gene expression profiles within each module.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBuilding prognostic NETs signatures using machine learning frameworks\u003c/h2\u003e \u003cp\u003eTo establish a robust NETs signature, six machine learning algorithms were employed in the TCGA cohort, including CoxBoost, elastic network (Enet), lasso, random forest (RF), Ridge regression, and support vector machine (SVM). Patients were divided into training and validation sets in a 7:3 ratio, and 10-fold cross-validation was used to evaluate model performance. The 1 years, 3 years, and 5 years AUC values were computed and compared for each model to select the best NETs signature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of nomogram model\u003c/h2\u003e \u003cp\u003eAfter selecting the best NETs signature, ssGSEA was used to calculate the NESs of the signature. Using the NESs of the NETs signature and other clinical features, a nomogram model was constructed to predict RCC patients' overall survival (OS) rates at 1 year, 3 years, and 5 years. Calibration curves were built to assess the consistency between actual survival and predicted survival. The R package \"rms\" was utilized to construct the nomogram model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eNETs signature annotation of tumor microenvironment, signaling pathways, and immune-related features\u003c/h2\u003e \u003cp\u003eWe collected 5 categories of immune modulators: antigen presentation, immune suppression, immune activation, chemokines, and receptors. Signal pathways related to targeted therapy and immunotherapy were gathered from the MSigDB database, and NESs were computed using ssGSEA. Five immune deconvolution methods assessed immune cell infiltration abundance in RCC patients, including QUANTISEQ, CIBERSORT, MCPCOUNTER, xCell, and EPIC. The Tumor Immune Phenotype (TIP) tracker (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biocchrbmu.edu.cn\u003c/span\u003e\u003cspan address=\"http://biocchrbmu.edu.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to evaluate anticancer immune activity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The TIDE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database was used for immune-related feature analysis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell lines and culture conditions\u003c/h2\u003e \u003cp\u003eHuman kidney cell carcinoma cell lines (Caki-1, ACHN, OS-RC-2, 786-O, Caki-2, and 769-P) and normal renal cell lines (HK-2) were obtained from the ATCC (Manassas, VA, USA). The kidney cell carcinoma cell lines were grown in Ham\u0026rsquo;s F-12K medium (Jiangsu Kaiji Biotechnology Co, Ltd) or RPMI-1640 medium (Gibco Laboratories, Grand Island, NY). And HK-2 was cultivated in DMEM medium (Gibco Laboratories, Grand Island, NY).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting assay and cell counting kit-8 (CCK-8) assay\u003c/h2\u003e \u003cp\u003eWestern blot analysis was conducted to determine the expression levels of genes. The blots were incubated with an antibody overnight at 4\u0026deg;C. The following antibodies were used: KCNN4 (Abcam, ab215990), GAPDH (Abcam, ab8245), PCNA (Abcam, ab29), N-cadherin (Abcam, ab76011), E-cadherin (Abcam, ab314063), Vimentin (Abcam, ab92547). We used the CCK-8 assay (Beyotime, Shanghai, China) to measure cell proliferation according to a specific protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWound healing assay\u003c/h2\u003e \u003cp\u003eAs previously described [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], RCC cell lines were cultured in serum-free media. Monolayers were scratched using a pipette tip and washed with PBS. After 24 hours, wound healing was captured under a microscope (Beckman).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnimal experiments\u003c/h2\u003e \u003cp\u003e All animal experiments were conducted in accordance with ethical standards and approved by the Ethics Committee of the Cancer Hospital, Chinese Academy of Medical Sciences. \u003cem\u003eIn vivo\u003c/em\u003e studies involved subcutaneous injection of 5\u0026times;10\u003csup\u003e6\u003c/sup\u003e Caki-1 and 769-P cell lines in 100 \u0026micro;L PBS into the flanks of nude mice. Tumor volumes were monitored weekly using the formula volume = (length\u0026times;width\u003csup\u003e2\u003c/sup\u003e)/2. Mice were euthanized on day 28 post-transplantation. Lung and subcutaneous tumor tissues were collected for further analysis. Lung samples were embedded in paraffin, and sections were stained with hematoxylin and eosin (H\u0026amp;E) to visualize the size of lung metastases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry\u003c/h2\u003e \u003cp\u003eThe mouse antibodies against KCNN4 (Abcam, ab215990), N-cadherin (Abcam, ab76011), E-cadherin (Abcam, ab314063), Vimentin (Abcam, ab92547, 1:200) and Ki-67 (Abcam, ab15580) were purchased from Abcam. IHC experiments were conducted according to standard procedures. ImageJ's IHC Profiler macro was used to quantify the protein intensity expression in the samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe used the \u0026ldquo;survminer\u0026rdquo; package (version 0.4.9) in R to compute the optimal cutpoint using the \u0026ldquo;surv_cutpoint\u0026rdquo; function, which grouped patients accordingly. Subsequently, we utilized both the \u0026ldquo;survminer\u0026rdquo; and \u0026ldquo;survival\u0026rdquo; packages (version 3.3-1) to plot Kaplan-Meier survival curves for different patient groups and to perform log-rank tests to assess the significance of differences between groups. ROC curve analysis was conducted using the \u0026ldquo;pROC\u0026rdquo; package (version 1.18.0). Graphical visualization was performed using the \u0026ldquo;ggplot2\u0026rdquo; package (version 3.3.5). All statistical analyses in this study were conducted using the R programming language (version 4.2.0). Unless otherwise stated, all statistical tests were two-sided, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and validation of NETs related gene subtypes\u003c/h2\u003e \u003cp\u003eAccording to the expression levels of 89 NETs-related genes, we performed NMF analysis on the TCGA cohort. The correlation plot indicated that k\u0026thinsp;=\u0026thinsp;4 was the optimal number of subtypes, thus RCC patients were divided into C1-C4 subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). The clinical data summary of patients with different subtypes of RCC is shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. There were no significant differences in age, gender, and N stage distribution among the four subtypes, whereas UICC stage, T stage, M stage, grade, and tumor location showed significant differences in distribution. The Kaplan-Meier survival curve analysis revealed significant differences in OS among the four subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Specifically, patients with the C2 subtype had better prognosis, while patients with C1 and C3 subtypes had shorter survival times. Enrichment analysis using ssGSEA on hallmark pathways showed that the C1 subtype enriched in the Angiogenesis pathway, while oxidative phosphorylation was enriched in the C2 subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The PI3K_AKT_mTOR_signaling pathway was enriched in the C3 subtype, and the C4 subtype was significantly enriched in Interferon_gamma_response and Interferon_alpha_response (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Deconvolution analysis revealed that the four subtypes enriched different immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Additionally, the tumor immune infiltration status was also significantly enriched in different subtypes; for example, the C2 subtype had higher levels of StromalScore, ImmuneScore, and ESTIMATEScore, while showing lower TumorPurity. In contrast, the C4 subtype had higher TumorPurity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF-I).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWGCNA network identifies subtype-related genes\u003c/h2\u003e \u003cp\u003eWe set a β soft threshold of 5 and constructed the WGCNA network (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.8, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). We identified the ME green module as the most significant module (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), which contained 824 genes (correlation coefficient 0.63, p\u0026thinsp;=\u0026thinsp;2.4e-92, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The univariate Cox regression analysis was performed on genes in the green module, filtering for 330 genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for further analysis (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Subsequently, we applied six machine learning algorithms to filter the 330 genes and select the optimal NETs signature. We computed the AUC values for each model at 1 year, 3 years, and 5 years to evaluate their prognostic performance for RCC patients. The results showed that the Ridge model selected genes with the highest AUC values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-G), identifying a total of 7 genes (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we conducted GO enrichment analysis and KEGG pathway analysis on the genes identified by the Ridge model. GO enrichment analysis revealed that these signature genes were enriched in tumor necrosis factor production (BP, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH), tertiary granule (CC, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI), and MHC class I receptor activity (MF, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ). KEGG pathway analysis indicated enrichment of these genes in osteoclast differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDetermination and verification of prognostic NETs signatures\u003c/h2\u003e \u003cp\u003eBased on ssGSEA, we calculated the NESs of the signature genes in RCC patients from the Ridge model and defined this as the NETs signature. Using the optimal cutoff value for the NETs signature, we divided patients into high and low groups. We first evaluated the clinical differences between the high and low NETs signature groups (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). It is evident that the high NETs signature group has a higher proportion of advanced-stage patients (Stages III-IV), while the low NETs signature group has a higher proportion of early-stage patients (Stages I-II). Further analysis of their impact on RCC prognosis revealed that the low NETs signature group has significantly longer survival times than the high NETs signature group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additionally, ROC analysis demonstrated that the NETs signature has higher predictive efficacy in RCC patients for 1-year, 3-year, and 5-year prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). To ensure the robustness of the analysis, we further validated the results using four validation cohorts. The results showed that the NETs signature performed well across all four validation cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo provide a quantitative method for clinicians to assess patient prognosis, we further explored the potential associations between the NETs signature and clinical-pathological characteristics in the TCGA cohort. We constructed a nomogram model incorporating the NETs signature and clinical-pathological characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Higher scores of the NETs signature in RCC patients were associated with poorer prognosis. Using the nomogram model's calibration curves, we predicted the survival probabilities of patients at 1 year, 3 years, and 5 years after diagnosis using the NETs signature. The calibration curves for 1-year, 3-year, and 5-year survival probabilities effectively predicted the actual survival probabilities at these time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH-J). These results indicate that the nomogram model based on the NETs signature has strong discrimination and calibration capabilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFeatures of NETs signatures in tumor microenvironment\u003c/h2\u003e \u003cp\u003eIn RCC patients, the NETs signature showed significant correlations with five categories of immune regulators (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Antigen presentation, immune suppression, immune activation, and receptor molecules were especially highly expressed in the high NETs signature group, whereas chemokines were highly expressed in the low NETs signature group. Various deconvolution algorithms were used to assess the abundance of immune cell infiltration between the two patient groups. In the high NETs signature group, RCC patients were enriched with immune-promoting cells, such as NK cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, and CD4\u003csup\u003e+\u003c/sup\u003e T cells. Conversely, in the low NETs signature group, RCC patients were enriched with immune-suppressive cells, such as myeloid-derived suppressor cells (MDSCs), neutrophils, mast cells, and fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C). We used online tools to calculate the TIP scores of RCC patients to explore the biological mechanisms associated with the NETs signature. The cancer immune cycle was more activated in the high NETs signature group, including tumor antigen release (Step 1), tumor antigen presentation (Step 2), immune cell activation (Step 3), recruitment of tumor-infiltrating immune cells (Step 4), and infiltration of immune cells (Step 5). In contrast, the killing of tumor cells (Step 7) was enriched in the low NETs signature group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Additionally, the NETs signature was significantly positively correlated with PD-1 immunotherapy, tumor-related miRNAs, and mismatch repair (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). We also observed significant positive correlations between the NETs signature and the HER2 and KIT signaling pathways, while significant negative correlations were observed with the RET and FLT3 signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eNETs related immune characteristics\u003c/h2\u003e \u003cp\u003eWe further explored the relationship between the NETs signature and various immunotherapy predictive factors. The TIDE score, Dysfunction score, IFNG levels, Merrck18, and CD8 molecules were higher in the high NETs signature group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In contrast, the Exclusion score, MDSCs, cancer-associated fibroblasts, and tumor-associated M2 macrophages were higher in the low NETs signature group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Additionally, we analyzed the relationship between the NETs signature and immunotherapy response rates. The results revealed that the low NETs signature group had a higher proportion of immunotherapy responders (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The NETs signature was further analyzed in multiple validation cohorts. It was evident that the low NETs signature group had a higher response rate to immunotherapy, while the high NETs signature group was relatively resistant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-F). Based on the TIDE algorithm, RCC patients with a low NETs signature had a better response to immunotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eKnockdown of KCNN4 inhibits mRNA and protein expression levels of KCNN4\u003c/h2\u003e \u003cp\u003eSince KCNN4 shows higher expression levels and survival correlation in RCC patients, we selected it for validation in both \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). First, we analyzed the expression differences of KCNN4 in various RCC cell lines and normal renal cell lines. We found that KCNN4 has higher mRNA expression levels in RCC cell lines, especially in Caki-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). To further analyze the role of KCNN4 in RCC patients, we first knocked down KCNN4 in the Caki-1 cell lines. After knocking down KCNN4, both the mRNA and protein expression levels of KCNN4 were significantly lower than those in the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), and similar results were shown in the Western blotting experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Next, KCNN4 was overexpressed in the 769-P cell lines, significantly increasing both mRNA and protein expression levels of KCNN4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eKnockdown of KCNN4 inhibits tumor cell growth and EMT ability\u003c/h2\u003e \u003cp\u003eWe further investigated KCNN4, first knocking it down (sh-KCNN4) in the Caki-1 cell lines. The CCK8 assay showed that sh-KCNN4 inhibited the proliferation of Caki-1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The colony formation assay indicated that sh-KCNN4 disrupted the colony formation of Caki-1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-C). The wound healing assay results demonstrated that sh-KCNN4 suppressed the invasion of Caki-1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-E). Western blotting assay of the EMT ability in Caki-1 cells revealed a slight increase in E-cadherin and a downregulation of N-cadherin and Vimentin when KCNN4 was inhibited (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). Next, we overexpressed KCNN4 in the 769-P cell lines. The CCK8 assay showed that KCNN4 overexpression promoted the proliferation of 769-P cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). The colony formation assay indicated that KCNN4 overexpression enhanced the colony formation of 769-P cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH-I). The wound healing assay results demonstrated that KCNN4 overexpression increased the invasion of 769-P cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ-K). Western blotting assay of the EMT ability in 769-P cells revealed a downregulation of E-cadherin and a slight increase in N-cadherin and Vimentin when KCNN4 was overexpressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eL).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInhibiting KCNN4 can inhibit tumor growth and metastasis\u003c/b\u003e \u003cb\u003ein vivo\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the \u003cem\u003ein vivo\u003c/em\u003e experiments, we established a subcutaneous xenograft tumor model in nude mice. Compared to the NC group, the sh-KCNN4 group exhibited significantly smaller and lighter tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B). The tumor growth curve indicated that sh-KCNN4 inhibited tumor growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Furthermore, HE staining of lung tissues showed that sh-KCNN4 reduced the formation of lung metastatic nodules and tumor thrombi \u003cem\u003ein vivo\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). IHC results demonstrated that the expression of KCNN4 was downregulated in the sh-KCNN4 group, along with a reduction in Ki-67 expression and suppression of EMT-related proteins such as N-cadherin and Vimentin, while E-cadherin was increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). In contrast, the tumors in the KCNN4 overexpression group were significantly larger and heavier than the NC group (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B). The tumor growth curve indicated that KCNN4 overexpression promoted tumor growth (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). Additionally, HE staining of lung tissues showed that KCNN4 overexpression increased the formation of lung metastatic nodules and tumor thrombi \u003cem\u003ein vivo\u003c/em\u003e (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD). IHC results demonstrated that the expression of KCNN4 was upregulated in the KCNN4 overexpression group, along with an increase in Ki-67 expression and enhancement of EMT-related proteins such as N-cadherin and Vimentin, while E-cadherin was decreased (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs key mediators of extracellular matrix formation, angiogenesis, and immune response, NETs play a crucial role in tumor progression and metastasis. NETs-related genes have been shown to be promising therapeutic targets in various cancers. Therefore, establishing a robust prognostic signature and exploring genes that mediate NETs formation may provide new therapeutic strategies for treating RCC.\u003c/p\u003e \u003cp\u003eBased on previously identified NETs-related genes, this study classified RCC patients into four subtypes. The tumor staging differences among the four subtypes were statistically significant, with the C2 subtype having a better prognosis and the C1 subtype having a poorer prognosis. In our study, six machine learning methods were used to predict patient survival. The Ridge algorithm demonstrated the best performance and was used to establish the NETs signature. Prognostic analysis indicated that the NETs signature is a risk marker for OS in RCC patients. ROC analysis further revealed that the NETs signature has high accuracy in predicting 1 year, 3 years, and 5 years of OS in RCC patients.\u003c/p\u003e \u003cp\u003ePatients in the high NETs signature group exhibited a large presence of anti-tumor immune cells in the TME, such as NK cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and CD4\u0026thinsp;+\u0026thinsp;T cells. Conversely, the low NETs signature group of RCC patients was enriched with immunosuppressive cells, including MDSCs, neutrophils, mast cells, and fibroblasts. Additionally, various immune regulators, such as antigen presentation, immunosuppression, immune stimulation, chemokines, and receptors, were upregulated in the high NETs signature group, inhibiting tumor cell recurrence and metastasis. The cancer immunity cycle was also more activated in the high NETs signature group. These factors suggest that patients in the high NETs signature group should have better prognoses. However, in our study, patients in the low NETs signature group achieved better outcomes. We need to explore further the mechanisms underlying this contradiction in future studies.\u003c/p\u003e \u003cp\u003eFrom the perspective of immunotherapy, the NETs signature can predict the response rate of RCC patients receiving anti-PD-1 or anti-PD-L1 treatment. Notably, patients in the high NETs signature group benefit less from immunotherapy. Several immunosuppressive markers are upregulated in the high NETs signature group, suggesting a potential association between the lower response rate and these immunosuppressive markers. Therefore, improving the expression levels of these immunosuppressive markers in the tumor microenvironment of the high NETs signature group should be a primary therapeutic focus.\u003c/p\u003e \u003cp\u003eFew studies have addressed the role of KCNN4 in RCC. Here, we identified the biological functions of KCNN4 through both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments. Briefly, KCNN4 is a risk factor for the survival of RCC patients and is associated with advanced pathological stages. Notably, the knockdown of KCNN4 not only inhibited tumor cell growth but also suppressed EMT capabilities. In a subcutaneous xenograft tumor model, we demonstrated that KCNN4 knockdown could inhibit tumor growth and reduce lung metastasis. Moreover, \u003cem\u003ein vivo\u003c/em\u003e experiments also showed that NETs formation-related proteins, including NE and Vimentin, were downregulated in the sh-KCNN4 group.\u003c/p\u003e \u003cp\u003eKCNN4 can induce neutrophil infiltration in RCC and regulate the formation of NETs. There is some evidence supporting this hypothesis. KCNN4 plays a crucial role in type I IFN signaling activation. IFN-α/IFN-γ, as important stimuli, can induce NETs formation, suggesting that KCNN4 may be involved in regulating NETs. Additionally, research by Kantari et al. has shown that KCNN4 interacts with proteinase 3 (PR3) and inhibits macrophage clearance of apoptotic neutrophils. This process contributes to pro-inflammatory effects and NETs formation.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Firstly, it is based on publicly available bulk data, which do not accurately reflect the cell-cell interaction effects of neutrophils and other immune cells. Moreover, due to the short lifespan of neutrophils, single-cell sequencing faces challenges in sample acquisition and relatively low sequencing depth. Additionally, although this study reveals the association between KCNN4 and NETs formation, the underlying mechanisms still need further validation at the pathway level.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAs mentioned above, NETs play an irreplaceable role in the tumor progression of RCC. We performed NMF analysis on RCC patients and identified four subtypes associated with NETs. Using various machine learning algorithms, this study established and validated a robust NETs signature for RCC patients. Subsequently, KCNN4 was further screened as a key gene and validated through \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments. We ultimately demonstrated that KCNN4 is detrimental to RCC tumor growth and is associated with EMT capability and NETs formation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYihao Zhu: data curation, writing\u0026ndash;original draft. Yajian Li and Xuwen Li: methodology, editing, validation. Yuan Yu: visualization, software. Can Chen: editing. Mingshuai Wang: IHC assistant. Dong Chen: cell cultural guidance. Nianzeng Xing: conceptualization. Xiongjun Ye and Feiya Yang: conceptualization, writing review, editing, check, and approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China (No. 62076007) and the Beijing Natural Science Foundation (No. 7232132).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublic datasets were analyzed in this study. TCGA RCC cohort was downloaded from the TCGA database (http://cancergenome.nih.gov/). GSE167573 and GSE29609 were downloaded from the GEO database(http://www.ncbi.nlm.nih.gov/geo/). CheckMate009/010 and CheckMate025 were obtained from Tumor Immunotherapy Gene Expression Resource databases (http://tiger.canceromics.org/). The original data and material were available when required from the corresponding author Xiongjun Ye.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committee of the Cancer Hospital, Chinese Academy of Medical Sciences (NCC2024A284) and was in accordance with ARRIVE guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the authors and patients signed the consent for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCapitanio U, Bensalah K, Bex A, Boorjian SA, Bray F, Coleman J, Gore JL, Sun M, Wood C, Russo 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11(9):5869-5878.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neutrophil Extracellular Traps, Renal Cell Carcinoma, KCNN4, Prognostic Model, Non-negative Matrix Factorization, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-4700747/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4700747/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeutrophil extracellular traps (NETs) represent a novel form of inflammatory cell death within neutrophils. Emerging research indicates that NETs promote cancer progression and metastasis in various ways. This study aims to provide prognostic NETs characteristics and therapeutic targets for patients with renal cell carcinoma (RCC). NMF analysis was conducted on 89 NET-related genes in the training cohort. Subsequently, WGCNA networks were utilized to study the subtype feature genes. Six machine learning algorithms were assessed for model training, and the optimal model was selected based on 1-year, 3-year, and 5-year AUC values. A NETs signature was then constructed to predict overall survival in RCC patients. Furthermore, multi-omics validation was performed based on NETs signature. Finally, stable knockout key gene RCC cell lines were established to verify the biological function of KCNN4 both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. This study highlights the emerging hot topic of NETs in RCC. We provide a prognostic NETs signature and identify multiple roles of KCNN4 in RCC. This work contributes to risk stratification and the identification of new therapeutic targets for RCC patients.\u003c/p\u003e","manuscriptTitle":"Constructing a Neutrophil Extracellular Trap Model Based on Machine Learning to Predict Clinical Outcomes and Immunotherapy Response in Renal Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-11 12:15:43","doi":"10.21203/rs.3.rs-4700747/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":"6fdaca80-e7fe-4e87-b5e6-5eacdfc19481","owner":[],"postedDate":"August 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-20T17:02:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-11 12:15:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4700747","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4700747","identity":"rs-4700747","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}