Identification of NEK6 as a potential biomarker for prognosis in glioma

Identification of NEK6 as a potential biomarker for prognosis in glioma | 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 Identification of NEK6 as a potential biomarker for prognosis in glioma Danwen Wang, Zisong Wang, Jian Xu, Yuxiang Cai, Xiaoping Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3754077/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 Never in mitosis gene A-related kinase 6 (NEK6) is involved in mitotic cell cycle. However, the characteristics and roles of NEK6 in pan-cancer remain incomplete. The objective of the present study is to comprehensively explore the prognostic value of NEK6 and its potential functions in multiple cancers, especially in gliomas. In this study, we conducted of comprehensive analyses of NEK6 in pan-cancer, including expression profile, immune characteristics and its relationship with clinical prognosis. We found that NEK6 was significantly upregulated in gliomas. And the increased level of NEK6 was significantly associated with poor clinical prognoses of tumor patients. Moreover, the single-cell analysis revealed that NEK6 overexpression was highly related to malignant cells and Mono/Macrophages in glioma tissue. spebrutinib and barasertib were identified to be targeted therapeutic drugs for gliomas. Then, the prognostic role of NEK6 was further validated using an independent glioma cohort, and confirmed that the highly expression of NEK6 in glioma was positively correlated with poor prognosis in patients with glioma. In vitro experiment demonstrated that knockdown of NEK6 hindered the growth and migration capacity of the glioma cells, leading to a halt in the G2/M phase of the cell cycle and triggering apoptosis in glioma cell lines. Taken together, our data uncovered the prognostic value, therapeutic potential, and molecular insight of NEK6 in glioma. Biological sciences/Cancer/Cancer genetics Biological sciences/Cancer/Cancer microenvironment Biological sciences/Cancer/Cns cancer Biological sciences/Cancer/Tumour biomarkers NEK6 glioma cancer biomarker tumor microenvironment cell cycle Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction As the main cause of death worldwide, cancer has become the most alarming disease that seriously affects public health with increasing incidence rate and mortality. The updated data of global cancer statistics in 2020 indicates that there were approximately 19.3 million new cancer cases, resulting in the deaths of nearly 10 million people worldwide [ 1 ]. At present, research on early tumor diagnosis and clinical treatment becomes increasingly crucial. Although significant advancements in cancer therapy strategies including radiotherapy, chemotherapy, surgery and molecular targeted therapeutic drugs, the side effects of medications and drug insensitivity have resulted in poor prognosis for cancer patients[ 2 , 3 ]. Remarkably, identifying useful diagnostic biomarkers and therapeutic targets is currently urgent problems to resolve. Recently, an increasing number of researches revealed the general characteristics and heterogeneity of tumor pathogenesis by pan-cancer analysis, which could be beneficial in identifying pivotal biomarkers and understanding the relevant molecular functions in tumor biological processes[ 4 – 6 ]. Glioma, the most common primary intracranial malignant tumor, accounts for almost 80% of malignancies in the central nervous system (CNS)[ 7 ]. Although our previous research and numerous other attempts to develop effective prognostic markers and stratifications for glioma, glioma remains a type of malignant tumor with extremely poor prognosis due to genetic heterogeneity[ 8 – 10 ]. Therefore, it remains needful and urgent to identify well-defined and effective biomarkers in glioma to comprehensively raise therapeutic effect. In human genome, a protein kinases superfamily has been identified as NIMA (Never In Mitosis Gene A)-related kinases (NEKs) composed of 11 NEKs in total[ 11 ]. As a characterized member of protein kinase superfamily, NIMA-related kinase-6 (NEK6) is a 313 amino acid serine/threonine kinase, which participated in the regulation of mitosis[ 12 ]. Moreover, recent researches revealed that NEK6 could play various roles in malignant tumors, including enhancing cancer aggressiveness, restraining of the p53-induced senescence and causing drug resistance due to hypoxia[ 13 – 15 ]. Specifically, it was reported that NEK6 was abundantly expressed in breast cancer tissues compared with the benign normal tissue, which was positively associated with tumor size and clinical pathological grades of breast cancer patients. These findings suggested that NEK6 could serve as an effective predictive factor for early and precise diagnosis and prognosis[ 15 ]. Besides, NEK6 has previously been shown to contribute to hepatic cancer progression, invasion as well as cell cycle G2/M phase arrest, implying that NEK6 could be an executable target for cancer treatment[ 16 ]. Nevertheless, considering the highly diverse and heterogeneous nature of tumors, the research on the roles of NEK6 in different kinds of cancer progression remains incomplete to date. Therefore, it is necessary for us to take advantage of pan-cancer analysis to analyze and summarize the possible role of NEK6 in glioma tumorigenesis and progression. In this study, we performed a comprehensive analysis of the expression patterns of NEK6 and its relationship with patient prognosis, immune infiltration and single-cell transcriptome levels. Moreover, we also conducted an analysis of protein interaction networks and enrichment pathways, which are implicated in the regulation of tumor progression by NEK6. We verified NEK6 expression in GBM tissues by immunohistochemistry (IHC) staining. To confirm the tumorigenic factors of NEK6, we carried out additional in vitro experiments. The collective findings demonstrated the functional roles of NEK6 in diverse kinds of cancers, especially in GBM, and providing beneficial orientations and strategies for the cancer clinical management. 2. Materials and Methods 2.1 Data Collection We downloaded the uniformly standardized RNA expression data from TCGA database and GTEx consortium portal[ 17 ]. Additionally, we obtained high-quality TCGA pan-cancer follow-up information from a previously published TCGA prognosis study [ 18 ]. For further analysis, we utilized the R package "CuratedCancerPrognosisData" [ 19 ] to obtain four glioma RNA sequencing datasets: TCGA-LGG from the TCGA [ 20 ], CGGA-693 and CGGA-325 from the CGGA [ 21 ], and GSE16011 from the GEO [ 22 ]. 2.2 Investigating the Prognostic and Immune Characteristics of NEK6 in Pan-Cancer Using the Sangerbox platform [ 23 ], we extracted the expression data of NEK6 using the TCGA and GTEx data. Subsequently, we filtered samples including primary tumor and normal tissue. The expression values were subjected to log2(x + 1) transformation, and cancer types with less than three samples were excluded, resulting in 34 cancer types with expression data. Non-paired Wilcoxon Rank Sum and Signed Rank Tests were utilized to analyze the significance of differences between cancer samples and adjacent normal samples. Next, from the TCGA Pan-Cancer dataset, we obtained the expression data of the NEK6 in various samples, which were then subjected to log2(x + 1) transformation. Survival information was extracted from the TCGA Pan-Cancer follow-up data, and samples with follow-up times less than 30 days were excluded. R package "survival" was used to establish a Cox proportional hazards regression model (Coxph) to analyze the prognostic relationship between gene expression and pan-cancer. The significance of the model was evaluated using Log-rank test. Furthermore, we retrieved 150 marker genes representing five immune pathways including chemokine, receptor, MHC, Immunoinhibitor, and Immunostimulator from the TISIDB database [ 24 ]. We calculated the Spearman’s correlation between NEK6 expression and the expression of these immune regulatory genes in pan-cancer. Lastly, we employed the "Immune" module of TIMER2.0 [ 25 ], integrating multiple tumor immune microenvironment estimation methods, and used the Spearman’s correlation to calculate the correlationship between NEK6 expression and the levels of 19 immune cell infiltrations in pan-cancer level. 2.3 Exploring the Association between NEK6 and Clinical Characteristics in Glioma Patients In the TCGA-LGG, CGGA-693, CGGA-325, and GSE16011 datasets, we first divided glioma patients into NEK6 high-expression and low-expression groups based on the median expression level of the NEK6. Subsequently, we compared the differences in age, gender, pathological type, grade, etc., between the two groups of patients, and conducted relevant analyses using R. Using the Kaplan-Meier method, we plotted the survival curves for the NEK6 high-expression and low-expression groups and used the R package "survRM2" to calculate the Restricted Mean Survival Time (RMST) ratio. Next, we included NEK6 expression groups (high and low) alone with other clinical information into a univariate Coxph. Furthermore, we employed a stepwise regression to select the best model and construct a multivariate Coxph. 2.4 Exploring the Correlation between NEK6 and Cancer-Associated Biomarkers We utilized the R package “IOBR” to extract a set of 255 published feature genes [ 26 ]. Subsequently, based on the sequencing data from the four datasets, we employed the Gene Set Variation Analysis (GSVA) method to calculate the GSVA scores for each sample [ 27 ]. Finally, for the NEK6 high-expression and low-expression groups, we used the R package “pheatmap” to create a heatmap displaying some significantly correlated biomarkers. 2.5 Investigating the Potential Functions of NEK6 in Glioma In the four datasets, we first used the R package "limma" to calculate the differentially expressed genes (DEGs) between the NEK6 high-expression and low-expression groups. DEGs were selected based on the criteria |log2(FC)| > 2 and adjusted p-value < 0.05. Next, using the set of DEGs, we conducted Gene Ontology (GO) analysis for Biological Process (BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using the R package "clusterProfiler" [ 28 ]. Enrichment with Fisher's test p-value less than 0.05 was considered statistically significant. 2.6 Investigation of NEK6 Expression and Cell Communication in Single Cells of Gliomas Utilizing the TISCH2 database [ 29 ], we analyzed single cell profiles of glioma based on the GSE131928 [ 30 ] data and conducted an examination of NEK6 expression patterns within individual cells, as well as the cell communication between different cell clusters. 2.7 Prediction of Potential Drugs Targeting Glioma Patients Based on NEK6 We employed R package "pRRophetic" to construct a ridge regression model for predicting drug IC50 values [ 31 ]. To this end, we utilized cell line gene expression profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) database and transcriptome expression data from the four databases. Furthermore, we acquired normalized RNA expression data (RNA: RNA-seq) and drug activity data (Compound activity: DTP NCI-60) from the CellMiner database [ 32 ]. For drug activity data that contained missing values, we utilized the R package "impute" with the K-nearest neighbors (KNN) method to assess and impute the missing values. Subsequently, we computed the Spearman’s correlation coefficient between NEK6 expression and different drugs to explore potential associations. 2.8 Tissue microarray and Immunohistochemical (IHC) staining We collected tissue samples from 38 cases of human low-grade gliomas and gathered corresponding patient follow-up information. This study received support from the Institutional Review Board of Zhongnan Hospital of Wuhan University (No. 2019048). We confirmed that all research was performed in accordance with relevant guidelines. And informed consent was obtained from all participants. First, we processed the tissue samples by embedding, sectioning, and Hematoxylin and Eosin (HE) staining. Subsequently, tissue microarrays were constructed. These histological microarrays were exposed in an oven at 62°C for 2 hours, followed by dewaxing in regular xylene and dehydration in alcohol. Subsequently, they were subjected to high-temperature (120°C) retrieval in EDTA buffer at a pH of 7.4 and allowed to cool naturally. During this process, to inhibit the activity of endogenous peroxidases, we used a solution of 0.3% hydrogen peroxide and methanol for 30 minutes. Next, we washed the samples three times with PBS, with each wash lasting for 10 minutes. Following this, blocking was performed at room temperature for 1 hour using goat serum, then incubated with anti-NEK6 antibodies (1:400, GeneTex, GTX13387) for a night. The following day, we added the relavant secondary antibodies and DAB solution into the microscope slide. Finally, we observed and photographed via a microscope. To obtain high-resolution images, we scanned the corresponding 40x tissue microarray images using a Hamamatsu NanoZoomer XR slide scanner. Subsequently, we employed QuPath-0.4.3 software for image recognition and calculating the positivity rate [ 33 ]. Based on the median positivity rate of NEK6 immunohistochemistry, we categorized patients into high and low expression groups, followed by the construction of survival curves and the performance of RMST tests to assess the impact of NEK6 on the prognosis of glioma patients. 2.9 Cell Culture and siRNA Transfection The cell lines were acquired from the Cell Bank of the Chinese Academy of Science and underwent authentication and testing to ensure absence of mycoplasma contamination. The medium with Dulbecco's modified Eagle's medium (DMEM) was used to culture the U251 and U87 glioma cell lines. And the medium was enhanced with 10% FBS (Gibco, Grand Island, NY, USA). Tsingke (Wuhan, China) provided the siRNAs, and the sequences (siNEK6-1, siNEK6-2, siNEK6-3) are listed below: siNEK6-1: 5′- AGUUCAGGGCCUTTAUCTTTG-3′; siLAP2α-2: 5′- GCGGTUACAATTTCCACGAGT-3′; siLAP2α-3: 5′- GCAATTTUGCCAACGTTGATA-3′; The siRNAs were transfected into the two glioma cell lines via Lipofectamine 3000 reagent. 2.10 RNA Extraction and qRT‒PCR The RNA extraction assay was conducted by the RNeasy mini kit (Qiagen). Afterwards, 1 µg RNAunderwent reverse transcription to cDNA. Next, qRT-PCR was conducted with guidelines provided by the PCR Mix manufacturer. The primer sequences were showed below: NEK6: 5′-CATCCCAACACGCTGTCTTTT-3′; 5′-TACACCTCGCTGAACTGTCCT-3′; GAPDH: 5′-GGAGCGAGTTCCCTCCAATTT-3′; 5′-GGCTGTTGTCATACTTCTCATGG-3′. 2.11 MTT Assay The 96-well plate was used to seed the cells, with a density of 1×10 3 cells per well, and they were cultured overnight. Following the specified duration of culturing, MTT was introduced into every well and allowed to incubate for 4 hours at a temperature. Next, the liquid above the sediment was removed, and 200 µl of DMSO was introduced into every well. 2.12 Cell Cycle and Apoptosis Assay For cell cycle assay, the cells were treated with DNA Staining Solution and appropriate permeabilization solution. The apoptosis test was performed using the Annexin V FITC Apoptosis Assay Kit. In total, 10 6 cells were placed in 6-well dishes, subsequently gathered (including cells in the supernatant), and subjected to a 5-minute treatment with 5 µl of Annexin V-APC and 10 µl of 7-AAD. Shortly after, the specimen was immediately identified using a flow cytometer. 2.13 Wound-healing Assay The U251 and U87 glioma cells (density 2.5 ×10 5 cells/well) transfected with siRNAs were inoculated in the 6-well plate for 24 h. After that, we used a 200 µL pipetting head to create a scratch on the plate. The serum-free medium was then replaced, and images were captured at 0 hours and 48 hours using an inverted microscope (XDS-100, Cai Kang Optical Instrument Co, Ltd, China). 2.14 Statistical Analysis All statistical analyses and data visualizations were performed using R software v4.3.1 ( https://www.r-project.org/ ) and GraphPad Prism 7 (USA). Comparisons between two groups were conducted using the Wilcoxon test, while the Kruskal-Wallis test was used for comparisons involving multiple groups. Spearman’s correlation analysis was utilized to assess correlations between variables. The correlation between NEK6 expression levels and clinical pathological characteristics of patients was evaluated using the Chi-square test. All the results were obtained from more than three independent experiments. Survival analysis was conducted using the Kaplan–Meier curve, and the differences were determined using the log-rank test. To analyze differences between groups, we utilized a two-tailed t test. The statistical significance was presented in the following manner: ns indicates no statistical significance, *p < 0.05, **p < 0.01, and ***p < 0.001. 3. Results 3.1 The Expression Profile and Immune Characteristics of NEK6 in Pan-Cancer Utilizing the GTEx and TCGA databases, we conducted a differential analysis of NEK6 expression in cancer samples and adjacent non-cancerous samples across 34 different cancer types. Our findings revealed elevated expression of NEK6 in cancer samples from various cancer types like glioblastoma multiforme (GBM), lower grade glioma (LGG), kidney renal papillary cell carcinoma (KIRP) and kidney renal clear cell carcinoma (KIRC). Conversely, NEK6 exhibited reduced expression in cancer samples of bladder urothelial carcinoma (BLCA) and kidney chromophobe (KICH) ( Fig. 1 A ) . According to Fig. 1 B, NEK6 expression were highest in liver, followed by basal ganglia and cerebral cortex. Given the pivotal role of immune regulation in tumorigenesis and progression, we conducted an analysis of 150 immune pathway-related genes spanning chemokine, receptor, MHC, Immunoinhibitor, and Immunostimulator categories (Supplementary Fig. 1) . Notably, NEK6 demonstrated significant correlations with multiple immune regulatory genes in the context of pan-cancer. Leveraging the TIMER 2.0, we employed diverse algorithms including CIBERSORT, XCELL, EPIC, MCPCOUNTER, QUANTISEQ, and TIMER to assess the correlations between NEK6 expression and immune cell infiltration levels in pan-cancer ( Fig. 1 C ) . Our results indicated a positive correlation between NEK6 expression and cancer-associated fibroblast infiltration in the majority of cancer types, with particular significance in GBM and LGG. Additionally, NEK6 expression was significantly correlated with immune cell infiltration levels such as memory B cells, CD4 + memory T cell, cancer-associated fibroblasts, and myeloid dendritic cells, underscoring its potential role in the immune microenvironment of glioma. 3.2 Correlation Between NEK6 Expression and Prognosis in Pan-cancers Integrating TCGA pan-cancer data with follow-up information, a forest plot depicted the prognostic relationship of NEK6 across diverse cancer types (Fig. 2A) . Specifically, NEK6 high-expression correlated with poorer prognosis in 12 tumor types such as GBMLGG, LGG, and ACC, while NEK6 low-expression was associated with worse prognosis in KIRC. Notably, the pan-cancer analysis of NEK6 has highlighted its significant overexpression in glioma, which is linked to a worse prognosis. Moreover, NEK6 has been found to correlate with immune infiltration levels in glioma. Therefore, we delved deeper into the characteristics of NEK6 in the context of glioma. Chi-square analysis demonstrated that both NEK6 high-expression and low-expression groups are significantly associated with chemotherapy, IDH mutation status, and tumor grade among glioma patients. However, no significant association was observed with variables such as race or grade (p-values > 0.05) (Supplementary Table 1–4) . Further exploration using Kaplan-Meier curves revealed that patients with NEK6 high-expression in glioma experienced a shorter overall survival period (Fig. 2B) , a finding corroborated by restricted mean survival time (RMST) analysis (Supplementary Table 5–8) . Utilizing both the univariate Cox proportional hazards regression models (Coxph) and multivariate Coxph based on stepwise regression, we further established that the NEK6 expression level serves as an independent prognostic factor for poor outcomes among glioma patients (Fig. 2C) . Figure 2. Correlation of NEK6 with the prognosis of glioblastoma. (A) Calculation of the HR values for NEK6 expression in pan-cancer with OS and DSS using CoxPH, where NEK6 is a significant risk factor for glioblastoma in both cases. (B) Kaplan-Meier curves for NEK6 high and low expression groups in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets. (C) Calculation of HR values for NEK6 high and low expression in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG using univariate and multivariate CoxPH with other clinical indicators. 3.3 NEK6 Demonstrates Significant Associations with Various Cancer Biomarkers Biomarkers can provide early warnings for the onset and progression of cancer, offering clinicians insights for risk stratification and targeted therapies. Therefore, leveraging a collection of 255 published feature genes, we conducted GSVA to investigate the relationship between NEK6 expression and multiple biomarkers in glioma (Fig. 3A-B) . Our analysis revealed that the NEK6 high-expression group exhibited elevated levels of CD8 + T effector cells, immune checkpoint molecules, macrophages, co-stimulation antigen-presenting cells (APC), antigen processing and presentation machinery, BCR signaling pathway components, chemokine receptors, cytokine receptors, natural killer cell cytotoxicity factors, TNF family members, and exosomal secretion pathways. Conversely, the levels of sirtuin nicotinamide metabolism were lower in the NEK6 high-expression group. Figure 3. Correlation of NEK6 with tumor-related biomarkers in glioblastoma. (A) Calculation of GSVA scores for tumor-related biomarkers in samples from CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets. Heatmap representation of biomarkers strongly correlated with NEK6 expression in all four datasets, with samples divided into NEK6 high and low expression groups. (B) Spearman's correlation between NEK6 expression and GSVA scores for immune checkpoint and EMT2 in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets. 3.4 NEK6's Involvement in Various Biological Functions in Glioma In order to elucidate the potential functions of NEK6 in glioma tissue, we conducted enrichment analysis based on DEGs between the NEK6 high-expression and low-expression groups. Utilizing the GO analysis for BP module, our results indicated that the NEK6 expression is associated with a range of functions, including but not limited to positive regulation of cytokine production, positive regulation of the MAPK cascade, regulation of nervous system development, activation of immune response, axon development, mononuclear cell proliferation, T cell costimulation, microglial cell activation, establishment of the endothelial barrier, regulation of T cell-mediated cytotoxicity, and glial cell activation (Fig. 4A & Supplementary Table 9) . Furthermore, the KEGG pathway enrichment analysis demonstrated that NEK6 was enriched in multiple major categories of signaling pathways such as organismal systems, metabolism, human diseases, genetic information processing, and environmental information processing (Fig. 4B) . Figure 4. Functional enrichment analysis of NEK6 in glioblastoma. (A) Functional enrichment analysis of BP modules using GO analysis for CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets. (B) Functional enrichment analysis using KEGG database for CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets, represented in bar graphs for five categories. 3.5 Single-Cell Transcriptome Analysis Unveils Elevated NEK6 Expression in Glioma To thoroughly investigate the expression patterns of NEK6 within the cellular population of glioma tissue, we harnessed the TISCH2 database to meticulously analyze the GSE131928 single-cell dataset (Fig. 5A) . The outcomes of this analysis reveal that NEK6 exhibits predominantly high expression levels in the malignant cells and Mono/Macro (Monocyte/Macrophage) populations within glioma tissue (Fig. 5B-C) . Another insight is visually depicted through a heatmap that effectively illustrates the communication patterns among these distinct cell types (Fig. 5D) . Figure 5. Expression characteristics of NEK6 at the single-cell level in glioblastoma. (A) Single-cell clustering in GSE131928. (B) Expression localization of NEK6 in cell clusters in GSE131928. (C) Violin plots illustrating NEK6 expression levels in different cell clusters in GSE131928. (D) Cell communication in GSE131928. 3.6 Identification of Potential Anti-Glioma Drugs Through NEK6 We conducted a thorough drug screening by utilizing the NCI-60 cancer cell line dataset sourced from the CellMiner database. The comprehensive results obtained underscore that NEK6 expression exhibits a negative correlation with the activity levels of Barasertib, Eribulin mesilate, TAK Plk inhibitor, etc. Conversely, NEK6 expression displayed a positive correlation with JNJ-38877605, Simvastatin, GSK-2636771, etc. (Fig. 6A) . In-depth analysis of the GDSC database demonstrated that NEK6 expression was negatively correlated with the IC50 values of MG-132, Paclitaxel, BI-2536, etc., while it was positively correlated with BAY 61-3606, Lisitinib, GW-2580, etc. (Fig. 6B-C) . Figure 6. Screening of anti-glioblastoma drugs based on NEK6. (A) Spearman correlation between NEK6 expression in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets and drug IC50 values from the GDSC database. (B) Spearman correlation between NEK6 expression and drugs from the CellMiner database. 3.7 Immunohistochemical Staining of NEK6 in Glioma Tissue Microarray To further substantiate our research findings, we collected tissue samples from 38 cases of human low-grade gliomas and gathered corresponding patient follow-up information. We performed immunohistochemical staining on these tissue samples to calculate the positivity rate of NEK6 (Fig. 7A) . Subsequently, based on the median positivity rate of NEK6, we categorized patients into high NEK6 expression group and low NEK6 expression group. According to the survival curves, it was evident that patients with high NEK6 expression had significantly lower survival rates (Fig. 7B) . This finding was further validated through RMST analysis (Supplementary Table 10) . Figure 7. Immunohistochemistry staining for NEK6. (A) Representative immunohistochemistry images for NEK6 in high, medium, and low expression groups. (B) Kaplan-Meier curves for NEK6 high and low expression groups. 3.8 The Effects of NEK6 Knockdown on Proliferation, Migration, Cell Cycle and Apoptosis of GBM Cells In order to confirm the molecular roles of NEK6 in glioma cells, we silenced the NEK6 expression in the U251 and U87 cell lines, individually (Fig. 8A) . The MTT assay findings revealed that the suppression of NEK6 greatly impeded the capacity of cell proliferation in both glioma cell lines (Fig. 8B) . Furthermore, the wound healing experiment demonstrated that the suppression of NEK6 could decelerate the migration speed of glioma cells (Fig. 8C) . Meanwhile, we conducted apoptosis and cell cycle assays to confirm that the decrease in NEK6 caused apoptosis and cell cycle arrest in glioma cells during the G2/M phase (Fig. 8D-E) . Figure 8. The effects of NEK6 knockdown on proliferation, migration, cell cycle and apoptosis of GBM cells. (A) The mRNA level of NEK6 was weakened by si-NEK6 transfection in U251 and U87 cell lines. (B) Knockdown of NEK6 inhibited the proliferation of the U251 and U87 cell lines. (C) Wound-healing assays indicated the knockdown of NEK6 could restrain the migration of GBM cells. (D-E) The si-NEK6 induced cell apoptosis increasing, cell G2/M phase cycle arrest in U251 and U87 cell lines. 4. Discussion Gliomas, accounting for over 70% of malignant brain tumors in adults, pose a significant challenge in the realm of cancer biology [ 34 ]. Patients with low-grade gliomas have a relatively extended median survival of 11.6 years, while those with glioblastoma, even with the current standard of maximal safe resection, have a median survival of less than 1 year [ 35 ]. The conventional treatments such as surgical resection, temozolomide, and radiation therapy are inadequate in combating cancer progression [ 36 ]. One of the major barriers is the blood-brain barrier, which limits the entry of most anti-tumor drugs into the brain [ 37 ]. Additionally, the immunosuppressive nature of gliomas has been well-documented, further complicating the development of anti-glioma drugs [ 38 ]. Thus, understanding new therapeutic targets and their relationship with the tumor microenvironment is of paramount importance for improving the prognosis of glioma patients. In this context, NEKs have gained attention. Initially recognized for their role in cell cycle regulation, the NEKs family, consisting of 11 members, has been linked to a variety of cellular functions, including centrosome organization, primary cilia function, gametogenesis, mRNA splicing, myogenesis, intercellular protein transport, mitochondrial homeostasis, and DNA damage repair [ 39 ]. NEK6, in particular, plays a pivotal role in promoting mitosis during the mid to late stages of the cell cycle [ 40 , 41 ]. Research has suggested that NEK6 may serve as a potential target in various tumors, such as breast cancer, hepatocellular carcinoma, colorectal cancer, prostate cancer, and thyroid cancer [ 15 , 16 , 39 , 42 – 45 ]. For instance, in breast cancer, the upregulation of NEK6 has been shown to promote cell cycle progression, cancer cell proliferation, and the formation of spheroids, all of which contribute to tumor growth. Furthermore, NEK6 has been found to be positively correlated with histological grading, tumor size, and TNM staging, indicating its potential role in the pathogenesis of breast cancer [ 15 , 46 ]. Besides, research has already confirmed an association between astrocytopathy and NEK6 regulation. NEK6 plays a significant role in this association as it influences the phosphorylation of the signaling protein STAT3 upon binding. Consequently, NEK6's activity is crucial for the induction of reactive astrocytic proliferation markers such as GFAP and PCNA. Importantly, aberrant regulation of NEK6 can lead to an increase in reactive astrocytic proliferation, thereby exacerbating the formation of brain lesions. Conversely, the downregulation of NEK6 is associated with a reduction in astrocyte activity and, consequently, a decrease in lesion size [ 47 ]. These findings not only suggest that NEK6 may hold a pivotal role in the life activities of neural cells but also imply the likelihood of NEK6's involvement in glioma, particularly in combination with its established role in many other cancers. However, despite these promising connections, there is currently no research that directly addresses the relationship between NEK6 and glioma. To fill this gap, our study conducted a comprehensive analysis. We initiated by examining NEK6 expression in various tumors through a pan-cancer analysis, assessing its prognostic implications and immune characteristics. Our findings suggested a potential link between NEK6 and the development of glioma. Subsequently, we delved deeper into this relationship by systematically analyzing NEK6 and glioma. Survival analysis substantiated a significant correlation between high NEK6 expression and poor prognosis in glioma patients. Furthermore, we explored the associations of NEK6 expression with tumor biomarkers, revealing potential links with cell cytokines, nervous system development, immune responses, and cell proliferation, among other biological processes. Single-cell analysis pinpointed elevated NEK6 expression primarily in malignant cells and Mono/Macrophages within glioma tissue. Based on the NEK6 expression, we have also identified promising anti-glioma drugs, such as spebrutinib and barasertib. For validation, we collected 38 samples from low-grade glioma patients, alongside their follow-up information. Based on this, we confirmed that high NEK6 expression was indeed associated with a worse prognosis in these patients. To gain a deeper understanding of the molecular role of NEK6 in glioma cells, we conducted experiments wherein we suppressed NEK6 expression. The results demonstrated that inhibiting NEK6 significantly hindered glioma cell proliferation, slowed down glioma cell migration, induced apoptosis in glioma cells during the G2/M phase, and caused cell cycle arrest. In previous study, NEK6 has been identified as a novel DNA damage checkpoint target. Inhibiting its activity is crucial for effective cell cycle arrest at the G2/M phase after DNA damage [ 48 ]. It's worth noting that research has also suggested that abnormalities in spindle formation, chromosome segregation, cell cycle arrest, and cell death can be related to the expression of NEK6. This underscores the essential role of NEK6 in regulating the progression of the cell cycle [ 40 ]. In our study, we confirmed, through cellular experiments, that the downregulation of NEK6 induces apoptosis and cell cycle arrest at the G2/M phase in glioma cells. This finding aligns with previous research and emphasizes the necessity of NEK6 for glioblastoma cells to complete cell cycle replication. As a result, it becomes evident that drugs designed to inhibit NEK6 expression hold significant potential for the treatment of glioma. Interestingly, previous study has suggested that the activation of NEK6 is mediated by NEK1 and Plk1, both of which are themselves activated by CDK9 [ 49 ]. Furthermore, NEK9 has been found to directly interact with NEK6 and phosphorylate it at the Ser-206 position during the process of mitosis [ 50 ]. The phosphorylation of NEK6 drives the protein Eg5 to regulate spindle formation following NEK9 activation [ 51 , 52 ]. NEK6 and NEK7 are likely to possess very similar properties and functions, as both of them serve as downstream substrates of NEK9 [ 53 ]. All these findings suggest that NEK6 interacts with other members of the NEKs family in the execution of various biological processes. Therefore, investigating the roles of other NEKs in glioma not only contributes to a deeper understanding of NEK6's mechanisms in the context of glioma but also holds the potential for the discovery of new therapeutic targets for it, which serves as a potential direction for future research. 5. Conclusions This study highlights NEK6 as a promising candidate for precision therapy in glioma. Our findings reveal that NEK6 expression independently predicts the prognosis of glioma patients. Furthermore, NEK6 expression in glioma exhibits strong correlations with the immune microenvironment and multiple tumor-related biomarkers. Leveraging the insights from NEK6 expression, we have identified potential anti-glioma drugs. Furthermore, our research showcases that by downregulating NEK6, we can not only impede the migration and proliferation of glioma cells but also trigger apoptosis and induce cell cycle arrest in the G2/M phase of these cells. Declarations Data availability statement The original contributions presented in the study are included in the main text/Supplementary Material. Further inquiries can be directed to the corresponding authors. Author contributions D.W. and Z.W. conceived the idea, analyzed the data, and drafted the work. D.W. performed the experimental verification. Z.W., Y.C., X.L., and Z.L. collected the patient sample and participated in the revision. X.L. and Z.L. supervised the study and provided funding support. All authors contributed to the article and approved the submitted version. Funding This research was supported by the project of Hubei province and the Translational Medicine Research Fund of Zhongnan Hospital of Wuhan University (YYXKNL2023011, ZNJC202206), the Fundamental Research Funds for the Central Universities (2042023kf0068), and National Natural Science Foundation of China (82303580). Institutional Review Board Statement Studies involving human participants were reviewed and approved by the Ethics Committee of the Zhongnan Hospital of Wuhan University (ethics No. 2019048). The patients provided written informed consent to participate in the study. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. 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Nek9 is a Plk1-activated kinase that controls early centrosome separation through Nek6/7 and Eg5. EMBO J 2011, 30, 2634–2647, doi: 10.1038/emboj.2011.179 . O'Regan, L.; Fry, A.M. The Nek6 and Nek7 protein kinases are required for robust mitotic spindle formation and cytokinesis. Mol Cell Biol 2009, 29, 3975–3990, doi: 10.1128/MCB.01867-08 . Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.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. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-3754077","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":264521428,"identity":"1cd12621-0ff2-4151-b394-79559f7d7611","order_by":0,"name":"Danwen Wang","email":"","orcid":"","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Danwen","middleName":"","lastName":"Wang","suffix":""},{"id":264521429,"identity":"ccd285a3-9198-450f-adf8-cf71c065d077","order_by":1,"name":"Zisong Wang","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Zisong","middleName":"","lastName":"Wang","suffix":""},{"id":264521430,"identity":"052a1613-9562-4157-96ae-06a13ee628e6","order_by":2,"name":"Jian Xu","email":"","orcid":"","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Xu","suffix":""},{"id":264521431,"identity":"610f0eab-214b-4233-a41d-92e4a143f23a","order_by":3,"name":"Yuxiang Cai","email":"","orcid":"","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Yuxiang","middleName":"","lastName":"Cai","suffix":""},{"id":264521432,"identity":"e9b7c461-6daf-40cb-85b9-cb6c8edb1d15","order_by":4,"name":"Xiaoping Liu","email":"","orcid":"","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Liu","suffix":""},{"id":264521433,"identity":"67c65723-12a7-4bb9-b5b5-9016b4a8778e","order_by":5,"name":"Zhiqiang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACPmYGNoYEBgkgk/kAM1joAAEtbAgtbAlEagEjMOAxIFILO/uzBw/KLOTN+dd8ky5sY5Dju5HA+LkAv8PSDRLOSRjunPF2m/TMNgZjyRsJzNIz8Gs5JpHYJsG44cbZbbd52xgSN9xIYGPmwauFsQ2kxX7DjTPPQFrqidDCzAbSkrjhfA8bSEuCAWEtbGwSQL8kb7jBZv6bB+ipmWceNkvj08LPf/yZ5I+yOtsN5w8/NuYps5HnO5588DM+LVC7gFgiAcQCxSljA0ENEC38B4hQOApGwSgYBSMSAABcL0UW/ti9AgAAAABJRU5ErkJggg==","orcid":"","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-12-14 14:44:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3754077/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3754077/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49083271,"identity":"d42d73dd-0709-4a4e-9ad7-a0c36229f482","added_by":"auto","created_at":"2024-01-02 20:22:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2126712,"visible":true,"origin":"","legend":"\u003cp\u003ePan-cancer analysis of NEK6 expression and its immunological implications. (A) Differential expression of NEK6 in cancer and adjacent tissues from the TCGA combined with GTEx database. (B) NEK6 expression levels in normal tissues and cell types, based on the Consensus dataset created by integrating the data from three transcriptomics datasets (HPA, GTEx and FANTOM5). (C) Correlation of NEK6 expression in pan-cancer with 19 different immune cell types, assessed using Spearman's correlation.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3754077/v1/7c4f203a901975982b70e3b6.png"},{"id":49082813,"identity":"cf611911-1a59-40c5-840a-55831cff53ee","added_by":"auto","created_at":"2024-01-02 20:14:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1921315,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of NEK6 with the prognosis of glioblastoma. (A) Calculation of the HR values for NEK6 expression in pan-cancer with OS and DSS using CoxPH, where NEK6 is a significant risk factor for glioblastoma in both cases. (B) Kaplan-Meier curves for NEK6 high and low expression groups in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets. (C) Calculation of HR values for NEK6 high and low expression in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG using univariate and multivariate CoxPH with other clinical indicators.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3754077/v1/ae385d0d794194aefc7c221f.png"},{"id":49083270,"identity":"f1e7d4dd-f541-4754-aa89-374d406e24c3","added_by":"auto","created_at":"2024-01-02 20:22:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1546942,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of NEK6 with tumor-related biomarkers in glioblastoma. (A) Calculation of GSVA scores for tumor-related biomarkers in samples from CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets. Heatmap representation of biomarkers strongly correlated with NEK6 expression in all four datasets, with samples divided into NEK6 high and low expression groups. (B) Spearman's correlation between NEK6 expression and GSVA scores for immune checkpoint and EMT2 in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3754077/v1/8ba787e25a4ba35dc5cefd12.png"},{"id":49082816,"identity":"41af1714-4876-45f5-839c-2e94f2eea738","added_by":"auto","created_at":"2024-01-02 20:14:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":928367,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of NEK6 in glioblastoma. (A) Functional enrichment analysis of BP modules using GO analysis for CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets. (B) Functional enrichment analysis using KEGG database for CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets, represented in bar graphs for five categories.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3754077/v1/2291c411b5fb6aa74704e8d2.png"},{"id":49082817,"identity":"90503ab2-b606-46ff-8285-42a596d05e8e","added_by":"auto","created_at":"2024-01-02 20:14:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1973895,"visible":true,"origin":"","legend":"\u003cp\u003eExpression characteristics of NEK6 at the single-cell level in glioblastoma. (A) Single-cell clustering in GSE131928. (B) Expression localization of NEK6 in cell clusters in GSE131928. (C) Violin plots illustrating NEK6 expression levels in different cell clusters in GSE131928. (D) Cell communication in GSE131928.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3754077/v1/088b6b826062e6ddd073131c.png"},{"id":49082812,"identity":"10519b11-6c87-4b88-acb4-534c63e79b9f","added_by":"auto","created_at":"2024-01-02 20:14:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":993494,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of anti-glioblastoma drugs based on NEK6. (A) Spearman correlation between NEK6 expression in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets and drug IC50 values from the GDSC database. (B) Spearman correlation between NEK6 expression and drugs from the CellMiner database.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3754077/v1/a63ad0cd9202af0b991fb275.png"},{"id":49082820,"identity":"4043948e-f496-4ff3-8201-8be03b18194f","added_by":"auto","created_at":"2024-01-02 20:14:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3669051,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemistry staining for NEK6. (A) Representative immunohistochemistry images for NEK6 in high, medium, and low expression groups. (B) Kaplan-Meier curves for NEK6 high and low expression groups.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3754077/v1/f02d3535db50686226becd41.png"},{"id":49083272,"identity":"5fc4f21b-fd97-4aed-9327-d4aa682ca659","added_by":"auto","created_at":"2024-01-02 20:22:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":13127208,"visible":true,"origin":"","legend":"\u003cp\u003eThe effects of NEK6 knockdown on proliferation, migration, cell cycle and apoptosis of GBM cells. (A) The mRNA level of NEK6 was weakened by si-NEK6 transfection in U251 and U87 cell lines. (B) Knockdown of NEK6 inhibited the proliferation of the U251 and U87 cell lines. (C) Wound-healing assays indicated the knockdown of NEK6 could restrain the migration of GBM cells. (D-E) The si-NEK6 induced cell apoptosis increasing, cell G2/M phase cycle arrest in U251 and U87 cell lines.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3754077/v1/d2c3180489947ec291b86eb8.png"},{"id":76726258,"identity":"ce3b7448-d7d9-4486-8429-83db0875e4ec","added_by":"auto","created_at":"2025-02-20 05:34:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":31252050,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3754077/v1/e49386df-abda-47db-b90e-e213607aada6.pdf"},{"id":49082818,"identity":"4b007754-a5b8-4878-ab57-568198e91f58","added_by":"auto","created_at":"2024-01-02 20:14:35","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":926322,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3754077/v1/1b18de4ebc5c6215f7418df6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of NEK6 as a potential biomarker for prognosis in glioma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs the main cause of death worldwide, cancer has become the most alarming disease that seriously affects public health with increasing incidence rate and mortality. The updated data of global cancer statistics in 2020 indicates that there were approximately 19.3\u0026nbsp;million new cancer cases, resulting in the deaths of nearly 10\u0026nbsp;million people worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. At present, research on early tumor diagnosis and clinical treatment becomes increasingly crucial. Although significant advancements in cancer therapy strategies including radiotherapy, chemotherapy, surgery and molecular targeted therapeutic drugs, the side effects of medications and drug insensitivity have resulted in poor prognosis for cancer patients[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Remarkably, identifying useful diagnostic biomarkers and therapeutic targets is currently urgent problems to resolve. Recently, an increasing number of researches revealed the general characteristics and heterogeneity of tumor pathogenesis by pan-cancer analysis, which could be beneficial in identifying pivotal biomarkers and understanding the relevant molecular functions in tumor biological processes[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Glioma, the most common primary intracranial malignant tumor, accounts for almost 80% of malignancies in the central nervous system (CNS)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although our previous research and numerous other attempts to develop effective prognostic markers and stratifications for glioma, glioma remains a type of malignant tumor with extremely poor prognosis due to genetic heterogeneity[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, it remains needful and urgent to identify well-defined and effective biomarkers in glioma to comprehensively raise therapeutic effect.\u003c/p\u003e \u003cp\u003eIn human genome, a protein kinases superfamily has been identified as NIMA (Never In Mitosis Gene A)-related kinases (NEKs) composed of 11 NEKs in total[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As a characterized member of protein kinase superfamily, NIMA-related kinase-6 (NEK6) is a 313 amino acid serine/threonine kinase, which participated in the regulation of mitosis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, recent researches revealed that NEK6 could play various roles in malignant tumors, including enhancing cancer aggressiveness, restraining of the p53-induced senescence and causing drug resistance due to hypoxia[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Specifically, it was reported that NEK6 was abundantly expressed in breast cancer tissues compared with the benign normal tissue, which was positively associated with tumor size and clinical pathological grades of breast cancer patients. These findings suggested that NEK6 could serve as an effective predictive factor for early and precise diagnosis and prognosis[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Besides, NEK6 has previously been shown to contribute to hepatic cancer progression, invasion as well as cell cycle G2/M phase arrest, implying that NEK6 could be an executable target for cancer treatment[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Nevertheless, considering the highly diverse and heterogeneous nature of tumors, the research on the roles of NEK6 in different kinds of cancer progression remains incomplete to date. Therefore, it is necessary for us to take advantage of pan-cancer analysis to analyze and summarize the possible role of NEK6 in glioma tumorigenesis and progression.\u003c/p\u003e \u003cp\u003eIn this study, we performed a comprehensive analysis of the expression patterns of NEK6 and its relationship with patient prognosis, immune infiltration and single-cell transcriptome levels. Moreover, we also conducted an analysis of protein interaction networks and enrichment pathways, which are implicated in the regulation of tumor progression by NEK6. We verified NEK6 expression in GBM tissues by immunohistochemistry (IHC) staining. To confirm the tumorigenic factors of NEK6, we carried out additional in vitro experiments. The collective findings demonstrated the functional roles of NEK6 in diverse kinds of cancers, especially in GBM, and providing beneficial orientations and strategies for the cancer clinical management.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Collection\u003c/h2\u003e \u003cp\u003eWe downloaded the uniformly standardized RNA expression data from TCGA database and \u003cb\u003eGTEx\u003c/b\u003e consortium portal[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, we obtained high-quality TCGA pan-cancer follow-up information from a previously published TCGA prognosis study [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For further analysis, we utilized the R package \"CuratedCancerPrognosisData\" [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] to obtain four glioma RNA sequencing datasets: TCGA-LGG from the TCGA [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], CGGA-693 and CGGA-325 from the CGGA [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and GSE16011 from the GEO [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Investigating the Prognostic and Immune Characteristics of NEK6 in Pan-Cancer\u003c/h2\u003e \u003cp\u003eUsing the Sangerbox platform [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], we extracted the expression data of NEK6 using the TCGA and GTEx data. Subsequently, we filtered samples including primary tumor and normal tissue. The expression values were subjected to log2(x\u0026thinsp;+\u0026thinsp;1) transformation, and cancer types with less than three samples were excluded, resulting in 34 cancer types with expression data. Non-paired Wilcoxon Rank Sum and Signed Rank Tests were utilized to analyze the significance of differences between cancer samples and adjacent normal samples.\u003c/p\u003e \u003cp\u003eNext, from the TCGA Pan-Cancer dataset, we obtained the expression data of the NEK6 in various samples, which were then subjected to log2(x\u0026thinsp;+\u0026thinsp;1) transformation. Survival information was extracted from the TCGA Pan-Cancer follow-up data, and samples with follow-up times less than 30 days were excluded. R package \"survival\" was used to establish a Cox proportional hazards regression model (Coxph) to analyze the prognostic relationship between gene expression and pan-cancer. The significance of the model was evaluated using Log-rank test.\u003c/p\u003e \u003cp\u003eFurthermore, we retrieved 150 marker genes representing five immune pathways including chemokine, receptor, MHC, Immunoinhibitor, and Immunostimulator from the TISIDB database [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We calculated the Spearman\u0026rsquo;s correlation between NEK6 expression and the expression of these immune regulatory genes in pan-cancer.\u003c/p\u003e \u003cp\u003eLastly, we employed the \"Immune\" module of TIMER2.0 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], integrating multiple tumor immune microenvironment estimation methods, and used the Spearman\u0026rsquo;s correlation to calculate the correlationship between NEK6 expression and the levels of 19 immune cell infiltrations in pan-cancer level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Exploring the Association between NEK6 and Clinical Characteristics in Glioma Patients\u003c/h2\u003e \u003cp\u003eIn the TCGA-LGG, CGGA-693, CGGA-325, and GSE16011 datasets, we first divided glioma patients into NEK6 high-expression and low-expression groups based on the median expression level of the NEK6. Subsequently, we compared the differences in age, gender, pathological type, grade, etc., between the two groups of patients, and conducted relevant analyses using R.\u003c/p\u003e \u003cp\u003eUsing the Kaplan-Meier method, we plotted the survival curves for the NEK6 high-expression and low-expression groups and used the R package \"survRM2\" to calculate the Restricted Mean Survival Time (RMST) ratio. Next, we included NEK6 expression groups (high and low) alone with other clinical information into a univariate Coxph. Furthermore, we employed a stepwise regression to select the best model and construct a multivariate Coxph.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Exploring the Correlation between NEK6 and Cancer-Associated Biomarkers\u003c/h2\u003e \u003cp\u003eWe utilized the R package \u0026ldquo;IOBR\u0026rdquo; to extract a set of 255 published feature genes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Subsequently, based on the sequencing data from the four datasets, we employed the Gene Set Variation Analysis (GSVA) method to calculate the GSVA scores for each sample [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Finally, for the NEK6 high-expression and low-expression groups, we used the R package \u0026ldquo;pheatmap\u0026rdquo; to create a heatmap displaying some significantly correlated biomarkers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Investigating the Potential Functions of NEK6 in Glioma\u003c/h2\u003e \u003cp\u003eIn the four datasets, we first used the R package \"limma\" to calculate the differentially expressed genes (DEGs) between the NEK6 high-expression and low-expression groups. DEGs were selected based on the criteria |log2(FC)| \u0026gt; 2 and adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eNext, using the set of DEGs, we conducted Gene Ontology (GO) analysis for Biological Process (BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using the R package \"clusterProfiler\" [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Enrichment with Fisher's test p-value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Investigation of NEK6 Expression and Cell Communication in Single Cells of Gliomas\u003c/h2\u003e \u003cp\u003eUtilizing the TISCH2 database [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], we analyzed single cell profiles of glioma based on the GSE131928 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] data and conducted an examination of NEK6 expression patterns within individual cells, as well as the cell communication between different cell clusters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Prediction of Potential Drugs Targeting Glioma Patients Based on NEK6\u003c/h2\u003e \u003cp\u003eWe employed R package \"pRRophetic\" to construct a ridge regression model for predicting drug IC50 values [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To this end, we utilized cell line gene expression profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) database and transcriptome expression data from the four databases. Furthermore, we acquired normalized RNA expression data (RNA: RNA-seq) and drug activity data (Compound activity: DTP NCI-60) from the CellMiner database [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. For drug activity data that contained missing values, we utilized the R package \"impute\" with the K-nearest neighbors (KNN) method to assess and impute the missing values. Subsequently, we computed the Spearman\u0026rsquo;s correlation coefficient between NEK6 expression and different drugs to explore potential associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Tissue microarray and Immunohistochemical (IHC) staining\u003c/h2\u003e \u003cp\u003eWe collected tissue samples from 38 cases of human low-grade gliomas and gathered corresponding patient follow-up information. This study received support from the Institutional Review Board of Zhongnan Hospital of Wuhan University (No. 2019048). We confirmed that all research was performed in accordance with relevant guidelines. And informed consent was obtained from all participants. First, we processed the tissue samples by embedding, sectioning, and Hematoxylin and Eosin (HE) staining. Subsequently, tissue microarrays were constructed. These histological microarrays were exposed in an oven at 62\u0026deg;C for 2 hours, followed by dewaxing in regular xylene and dehydration in alcohol. Subsequently, they were subjected to high-temperature (120\u0026deg;C) retrieval in EDTA buffer at a pH of 7.4 and allowed to cool naturally. During this process, to inhibit the activity of endogenous peroxidases, we used a solution of 0.3% hydrogen peroxide and methanol for 30 minutes. Next, we washed the samples three times with PBS, with each wash lasting for 10 minutes. Following this, blocking was performed at room temperature for 1 hour using goat serum, then incubated with anti-NEK6 antibodies (1:400, GeneTex, GTX13387) for a night. The following day, we added the relavant secondary antibodies and DAB solution into the microscope slide. Finally, we observed and photographed via a microscope. To obtain high-resolution images, we scanned the corresponding 40x tissue microarray images using a Hamamatsu NanoZoomer XR slide scanner. Subsequently, we employed QuPath-0.4.3 software for image recognition and calculating the positivity rate [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Based on the median positivity rate of NEK6 immunohistochemistry, we categorized patients into high and low expression groups, followed by the construction of survival curves and the performance of RMST tests to assess the impact of NEK6 on the prognosis of glioma patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Cell Culture and siRNA Transfection\u003c/h2\u003e \u003cp\u003eThe cell lines were acquired from the Cell Bank of the Chinese Academy of Science and underwent authentication and testing to ensure absence of mycoplasma contamination. The medium with Dulbecco's modified Eagle's medium (DMEM) was used to culture the U251 and U87 glioma cell lines. And the medium was enhanced with 10% FBS (Gibco, Grand Island, NY, USA). Tsingke (Wuhan, China) provided the siRNAs, and the sequences (siNEK6-1, siNEK6-2, siNEK6-3) are listed below: siNEK6-1: 5\u0026prime;- AGUUCAGGGCCUTTAUCTTTG-3\u0026prime;; siLAP2α-2: 5\u0026prime;- GCGGTUACAATTTCCACGAGT-3\u0026prime;; siLAP2α-3: 5\u0026prime;- GCAATTTUGCCAACGTTGATA-3\u0026prime;; The siRNAs were transfected into the two glioma cell lines via Lipofectamine 3000 reagent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 RNA Extraction and qRT‒PCR\u003c/h2\u003e \u003cp\u003eThe RNA extraction assay was conducted by the RNeasy mini kit (Qiagen). Afterwards, 1 \u0026micro;g RNAunderwent reverse transcription to cDNA. Next, qRT-PCR was conducted with guidelines provided by the PCR Mix manufacturer. The primer sequences were showed below: NEK6: 5\u0026prime;-CATCCCAACACGCTGTCTTTT-3\u0026prime;; 5\u0026prime;-TACACCTCGCTGAACTGTCCT-3\u0026prime;; GAPDH: 5\u0026prime;-GGAGCGAGTTCCCTCCAATTT-3\u0026prime;; 5\u0026prime;-GGCTGTTGTCATACTTCTCATGG-3\u0026prime;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 MTT Assay\u003c/h2\u003e \u003cp\u003eThe 96-well plate was used to seed the cells, with a density of 1\u0026times;10\u003csup\u003e3\u003c/sup\u003e cells per well, and they were cultured overnight. Following the specified duration of culturing, MTT was introduced into every well and allowed to incubate for 4 hours at a temperature. Next, the liquid above the sediment was removed, and 200 \u0026micro;l of DMSO was introduced into every well.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Cell Cycle and Apoptosis Assay\u003c/h2\u003e \u003cp\u003eFor cell cycle assay, the cells were treated with DNA Staining Solution and appropriate permeabilization solution. The apoptosis test was performed using the Annexin V FITC Apoptosis Assay Kit. In total, 10\u003csup\u003e6\u003c/sup\u003e cells were placed in 6-well dishes, subsequently gathered (including cells in the supernatant), and subjected to a 5-minute treatment with 5 \u0026micro;l of Annexin V-APC and 10 \u0026micro;l of 7-AAD. Shortly after, the specimen was immediately identified using a flow cytometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Wound-healing Assay\u003c/h2\u003e \u003cp\u003eThe U251 and U87 glioma cells (density 2.5 \u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/well) transfected with siRNAs were inoculated in the 6-well plate for 24 h. After that, we used a 200 \u0026micro;L pipetting head to create a scratch on the plate. The serum-free medium was then replaced, and images were captured at 0 hours and 48 hours using an inverted microscope (XDS-100, Cai Kang Optical Instrument Co, Ltd, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses and data visualizations were performed using R software v4.3.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GraphPad Prism 7 (USA). Comparisons between two groups were conducted using the Wilcoxon test, while the Kruskal-Wallis test was used for comparisons involving multiple groups. Spearman\u0026rsquo;s correlation analysis was utilized to assess correlations between variables. The correlation between NEK6 expression levels and clinical pathological characteristics of patients was evaluated using the Chi-square test. All the results were obtained from more than three independent experiments. Survival analysis was conducted using the Kaplan\u0026ndash;Meier curve, and the differences were determined using the log-rank test. To analyze differences between groups, we utilized a two-tailed t test. The statistical significance was presented in the following manner: ns indicates no statistical significance, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The Expression Profile and Immune Characteristics of NEK6 in Pan-Cancer\u003c/h2\u003e \u003cp\u003eUtilizing the GTEx and TCGA databases, we conducted a differential analysis of NEK6 expression in cancer samples and adjacent non-cancerous samples across 34 different cancer types. Our findings revealed elevated expression of NEK6 in cancer samples from various cancer types like glioblastoma multiforme (GBM), lower grade glioma (LGG), kidney renal papillary cell carcinoma (KIRP) and kidney renal clear cell carcinoma (KIRC). Conversely, NEK6 exhibited reduced expression in cancer samples of bladder urothelial carcinoma (BLCA) and kidney chromophobe (KICH) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, NEK6 expression were highest in liver, followed by basal ganglia and cerebral cortex. Given the pivotal role of immune regulation in tumorigenesis and progression, we conducted an analysis of 150 immune pathway-related genes spanning chemokine, receptor, MHC, Immunoinhibitor, and Immunostimulator categories \u003cb\u003e(Supplementary Fig.\u0026nbsp;1)\u003c/b\u003e. Notably, NEK6 demonstrated significant correlations with multiple immune regulatory genes in the context of pan-cancer. Leveraging the TIMER 2.0, we employed diverse algorithms including CIBERSORT, XCELL, EPIC, MCPCOUNTER, QUANTISEQ, and TIMER to assess the correlations between NEK6 expression and immune cell infiltration levels in pan-cancer \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Our results indicated a positive correlation between NEK6 expression and cancer-associated fibroblast infiltration in the majority of cancer types, with particular significance in GBM and LGG. Additionally, NEK6 expression was significantly correlated with immune cell infiltration levels such as memory B cells, CD4\u003csup\u003e+\u003c/sup\u003e memory T cell, cancer-associated fibroblasts, and myeloid dendritic cells, underscoring its potential role in the immune microenvironment of glioma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation Between NEK6 Expression and Prognosis in Pan-cancers\u003c/h2\u003e \u003cp\u003eIntegrating TCGA pan-cancer data with follow-up information, a forest plot depicted the prognostic relationship of NEK6 across diverse cancer types \u003cb\u003e(Fig.\u0026nbsp;2A)\u003c/b\u003e. Specifically, NEK6 high-expression correlated with poorer prognosis in 12 tumor types such as GBMLGG, LGG, and ACC, while NEK6 low-expression was associated with worse prognosis in KIRC. Notably, the pan-cancer analysis of NEK6 has highlighted its significant overexpression in glioma, which is linked to a worse prognosis. Moreover, NEK6 has been found to correlate with immune infiltration levels in glioma. Therefore, we delved deeper into the characteristics of NEK6 in the context of glioma. Chi-square analysis demonstrated that both NEK6 high-expression and low-expression groups are significantly associated with chemotherapy, IDH mutation status, and tumor grade among glioma patients. However, no significant association was observed with variables such as race or grade (p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05) \u003cb\u003e(Supplementary Table\u0026nbsp;1\u0026ndash;4)\u003c/b\u003e. Further exploration using Kaplan-Meier curves revealed that patients with NEK6 high-expression in glioma experienced a shorter overall survival period \u003cb\u003e(Fig.\u0026nbsp;2B)\u003c/b\u003e, a finding corroborated by restricted mean survival time (RMST) analysis \u003cb\u003e(Supplementary Table\u0026nbsp;5\u0026ndash;8)\u003c/b\u003e. Utilizing both the univariate Cox proportional hazards regression models (Coxph) and multivariate Coxph based on stepwise regression, we further established that the NEK6 expression level serves as an independent prognostic factor for poor outcomes among glioma patients \u003cb\u003e(Fig.\u0026nbsp;2C)\u003c/b\u003e. \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2.\u003c/b\u003e Correlation of NEK6 with the prognosis of glioblastoma. (A) Calculation of the HR values for NEK6 expression in pan-cancer with OS and DSS using CoxPH, where NEK6 is a significant risk factor for glioblastoma in both cases. (B) Kaplan-Meier curves for NEK6 high and low expression groups in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets. (C) Calculation of HR values for NEK6 high and low expression in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG using univariate and multivariate CoxPH with other clinical indicators.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3 NEK6 Demonstrates Significant Associations with Various Cancer Biomarkers\u003c/h2\u003e \u003cp\u003eBiomarkers can provide early warnings for the onset and progression of cancer, offering clinicians insights for risk stratification and targeted therapies. Therefore, leveraging a collection of 255 published feature genes, we conducted GSVA to investigate the relationship between NEK6 expression and multiple biomarkers in glioma \u003cb\u003e(Fig.\u0026nbsp;3A-B)\u003c/b\u003e. Our analysis revealed that the NEK6 high-expression group exhibited elevated levels of CD8\u003csup\u003e+\u003c/sup\u003e T effector cells, immune checkpoint molecules, macrophages, co-stimulation antigen-presenting cells (APC), antigen processing and presentation machinery, BCR signaling pathway components, chemokine receptors, cytokine receptors, natural killer cell cytotoxicity factors, TNF family members, and exosomal secretion pathways. Conversely, the levels of sirtuin nicotinamide metabolism were lower in the NEK6 high-expression group. \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 3.\u003c/b\u003e Correlation of NEK6 with tumor-related biomarkers in glioblastoma. (A) Calculation of GSVA scores for tumor-related biomarkers in samples from CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets. Heatmap representation of biomarkers strongly correlated with NEK6 expression in all four datasets, with samples divided into NEK6 high and low expression groups. (B) Spearman's correlation between NEK6 expression and GSVA scores for immune checkpoint and EMT2 in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 NEK6's Involvement in Various Biological Functions in Glioma\u003c/h2\u003e \u003cp\u003eIn order to elucidate the potential functions of NEK6 in glioma tissue, we conducted enrichment analysis based on DEGs between the NEK6 high-expression and low-expression groups. Utilizing the GO analysis for BP module, our results indicated that the NEK6 expression is associated with a range of functions, including but not limited to positive regulation of cytokine production, positive regulation of the MAPK cascade, regulation of nervous system development, activation of immune response, axon development, mononuclear cell proliferation, T cell costimulation, microglial cell activation, establishment of the endothelial barrier, regulation of T cell-mediated cytotoxicity, and glial cell activation \u003cb\u003e(Fig.\u0026nbsp;4A \u0026amp; Supplementary Table\u0026nbsp;9)\u003c/b\u003e. Furthermore, the KEGG pathway enrichment analysis demonstrated that NEK6 was enriched in multiple major categories of signaling pathways such as organismal systems, metabolism, human diseases, genetic information processing, and environmental information processing \u003cb\u003e(Fig.\u0026nbsp;4B)\u003c/b\u003e. \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 4.\u003c/b\u003e Functional enrichment analysis of NEK6 in glioblastoma. (A) Functional enrichment analysis of BP modules using GO analysis for CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets. (B) Functional enrichment analysis using KEGG database for CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets, represented in bar graphs for five categories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Single-Cell Transcriptome Analysis Unveils Elevated NEK6 Expression in Glioma\u003c/h2\u003e \u003cp\u003eTo thoroughly investigate the expression patterns of NEK6 within the cellular population of glioma tissue, we harnessed the TISCH2 database to meticulously analyze the GSE131928 single-cell dataset \u003cb\u003e(Fig.\u0026nbsp;5A)\u003c/b\u003e. The outcomes of this analysis reveal that NEK6 exhibits predominantly high expression levels in the malignant cells and Mono/Macro (Monocyte/Macrophage) populations within glioma tissue \u003cb\u003e(Fig.\u0026nbsp;5B-C)\u003c/b\u003e. Another insight is visually depicted through a heatmap that effectively illustrates the communication patterns among these distinct cell types \u003cb\u003e(Fig.\u0026nbsp;5D)\u003c/b\u003e. \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 5.\u003c/b\u003e Expression characteristics of NEK6 at the single-cell level in glioblastoma. (A) Single-cell clustering in GSE131928. (B) Expression localization of NEK6 in cell clusters in GSE131928. (C) Violin plots illustrating NEK6 expression levels in different cell clusters in GSE131928. (D) Cell communication in GSE131928.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Identification of Potential Anti-Glioma Drugs Through NEK6\u003c/h2\u003e \u003cp\u003eWe conducted a thorough drug screening by utilizing the NCI-60 cancer cell line dataset sourced from the CellMiner database. The comprehensive results obtained underscore that NEK6 expression exhibits a negative correlation with the activity levels of Barasertib, Eribulin mesilate, TAK Plk inhibitor, etc. Conversely, NEK6 expression displayed a positive correlation with JNJ-38877605, Simvastatin, GSK-2636771, etc. \u003cb\u003e(Fig.\u0026nbsp;6A)\u003c/b\u003e. In-depth analysis of the GDSC database demonstrated that NEK6 expression was negatively correlated with the IC50 values of MG-132, Paclitaxel, BI-2536, etc., while it was positively correlated with BAY 61-3606, Lisitinib, GW-2580, etc. \u003cb\u003e(Fig.\u0026nbsp;6B-C)\u003c/b\u003e. \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 6.\u003c/b\u003e Screening of anti-glioblastoma drugs based on NEK6. (A) Spearman correlation between NEK6 expression in CGGA-325, CGGA-693, GSE16011, and TCGA-LGG datasets and drug IC50 values from the GDSC database. (B) Spearman correlation between NEK6 expression and drugs from the CellMiner database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Immunohistochemical Staining of NEK6 in Glioma Tissue Microarray\u003c/h2\u003e \u003cp\u003eTo further substantiate our research findings, we collected tissue samples from 38 cases of human low-grade gliomas and gathered corresponding patient follow-up information. We performed immunohistochemical staining on these tissue samples to calculate the positivity rate of NEK6 \u003cb\u003e(Fig.\u0026nbsp;7A)\u003c/b\u003e. Subsequently, based on the median positivity rate of NEK6, we categorized patients into high NEK6 expression group and low NEK6 expression group. According to the survival curves, it was evident that patients with high NEK6 expression had significantly lower survival rates \u003cb\u003e(Fig.\u0026nbsp;7B)\u003c/b\u003e. This finding was further validated through RMST analysis \u003cb\u003e(Supplementary Table\u0026nbsp;10)\u003c/b\u003e. \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 7.\u003c/b\u003e Immunohistochemistry staining for NEK6. (A) Representative immunohistochemistry images for NEK6 in high, medium, and low expression groups. (B) Kaplan-Meier curves for NEK6 high and low expression groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.8 The Effects of NEK6 Knockdown on Proliferation, Migration, Cell Cycle and Apoptosis of GBM Cells\u003c/h2\u003e \u003cp\u003eIn order to confirm the molecular roles of NEK6 in glioma cells, we silenced the NEK6 expression in the U251 and U87 cell lines, individually \u003cb\u003e(Fig.\u0026nbsp;8A)\u003c/b\u003e. The MTT assay findings revealed that the suppression of NEK6 greatly impeded the capacity of cell proliferation in both glioma cell lines \u003cb\u003e(Fig.\u0026nbsp;8B)\u003c/b\u003e. Furthermore, the wound healing experiment demonstrated that the suppression of NEK6 could decelerate the migration speed of glioma cells \u003cb\u003e(Fig.\u0026nbsp;8C)\u003c/b\u003e. Meanwhile, we conducted apoptosis and cell cycle assays to confirm that the decrease in NEK6 caused apoptosis and cell cycle arrest in glioma cells during the G2/M phase \u003cb\u003e(Fig.\u0026nbsp;8D-E)\u003c/b\u003e. \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 8.\u003c/b\u003e The effects of NEK6 knockdown on proliferation, migration, cell cycle and apoptosis of GBM cells. (A) The mRNA level of NEK6 was weakened by si-NEK6 transfection in U251 and U87 cell lines. (B) Knockdown of NEK6 inhibited the proliferation of the U251 and U87 cell lines. (C) Wound-healing assays indicated the knockdown of NEK6 could restrain the migration of GBM cells. (D-E) The si-NEK6 induced cell apoptosis increasing, cell G2/M phase cycle arrest in U251 and U87 cell lines.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eGliomas, accounting for over 70% of malignant brain tumors in adults, pose a significant challenge in the realm of cancer biology [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Patients with low-grade gliomas have a relatively extended median survival of 11.6 years, while those with glioblastoma, even with the current standard of maximal safe resection, have a median survival of less than 1 year [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The conventional treatments such as surgical resection, temozolomide, and radiation therapy are inadequate in combating cancer progression [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. One of the major barriers is the blood-brain barrier, which limits the entry of most anti-tumor drugs into the brain [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, the immunosuppressive nature of gliomas has been well-documented, further complicating the development of anti-glioma drugs [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Thus, understanding new therapeutic targets and their relationship with the tumor microenvironment is of paramount importance for improving the prognosis of glioma patients.\u003c/p\u003e \u003cp\u003eIn this context, NEKs have gained attention. Initially recognized for their role in cell cycle regulation, the NEKs family, consisting of 11 members, has been linked to a variety of cellular functions, including centrosome organization, primary cilia function, gametogenesis, mRNA splicing, myogenesis, intercellular protein transport, mitochondrial homeostasis, and DNA damage repair [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. NEK6, in particular, plays a pivotal role in promoting mitosis during the mid to late stages of the cell cycle [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Research has suggested that NEK6 may serve as a potential target in various tumors, such as breast cancer, hepatocellular carcinoma, colorectal cancer, prostate cancer, and thyroid cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. For instance, in breast cancer, the upregulation of NEK6 has been shown to promote cell cycle progression, cancer cell proliferation, and the formation of spheroids, all of which contribute to tumor growth. Furthermore, NEK6 has been found to be positively correlated with histological grading, tumor size, and TNM staging, indicating its potential role in the pathogenesis of breast cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Besides, research has already confirmed an association between astrocytopathy and NEK6 regulation. NEK6 plays a significant role in this association as it influences the phosphorylation of the signaling protein STAT3 upon binding. Consequently, NEK6's activity is crucial for the induction of reactive astrocytic proliferation markers such as GFAP and PCNA. Importantly, aberrant regulation of NEK6 can lead to an increase in reactive astrocytic proliferation, thereby exacerbating the formation of brain lesions. Conversely, the downregulation of NEK6 is associated with a reduction in astrocyte activity and, consequently, a decrease in lesion size [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These findings not only suggest that NEK6 may hold a pivotal role in the life activities of neural cells but also imply the likelihood of NEK6's involvement in glioma, particularly in combination with its established role in many other cancers. However, despite these promising connections, there is currently no research that directly addresses the relationship between NEK6 and glioma.\u003c/p\u003e \u003cp\u003eTo fill this gap, our study conducted a comprehensive analysis. We initiated by examining NEK6 expression in various tumors through a pan-cancer analysis, assessing its prognostic implications and immune characteristics. Our findings suggested a potential link between NEK6 and the development of glioma. Subsequently, we delved deeper into this relationship by systematically analyzing NEK6 and glioma. Survival analysis substantiated a significant correlation between high NEK6 expression and poor prognosis in glioma patients. Furthermore, we explored the associations of NEK6 expression with tumor biomarkers, revealing potential links with cell cytokines, nervous system development, immune responses, and cell proliferation, among other biological processes. Single-cell analysis pinpointed elevated NEK6 expression primarily in malignant cells and Mono/Macrophages within glioma tissue. Based on the NEK6 expression, we have also identified promising anti-glioma drugs, such as spebrutinib and barasertib.\u003c/p\u003e \u003cp\u003eFor validation, we collected 38 samples from low-grade glioma patients, alongside their follow-up information. Based on this, we confirmed that high NEK6 expression was indeed associated with a worse prognosis in these patients. To gain a deeper understanding of the molecular role of NEK6 in glioma cells, we conducted experiments wherein we suppressed NEK6 expression. The results demonstrated that inhibiting NEK6 significantly hindered glioma cell proliferation, slowed down glioma cell migration, induced apoptosis in glioma cells during the G2/M phase, and caused cell cycle arrest.\u003c/p\u003e \u003cp\u003eIn previous study, NEK6 has been identified as a novel DNA damage checkpoint target. Inhibiting its activity is crucial for effective cell cycle arrest at the G2/M phase after DNA damage [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. It's worth noting that research has also suggested that abnormalities in spindle formation, chromosome segregation, cell cycle arrest, and cell death can be related to the expression of NEK6. This underscores the essential role of NEK6 in regulating the progression of the cell cycle [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In our study, we confirmed, through cellular experiments, that the downregulation of NEK6 induces apoptosis and cell cycle arrest at the G2/M phase in glioma cells. This finding aligns with previous research and emphasizes the necessity of NEK6 for glioblastoma cells to complete cell cycle replication. As a result, it becomes evident that drugs designed to inhibit NEK6 expression hold significant potential for the treatment of glioma.\u003c/p\u003e \u003cp\u003eInterestingly, previous study has suggested that the activation of NEK6 is mediated by NEK1 and Plk1, both of which are themselves activated by CDK9 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Furthermore, NEK9 has been found to directly interact with NEK6 and phosphorylate it at the Ser-206 position during the process of mitosis [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The phosphorylation of NEK6 drives the protein Eg5 to regulate spindle formation following NEK9 activation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. NEK6 and NEK7 are likely to possess very similar properties and functions, as both of them serve as downstream substrates of NEK9 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. All these findings suggest that NEK6 interacts with other members of the NEKs family in the execution of various biological processes. Therefore, investigating the roles of other NEKs in glioma not only contributes to a deeper understanding of NEK6's mechanisms in the context of glioma but also holds the potential for the discovery of new therapeutic targets for it, which serves as a potential direction for future research.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study highlights NEK6 as a promising candidate for precision therapy in glioma. Our findings reveal that NEK6 expression independently predicts the prognosis of glioma patients. Furthermore, NEK6 expression in glioma exhibits strong correlations with the immune microenvironment and multiple tumor-related biomarkers. Leveraging the insights from NEK6 expression, we have identified potential anti-glioma drugs. Furthermore, our research showcases that by downregulating NEK6, we can not only impede the migration and proliferation of glioma cells but also trigger apoptosis and induce cell cycle arrest in the G2/M phase of these cells.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the main text/Supplementary Material. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.W. and Z.W. conceived the idea, analyzed the data, and drafted the work. D.W. performed the experimental verification. Z.W., Y.C., X.L., and Z.L. collected the patient sample and participated in the revision. X.L. and Z.L. supervised the study and provided funding support. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the project of Hubei province and the Translational Medicine Research Fund of Zhongnan Hospital of Wuhan University (YYXKNL2023011, ZNJC202206), the Fundamental Research Funds for the Central Universities (2042023kf0068), and National Natural Science Foundation of China (82303580).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudies involving human participants were reviewed and approved by the Ethics Committee of the Zhongnan Hospital of Wuhan University (ethics No. 2019048). The patients provided written informed consent to participate in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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The Nek6 and Nek7 protein kinases are required for robust mitotic spindle formation and cytokinesis. Mol Cell Biol 2009, 29, 3975\u0026ndash;3990, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/MCB.01867-08\u003c/span\u003e\u003cspan address=\"10.1128/MCB.01867-08\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NEK6, glioma, cancer biomarker, tumor microenvironment, cell cycle","lastPublishedDoi":"10.21203/rs.3.rs-3754077/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3754077/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNever in mitosis gene A-related kinase 6 (NEK6) is involved in mitotic cell cycle. However, the characteristics and roles of NEK6 in pan-cancer remain incomplete. The objective of the present study is to comprehensively explore the prognostic value of NEK6 and its potential functions in multiple cancers, especially in gliomas. In this study, we conducted of comprehensive analyses of NEK6 in pan-cancer, including expression profile, immune characteristics and its relationship with clinical prognosis. We found that NEK6 was significantly upregulated in gliomas. And the increased level of NEK6 was significantly associated with poor clinical prognoses of tumor patients. Moreover, the single-cell analysis revealed that NEK6 overexpression was highly related to malignant cells and Mono/Macrophages in glioma tissue. spebrutinib and barasertib were identified to be targeted therapeutic drugs for gliomas. Then, the prognostic role of NEK6 was further validated using an independent glioma cohort, and confirmed that the highly expression of NEK6 in glioma was positively correlated with poor prognosis in patients with glioma. In vitro experiment demonstrated that knockdown of NEK6 hindered the growth and migration capacity of the glioma cells, leading to a halt in the G2/M phase of the cell cycle and triggering apoptosis in glioma cell lines. Taken together, our data uncovered the prognostic value, therapeutic potential, and molecular insight of NEK6 in glioma.\u003c/p\u003e","manuscriptTitle":"Identification of NEK6 as a potential biomarker for prognosis in glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 20:14:30","doi":"10.21203/rs.3.rs-3754077/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":"745b9244-10bd-480b-b524-99ea92322ab1","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":27877608,"name":"Biological sciences/Cancer/Cancer genetics"},{"id":27877609,"name":"Biological sciences/Cancer/Cancer microenvironment"},{"id":27877610,"name":"Biological sciences/Cancer/Cns cancer"},{"id":27877611,"name":"Biological sciences/Cancer/Tumour biomarkers"}],"tags":[],"updatedAt":"2025-02-20T05:24:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-02 20:14:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3754077","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3754077","identity":"rs-3754077","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}