A Novel Discovery of CXCL5 in Prognosis Prediction and Targeted Therapy of Glioblastomas

A Novel Discovery of CXCL5 in Prognosis Prediction and Targeted Therapy of Glioblastomas | 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 A Novel Discovery of CXCL5 in Prognosis Prediction and Targeted Therapy of Glioblastomas Hui Li, Han Lu, Jianxin Xi, Zhishan Du, Bo Wu, Jiaxin Ren, Wenzhuo Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4738447/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 Glioblastoma (GBM) patients face a grim prognosis, with many treatments failing to achieve significant improvements. Recent research has focused on the immunosuppressive environment within GBM tumors. One particular protein, C-X-C chemokine ligand 5 (CXCL5), is highly expressed in various cancers and is known to affect the immune environment, tumor invasion, metastasis, and overall prognosis. In our study, we investigated the role of CXCL5 in the immunosuppressive environment of GBM. We aimed to develop a CXCL5-associated immune prognostic signature (IPS) to predict patient outcomes and identify potential treatments targeting the CXCL5/CXCR2 axis. Initially, we performed enzyme-linked immunosorbent assays (ELISA) on 80 high-grade glioma samples to measure CXCL5 levels. We also analyzed RNA-seq data from 169 GBM samples obtained from the TCGA dataset, dividing them into high (CXCL5_H) and low (CXCL5_L) CXCL5 expression groups. Our analysis revealed that the CXCL5_H group had higher expression of immune-related genes but a poorer prognosis compared to the CXCL5_L group. Using the least absolute shrinkage and selection operator (LASSO) Cox analysis, we constructed a CXCL5-associated IPS, which we confirmed as an independent prognostic factor for GBM through univariate and multivariate Cox analyses. We developed a nomogram based on the three-gene IPS to predict overall survival in GBM patients. Moreover, our study identified the CXCL5/CXCR2 axis as a promising target for GBM treatment. We employed computational techniques to screen for potential inhibitors of this axis and validated their effectiveness in vitro. In conclusion, our study provides a new prognostic model and suggests targeted therapeutic options for GBM by elucidating the role of CXCL5 in the tumor's immunosuppressive environment. This work may pave the way for improved patient outcomes and more effective treatments for this challenging cancer. Glioblastoma (GBM) CXCL5/CXCR2 immune Prognostic Signature (IPS) inhibitors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by a high recurrence rate and poor prognosis. Despite advanced treatments, including surgical resection, temozolomide chemotherapy, and chemoradiotherapy, GBM often recurs, sometimes in distant organs. Research has increasingly focused on the immunosuppressive microenvironment of GBM, highlighting the need to predict patient prognosis from an immunological perspective and develop more effective treatments. Immunotherapy has shown significant antitumor activity in other cancers such as melanoma, non-small cell lung cancer, renal cancer, and prostate cancer by restoring the body's immune response against tumors. Recent studies have identified immune cells in the central nervous system, challenging the notion of the brain as an immune-privileged site [ 1 – 3 ]. However, GBM's specific immunosuppressive environment, characterized by various immunosuppressive factors like IL-1, TGF-β, CD70, CD95, CSF-1, and PD-L1, hampers the efficacy of immunotherapy [ 4 – 8 ]. Clinical trials targeting these factors have yielded poor results, underscoring the importance of understanding GBM's immunosuppressive mechanisms and identifying new immunosuppressive targets. C-X-C motif chemokine ligand 5 (CXCL5) is a chemokine that binds to the interleukin 8B (IL-8B) receptor known as C-X-C motif chemokine receptor 2 (CXCR2) [ 9 ]. In various cancers, including rhabdomyosarcoma, nasopharyngeal carcinoma, breast cancer, bladder cancer, and papillary thyroid carcinoma, the CXCL5/CXCR2 axis promotes tumor cell migration and invasion [ 10 – 12 ]. Chemokines, like CXCL5, as significant components of the tumor microenvironment (TME), play crucial roles in biological processes, including immune cell recruitment, angiogenesis, tumor growth, and metastasis [ 13 – 18 ]. Overexpression of CXCL5 is linked to poor survival, recurrence, and metastasis in cancer patients [ 19 , 20 ]. In glioma, CXCL5 is upregulated compared to normal brain tissue, but its role in the GBM immunosuppressive microenvironment remains unexplored [ 21 ]. In our study, we conducted an enzyme-linked immunosorbent assay (ELISA) for CXCL5 on tissue samples from 72 high-grade glioma patients and analyzed their survival times. We also analyzed RNA-seq data of GBM samples from the TCGA dataset, categorizing them into CXCL5_H and CXCL5_L groups based on CXCL5 expression levels. We then performed immunogenetic and prognostic analyses between these groups and identified differentially expressed immune genes (DEIGs). Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we constructed an immune prognostic signature (IPS) and developed a predictive nomogram model to estimate overall survival (OS) for GBM patients. Our study also identified the CXCL5/CXCR2 axis as a potential therapeutic target, leading to the screening and evaluating potential inhibitors for this axis. In summary, our research elucidates the role of CXCL5 in the immunosuppressive microenvironment of GBM, provides a novel IPS and nomogram for prognosis prediction, and promotes the development of inhibitors targeting the CXCL5/CXCR2 axis for GBM treatment. Results Expression of CXCL5 correlates with prognosis We conducted an enzyme-linked immunosorbent assay (ELISA) to measure CXCL5 levels in tissue samples from 80 high-grade glioma patients. Subsequently, we analyzed the patients' survival times. Figure 1 A-C illustrates the clinical characteristics of patients in the CXCL5_H and CXCL5_L groups, including the distribution by number, gender, and age. Based on CXCL5 protein expression levels, the patients were categorized into two groups: CXCL5_H (n = 40) and CXCL5_L (n = 40) (Fig. 1 D). Our survival analysis revealed a significant correlation between high CXCL5 protein levels and poor overall survival (OS) (P = 0.011) (Fig. 1 E). This finding suggests that elevated CXCL5 expression may serve as a prognostic marker for reduced survival in high-grade glioma patients. This study highlights the potential role of CXCL5 in glioma progression and its value as a prognostic indicator. Further research is warranted to explore the mechanisms by which CXCL5 influences tumor behavior and to evaluate its potential as a therapeutic target. Comparative immunogenomic analysis between CXCL5_L and CXCL5_H We obtained RNA-seq data of GBM samples from the TCGA dataset and categorized them into CXCL5_H and CXCL5_L groups based on CXCL5 expression levels. Immunogenetic and prognostic analyses were conducted between these groups, revealing no significant differences in age and sex (age: Mann-Whitney test, P > 0.05; sex: chi-squared test, P > 0.05). Clinical details and their association with CXCL5 protein levels are summarized in Supplementary Table 1. We examined 29 sets of immune-associated genes representing various functions, pathways, and immune cell types (Supplementary Table 2). Using single-sample gene set enrichment analysis (ssGSEA), we found that the CXCL5_H group was significantly enriched for immune cell types, functions, and pathways (Fig. 2 A). In the HLA gene analysis, CXCL5_H showed higher expression than CXCL5_L, except for HLA-DOB (ANOVA test, P < 0.05) (Fig. 2 B). Estimation results indicated that the CXCL5_H group had higher estimation, stromal, and immune scores compared to the CXCL5_L group (Kruskal-Wallis test, P < 0.001 for all) (Fig. 2 D). Conversely, tumor purity was higher in the CXCL5_L group (Kruskal-Wallis test, P < 0.001) (Fig. 2 D). Our analysis of Tim-3 and PD-1, markers of T lymphocyte exhaustion, showed higher expression in the CXCL5_H group, indicating more significant lymphocyte depletion (Fig. 2 E) [ 22 ]. Additionally, T cell-associated negative immunomodulatory factors such as CD274 (PD-L1), PD-1 (PDCD1), ICOS, TIM3, IDO1, and LAG3 were more highly expressed in the CXCL5_H group (ANOVA test, P < 0.05), suggesting a more immunosuppressive microenvironment (Fig. 2 E) [ 23 ]. CXCL5 expression was also positively correlated with immune infiltration of regulatory T cells (Tregs_XCELL: P < 0.05, Tregs_CIBERSORT-ABS: P < 0.05) (Supplementary Fig. 1A, 1B). Moreover, the overall survival rate was better in the CXCL5_L group compared to the CXCL5_H group, consistent with the ELISA results (Fig. 2 C). These findings suggest that CXCL5 contributes to an immunosuppressive microenvironment in GBM, which may lead to suboptimal responses to immunotherapy and poorer prognosis for GBM patients. Identifying differentially expressed immune genes (DEIGs) We identified 3,755 differentially expressed immune genes (DEIGs) using the criteria of FDR < 0.05 and |log2 fold change (FC)| ≥ 1. Of these, 2,378 genes were up-regulated, and 1,377 were down-regulated (Fig. 3 A). To explore the functional implications of these DEIGs, we used the DAVID website to identify Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (Fig. 3 B). The DEIGs were significantly enriched in pathways and processes such as myeloid leukocyte activation, Interleukin-10 signaling, extracellular matrix organization, regulation of inflammatory response, blood vessel development, regulation of cell adhesion, negative regulation of cell proliferation, acute inflammatory response, adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin, mononuclear cell migration, regulation of cytokine production, NF-kappa B signaling pathway, and complement and coagulation cascades. We selected a subset and visualized it as a network diagram to better understand the relationships between these enriched terms (Fig. 3 C, 3 D). This network highlights the complex interactions and regulatory mechanisms involving these DEIGs, providing insights into the role of CXCL5 in the immunosuppressive microenvironment of GBM. Establishment and validation of an immunoprognostic signature (IPS) in association with CXCL5 We constructed the Immune Prognostic Score (IPS) for the training set using LASSO Cox regression analysis (Fig. 4 A-C). Before applying the Cox regression model, we ensured that the proportional hazards (PH) assumption was met. The IPS included in the Cox PH model adhered to this assumption. Risk scores for each sample were calculated using the formula: risk score = MIR222HG*0.42 + TGM2*0.052 - ZNF560*0.182. Based on the optimal cutoff value determined by the Survminer R package (0.7179717), GBM patients in the training set were classified into low-risk and high-risk groups. Kaplan-Meier analysis demonstrated that patients with high risk scores had significantly worse prognoses than those with low risk scores (Fig. 4 D). In the training set, the ROC (receiver operating characteristic) curve analysis for IPS yielded an AUC (area under the curve) value of 0.742, indicating good predictive power for overall survival (OS) (Fig. 4 E). The distribution of risk scores and gene expression patterns for the training set is depicted in Fig. 4 H. To validate the prognostic value of IPS, we applied the same formula to the test set. Patients in the test set were similarly divided into high-risk and low-risk groups based on the corresponding cutoff values. The results mirrored those of the training set, with high risk scores correlating with poorer OS (Fig. 4 F). The distribution of risk scores and gene expression patterns in the test set is shown in Fig. 4 I. The AUC value for the ROC curve analysis in the test set was 0.751, demonstrating that the IPS possesses high sensitivity and specificity with substantial predictive value (Fig. 4 G). Development of a three-gene IPS-based nomogram model Univariate and multivariate Cox analyses identified the Immune Prognostic Score (IPS) as an independent prognostic factor (multivariate Cox analysis: HR = 2.044, 95% CI: 1.377–3.035, P < 0.001; univariate Cox analysis: HR = 2.064, 95% CI: 1.393–3.060, P < 0.001) (Figs. 5 A, 5 B). Based on the three-gene IPS, we developed a nomogram model (Fig. 5 C). ROC curve analysis of this nomogram showed AUC values of 0.74, 0.71, and 0.89 for 1, 2, and 3 years overall survival (OS), respectively, indicating high sensitivity, specificity, and predictive value (Fig. 5 D). The nomogram model demonstrated robust specificity discrimination with a C-index of 0.684. Calibration plots of observed versus predicted probabilities for 1, 2, and 3 years OS showed strong agreement (Fig. 5 E). These findings underscore the utility of the IPS as a prognostic tool in GBM, providing valuable insights for patient stratification and treatment planning. Virtual screening of potential CXCR2 inhibitors We identified two binding pockets on CXCR2 as reference docking regions for virtual screening. The first binding site was located at the N-terminal alpha helix of CXCR2, where the interacting amino acid residues between CXCR2-CXCL5 and CXCR2-anti-CXCR2 overlapped. This overlap region was used to create a binding sphere designated as site 1 for drug screening (Fig. 6 ). The second binding site, site 2, was selected based on the intracellular xenobiotic binding domain formed by TM1, TM2, TM3, TM6, and the cytoplasmic portion of CXCR2 in intracellular loop 1 (ICL1), known to bind antagonists [ 24 ]. We downloaded 17,799 natural, named, and available molecules from the ZINC15 database and performed Libdock docking on the two identified CXCR2 sites. The higher the Libdock score, the more stable the binding between the small molecule and the protein. These docking studies aim to identify potential inhibitors targeting CXCL5-CXCR2 interactions, which may provide new therapeutic approaches for GBM. Prediction of drug candidates' toxicological and pharmacological properties The pharmacological and toxicological properties of the top 20 molecules, as determined by their Libdock scores, were analyzed using the DS 4.5 ADME and TOPKAT modules. The ADME module evaluated brain-blood barrier (BBB) permeability, water solubility, human intestinal absorption, cytochrome P450 2D6 (CYP2D6) binding, hepatotoxicity, and plasma protein binding (PPB) properties of these compounds (Supplementary Table 4). Meanwhile, the TOPKAT module assessed rodent carcinogenicity (based on the National Toxicology Program dataset), developmental toxicity potential (DTP), and Ames mutagenicity to confirm their safety (Supplementary Table 5). Considering these results, ZINC000000537805 and ZINC000053147179 were selected as ideal compounds for CXCR2 sites 1 and 2. Both compounds are not CYP2D6 inhibitors and exhibit low carcinogenicity and mutagenicity in rodent models, making them promising candidates for further development. These findings contribute to the potential therapeutic targeting of CXCL5-CXCR2 interactions in GBM, providing a foundation for future drug development efforts. Binding interactions, pharmacophore analysis, and molecular dynamics simulations between selected compounds and CXCR2 The CDOCKER interaction energies of ZINC000053147179 and ZINC000000537805 with CXCR2 were − 51.7992 kcal/mol and − 34.7995 kcal/mol, respectively, indicating strong affinities for CXCR2. All docked conformations were visualized using PyMol and Schrodinger (Fig. 7 , Tables 1 and 2 ). ZINC000053147179 formed five hydrogen bonds with CXCR2, while ZINC000000537805 formed three. Table 1 CDOCKER interaction energy of compounds with CXCL5. Compound CDOCKER interaction energy (kcal/mol) ZINC000000537805 -34.7995 ZINC000053147179 -51.7992 Table 2 Hydrogen bond interaction parameters for each compound with CXCR2 residues. Receptor Compound Donor atom Receptor atom Distances (Å) CXCR2 ZINC000000537805 A:LYS16:HZ3 ZINC000000537805:O21 2.1 A:ASP13:OD1 ZINC000000537805:H40 2.2 A:GLU12:O ZINC000000537805:H48 1.8 ZINC000053147179 A:LYS320:HZ2 ZINC000053147179:O15 2.4 A:ASP84:OD1 ZINC000053147179:H44 2.0 A:ASP84:OD1 ZINC000053147179:H45 2.2 A:ASP84:OD2 ZINC000053147179:H48 1.9 A:SER76:OG ZINC000053147179:H61 2.2 Pharmacological analyses of these compounds revealed that ZINC000000537805 and ZINC000053147179 had 25 and 35 characteristic pharmacophores, respectively (Figs. 8 A, 8 B). ZINC000000537805 featured 14 hydrogen acceptors, 2 hydrogen donors, 4 hydrophobic centers, 4 aromatic rings, and 1 ionizable positive group. ZINC000053147179 had 12 hydrogen acceptors, 13 hydrogen donors, 3 hydrophobic centers, 6 aromatic rings, and 1 ionizable positive group. Molecular dynamics simulations showed that the RMSD and potential energy of the ligand-CXCR2 complex remained stable over time (Figs. 8 C, 8 D). ZINC000000537805, also known as glibenclamide, is a sulfonylurea hypoglycemic agent indicated for type 2 diabetes. Beyond its metabolic effects, glibenclamide has anti-inflammatory properties through NLRP3 inhibition [ 25 , 26 ]. Additionally, growing evidence suggests that glibenclamide exerts antitumor effects by inhibiting the KATP channel or ATP-binding cassette superfamily proteins [ 27 – 29 ]. Therefore, we selected glibenclamide for further experimental validation. These findings highlight the potential of glibenclamide and ZINC000053147179 as therapeutic agents targeting CXCL5-CXCR2 interactions in GBM, warranting further investigation. Verifying the effects of the selected drugs through in vitro experiments The Cell Counting Kit-8 (CCK8) assay was used to assess the effect of glibenclamide on the proliferation of U-373 MG cells. The results demonstrated that glibenclamide inhibited cell proliferation after 48 hours, with the inhibition rate increasing in a dose-dependent manner (Fig. 9 A). Additionally, the scratch assay revealed that the migration width ratio of cells treated with glibenclamide was significantly smaller compared to the control group. This inhibitory effect on cell migration was more pronounced at higher drug concentrations (Fig. 9 B). These findings suggest that glibenclamide effectively inhibits the proliferation and migration of U-373 MG cells, indicating its potential as a therapeutic agent in GBM. Methods and materials Collection of clinical specimens and enzyme-linked immunosorbent assay (ELISA) Eighty specimens of WHO grade IV glioblastoma were collected from the pathology archive of Sun Yat-sen University Cancer Centre (SYSUCC) between January 2010 and June 2020. These specimens were used to perform an enzyme-linked immunosorbent assay (ELISA) to assess the clinical and prognostic significance of CXCL5 expression. Clinicopathological data and follow-up information were obtained from medical records under the approval of the Ethics Committee of Sun Yat-sen University Cancer Centre, with written informed consent obtained from all patients. All methods were performed in accordance with the relevant guidelines and regulations. Acquisition and processing of gene expression data RNA-seq data from The Cancer Genome Atlas (TCGA) repository were downloaded for 169 glioblastoma (GBM) patients with survival data available. Patients were classified into CXCL5 high (CXCL5_H, n = 85) and CXCL5 low (CXCL5_L, n = 84) groups based on their CXCL5 expression levels. Gene expression quantification utilized FPKM values, which were log2-transformed for subsequent analysis of RNA transcriptome maps. Comparative immunogenomic analysis between CXCL5_H and CXCL5_L Twenty-nine immunomarkers relevant to GBM were selected for analysis. Single-sample gene set enrichment analysis (ssGSEA) scores were used to quantify the enrichment levels of these immunomarkers across GBM patient samples [ 30 , 31 ]. Additionally, ESTIMATE was employed to evaluate immune cell infiltration, stromal content, and tumor purity [ 32 ]. The expression profiles of HLA genes, markers of T lymphocyte exhaustion, and negative immune regulators associated with T cells were compared between CXCL5_H and CXCL5_L groups using ANOVA. Prognostic analysis Survival analysis using the log-rank test with a significance threshold of P < 0.05 was conducted to assess differences in overall survival (OS) between CXCL5_H and CXCL5_L groups. Kaplan-Meier curves were plotted to visualize survival outcomes. Analysis of the differential expression Differentially expressed genes between CXCL5_H and CXCL5_L were processed and performed using the Limma package of RStudio software and the Wilcoxon Rank Sum and Signed Rank test [ 33 ]. Genes with adjusted P values 1 were selected as cut-off points for analysis. Differentially expressed immune genes (DEIGs) were identified using the ImmPort database to facilitate further studies. Analyzing functional enrichment To analyze DEIGs for gene ontology and signaling pathway enrichment, we uploaded DEIGs to the online tools: David (Database for Annotation, Visualization, and Integrated Discovery) and Metascape, an online site that provides gene annotation, visualization, and gene properties online [ 34 – 36 ]. GO analyses included classical pathways (biological processes), extracellular structural domains (cellular composition), antigen binding (molecular function), complement activation and cytokine-cytokine receptor KEGG analysis. In addition, we constructed a network graph with a similarity of > 0.3. They are connected by edges. The terms with the best p-value were selected from each cluster to form a total of 250 terms, with a maximum of 15 terms per cluster. Building and validating the immune prognostic signature (IPS) The LASSO-Cox analysis is widely used for high-dimensional predictive regression [ 37 ]. It allows the selection of the best penalty parameter lambda by cross-validation for shrinkage and variable identification, thus preventing over-fitting [ 38 ]. All DEIGs were entered into the LASSO Cox analysis to screen out genes with prognostic impact as candidates for IPS construction. We then randomly divided the TCGA dataset into two groups, a training group, and a validation group, and then entered data from both groups into the LASSO Cox analysis to calculate a risk score for each patient to construct the IPS. Optimal critical values were obtained, and patients were classified as low and high risk according to the analysis of the "glmnet" R package [ 39 , 40 ]. Sensitivity and specificity were then assessed using the "Survival ROC" R package [ 41 ], and ROC curves were plotted. Area under the curve (AUC) values were also calculated. The ability of the IPS prediction set was further validated. Developing the Nomogram The independent prognostic ability of IPS was assessed by univariate and multivariate Cox analyses. Based on the results of the Cox analysis, an innovative nomogram was developed using the RMS software package. The ROC package "Survival ROC" was then used to assess sensitivity and specificity [ 41 ], and the ROC receiver operating characteristic curve was plotted. Area under the curve (AUC) values were also calculated. A calibration plot was made to assess the accuracy of the nomogram based on the observed and predicted probabilities for 1, 2, and 3-year OS. The discriminatory ability of the model was evaluated using the coordination index (C-index). In addition, the C-index was modified by calculating the bootstrap number [ 42 ]. Virtual filtering with Libdock The Libdock module analyses the conformations of small molecules and receptors and their interaction hotspots and tightly binds the matching conformation to the binding pocket of the receptor. A total of two CXCR2 binding pockets were selected as reference docking regions for the virtual screen. We downloaded the Fab sequences of human CXCR2 (code P25025) and CXCL5 (code 2MGS) from the AlphaFold protein structure database and the CXCR2 antibody abN48 (code 6KVF) from the PDB database. Based on the CXCR2-CXCL5, CXCR2-anti-CXCR2 protein interactions shown by HADDOCK 2.4 and PyMol (Fig. 5 ), the amino acids of the N-terminal alpha helix of CXCR2 were selected as the site for the extracellular drug screen.1 In addition, an intracellular allosteric binding domain formed by TM1, TM2, TM3, TM6, and the cytoplasmic portion of intracellular loop 1 (ICL1) of CXCR2 has been reported to bind antagonists and was therefore selected as site 2 for drug screening [ 24 ]. In addition, 17,799 natural, named, and purchasable molecular files were downloaded from the ZINC15 database for the virtual screening of CXCR2 inhibitors. Finally, all ligands docked to CXCR2 at different positions were ranked according to their Libdock scores. The 20 compounds with the highest Libdock scores were also individually selected for further analysis. Predicting toxicological and pharmacological properties The pharmacological properties of the selected molecules were calculated using the DS 4.5 ADME module, including water solubility, cytochrome P450 2D6 (CYP2D6) inhibition, plasma protein binding (PPB) levels, blood-brain barrier (BBB) permeation, hepatotoxicity, and human intestinal absorption. In addition, DS 4.5's TOPKAT (Toxicity Prediction by Computer Assisted Technology) module plays a vital role in assessing the toxicity of all potential compounds, including rodent carcinogenicity (based on the National Toxicology Program dataset), developmental toxicity potential (DTP) and Ames mutagenesis (Ames). The TOPKAT module quickly and accurately calculates and validates the toxicity and environmental impact of compounds based on 2D molecular structures. A series of robust, cross-validated Quantitative Structure-Toxicity Relationship (QSTR) models were used to evaluate different toxicity predictions. Finally, all of the above pharmacological and toxicological properties were considered in selecting CXCR2 drug candidates. Binding interactions, pharmacophore analysis, and molecular dynamics simulations between selected compounds and CXCR2 The CDOCKER module is based on the CHARMm36 force field. It is used for precise docking between the ligand and CXCR2. During docking, the receptor remains rigid, while the ligand can be flexible. The interaction energy, which reflects the binding affinity of the ligand, is also calculated in the pose of each complex. For each ligand, 10 docking poses were generated, and the optimal one was selected based on suitable docking orientation and high docking percentage [ 43 , 44 ]. The CDOCKER interaction energies were also evaluated separately for each of the different poses of each tested molecule. Furthermore, the CDOCKER docking results were visualized using Schrodinger and PyMol. The pharmacology of the selected molecules was also analyzed using the 3D-QSAR pharmacology module of DS 4.5. In addition, the molecular dynamics simulation module was used to assess the stability of the ligand-CXCR2 complexes in their natural environment. The RMSD and potential energy of these complexes were evaluated. Combining these results, a drug was selected for further experimental validation. The Cell Counting Kit-8 (CCK8) and scratch assays to verify the effect of the selected drug Human glioma cells U-373 MG were cultured in vitro and divided into control and experimental groups. Different concentrations of glibenclamide were added to the cells in the experimental group for 48 h. The concentrations were 10, 20, 50, 100, 200, 500, 1000 and 1500 µmol/L. The control group was operated in the same way as the experimental group, except that glibenclamide was not added. The cell activity of each group was measured by CCK8, and the migration and invasion of cells were detected by the scratch method. Discussion Patients with glioblastoma (GBM) face a poor prognosis, and many treatments fail to achieve the desired outcomes. CXCL5 has been shown to play a significant role in immune cell recruitment, angiogenesis, tumor growth, and metastasis within the tumor microenvironment (TME) [ 15 – 18 ]. Overexpression of CXCL5 is closely associated with poorer survival, recurrence, and metastasis in cancer patients [ 19 , 20 ]. In gliomas, CXCL5 is upregulated compared to normal brain tissue[ 21 ]. Despite the attention to the immunosuppressive microenvironment of GBM, the relationship between CXCL5 and this environment has not been explored.[ 21 ]. Despite the attention to the immunosuppressive microenvironment of GBM, the relationship between CXCL5 and this environment has not been explored. In our study, we measured CXCL5 levels using ELISA in tissue samples from 80 high-grade glioma patients and analyzed their survival data. Patients with higher CXCL5 protein expression had poorer overall survival (OS) (p = 0.011) (Fig. 1 E). We then analyzed RNA-seq data of GBM samples from the TCGA dataset, dividing them into CXCL5_H and CXCL5_L groups based on CXCL5 expression. Using single-sample gene set enrichment analysis (ssGSEA), we found that CXCL5_H samples had higher enrichment of immune cells, functions, and pathways than CXCL5_L samples. Studies indicate that immunocompetent patients with various tumors, including triple-negative breast cancer, have better survival rates than immunosuppressed patients [ 45 ]. Our analysis showed that most HLA genes were expressed at higher levels in CXCL5_H than in CXCL5_L. HLA plays a crucial role in presenting tumor-associated antigens and the anti-tumor immune response, with its expression correlated with tumor type and grade [ 46 ]. In gliomas, patients with low HLA expression benefit more from immunotherapy [ 47 ]. This suggests that patients with lower CXCL5 expression might be more suitable for immunotherapy and have a better prognosis. Previous studies have shown that GBM patients often resist immunotherapy, with no survival advantage in recurrent cases [ 48 , 49 ]. Therefore, classifying GBM patients based on CXCL5 expression could enable more personalized and precise treatments. We also found higher levels of markers for T lymphocyte depletion, such as Tim-3 and PD-1, in CXCL5_H samples. Despite high immune activity, immune cells in CXCL5_H are often hypofunctional and highly depleted. T cell-associated negative immunomodulatory factors like CD274 (PDL1), PD-1 (PDCD1), ICOS, LAG3, TIM3, and IDO1 were also expressed at higher levels in CXCL5_H, indicating a more immunosuppressive microenvironment [ 23 ]. Additionally, CXCL5 expression correlated positively with Treg cell infiltration. CXCL5 has been reported to promote the recruitment of myeloid-derived suppressor cells (MDSCs) via the CXCL5/CXCR2 axis, contributing to cancer progression in breast and prostate cancers [ 50 , 51 ]. In GBM, MDSCs suppress immune responses, inhibit cytotoxic T-cell activity, and impair NK cell, macrophage, and dendritic cell functions [ 52 – 56 ]. The presence of MDSCs correlates with a poor prognosis in GBM patients [ 2 ]. Consistent with our ELISA results, patients with lower CXCL5 expression had better OS (p < 0.05). We speculate that CXCL5 enhances the immunosuppressive microenvironment of GBM, leading to suboptimal immunotherapy outcomes and poor prognosis. We also found higher levels of markers for T lymphocyte depletion, such as Tim-3 and PD-1, in CXCL5_H samples. Despite high immune activity, immune cells in CXCL5_H are often hypofunctional and highly depleted. T cell-associated negative immunomodulatory factors like CD274 (PDL1), PD-1 (PDCD1), ICOS, LAG3, TIM3, and IDO1 were also expressed at higher levels in CXCL5_H, indicating a more immunosuppressive microenvironment [ 23 ]. Additionally, CXCL5 expression correlated positively with Treg cell infiltration. CXCL5 has been reported to promote the recruitment of myeloid-derived suppressor cells (MDSCs) via the CXCL5/CXCR2 axis, contributing to cancer progression in breast and prostate cancers [ 50 , 51 ]. In GBM, MDSCs suppress immune responses, inhibit cytotoxic T-cell activity, and impair NK cell, macrophage, and dendritic cell functions [ 52 ][ 53 ][ 54 ][ 55 , 56 ]. The presence of MDSCs correlates with a poor prognosis in GBM patients [ 2 ]. Consistent with our ELISA results, patients with lower CXCL5 expression had better OS (p < 0.05). We speculate that CXCL5 enhances the immunosuppressive microenvironment of GBM, leading to suboptimal immunotherapy outcomes and poor prognosis. We further analyzed differentially expressed genes (DEGs) between CXCL5_H and CXCL5_L, identifying 3755 DEGs. Gene ontology (GO) and KEGG analysis showed significant enrichment of DEGs in immune-related pathways, including immune cell regulation, cytokines, complement, immunoglobulins, cell adhesion, proliferation, and vascular development. This suggests CXCL5's vital role in GBM's immune microenvironment and cell proliferation, invasion, and metastasis. Interestingly, DEGs were enriched in bone marrow leukocyte activation, indicating that CXCL5 may facilitate MDSC recruitment to GBM sites. We developed a CXCL5-associated immune prognostic signature (IPS) with AUC values of 0.742 and 0.751 in training and validation sets, respectively. LASSO Cox regression identified ZNF560, TGM2, and MIR222HG (miR222/221 cluster host gene) as essential genes in our IPS. Dysregulation of miRNAs, including miRNA-221/222, is a hallmark of cancer associated with glioma prognosis [ 59 ][ 60 ]. TGM2 is highly expressed in several malignancies and promotes tumor cell proliferation and metastasis [ 61 ][ 62 ]. Although TGM2's effects on glioma are not well-documented, ZNF560 has been implicated in colorectal cancer studies [ 63 ]. Therefore, these genes represent potential prognostic and therapeutic markers for GBM. Our IPS was an independent prognostic factor in univariate and multivariate Cox analyses. We developed a nomogram based on the three-gene IPS for predicting 1-, 2-, and 3-year survival in GBM patients, offering a new perspective for prognosis prediction. Moreover, the CXCL5/CXCR2 pathway is a promising target for GBM treatment. Currently, there are no drugs specifically targeting this axis for GBM, and existing CXCR2 inhibitors like reparixin and antileukin are either limited to basic research or not specific. Therefore, drug screens targeting the CXCL5/CXCR2 axis are crucial for developing new GBM treatments and improving patient prognosis. Using computational techniques, we screened potential CXCR2 inhibitors and identified ZINC000000537805 and ZINC000053147179 as potent and safe candidates. ZINC000000537805 (glibenclamide) has shown efficacy in reducing the risk and progression of various cancers [ 27 – 29 , 64 – 67 ], though its effect on glioma's immune microenvironment is unstudied. Our in vitro experiments demonstrated that glibenclamide inhibits glioma cell growth, migration, and invasion, offering a potential new treatment for GBM. In conclusion, this study highlights CXCL5's role in GBM's immunosuppressive microenvironment, provides a novel IPS and nomogram for predicting GBM prognosis, and facilitates the development of CXCL5/CXCR2 axis inhibitors for GBM treatment. Conclusions In this study, we demonstrated that CXCL5 significantly enhances the immunosuppressive microenvironment of GBM, leading to poor immunotherapy outcomes and prognosis for patients. We established a CXCL5-associated immune prognostic signature (IPS), enabling stratification of patients into high-risk or low-risk groups. This IPS was also used to create a new nomogram for predicting GBM patient prognosis. Furthermore, we identified the potential of CXCL5 as a therapeutic target for GBM aimed at modulating the immunosuppressive microenvironment. Through a virtual drug screen and subsequent experiments, we discovered therapeutic agents targeting the CXCL5/CXCR2 axis. These findings offer new insights into GBM treatment strategies and prognostic assessment. Declarations Acknowledgments This work was supported by the National Natural Science Foundation of China (grant number 81872324, 82203219), Guangdong Basic and Applied Basic Research Foundation (2020A1515110203), Fundamental and Applied Fundamental Research Fund of Guangdong Province (2021A1515111198) and Beijing New-star Plan of Science and Technology (Z201100006820148 to YC). Funders had no role in study design, data collection and analysis, publication decisions, or manuscript preparation. Author Contribution statement This study is a team effort. Each author has made substantial contributions to the study. Hui Li carried out the design of the work. Han Lu was responsible for creating new software used in the work. Jianxin Xi carried out the experiments and data collection. Zhishan Du contributed significantly to the interpretation of the data. Bo Wu drafted the paper. Wenzhuo Yang and Jiaxin Ren created the enzyme-linked immunosorbent assay (ELISA). Sheng Zhong developed the concept and made substantial revisions. Competing Interest Statement All authors declare no competing interests related to this manuscript and have agreed to publish. Data Availability 1.The RNA-seq data supporting this research article are from previously reported studies and datasets, which have been cited. The processed data are available from the TCGA website (https://portal.gdc.cancer.gov/repository). 2.The 80 high-grade glioma specimens data used to support the findings of this study are restricted by the Ethics Committee of Sun Yat-sen University Cancer Centre in order to protect patient privacy. 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Li, H., et al., Cancer-expanded myeloid-derived suppressor cells induce anergy of NK cells through membrane-bound TGF-beta 1. J Immunol, 2009. 182 (1): p. 240-9. Ugolini, A., et al., Polymorphonuclear myeloid-derived suppressor cells limit antigen cross-presentation by dendritic cells in cancer. JCI Insight, 2020. 5 (15). Huang, B., et al., Gr-1+CD115+ immature myeloid suppressor cells mediate the development of tumor-induced T regulatory cells and T-cell anergy in tumor-bearing host. Cancer Res, 2006. 66 (2): p. 1123-31. Ye, Z.P., et al., Glioma-derived ADAM10 induces regulatory B cells to suppress CD8+ T cells. PLoS One, 2014. 9 (8): p. e105350. Veglia, F., et al., Fatty acid transport protein 2 reprograms neutrophils in cancer. Nature, 2019. 569 (7754): p. 73-78. Veglia, F., M. Perego, and D. Gabrilovich, Myeloid-derived suppressor cells coming of age. Nat Immunol, 2018. 19 (2): p. 108-119. Garofalo, M., et al., miR221/222 in cancer: their role in tumor progression and response to therapy. Curr Mol Med, 2012. 12 (1): p. 27-33. Zhang, R., et al., Plasma miR-221/222 Family as Novel Descriptive and Prognostic Biomarkers for Glioma. Mol Neurobiol, 2016. 53 (3): p. 1452-1460. Budillon, A., C. Carbone, and E. Di Gennaro, Tissue transglutaminase: a new target to reverse cancer drug resistance. Amino Acids, 2013. 44 (1): p. 63-72. Li, B., R.A. Cerione, and M. Antonyak, Tissue transglutaminase and its role in human cancer progression. Adv Enzymol Relat Areas Mol Biol, 2011. 78 : p. 247-93. Zhu, H., et al., Screening for differentially expressed genes between left- and right-sided colon carcinoma by microarray analysis. Oncology Letters, 2013. 6 (2): p. 353-358. Abdul, M. and N. Hoosein, Expression and activity of potassium ion channels in human prostate cancer. Cancer Lett, 2002. 186 (1): p. 99-105. Wondergem, R., et al., Membrane potassium channels and human bladder tumor cells: II. Growth properties. J Membr Biol, 1998. 161 (3): p. 257-62. Malhi, H., et al., KATP channels regulate mitogenically induced proliferation in primary rat hepatocytes and human liver cell lines. Implications for liver growth control and potential therapeutic targeting. J Biol Chem, 2000. 275 (34): p. 26050-7. Zhou, Q., et al., Blockage of voltage-gated K+ channels inhibits adhesion and proliferation of hepatocarcinoma cells. Int J Mol Med, 2003. 11 (2): p. 261-6. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.docx SupplementaryTable2.docx SupplementaryTable3.docx SupplementaryTable4.docx Supplementaryfigure1.tif Supplementary figure 1. (A, B) Comparison of the immune infiltration of Treg cells between CXCL5_H and CXCL5_L (ANOVA test). 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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-4738447","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":338277508,"identity":"41df41e9-0412-4861-b470-30b437c1f347","order_by":0,"name":"Hui Li","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""},{"id":338277509,"identity":"865003dd-2225-4074-944d-7374a30ddbfc","order_by":1,"name":"Han Lu","email":"","orcid":"","institution":"the First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Lu","suffix":""},{"id":338277510,"identity":"48505157-b803-4c26-aebd-1bedc1455047","order_by":2,"name":"Jianxin Xi","email":"","orcid":"","institution":"the First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jianxin","middleName":"","lastName":"Xi","suffix":""},{"id":338277511,"identity":"0d16aa89-4329-4800-ba59-1305cf1a992e","order_by":3,"name":"Zhishan Du","email":"","orcid":"","institution":"Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhishan","middleName":"","lastName":"Du","suffix":""},{"id":338277512,"identity":"9ab6ad86-1698-4c6b-b493-f3c77d351094","order_by":4,"name":"Bo Wu","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Wu","suffix":""},{"id":338277513,"identity":"9f513c66-1449-4b15-ac0b-944d6776de16","order_by":5,"name":"Jiaxin Ren","email":"","orcid":"","institution":"the First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Ren","suffix":""},{"id":338277514,"identity":"f556db98-5938-4dfa-93d4-4ba6a701c02a","order_by":6,"name":"Wenzhuo Yang","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Wenzhuo","middleName":"","lastName":"Yang","suffix":""},{"id":338277515,"identity":"79ccf48d-8434-47e2-8975-e354b53d33f8","order_by":7,"name":"Sheng Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACfvbGxgcSFf/s+JmZDxCnRbLncLOBxZkDyZLtbQnEaTG4kd4mUdl2gHFDzxkDIl125mCzwc22O8wGEjkfb7xhsJPTbSCgg7G9sfHhjHPP+MwlcjdbzmFINjY7QEALM8/BZmOJMmZmyxm526R5GA4kbiOkhU0isU36Dxsz44YbOc+I08ID1CIh0XaYccOZM2zEaZEAOsxA4kwaKJCNLecYEOEX++PtD4FRaQOKyoc33lTYyRHUgmYlsVGDpIVUHaNgFIyCUTAiAAARxkiKCLt01QAAAABJRU5ErkJggg==","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2024-07-14 12:55:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4738447/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4738447/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62729081,"identity":"23b9e4d5-83a5-4b38-83dc-a015b2a9a243","added_by":"auto","created_at":"2024-08-18 22:58:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1225950,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical information and Enzyme-linked immunosorbent assay (ELISA) for CXCL5 of 80 high-grade glioma patients’ tissues.\u003c/strong\u003e (A- C) Clinical information between the CXCL5_H and CXCL5_L groups, including the number, gender, and age of people. (D) Comparison of the expression level of CXCL5 protein between CXCL5_H and CXCL5_L performed by Enzyme-linked immunosorbent assay (ELISA) of 80 high-grade glioma patients’ tissues. *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001. It also applies to following figures. (E) Comparison of survival prognosis between CXCL5_H and CXCL5_L (log-rank test).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/3a8dc2b7789a1f8b7e43db3c.jpg"},{"id":62729545,"identity":"9edd449d-196d-4990-ae20-187c62b6256a","added_by":"auto","created_at":"2024-08-18 23:06:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1791218,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunogenomic analyses between CXCL5_H and CXCL5_L. \u003c/strong\u003e(A) The enrichment levels of the 29-immune signature by ssGSEA score in each GBM sample. ESTIMATE was used to evaluate Tumor purity, Stromal score and Immune score. (B) Comparison of the expression levels of HLA genes between CXCL5_H and CXCL5_L (ANOVA test). (C) Comparison of survival prognosis between CXCL5_H and CXCL5_L (log-rank test). (D) Comparison of the Immune score, Stromal score, ESTIMATE score, Tumor purity between CXCL5_H and CXCL5_L (Kruskal–Wallis test). (E) Comparison of genes expression levels of T lymphocyte exhaustion’s markers and T cell-related negative immune regulators between CXCL5_H and CXCL5_L (ANOVA test).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/ee36e9c98f078b565caa9e4f.jpg"},{"id":62729546,"identity":"5de25512-1871-491f-b6d3-63f44a5addef","added_by":"auto","created_at":"2024-08-18 23:06:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1958993,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentified CXCL5-associated immune genes.\u003c/strong\u003e (A) Volcano plot of 309 immune genes differentially expressed between CXCL5_H and CXCL5_L. (B) GO terms and KEGG pathways enrichment of DEIGs. (C) Enriched terms are colored by cluster ID, where nodes that share the same cluster ID are typically close to each other in DEIGs. (D) Enriched terms are colored by p-value, where terms containing more genes tend to have a more significant P-value in DEIGs.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/9e8c26b000cf0a6afef491e1.jpg"},{"id":62729088,"identity":"4bdb6c6e-7997-4125-9597-b7a7eb76180d","added_by":"auto","created_at":"2024-08-18 22:58:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1512566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the CXCL5-related immune prognostic signature. \u003c/strong\u003e(A-C) LASSO Cox analysis identified three genes most correlated to overall survival in train set. (D, F) Kaplan–Meier curves of overall survival based on the IPS in training set and test set. (E, G) ROC curve analysis of the IPS. (H, I) Risk scores distribution, survival status of each patient, and heatmaps of prognostic three-gene signature in training set and test set.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/5613e90639da9b44dcff4e04.jpg"},{"id":62729095,"identity":"47a6b50c-7917-4c29-be8b-9a177350532d","added_by":"auto","created_at":"2024-08-18 22:58:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2679974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the nomogram model.\u003c/strong\u003e (A) Univariate Cox analyses indicated that IPS was significantly associated with OS. (B) Multivariate Cox analyses indicated that IPS was significantly associated with OS. (C) Nomogram model for predicting the probability of 1-, 2- and 3-year OS in GBMs. (D) ROC curve analysis of the nomogram model. (E) Calibration plots of the nomogram for predicting the probability of OS at 1-, 2- and 3- year.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/13787ed8280d05326a5980c5.jpg"},{"id":62729096,"identity":"1c8b1c13-df90-4a60-af7b-555f27008314","added_by":"auto","created_at":"2024-08-18 22:58:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":11667698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-protein interaction between CXCR2 and CXCL5, CXCR2 and anti- CXCR2. \u003c/strong\u003e(A) The molecular structure of CXCR2 and CXCL5 complex. Initial molecular structure and the Hydrogen bonds between CXCR2 and CXCL5 were shown. (B) The molecular structure of CXCR2 and CXCL5 complex. The surface of the complex was added, green for CXCR2, light blue for CXCL5, red for binding site residues. The amino acid residues GLU-7, ASP-9, and SER-10 of CXCR2 formed hydrogen bond interactions with the amino acid residues LYS-62, LYS-18, and HIS-16 of CXCL5, respectively. (C) The molecular structure of CXCR2 and anti- CXCR2 complex. Initial molecular structure and the Hydrogen bonds between CXCR2 and anti- CXCR2 were shown. (D) The molecular structure of CXCR2 and anti- CXCR2 complex. The surface of the complex was added, green for CXCR2, navy blue for anti- CXCR2, red for binding site residues. The amino acid residues LYS-16, GLY-17 ASP-19, LEU-20, and SER-21 of CXCR2 form hydrogen bonds with the amino acid residues THR-57, TYR-59, THR-68, GLU-81, and LYS-18 of anti-CXCR2.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/4346146a17a124132d1998ae.jpg"},{"id":62729544,"identity":"af16336e-9a74-462a-be2e-6554b272aabd","added_by":"auto","created_at":"2024-08-18 23:06:15","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4354645,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe 3D and 2D schematic drawing of interactions between ligands and CXCR2 by Schrodinger and PyMol. \u003c/strong\u003e(A) The molecular structure of CXCR2 with ZINC000000537805complex. Initial molecular structure and the Hydrogen bonds between CXCR2 and ZINC000000537805 were shown. (B) The surface of CXCR2 with ZINC000000537805 complex was added, green for CXCR2, orange forZINC000000537805. 3D and 2D schematic drawing of interactions were displayed between ligand and CXCR2. (C) The molecular structure of CXCR2 with ZINC000053147179complex. Initial molecular structure and the Hydrogen bonds between CXCR2 and ZINC000053147179 were shown. (D) The surface of CXCR2 with ZINC000053147179 complex was added, green for CXCR2, orange forZINC000053147179. 3D and 2D schematic drawing of interactions were displayed between ligand and CXCR2.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/52e467e747b20ae71328f937.jpg"},{"id":62729089,"identity":"fe532b98-530b-46fc-b8a0-0bac40174080","added_by":"auto","created_at":"2024-08-18 22:58:13","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5216211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePharmacophore predictions of selected ligands and Molecular dynamics simulation by DS 4.5. \u003c/strong\u003e(A) Pharmacophore predictions of ZINC000000537805: green represents hydrogen acceptor; blue represents the hydrophobic center; purple represents hydrogen donor; yellow represents aromatic ring; red represents inozable positive. (B) Pharmacophore predictions ZINC000053147179: green represents hydrogen acceptor; blue represents the hydrophobic center; purple represents hydrogen donor; yellow represents aromatic ring; red represents inozable positive. (C) Potential Energy by molecular dynamics simulations of ZINC000000537805 and ZINC000053147179. (D) Average backbone RMSD of molecular dynamics simulations to ZINC000000537805 and ZINC000053147179.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/9fc66049189ad773d3c1252f.jpg"},{"id":62729094,"identity":"f67080dd-90ec-4ce0-b061-0df5f33fab4a","added_by":"auto","created_at":"2024-08-18 22:58:13","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1065431,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of proliferation and migration inhibition rates by selected drug. \u003c/strong\u003e(A) Effect of different concentrations of glibenclamide on cell survival. (B) Effect of glibenclamide on cell migration width as revealed by scratching method.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/28a03381aca0728a4e8ac74e.jpg"},{"id":67083712,"identity":"de01e280-97af-4c06-9305-5ee9e08fda92","added_by":"auto","created_at":"2024-10-21 05:31:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":32546674,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/c2d88b3e-a1a7-4b8b-b115-2fc409216722.pdf"},{"id":62729086,"identity":"59c7248a-85e0-4524-8c8d-19d34723e5ce","added_by":"auto","created_at":"2024-08-18 22:58:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17396,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/87707a18d82c8e13d0f1d63d.docx"},{"id":62729541,"identity":"57fc6d08-c533-466f-9d7d-c7f6a7426d37","added_by":"auto","created_at":"2024-08-18 23:06:13","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23448,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/f5102d7e90a6ca95d8eb39bc.docx"},{"id":62729542,"identity":"8e141d73-2c65-401e-9f80-c620e2afa381","added_by":"auto","created_at":"2024-08-18 23:06:13","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18864,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/d6e37a77d58771d3b10f410e.docx"},{"id":62730286,"identity":"ab8ab2fe-207c-48df-9e4e-90d0c1c10ebc","added_by":"auto","created_at":"2024-08-18 23:14:15","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":23695,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/303d054f4002d78eb8b2b1bc.docx"},{"id":62729091,"identity":"6081ce96-82ee-47f8-b05f-b56323f03a59","added_by":"auto","created_at":"2024-08-18 22:58:13","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3604328,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary figure 1. \u003c/strong\u003e(A, B) Comparison of the immune infiltration of Treg cells between CXCL5_H and CXCL5_L (ANOVA test).\u003c/p\u003e","description":"","filename":"Supplementaryfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/5cd6f99f574a11ff58c7af2f.tif"},{"id":62729543,"identity":"28b1d6f3-1947-474c-8bf4-a50a6d363787","added_by":"auto","created_at":"2024-08-18 23:06:15","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":24266,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.docx","url":"https://assets-eu.researchsquare.com/files/rs-4738447/v1/988a287699ccda8d97680b78.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Discovery of CXCL5 in Prognosis Prediction and Targeted Therapy of Glioblastomas","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioblastoma (GBM) is the most aggressive primary brain tumor, characterized by a high recurrence rate and poor prognosis. Despite advanced treatments, including surgical resection, temozolomide chemotherapy, and chemoradiotherapy, GBM often recurs, sometimes in distant organs. Research has increasingly focused on the immunosuppressive microenvironment of GBM, highlighting the need to predict patient prognosis from an immunological perspective and develop more effective treatments.\u003c/p\u003e \u003cp\u003eImmunotherapy has shown significant antitumor activity in other cancers such as melanoma, non-small cell lung cancer, renal cancer, and prostate cancer by restoring the body's immune response against tumors. Recent studies have identified immune cells in the central nervous system, challenging the notion of the brain as an immune-privileged site [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, GBM's specific immunosuppressive environment, characterized by various immunosuppressive factors like IL-1, TGF-β, CD70, CD95, CSF-1, and PD-L1, hampers the efficacy of immunotherapy [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Clinical trials targeting these factors have yielded poor results, underscoring the importance of understanding GBM's immunosuppressive mechanisms and identifying new immunosuppressive targets.\u003c/p\u003e \u003cp\u003eC-X-C motif chemokine ligand 5 (CXCL5) is a chemokine that binds to the interleukin 8B (IL-8B) receptor known as C-X-C motif chemokine receptor 2 (CXCR2) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In various cancers, including rhabdomyosarcoma, nasopharyngeal carcinoma, breast cancer, bladder cancer, and papillary thyroid carcinoma, the CXCL5/CXCR2 axis promotes tumor cell migration and invasion [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Chemokines, like CXCL5, as significant components of the tumor microenvironment (TME), play crucial roles in biological processes, including immune cell recruitment, angiogenesis, tumor growth, and metastasis [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Overexpression of CXCL5 is linked to poor survival, recurrence, and metastasis in cancer patients [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In glioma, CXCL5 is upregulated compared to normal brain tissue, but its role in the GBM immunosuppressive microenvironment remains unexplored [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, we conducted an enzyme-linked immunosorbent assay (ELISA) for CXCL5 on tissue samples from 72 high-grade glioma patients and analyzed their survival times. We also analyzed RNA-seq data of GBM samples from the TCGA dataset, categorizing them into CXCL5_H and CXCL5_L groups based on CXCL5 expression levels. We then performed immunogenetic and prognostic analyses between these groups and identified differentially expressed immune genes (DEIGs). Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we constructed an immune prognostic signature (IPS) and developed a predictive nomogram model to estimate overall survival (OS) for GBM patients. Our study also identified the CXCL5/CXCR2 axis as a potential therapeutic target, leading to the screening and evaluating potential inhibitors for this axis. In summary, our research elucidates the role of CXCL5 in the immunosuppressive microenvironment of GBM, provides a novel IPS and nomogram for prognosis prediction, and promotes the development of inhibitors targeting the CXCL5/CXCR2 axis for GBM treatment.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExpression of CXCL5 correlates with prognosis\u003c/h2\u003e \u003cp\u003eWe conducted an enzyme-linked immunosorbent assay (ELISA) to measure CXCL5 levels in tissue samples from 80 high-grade glioma patients. Subsequently, we analyzed the patients' survival times. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-C illustrates the clinical characteristics of patients in the CXCL5_H and CXCL5_L groups, including the distribution by number, gender, and age. Based on CXCL5 protein expression levels, the patients were categorized into two groups: CXCL5_H (n = 40) and CXCL5_L (n = 40) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Our survival analysis revealed a significant correlation between high CXCL5 protein levels and poor overall survival (OS) (P = 0.011) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). This finding suggests that elevated CXCL5 expression may serve as a prognostic marker for reduced survival in high-grade glioma patients. This study highlights the potential role of CXCL5 in glioma progression and its value as a prognostic indicator. Further research is warranted to explore the mechanisms by which CXCL5 influences tumor behavior and to evaluate its potential as a therapeutic target.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eComparative immunogenomic analysis between CXCL5_L and CXCL5_H\u003c/h2\u003e \u003cp\u003eWe obtained RNA-seq data of GBM samples from the TCGA dataset and categorized them into CXCL5_H and CXCL5_L groups based on CXCL5 expression levels. Immunogenetic and prognostic analyses were conducted between these groups, revealing no significant differences in age and sex (age: Mann-Whitney test, P \u0026gt; 0.05; sex: chi-squared test, P \u0026gt; 0.05). Clinical details and their association with CXCL5 protein levels are summarized in Supplementary Table\u0026nbsp;1. We examined 29 sets of immune-associated genes representing various functions, pathways, and immune cell types (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eUsing single-sample gene set enrichment analysis (ssGSEA), we found that the CXCL5_H group was significantly enriched for immune cell types, functions, and pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In the HLA gene analysis, CXCL5_H showed higher expression than CXCL5_L, except for HLA-DOB (ANOVA test, P \u0026lt; 0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Estimation results indicated that the CXCL5_H group had higher estimation, stromal, and immune scores compared to the CXCL5_L group (Kruskal-Wallis test, P \u0026lt; 0.001 for all) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Conversely, tumor purity was higher in the CXCL5_L group (Kruskal-Wallis test, P \u0026lt; 0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur analysis of Tim-3 and PD-1, markers of T lymphocyte exhaustion, showed higher expression in the CXCL5_H group, indicating more significant lymphocyte depletion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, T cell-associated negative immunomodulatory factors such as CD274 (PD-L1), PD-1 (PDCD1), ICOS, TIM3, IDO1, and LAG3 were more highly expressed in the CXCL5_H group (ANOVA test, P \u0026lt; 0.05), suggesting a more immunosuppressive microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCXCL5 expression was also positively correlated with immune infiltration of regulatory T cells (Tregs_XCELL: P \u0026lt; 0.05, Tregs_CIBERSORT-ABS: P \u0026lt; 0.05) (Supplementary Fig.\u0026nbsp;1A, 1B). Moreover, the overall survival rate was better in the CXCL5_L group compared to the CXCL5_H group, consistent with the ELISA results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). These findings suggest that CXCL5 contributes to an immunosuppressive microenvironment in GBM, which may lead to suboptimal responses to immunotherapy and poorer prognosis for GBM patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying differentially expressed immune genes (DEIGs)\u003c/h2\u003e \u003cp\u003eWe identified 3,755 differentially expressed immune genes (DEIGs) using the criteria of FDR \u0026lt; 0.05 and |log2 fold change (FC)| ≥ 1. Of these, 2,378 genes were up-regulated, and 1,377 were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo explore the functional implications of these DEIGs, we used the DAVID website to identify Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The DEIGs were significantly enriched in pathways and processes such as myeloid leukocyte activation, Interleukin-10 signaling, extracellular matrix organization, regulation of inflammatory response, blood vessel development, regulation of cell adhesion, negative regulation of cell proliferation, acute inflammatory response, adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin, mononuclear cell migration, regulation of cytokine production, NF-kappa B signaling pathway, and complement and coagulation cascades.\u003c/p\u003e \u003cp\u003eWe selected a subset and visualized it as a network diagram to better understand the relationships between these enriched terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). This network highlights the complex interactions and regulatory mechanisms involving these DEIGs, providing insights into the role of CXCL5 in the immunosuppressive microenvironment of GBM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment and validation of an immunoprognostic signature (IPS) in association with CXCL5\u003c/h2\u003e \u003cp\u003eWe constructed the Immune Prognostic Score (IPS) for the training set using LASSO Cox regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C). Before applying the Cox regression model, we ensured that the proportional hazards (PH) assumption was met. The IPS included in the Cox PH model adhered to this assumption. Risk scores for each sample were calculated using the formula: risk score = MIR222HG*0.42 + TGM2*0.052 - ZNF560*0.182. Based on the optimal cutoff value determined by the Survminer R package (0.7179717), GBM patients in the training set were classified into low-risk and high-risk groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKaplan-Meier analysis demonstrated that patients with high risk scores had significantly worse prognoses than those with low risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In the training set, the ROC (receiver operating characteristic) curve analysis for IPS yielded an AUC (area under the curve) value of 0.742, indicating good predictive power for overall survival (OS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The distribution of risk scores and gene expression patterns for the training set is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH.\u003c/p\u003e \u003cp\u003eTo validate the prognostic value of IPS, we applied the same formula to the test set. Patients in the test set were similarly divided into high-risk and low-risk groups based on the corresponding cutoff values. The results mirrored those of the training set, with high risk scores correlating with poorer OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The distribution of risk scores and gene expression patterns in the test set is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI. The AUC value for the ROC curve analysis in the test set was 0.751, demonstrating that the IPS possesses high sensitivity and specificity with substantial predictive value (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of a three-gene IPS-based nomogram model\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate Cox analyses identified the Immune Prognostic Score (IPS) as an independent prognostic factor (multivariate Cox analysis: HR = 2.044, 95% CI: 1.377–3.035, P \u0026lt; 0.001; univariate Cox analysis: HR = 2.064, 95% CI: 1.393–3.060, P \u0026lt; 0.001) (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Based on the three-gene IPS, we developed a nomogram model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). ROC curve analysis of this nomogram showed AUC values of 0.74, 0.71, and 0.89 for 1, 2, and 3 years overall survival (OS), respectively, indicating high sensitivity, specificity, and predictive value (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe nomogram model demonstrated robust specificity discrimination with a C-index of 0.684. Calibration plots of observed versus predicted probabilities for 1, 2, and 3 years OS showed strong agreement (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). These findings underscore the utility of the IPS as a prognostic tool in GBM, providing valuable insights for patient stratification and treatment planning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVirtual screening of potential CXCR2 inhibitors\u003c/h2\u003e \u003cp\u003eWe identified two binding pockets on CXCR2 as reference docking regions for virtual screening. The first binding site was located at the N-terminal alpha helix of CXCR2, where the interacting amino acid residues between CXCR2-CXCL5 and CXCR2-anti-CXCR2 overlapped. This overlap region was used to create a binding sphere designated as site 1 for drug screening (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The second binding site, site 2, was selected based on the intracellular xenobiotic binding domain formed by TM1, TM2, TM3, TM6, and the cytoplasmic portion of CXCR2 in intracellular loop 1 (ICL1), known to bind antagonists [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We downloaded 17,799 natural, named, and available molecules from the ZINC15 database and performed Libdock docking on the two identified CXCR2 sites. The higher the Libdock score, the more stable the binding between the small molecule and the protein. These docking studies aim to identify potential inhibitors targeting CXCL5-CXCR2 interactions, which may provide new therapeutic approaches for GBM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of drug candidates' toxicological and pharmacological properties\u003c/h2\u003e \u003cp\u003eThe pharmacological and toxicological properties of the top 20 molecules, as determined by their Libdock scores, were analyzed using the DS 4.5 ADME and TOPKAT modules. The ADME module evaluated brain-blood barrier (BBB) permeability, water solubility, human intestinal absorption, cytochrome P450 2D6 (CYP2D6) binding, hepatotoxicity, and plasma protein binding (PPB) properties of these compounds (Supplementary Table\u0026nbsp;4). Meanwhile, the TOPKAT module assessed rodent carcinogenicity (based on the National Toxicology Program dataset), developmental toxicity potential (DTP), and Ames mutagenicity to confirm their safety (Supplementary Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eConsidering these results, ZINC000000537805 and ZINC000053147179 were selected as ideal compounds for CXCR2 sites 1 and 2. Both compounds are not CYP2D6 inhibitors and exhibit low carcinogenicity and mutagenicity in rodent models, making them promising candidates for further development. These findings contribute to the potential therapeutic targeting of CXCL5-CXCR2 interactions in GBM, providing a foundation for future drug development efforts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBinding interactions, pharmacophore analysis, and molecular dynamics simulations between selected compounds and CXCR2\u003c/h2\u003e \u003cp\u003eThe CDOCKER interaction energies of ZINC000053147179 and ZINC000000537805 with CXCR2 were − 51.7992 kcal/mol and − 34.7995 kcal/mol, respectively, indicating strong affinities for CXCR2. All docked conformations were visualized using PyMol and Schrodinger (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). ZINC000053147179 formed five hydrogen bonds with CXCR2, while ZINC000000537805 formed three.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCDOCKER interaction energy of compounds with CXCL5.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDOCKER interaction energy (kcal/mol)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZINC000000537805\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-34.7995\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZINC000053147179\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-51.7992\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHydrogen bond interaction parameters for each compound with CXCR2 residues.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceptor\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDonor atom\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReceptor atom\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDistances (Å)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eCXCR2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eZINC000000537805\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA:LYS16:HZ3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZINC000000537805:O21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA:ASP13:OD1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZINC000000537805:H40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA:GLU12:O\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZINC000000537805:H48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eZINC000053147179\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA:LYS320:HZ2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZINC000053147179:O15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA:ASP84:OD1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZINC000053147179:H44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA:ASP84:OD1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZINC000053147179:H45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA:ASP84:OD2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZINC000053147179:H48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA:SER76:OG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZINC000053147179:H61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003ePharmacological analyses of these compounds revealed that ZINC000000537805 and ZINC000053147179 had 25 and 35 characteristic pharmacophores, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). ZINC000000537805 featured 14 hydrogen acceptors, 2 hydrogen donors, 4 hydrophobic centers, 4 aromatic rings, and 1 ionizable positive group. ZINC000053147179 had 12 hydrogen acceptors, 13 hydrogen donors, 3 hydrophobic centers, 6 aromatic rings, and 1 ionizable positive group. Molecular dynamics simulations showed that the RMSD and potential energy of the ligand-CXCR2 complex remained stable over time (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eZINC000000537805, also known as glibenclamide, is a sulfonylurea hypoglycemic agent indicated for type 2 diabetes. Beyond its metabolic effects, glibenclamide has anti-inflammatory properties through NLRP3 inhibition [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, growing evidence suggests that glibenclamide exerts antitumor effects by inhibiting the KATP channel or ATP-binding cassette superfamily proteins [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, we selected glibenclamide for further experimental validation. These findings highlight the potential of glibenclamide and ZINC000053147179 as therapeutic agents targeting CXCL5-CXCR2 interactions in GBM, warranting further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVerifying the effects of the selected drugs through in vitro experiments\u003c/h2\u003e \u003cp\u003eThe Cell Counting Kit-8 (CCK8) assay was used to assess the effect of glibenclamide on the proliferation of U-373 MG cells. The results demonstrated that glibenclamide inhibited cell proliferation after 48 hours, with the inhibition rate increasing in a dose-dependent manner (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Additionally, the scratch assay revealed that the migration width ratio of cells treated with glibenclamide was significantly smaller compared to the control group. This inhibitory effect on cell migration was more pronounced at higher drug concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). These findings suggest that glibenclamide effectively inhibits the proliferation and migration of U-373 MG cells, indicating its potential as a therapeutic agent in GBM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Methods and materials","content":"\u003ch2\u003eCollection of clinical specimens and enzyme-linked immunosorbent assay (ELISA)\u003c/h2\u003e\u003cp\u003eEighty specimens of WHO grade IV glioblastoma were collected from the pathology archive of Sun Yat-sen University Cancer Centre (SYSUCC) between January 2010 and June 2020. These specimens were used to perform an enzyme-linked immunosorbent assay (ELISA) to assess the clinical and prognostic significance of CXCL5 expression. Clinicopathological data and follow-up information were obtained from medical records under the approval of the Ethics Committee of Sun Yat-sen University Cancer Centre, with written informed consent obtained from all patients. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\u003ch2\u003eAcquisition and processing of gene expression data\u003c/h2\u003e\u003cp\u003eRNA-seq data from The Cancer Genome Atlas (TCGA) repository were downloaded for 169 glioblastoma (GBM) patients with survival data available. Patients were classified into CXCL5 high (CXCL5_H, n = 85) and CXCL5 low (CXCL5_L, n = 84) groups based on their CXCL5 expression levels. Gene expression quantification utilized FPKM values, which were log2-transformed for subsequent analysis of RNA transcriptome maps.\u003c/p\u003e\u003ch2\u003eComparative immunogenomic analysis between CXCL5_H and CXCL5_L\u003c/h2\u003e\u003cp\u003eTwenty-nine immunomarkers relevant to GBM were selected for analysis. Single-sample gene set enrichment analysis (ssGSEA) scores were used to quantify the enrichment levels of these immunomarkers across GBM patient samples [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, ESTIMATE was employed to evaluate immune cell infiltration, stromal content, and tumor purity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The expression profiles of HLA genes, markers of T lymphocyte exhaustion, and negative immune regulators associated with T cells were compared between CXCL5_H and CXCL5_L groups using ANOVA.\u003c/p\u003e\u003ch2\u003ePrognostic analysis\u003c/h2\u003e\u003cp\u003eSurvival analysis using the log-rank test with a significance threshold of P \u0026lt; 0.05 was conducted to assess differences in overall survival (OS) between CXCL5_H and CXCL5_L groups. Kaplan-Meier curves were plotted to visualize survival outcomes.\u003c/p\u003e\u003ch2\u003eAnalysis of the differential expression\u003c/h2\u003e\u003cp\u003eDifferentially expressed genes between CXCL5_H and CXCL5_L were processed and performed using the Limma package of RStudio software and the Wilcoxon Rank Sum and Signed Rank test [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Genes with adjusted P values \u0026lt; 0.05 and |log2 fold change (FC)| \u0026gt;1 were selected as cut-off points for analysis. Differentially expressed immune genes (DEIGs) were identified using the ImmPort database to facilitate further studies.\u003c/p\u003e\u003ch2\u003eAnalyzing functional enrichment\u003c/h2\u003e\u003cp\u003eTo analyze DEIGs for gene ontology and signaling pathway enrichment, we uploaded DEIGs to the online tools: David (Database for Annotation, Visualization, and Integrated Discovery) and Metascape, an online site that provides gene annotation, visualization, and gene properties online [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e–\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. GO analyses included classical pathways (biological processes), extracellular structural domains (cellular composition), antigen binding (molecular function), complement activation and cytokine-cytokine receptor KEGG analysis. In addition, we constructed a network graph with a similarity of \u0026gt; 0.3. They are connected by edges. The terms with the best p-value were selected from each cluster to form a total of 250 terms, with a maximum of 15 terms per cluster.\u003c/p\u003e\u003ch2\u003eBuilding and validating the immune prognostic signature (IPS)\u003c/h2\u003e\u003cp\u003eThe LASSO-Cox analysis is widely used for high-dimensional predictive regression [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. It allows the selection of the best penalty parameter lambda by cross-validation for shrinkage and variable identification, thus preventing over-fitting [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. All DEIGs were entered into the LASSO Cox analysis to screen out genes with prognostic impact as candidates for IPS construction. We then randomly divided the TCGA dataset into two groups, a training group, and a validation group, and then entered data from both groups into the LASSO Cox analysis to calculate a risk score for each patient to construct the IPS. Optimal critical values were obtained, and patients were classified as low and high risk according to the analysis of the \"glmnet\" R package [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Sensitivity and specificity were then assessed using the \"Survival ROC\" R package [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and ROC curves were plotted. Area under the curve (AUC) values were also calculated. The ability of the IPS prediction set was further validated.\u003c/p\u003e\u003ch2\u003eDeveloping the Nomogram\u003c/h2\u003e\u003cp\u003eThe independent prognostic ability of IPS was assessed by univariate and multivariate Cox analyses. Based on the results of the Cox analysis, an innovative nomogram was developed using the RMS software package. The ROC package \"Survival ROC\" was then used to assess sensitivity and specificity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and the ROC receiver operating characteristic curve was plotted. Area under the curve (AUC) values were also calculated. A calibration plot was made to assess the accuracy of the nomogram based on the observed and predicted probabilities for 1, 2, and 3-year OS. The discriminatory ability of the model was evaluated using the coordination index (C-index). In addition, the C-index was modified by calculating the bootstrap number [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eVirtual filtering with Libdock\u003c/h2\u003e\u003cp\u003eThe Libdock module analyses the conformations of small molecules and receptors and their interaction hotspots and tightly binds the matching conformation to the binding pocket of the receptor. A total of two CXCR2 binding pockets were selected as reference docking regions for the virtual screen. We downloaded the Fab sequences of human CXCR2 (code P25025) and CXCL5 (code 2MGS) from the AlphaFold protein structure database and the CXCR2 antibody abN48 (code 6KVF) from the PDB database. Based on the CXCR2-CXCL5, CXCR2-anti-CXCR2 protein interactions shown by HADDOCK 2.4 and PyMol (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the amino acids of the N-terminal alpha helix of CXCR2 were selected as the site for the extracellular drug screen.1 In addition, an intracellular allosteric binding domain formed by TM1, TM2, TM3, TM6, and the cytoplasmic portion of intracellular loop 1 (ICL1) of CXCR2 has been reported to bind antagonists and was therefore selected as site 2 for drug screening [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In addition, 17,799 natural, named, and purchasable molecular files were downloaded from the ZINC15 database for the virtual screening of CXCR2 inhibitors. Finally, all ligands docked to CXCR2 at different positions were ranked according to their Libdock scores. The 20 compounds with the highest Libdock scores were also individually selected for further analysis.\u003c/p\u003e\u003ch2\u003ePredicting toxicological and pharmacological properties\u003c/h2\u003e\u003cp\u003eThe pharmacological properties of the selected molecules were calculated using the DS 4.5 ADME module, including water solubility, cytochrome P450 2D6 (CYP2D6) inhibition, plasma protein binding (PPB) levels, blood-brain barrier (BBB) permeation, hepatotoxicity, and human intestinal absorption. In addition, DS 4.5's TOPKAT (Toxicity Prediction by Computer Assisted Technology) module plays a vital role in assessing the toxicity of all potential compounds, including rodent carcinogenicity (based on the National Toxicology Program dataset), developmental toxicity potential (DTP) and Ames mutagenesis (Ames). The TOPKAT module quickly and accurately calculates and validates the toxicity and environmental impact of compounds based on 2D molecular structures. A series of robust, cross-validated Quantitative Structure-Toxicity Relationship (QSTR) models were used to evaluate different toxicity predictions. Finally, all of the above pharmacological and toxicological properties were considered in selecting CXCR2 drug candidates.\u003c/p\u003e\u003ch2\u003eBinding interactions, pharmacophore analysis, and molecular dynamics simulations between selected compounds and CXCR2\u003c/h2\u003e\u003cp\u003eThe CDOCKER module is based on the CHARMm36 force field. It is used for precise docking between the ligand and CXCR2. During docking, the receptor remains rigid, while the ligand can be flexible. The interaction energy, which reflects the binding affinity of the ligand, is also calculated in the pose of each complex. For each ligand, 10 docking poses were generated, and the optimal one was selected based on suitable docking orientation and high docking percentage [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The CDOCKER interaction energies were also evaluated separately for each of the different poses of each tested molecule. Furthermore, the CDOCKER docking results were visualized using Schrodinger and PyMol. The pharmacology of the selected molecules was also analyzed using the 3D-QSAR pharmacology module of DS 4.5. In addition, the molecular dynamics simulation module was used to assess the stability of the ligand-CXCR2 complexes in their natural environment. The RMSD and potential energy of these complexes were evaluated. Combining these results, a drug was selected for further experimental validation.\u003c/p\u003e\u003cp\u003e \u003cb\u003eThe Cell Counting Kit-8 (CCK8) and scratch assays to verify the effect of the selected drug\u003c/b\u003e \u003c/p\u003e\u003cp\u003eHuman glioma cells U-373 MG were cultured in vitro and divided into control and experimental groups. Different concentrations of glibenclamide were added to the cells in the experimental group for 48 h. The concentrations were 10, 20, 50, 100, 200, 500, 1000 and 1500 µmol/L. The control group was operated in the same way as the experimental group, except that glibenclamide was not added. The cell activity of each group was measured by CCK8, and the migration and invasion of cells were detected by the scratch method.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePatients with glioblastoma (GBM) face a poor prognosis, and many treatments fail to achieve the desired outcomes. CXCL5 has been shown to play a significant role in immune cell recruitment, angiogenesis, tumor growth, and metastasis within the tumor microenvironment (TME) [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Overexpression of CXCL5 is closely associated with poorer survival, recurrence, and metastasis in cancer patients [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In gliomas, CXCL5 is upregulated compared to normal brain tissue[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Despite the attention to the immunosuppressive microenvironment of GBM, the relationship between CXCL5 and this environment has not been explored.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Despite the attention to the immunosuppressive microenvironment of GBM, the relationship between CXCL5 and this environment has not been explored.\u003c/p\u003e \u003cp\u003eIn our study, we measured CXCL5 levels using ELISA in tissue samples from 80 high-grade glioma patients and analyzed their survival data. Patients with higher CXCL5 protein expression had poorer overall survival (OS) (p\u0026thinsp;=\u0026thinsp;0.011) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). We then analyzed RNA-seq data of GBM samples from the TCGA dataset, dividing them into CXCL5_H and CXCL5_L groups based on CXCL5 expression. Using single-sample gene set enrichment analysis (ssGSEA), we found that CXCL5_H samples had higher enrichment of immune cells, functions, and pathways than CXCL5_L samples. Studies indicate that immunocompetent patients with various tumors, including triple-negative breast cancer, have better survival rates than immunosuppressed patients [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analysis showed that most HLA genes were expressed at higher levels in CXCL5_H than in CXCL5_L. HLA plays a crucial role in presenting tumor-associated antigens and the anti-tumor immune response, with its expression correlated with tumor type and grade [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In gliomas, patients with low HLA expression benefit more from immunotherapy [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This suggests that patients with lower CXCL5 expression might be more suitable for immunotherapy and have a better prognosis. Previous studies have shown that GBM patients often resist immunotherapy, with no survival advantage in recurrent cases [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Therefore, classifying GBM patients based on CXCL5 expression could enable more personalized and precise treatments.\u003c/p\u003e \u003cp\u003eWe also found higher levels of markers for T lymphocyte depletion, such as Tim-3 and PD-1, in CXCL5_H samples. Despite high immune activity, immune cells in CXCL5_H are often hypofunctional and highly depleted. T cell-associated negative immunomodulatory factors like CD274 (PDL1), PD-1 (PDCD1), ICOS, LAG3, TIM3, and IDO1 were also expressed at higher levels in CXCL5_H, indicating a more immunosuppressive microenvironment [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, CXCL5 expression correlated positively with Treg cell infiltration. CXCL5 has been reported to promote the recruitment of myeloid-derived suppressor cells (MDSCs) via the CXCL5/CXCR2 axis, contributing to cancer progression in breast and prostate cancers [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In GBM, MDSCs suppress immune responses, inhibit cytotoxic T-cell activity, and impair NK cell, macrophage, and dendritic cell functions [\u003cspan additionalcitationids=\"CR53 CR54 CR55\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The presence of MDSCs correlates with a poor prognosis in GBM patients [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consistent with our ELISA results, patients with lower CXCL5 expression had better OS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We speculate that CXCL5 enhances the immunosuppressive microenvironment of GBM, leading to suboptimal immunotherapy outcomes and poor prognosis.\u003c/p\u003e \u003cp\u003eWe also found higher levels of markers for T lymphocyte depletion, such as Tim-3 and PD-1, in CXCL5_H samples. Despite high immune activity, immune cells in CXCL5_H are often hypofunctional and highly depleted. T cell-associated negative immunomodulatory factors like CD274 (PDL1), PD-1 (PDCD1), ICOS, LAG3, TIM3, and IDO1 were also expressed at higher levels in CXCL5_H, indicating a more immunosuppressive microenvironment [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, CXCL5 expression correlated positively with Treg cell infiltration. CXCL5 has been reported to promote the recruitment of myeloid-derived suppressor cells (MDSCs) via the CXCL5/CXCR2 axis, contributing to cancer progression in breast and prostate cancers [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In GBM, MDSCs suppress immune responses, inhibit cytotoxic T-cell activity, and impair NK cell, macrophage, and dendritic cell functions [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e][\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e][\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e][\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The presence of MDSCs correlates with a poor prognosis in GBM patients [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consistent with our ELISA results, patients with lower CXCL5 expression had better OS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We speculate that CXCL5 enhances the immunosuppressive microenvironment of GBM, leading to suboptimal immunotherapy outcomes and poor prognosis.\u003c/p\u003e \u003cp\u003eWe further analyzed differentially expressed genes (DEGs) between CXCL5_H and CXCL5_L, identifying 3755 DEGs. Gene ontology (GO) and KEGG analysis showed significant enrichment of DEGs in immune-related pathways, including immune cell regulation, cytokines, complement, immunoglobulins, cell adhesion, proliferation, and vascular development. This suggests CXCL5's vital role in GBM's immune microenvironment and cell proliferation, invasion, and metastasis. Interestingly, DEGs were enriched in bone marrow leukocyte activation, indicating that CXCL5 may facilitate MDSC recruitment to GBM sites.\u003c/p\u003e \u003cp\u003eWe developed a CXCL5-associated immune prognostic signature (IPS) with AUC values of 0.742 and 0.751 in training and validation sets, respectively. LASSO Cox regression identified ZNF560, TGM2, and MIR222HG (miR222/221 cluster host gene) as essential genes in our IPS. Dysregulation of miRNAs, including miRNA-221/222, is a hallmark of cancer associated with glioma prognosis [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e][\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. TGM2 is highly expressed in several malignancies and promotes tumor cell proliferation and metastasis [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e][\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Although TGM2's effects on glioma are not well-documented, ZNF560 has been implicated in colorectal cancer studies [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Therefore, these genes represent potential prognostic and therapeutic markers for GBM.\u003c/p\u003e \u003cp\u003eOur IPS was an independent prognostic factor in univariate and multivariate Cox analyses. We developed a nomogram based on the three-gene IPS for predicting 1-, 2-, and 3-year survival in GBM patients, offering a new perspective for prognosis prediction.\u003c/p\u003e \u003cp\u003eMoreover, the CXCL5/CXCR2 pathway is a promising target for GBM treatment. Currently, there are no drugs specifically targeting this axis for GBM, and existing CXCR2 inhibitors like reparixin and antileukin are either limited to basic research or not specific. Therefore, drug screens targeting the CXCL5/CXCR2 axis are crucial for developing new GBM treatments and improving patient prognosis. Using computational techniques, we screened potential CXCR2 inhibitors and identified ZINC000000537805 and ZINC000053147179 as potent and safe candidates. ZINC000000537805 (glibenclamide) has shown efficacy in reducing the risk and progression of various cancers [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR65 CR66\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], though its effect on glioma's immune microenvironment is unstudied. Our in vitro experiments demonstrated that glibenclamide inhibits glioma cell growth, migration, and invasion, offering a potential new treatment for GBM.\u003c/p\u003e \u003cp\u003eIn conclusion, this study highlights CXCL5's role in GBM's immunosuppressive microenvironment, provides a novel IPS and nomogram for predicting GBM prognosis, and facilitates the development of CXCL5/CXCR2 axis inhibitors for GBM treatment.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we demonstrated that CXCL5 significantly enhances the immunosuppressive microenvironment of GBM, leading to poor immunotherapy outcomes and prognosis for patients. We established a CXCL5-associated immune prognostic signature (IPS), enabling stratification of patients into high-risk or low-risk groups. This IPS was also used to create a new nomogram for predicting GBM patient prognosis. Furthermore, we identified the potential of CXCL5 as a therapeutic target for GBM aimed at modulating the immunosuppressive microenvironment. Through a virtual drug screen and subsequent experiments, we discovered therapeutic agents targeting the CXCL5/CXCR2 axis. These findings offer new insights into GBM treatment strategies and prognostic assessment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grant number 81872324, 82203219), Guangdong Basic and Applied Basic Research Foundation (2020A1515110203), Fundamental and Applied Fundamental Research Fund of Guangdong Province (2021A1515111198) and Beijing New-star Plan of Science and Technology (Z201100006820148 to YC). Funders had no role in study design, data collection and analysis, publication decisions, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a team effort. Each author has made substantial contributions to the study. Hui Li carried out the design of the work. Han Lu was responsible for creating new software used in the work. Jianxin Xi carried out the experiments and data collection. Zhishan Du contributed significantly to the interpretation of the data. Bo Wu drafted the paper.\u0026nbsp;Wenzhuo Yang\u0026nbsp;and Jiaxin Ren created the enzyme-linked immunosorbent assay (ELISA). Sheng Zhong developed the concept and made substantial revisions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests related to this manuscript and have agreed to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.The RNA-seq data supporting this research article are from previously reported studies and datasets, which have been cited. The processed data are available from the TCGA website (https://portal.gdc.cancer.gov/repository).\u003c/p\u003e\n\u003cp\u003e2.The 80 high-grade glioma specimens data used to support the findings of this study are restricted by the Ethics Committee of Sun Yat-sen University Cancer Centre in order to protect patient privacy. Data are available from shengzhong, [email protected] for researchers who meet the criteria for access to confidential data.\u003c/p\u003e\n\u003cp\u003e3.The molecular files data supporting this research article are from previously reported studies and datasets, which have been cited. The processed data are available from the ZINC15 database.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLouveau, A., et al., \u003cem\u003eCorrigendum: Structural and functional features of central nervous system lymphatic vessels.\u003c/em\u003e Nature, 2016. \u003cstrong\u003e533\u003c/strong\u003e(7602): p. 278.\u003c/li\u003e\n\u003cli\u003eDe Leo, A., A. Ugolini, and F. 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Implications for liver growth control and potential therapeutic targeting.\u003c/em\u003e J Biol Chem, 2000. \u003cstrong\u003e275\u003c/strong\u003e(34): p. 26050-7.\u003c/li\u003e\n\u003cli\u003eZhou, Q., et al., \u003cem\u003eBlockage of voltage-gated K+ channels inhibits adhesion and proliferation of hepatocarcinoma cells.\u003c/em\u003e Int J Mol Med, 2003. \u003cstrong\u003e11\u003c/strong\u003e(2): p. 261-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glioblastoma (GBM), CXCL5/CXCR2, immune Prognostic Signature (IPS), inhibitors","lastPublishedDoi":"10.21203/rs.3.rs-4738447/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4738447/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlioblastoma (GBM) patients face a grim prognosis, with many treatments failing to achieve significant improvements. Recent research has focused on the immunosuppressive environment within GBM tumors. One particular protein, C-X-C chemokine ligand 5 (CXCL5), is highly expressed in various cancers and is known to affect the immune environment, tumor invasion, metastasis, and overall prognosis. In our study, we investigated the role of CXCL5 in the immunosuppressive environment of GBM. We aimed to develop a CXCL5-associated immune prognostic signature (IPS) to predict patient outcomes and identify potential treatments targeting the CXCL5/CXCR2 axis. Initially, we performed enzyme-linked immunosorbent assays (ELISA) on 80 high-grade glioma samples to measure CXCL5 levels. We also analyzed RNA-seq data from 169 GBM samples obtained from the TCGA dataset, dividing them into high (CXCL5_H) and low (CXCL5_L) CXCL5 expression groups. Our analysis revealed that the CXCL5_H group had higher expression of immune-related genes but a poorer prognosis compared to the CXCL5_L group. Using the least absolute shrinkage and selection operator (LASSO) Cox analysis, we constructed a CXCL5-associated IPS, which we confirmed as an independent prognostic factor for GBM through univariate and multivariate Cox analyses. We developed a nomogram based on the three-gene IPS to predict overall survival in GBM patients. Moreover, our study identified the CXCL5/CXCR2 axis as a promising target for GBM treatment. We employed computational techniques to screen for potential inhibitors of this axis and validated their effectiveness in vitro. In conclusion, our study provides a new prognostic model and suggests targeted therapeutic options for GBM by elucidating the role of CXCL5 in the tumor's immunosuppressive environment. This work may pave the way for improved patient outcomes and more effective treatments for this challenging cancer.\u003c/p\u003e","manuscriptTitle":"A Novel Discovery of CXCL5 in Prognosis Prediction and Targeted Therapy of Glioblastomas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-18 22:58:07","doi":"10.21203/rs.3.rs-4738447/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":"4c0ac722-6960-4186-854e-a1d1f9fbd633","owner":[],"postedDate":"August 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T05:23:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-18 22:58:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4738447","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4738447","identity":"rs-4738447","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}