A Novel 3 chemotactic activity-related gene signature for Predicting prognosis of bladder Cancer

A Novel 3 chemotactic activity-related gene signature for Predicting prognosis of bladder Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Novel 3 chemotactic activity-related gene signature for Predicting prognosis of bladder Cancer Ming Zhang, Xing Dong, Weijie Yang, Qian Wu, Mingyang Chang, Jianing Lv, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3385390/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Bladder cancer is one of the most common malignant tumors of the urinary system. Both cancer and stromal cells, including bladder cancer, express chemokines and their corresponding receptors. Their altered expression controls angiogenesis, cancer cell proliferation, metastasis, and immune cell recruitment and activation in a variety of malignancies. Therefore, it is necessary to investigate the association between chemotactic activity-related genes and the prognosis of bladder cancer patients. Methods Download the The Cancer Genome Atlas (TCGA) database's expression profiles for chemotactic activity-related genes and clinical information. Create a prognostic model by using the univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model. Additionally, the validation cohort for the GSE13507 and GSE48276 datasets is used to verify the signature's predictive power. Results We identified 3 chemotactic activity-related genes related to BLCA patients’ overall survival (OS) and established a prognostic model based on their expression. According on the findings of the LASSO regression analysis, patients were split into high-risk and low-risk groups during the study. The survival time of the low-risk group was significantly longer than that of the high-risk group (P < 0.001). The riskscore and clinical prognostic indicators were combined to create a nomogram, which demonstrated strong predictive capacity in the training and validation groups. Conclusions With the use of CXCL12, ACKR3, and CXCL10, we have created a chemotactic activity-related predictive model in this study that may aid doctors in making conclusions regarding BLCA patients and provide useful information for tailored management. bladder cancer chemokine chemokine receptors prognosis immune infiltrates Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Bladder cancer is one of the most common malignant tumors of the urinary system, account for 500 000 new cases and 200 000 deaths worldwide each year 1 . Bladder urothelial carcinoma (BLCA) accounts for approximately 95% of bladder tumors. Histologically, approximately 75% of bladder cancers are classified as pure urothelial carcinoma, with histological variants accounting for the remaining 25% 2 , which poses a unique challenge for both clinical treatment and monitoring of tumor prognosis. The 5-year survival rate of advanced BLCA patients is only about 6% 3,4 , with 12–14 months of the OS 3 .Although some biomarkers are related to the tumorigenesis and prognosis of BLCA have been evaluated 1 , 5 , the methods that could accurately predict the prognosis of BLCA patients remain limited. Therefore, it is an urgent need to develop predictive models for the improved prognosis and treatment of BLCA patients. In recent years, immunotherapy has become a hot topic in the treatment of various tumors, and chemokines and their receptors, which are important components of the immune system, have gradually attracted attention. Chemokines, a large family of cytokines with chemotactic activity, and their cognate receptors are expressed by both cancer and stromal cells. Their altered expression dictates immunocyte recruitment and activation, angiogenesis, cancer cell proliferation, and metastasis in all the stages of various cancers 6 .In recent study showed that CXCR6 is critical for cytotoxic T lymphocyte(CTL) -mediated tumor control, and uncover the central role of the chemokine receptor CXCR6 in positioning tumor-infiltrating CTLs provides critical survival and proliferation signals to prolong survival in human cancer patients 7 .In prostate cancer, CXCL12/CXCR4 are identified as important prognostic markers of specific vasculature alterations, and inhibition of CXCR4 has been shown to effectively reduce the proliferative capacity and inhibit migration of tumor endothelial cells 8 , the same results were found for CXCR7(ACKR3) in breast and lung tumor 9 ; Another study found that activation of MAPK pathway by CXCR7 through recruitment of ARRB2 is a mechanism of resistance to second generation anti-androgen therapy 10 . HCC tumors derived IRF-1 activates immune cells to induce apoptosis of tumor cells through the CXCL10/CXCR3 axis in murine HCC tumor 11 . The importance of chemokines and their receptors in the cancerous process is becoming more and more clear from research. To effectively treat BLCA, it is crucial to thoroughly understand the function of bladder carcinogenesis and progression as well as to develop an accurate prognostic model of chemokines and their receptors. In this work, we explored the transcriptional profiles and clinical relevance of chemokines and their receptors in BLCA patients. We initially developed and validated the chemokines- and chemokine receptors-based prognostic risk model using 617 cases of BLCA from 3 independent cohorts. Materials and methods Data acquisition and preprocessing The RNA sequencing (RNA-seq) data of 406 BLCA samples and 19 normal pancreatic samples with their clinical pathological parameters were downloaded from the cancer genome atlas (TCGA, https://portal.gdc.cancer.gov ) databases as the training cohort. The RNA-seq data and clinical pathological features of 219 BLCA samples were downloaded from Gene Expression Omnibus GSE13507 and GSE48276 datasets ( https://www.ncbi.nlm.nih.gov/geo/ ) as the validation cohort. Identification of differentially expressed chemotactic activity-related genes The “limma” package was used to identify differentially expressed chemotactic activity-related genes in TCGA-BLCA, p 0.8 were set as cut-of values. Protein protein interactions (PPIs) were plotted by using string database, the minimum interaction score required for PPI analysis was set at 0.4 (medium confidence). Identification of prognostic genes To investigate the relationship between the expression levels of chemotactic activity-related genes and OS of BLCA patients, we conducted a univariate Cox regression analysis using the SPSS software (IBM, v22.0, Chicago, IL). A significant filtering criterion was set at p < 0.05 for further analysis. We next eliminated gene collinearity and reduced the number of genes using LASSO Cox regression. LASSO-COX dimension reduction analysis was performed by glmnet and survival packages in R. The λ value corresponding to the minimum partial likelihood deviance was selected as the optimal λ in our study. Construction and validation of a prognostic model based on chemotactic activity-related genes The risk score was calculated according to the standardized BLCA mRNA expression data in the training set. Risk score = expr CXCL12 × λ CXCL12 + expr ACKR3 × λ ACKR3 + expr CXCL10 × λ CXCL10 .where expr gene was the expression level of the gene and λ gene was the corresponding lambda value. BLCA patients were divided into high-risk and low-risk groups based on the median risk score, and the OS between these two groups was analyzed. To make the model more convincing, we utilized the BLCA cohort in the GEO(GSE13507 and GSE48276) database for validation. The expression of each chemotactic activity-related gene was also normalized, and the risk score was then calculated by the above formula. Next, to determine if risk score was an independent prognostic factor for OS in BLCA patients in the train set, univariate and multivariate Cox regression analyses were conducted. Covariates included age, gender, T, N and M. Nomogram construction Nomogram analysis was constructed in the training group by rms package in R. The upper part is the scoring system and the lower part is the prediction system. The 1-, 3-, and 5-year survival BLCA patients could exactly be predicted by total points, sum points of every factor. Verification of the prediction accuracy of OS was performed in patients of the validation group. Receiver operating characteristic (ROC) curves were produced by the timeROC package. Calibration curves and ROC values were used to show the accuracy of the survival prediction. Statistical analysis Statistical analyses were executed using R ( https://www.r-project.org/,v4.1.3 ), SPSS software (IBM, v22.0, Chicago, IL), and GraphPad Prism (v9.0, La Jolla, CA). The prognostic value was evaluated by Kaplan-Meier analysis and COX analysis. GSEA analyses were implemented with GSEA package in java software ( http://software.broadinstitute.org/gsea/index.jsp ) and Gene Ontology (GO ) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID portal website ( https://david.ncifcrf.gov/summary.jsp ). For all statistical methods, P < 0.05 was considered a significant difference. Results Identification of differentially expressed chemotactic activity-related genes in BLCA We obtained 406 BLCA patients and 19 normal tissues from TCGA. A total of 11 chemotactic activity-related differentially expressed genes(DEGs) were identified based on the cutoff criteria of |log2 (fold change) |>0.8 and P < 0.05 from 58 chemotactic activity-related genes. 8 chemotactic activity-related genes were considerably downregulated in BLCA patients, while 3 genes were upregulated, according to volcano plots and heatmaps (Fig. 1 A, D). This differentially expressed chemotactic activity-related genes' protein-protein interaction network was displayed in (Fig. 1 B). Furthermore, multiple mutations have been found in these genes associated with variable chemotactic activity in BLCA patients (Fig. 1 C). Functional enrichment analysis To explore the biological functions and pathways associated with the chemotactic activity-related DEGs, GO and KEGG functional enrichment analysis were performed. The analysis of GO enrichment revealed that apart from its role in the chemotactic activity, these DEGs were primarily associated with the killing of cells of other organism, inflammatory response, antimicrobial humoral immune response mediated by antimicrobial peptide, immune response and G-protein coupled receptor signaling pathway(Fig. 1 E); Additionally, KEGG analysis showed that these DEGs were connected to viral protein interactions with cytokines and cytokine receptors, the NF-kappa B signaling pathway, the TNF signaling pathway, human cytomegalovirus infection and rheumatoid arthritis (Fig. 1 F). This suggests that these genes are involved in biological processes other than chemotactic activity. Construction of a prognostic model based on chemotactic activity-related genes in the train set As shown in Fig. 2 A, we screened out 3 chemotactic activity-related genes with P < 0.05 using univariate Cox regression analysis, including 2 potential risky genes (CXCL12, ACKR3) and one potential protective genes (CXCL10). We examined whether the expression of genes identified with prognostic chemotactic activity was associated with the prognosis of BLCA using the Kaplan-Meier plotter.Our results demonstrate that BLCA patients with elevated CXCL10 expression had a better prognosis, while BLCA patients with low CXCL12 and ACKR3 expression were found to have a better prognosis (Fig. 2 B). We then conducted LASSO regression analysis based on the results of the multivariate Cox regression. (Fig. 2 C).The predictive chemotactic activity-related model was then created by LASSO regression using CXCL12, ACKR3, and CXCL10. (Fig. 2 C).For each cancer sample, we created a prognostic score using the following formula: Risk score = expr CXCL12 × 0.011159888 + expr ACKR3 × 0.002573076 + expr CXCL10 × -0.001526840.We split the 398 patients into high-risk and low-risk groups based on the median risk score for the purpose to determine whether this chemotactic activity-related model could accurately forecast the prognosis of patients with BLCA. In comparison to the low-risk group, the high-risk group had a higher mortality rate and a shorter survival period. For BLCA patients, higher scores were linked to a worse outcome (Fig. 3 A).Patients in the high-risk group had a worse outcome, according to Kaplan Meier curves (Fig. 3 C).The validation cohort also verified similar results (Fig. 3 B,D). The correlation of risk score and characteristics To investigate the clinical characterstics of the gene signature, we examined the association between the riskscore and clinic pathological information. First, we evaluated the connection between riskscore and T-staging and discovered that, in both datasets, the T3/T4 group is enriched for greater riskscore.( Fig. S1 A,C).In TCGA cohort, BLCA patients in the high risk group were older at diagnosis, ( Fig. S1 B).The risk score trended in the same direction in the validation cohort, though this difference was not statistically significant ( Fig. S1 D).However, the risk score did not correlate with N and M staging in either the training or validation databases ( Fig. File:S1 E,F,G,H).Overall, these suggesting that the signature predict malignant progression. independent evaluation of the prognosis using clinical characteristics and riskscore We carried out both univariate and multivariate independently prognosis analyses (Table 1 ) to see whether the risk score and clinical features could function as separate indicators of prognosis. Age, T stage, N stage, and risk score were substantially linked with the OS of BLCA patients, according to the findings of a univariate independent prognostic analysis. An independent multivariable analysis of prognosis showed that risk score may be the most independent predictor (p < 0.001). Table 1 Univariate and multivariate analysis of prognostic parameters in training group (OS) Variable Univariate analysis Multivariate analysis HR (95% CI) p Value HR (95% CI) p Value age 1.028 (1-1.057) 0.047 1.027 (0.998–1.056) 0.07 gender 0.617(0.354–1.077) 0.053 0.742 (0.406–1.354) 0.331 Tx 1.724(1.16–2.562) 0.007 1.525(0.979–2.376) 0.062 N 1.566 (1.192–2.057) 0.001 1.491 (1.085–2.05) 0.014 M 2.069 (0.745–5.746) 0.163 0.907(0.286–2.872) 0.868 Riskscore 3.49 (1.926–6.326) ༜0.0001 3.586 (1.805–7.127) ༜0.0001 Construction and verification The individualized prediction model We developed a nomogram incorporating age, gender, T, N, M, and risk score to make it easier for clinicians to apply this model to gain accurate prognostic information for each patient. The nomogram demonstrated that among many clinical criteria, the risk score was a significant contributor (Fig. 4 A). In the training and validation databases, the nomogram and actual observations in the calibration curve displayed an acceptable overlap, indicating great predictive accuracy (Fig. 4 B, C). The prognosis accuracy of OS was 0.715 at 1 year, 0.766 at 3 years, and 0.760 at 5 years, according to a time-dependent ROC analysis (Fig. 4 D). Similarly, our model shows strong predictive accuracy in the validation cohort (Fig. 4 F).These research' findings suggested that our model would be beneficial in forecasting BLCA prognostic, and the model we developed has a very high prognosis prediction accuracy. Functional enrichment analysis for different risk groups Next, To learn more about the connection between signatures and biological functions and activities, GSEA analysis was carried out. The examination of the identifiable gene sets revealed that DNA_REPLICATION_PRE_INITIATION, CELLULAR_RESPONSE_TO_HYPOXIA, STABILIZATION_OF_P53, NEGATIVE_REGULATION_OF_NOTCH4_SIGNALING and CYTOPROTECTION_BY_HMOX1 were significantly enriched in low-risk group, and ION_HOMEOSTASIS and GLYCOSAMINOGLYCAN_METABOLISM was significantly enriched in high-risk group (Fig. 5 ). The dysregulation of these pathways may be one of the factors affecting the prognosis of BLCA. Analysis of Tumor Microenvironment and Immune cell infiltration The development, progression, metastasis, and therapeutic response of tumors are all significantly influenced by the tumor microenvironment 12 .In order to investigate the relationship between these scores and the risk score, we merged the stromal score with the ESTIMATE score and the TIS score. The findings revealed a strong correlation between risk score and stromal score (P = 0.001, Fig. 6 A) and estimate score (P = 0.039, Fig. 6 B). Tumor inflammation signature (TIS) scores could well-predicted the cancers’ responsiveness to checkpoint blockade 13 .According to Fig. 6 C of our study, patients in the lower risk group had higher TIS scores, which suggests that they would respond more favorably to immune checkpoint inhibitors. Using the CIBERSORT algorithm, the proportion of 22 different types of immune cells in BLCA patients was determined, and a cut-off value of P-value 0.05 was used (Fig. 6 D). The high-risk group had higher levels of B cell naive infiltration and mast cell resting than the low-risk group did. However, the low-risk group had higher levels of infiltration of T cells CD4 memory activated, NK cells dormant, T cells follicular helper, T cells CD8, and Macrophages M1 than the high-risk group did (Fig. 6 E). The association of the levels of chemotactic activity-related genes with the infiltration level of immune cells in BLCA We also discovered a negative correlation between the expression of all three genes in BLCA tissues and tumor purity. The infiltration rates of CD4 + T cells, CD8 + T cells, B cells, macrophages, neutrophils, and DCs in BLCA were favorably correlated with the expression of CXCL12 mRNA. However, only B cells and macrophages showed a substantial and positive correlation with ACKR3 expression. In the end, we discovered that, with the exception of B cells, the CXCL10 was positively connected with the infiltration of CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells (Fig. 7 .A-C). Inflammatory response and tumor immunity were both highly correlated with genes associated to chemotactic activity, as was already mentioned. Discussion The chemokine superfamily, consisting of 48 chemokines and 23 receptors, is closely correlated with several hallmarks of cancer 14 . The four main classes of chemokines—CC, CXC, C, and CX3C—make up the biggest subfamily of cytokines 15 . All leukocytes and many nonhematopoietic cells, including cancer cells, express chemokine receptors differently. These receptors can be categorized into two groups: Atypical chemokine receptors and G protein-coupled chemokine receptors 16 . Together they coordinate and control the migration and localization of all immune cells in the body 17 . Therefore, chemokine systems constitute highly relevant therapeutic targets for a wide range of immune-related and inflammatory diseases. In particular, the chemokine system is highly associated with cancer and is reported in almost all disease hallmarks, such as promoting angiogenesis, metastasis, and immunosuppression of the tumor microenvironment 18 . Although some chemokines, such as CXCL13, have been found to modulate the structural organization of mature TLS in human tumors to promote an effective anti-tumor immune response, the role of most chemotactic activity-related genes in BLCA progression has not been reported. Therefore, a comprehensive analysis and the molecular characterization of chemotactic activity-related genes in BLCA will advance our understanding of the antitumor immune response and prognosis of BLCA. In our current studies, we first analyzed the differentially expressed chemotactic activity-related genes by using the information from TCGA database,then, selected the top 11 chemotactic activity-related genes that exhibited significantly differential expression between normal and BLCA tissue for further investigation of their potential mechanisms and biological functions. As expected, these genes were predominantly enriched in chemokinetic functionally relevant gene sets and signaling pathways, in addition to being involved in the killing of cells of other organism, inflammatory response, antimicrobial humoral immune response mediated by antimicrobial peptide, immune response, G-protein coupled receptor signaling pathway(GO) and Viral protein interaction with cytokine and cytokine receptors, NF-kappa B signaling pathway, TNF signaling pathway, Human cytomegalovirus infection and Rheumatoid arthritis(KEGG). Using the use of univariate Cox and Lasso Cox regression analysis, we created a prognostic risk model based on 3 genes (CXCL12, ACKR3, and CXCL10). Meanwhile, we analyzed these three genes in pan-cancer and found significant differences in various tumors(Fig. 8 ).In the previous study, CXCL12 is a CXC chemokine that traditionally has been classified as a homeostatic chemokine 19 , It contributes to physiological processes such as hematopoiesis, cardiogenesis, vascular formation, neurogenesis, and lastly maintenance of tissue stem cells 19 , 20 . Six different splice variants have been identified in humans (CXCL12α to ϕ) 21 , only CXCL12 β mRNA levels were significantly elevated in bladder cancer compared to normal bladder tissue and were associated with recurrence, metastasis and poor survival, in addition,CXCL12-β mRNA levels in exfoliated cells are highly sensitive to bladder cancer and a potential predictor of future recurrence 22 . Margitta M et al reported CXCL12 induces actin polymerization in bladder cancer cells and promotes bladder cancer migration 23 .ACKR3 is an atypical chemokine receptor first identified and was originally identified as orphan chemokine receptor RDC-1 / CXCR7 with high affinity for CXCL11 and CXCL12 16,24 . However, some studies indicate that ACKR3 does not couple or activate G proteins and does not trigger the typical chemokine receptor signaling pathway 25 , thus it behaves more like an atypical chemokine receptor and has therefore been renamed ACKR3.Until now, most research has focused on the potential role of CXCR7 expression in cancer cells. ACKR3 is expressed on tumors of hematopoietic origin, such as lymphomas 26 and of mesenchymal origin, such as sarcomas 27 , 28 as well as prostate and breast cancer 9 , 10 . In addition to increasing tumor cell proliferation and reducing trail-mediated apoptosis, it also plays pivotal parts in a plethora of physiological processes such as induces EGFR activation 29 , leads to increased endothelial cell migration (angiogenesis) 9 , enriches the cancer stem cells (CSCs) sub-population 30 .In vitro and in vivo studies of bladder cancer cell lines showed that alterations in ACKR3 expression were associated with proliferation, apoptosis, migration, invasion, angiogenesis and tumor growth activity. Furthermore, ACKR3 expression modulates the expression of pro-angiogenic factors IL-8 or VEGF, which may be involved in the regulation of tumor angiogenesis 31 . CXCL10, also known as interferon -induced protein 10 (IP10), is a 10-kDa protein that is functionally classified as an "inflammatory" chemokine. Moreover, due to lacking ELR motif, CXCL10 suppresses neovascularization and functions as an “angiostatic factor". In addition to inhibiting basal fibroblast growth factor (bFGF) and VEGF-induced angiogenesis, CXCL10 has potent anti-tumor activity against tumors by recruiting CTLs 32 , 33 .CXCL10 binds to CXCR3 and regulates the immune response by activating and recruiting leukocytes, including T cells, eosinophils, monocytes and NK cells 34 . For BC patients that responded to BCG therapy, CXCL10 exhibited a consistent pattern of stepwise increases, suggesting that CXCL10 expression may be related to the strength of the immune anti-tumor response 35 . We further performed GSEA to determine the difference in signaling pathway enrichment between the high- and low-risk groups. Our results showed that the low-risk group was significantly enriched in stabilization of P53 and negative regulation of NOTCH4 signaling, while the high-risk group showed no significant enrichment. P53 tumor suppressor is the most frequently mutated gene in human cancer. The fundamental role of P53 in tumor suppression is unequivocal and involved in a variety of biological processes: cell cycle arrest, DNA repair, apoptosis, aging, autophagy, iron downfall, or metabolism 36 , 37 .Unfortunately, TP53 is inactivated in most, if not all, human cancers. NOTCH is considered an ancient and highly conserved signaling pathway. Potential mechanisms of tumorigenesis involving NOTCH signaling include controlling of tumor-initiating cell phenotypes, regulation of known upstream or downstream tumor-associated signaling factors such as MYC or P53, promotion of angiogenesis or tumor invasion, and regulation of cell cycle 38 , 39 . The abnormalities of these pathways may be the cause for the difference in prognosis between high-risk and low-risk patients. In this study, a nomogram incorporating age, gender, T, N, M, and risk score was created. The risk value that was determined after the model was created is a trustworthy independent prognostic index. The risk score produced by the aforementioned three gene expressions can more accurately predict patient survival when compared to the traditional prognostic indexes such as "age," "gender," "T," "N," and "M." This finding further supports the notion that the gene-based expression signal can reliably predict the prognosis of BC patients. The model's adequate predictive power is demonstrated by the calibration curves and time-dependent ROC curves. However, our study has some limitations. Firstly, our study is a retrospective study based on an online database, the nature of the retrospective study design is one of the weaknesses of this study. In addition, the predictive power of this model in BC patients requires further validation by clinicians for better prognostic stratification and treatment management. Moreover, the biological function of above three genes requires further experimental validation. Conclusions With the use of CXCL12, ACKR3, and CXCL10, we have created a chemotactic activity-related predictive model in this study that may aid doctors in making conclusions regarding BLCA patients and provide useful information for tailored management. Declarations Ethics approval and consent to participate The results of our research data were obtained from a freely available website and did not include any information from human participants. All procedures were carried out in conformity with the pertinent rules and regulations. Availability of data and materials On reasonable request, the corresponding author will provide access to all the data analyzed during the current study. In this work, publicly accessible datasets from Gene Expression Omnibus (GSE13507 and GSE48276) and The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) were evaluated. Conflicts of interest In this domain, the writers affirm their claim to had no competing interests. Authors' contributions Jingyan Tian and Xiaoqing Wang supervised this project and were responsible for designing the paper. Zhang Ming conducted data analysis and prepared all the figures and tables. Zhang Ming, Xing Dong, Weijie Yang, Mingyang Chang, Jianing Lv1 drafted the manuscript. Jingyan Tian, Xiaoqing Wang and Wu Qian edited and revised the manuscript. All authors read and approved the final manuscript. References Lenis AT, Lec PM, Chamie K, Mshs MD. Bladder Cancer: A Review. JAMA Nov. 2020;17(19):1980–91. 10.1001/jama.2020.17598 . Lobo N, Shariat SF, Guo CC, Fernandez MI, Kassouf W, Choudhury A, et al. What Is the Significance of Variant Histology in Urothelial Carcinoma? Eur Urol Focus Jul. 2020;15(4):653–63. 10.1016/j.euf.2019.09.003 . Witjes JA, Bruins HM, Cathomas R, Comperat EM, Cowan NC, Gakis G, et al. European Association of Urology Guidelines on Muscle-invasive and Metastatic Bladder Cancer: Summary of the 2020 Guidelines. 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PLoS ONE. 2018;13(6):e0198789. 10.1371/journal.pone.0198789 . Desnoyer A, Dupin N, Assoumou L, Carlotti A, Gaudin F, Deback C, et al. Expression pattern of the CXCL12/CXCR4-CXCR7 trio in Kaposi sarcoma skin lesions. Br J Dermatol Dec. 2016;175(6):1251–62. 10.1111/bjd.14748 . Bennani-Baiti IM, Cooper A, Lawlor ER, Kauer M, Ban J, Aryee DN, et al. Intercohort gene expression co-analysis reveals chemokine receptors as prognostic indicators in Ewing's sarcoma. Clin Cancer Res Jul. 2010;15(14):3769–78. 10.1158/1078-0432.CCR-10-0558 . Salazar N, Mu?Oz D, Kallifatidis G, Singh RK, Jordà M, Lokeshwar BL. The chemokine receptor CXCR7 interacts with EGFR to promote breast cancer cell proliferation. Mol Cancer. 2014;13(1):198. Morein D, Erlichman N, Ben-Baruch A. Beyond Cell Motility: The Expanding Roles of Chemokines and Their Receptors in Malignancy. Front Immunol. 2020;11:952. 10.3389/fimmu.2020.00952 . Hao M, Zheng J, Hou K, Wang J, Chen X, Lu X, et al. Role of chemokine receptor CXCR7 in bladder cancer progression. Biochem Pharmacol Jul. 2012;15(2):204–14. 10.1016/j.bcp.2012.04.007 . Nazari A, Ahmadi Z, Hassanshahi G, Abbasifard M, Khorramdelazad H. Effective Treatments for Bladder Cancer Affecting CXCL9/CXCL10/CXCL11/ CXCR3 Axis: A Review. Oman Med J. 2020;35(2):e103–3. Karin N, Razon H. Chemokines beyond chemo-attraction: CXCL10 and its significant role in cancer and autoimmunity. Cytokine Sep. 2018;109:24–8. 10.1016/j.cyto.2018.02.012 . Ahmadi Z, Arababadi MK, Hassanshahi G. CXCL10 Activities, Biological Structure, and Source Along with Its Significant Role Played in Pathophysiology of Type I Diabetes Mellitus. Inflammation. 2013;36(2):364–71. Ashiru O, Esteso G, Garcia-Cuesta EM, Castellano E, Samba C, Escudero-Lopez E, et al. BCG Therapy of Bladder Cancer Stimulates a Prolonged Release of the Chemoattractant CXCL10 (IP10) in Patient Urine. Cancers (Basel) Jul. 2019;4(7). 10.3390/cancers11070940 . Boutelle AM, Attardi LD. p53 and Tumor Suppression: It Takes a Network. Trends Cell Biol Apr. 2021;31(4):298–310. 10.1016/j.tcb.2020.12.011 . Duffy MJ, Synnott NC, O’Grady S, Crown J. Targeting p53 for the treatment of cancer. Sem Cancer Biol. 2022;79:58–67. 10.1016/j.semcancer.2020.07.005 . Aster JC, Pear WS, Blacklow SC. The Varied Roles of Notch in Cancer. Annu Rev Pathol Jan. 2017;24:12:245–75. 10.1146/annurev-pathol-052016-100127 . Zhou B, Lin W, Long Y, Yang Y, Zhang H, Wu K, et al. Notch signaling pathway: architecture, disease, and therapeutics. Signal Transduct Target Ther Mar. 2022;24(1):95. 10.1038/s41392-022-00934-y . Additional Declarations No competing interests reported. Supplementary Files supplementary.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-3385390","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":245313247,"identity":"6d9c8c1d-c216-4cba-8085-123e4750b8fd","order_by":0,"name":"Ming Zhang","email":"","orcid":"","institution":"Department of urology, Lequn branch,The First Hospital of Jilin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Zhang","suffix":""},{"id":245313248,"identity":"ea51395e-7ad3-4cc3-81f3-b381dc876e87","order_by":1,"name":"Xing Dong","email":"","orcid":"","institution":"the First Hospital of Jilin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Dong","suffix":""},{"id":245313249,"identity":"41153458-3252-439d-a64f-18104996bb02","order_by":2,"name":"Weijie Yang","email":"","orcid":"","institution":"the First Hospital of Jilin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weijie","middleName":"","lastName":"Yang","suffix":""},{"id":245313250,"identity":"4c91051a-66fd-423c-acd3-9237f694dfb8","order_by":3,"name":"Qian Wu","email":"","orcid":"","institution":"Jeonbuk National University Medical School","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Wu","suffix":""},{"id":245313251,"identity":"908cc472-3bfb-453d-8d93-48e51d9e13aa","order_by":4,"name":"Mingyang Chang","email":"","orcid":"","institution":"the First Hospital of Jilin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingyang","middleName":"","lastName":"Chang","suffix":""},{"id":245313252,"identity":"b77b84d8-5c80-46f6-8768-47ab5de8a89a","order_by":5,"name":"Jianing Lv","email":"","orcid":"","institution":"the First Hospital of Jilin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianing","middleName":"","lastName":"Lv","suffix":""},{"id":245313253,"identity":"db261de9-a875-44c2-a31b-1524028edcc1","order_by":6,"name":"Xiaoqing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3RMQrCMBSA4UihLsHZDnqGB4GIIHqVBMGpiKPgoOCuq4KHyBGedOhSca24ZHKxQ8FV0JQ6p3UTzA8hy/tCQghxuX42MKu5kmi2xqo+oSi+IUVtQeoRiE/Rnc6G3dZF64iSQUehd9NWkkwn/R2MWXAVwpAJU+j3wEY4hhxy8KQqSSQVUr9tJeeMg4ClVBcsyKsGSUOmczCHp6QgWE1GacYbO4hZkAhxPJhH7SOfW0mwDdmDPhfdVpxInc2HnU28vlmJ6XMNpKL8U69ivhjJS9LE6lmXy+X6y95MK1C/3vI0/gAAAABJRU5ErkJggg==","orcid":"","institution":"Department of urology, Lequn branch,The First Hospital of Jilin University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiaoqing","middleName":"","lastName":"Wang","suffix":""},{"id":245313254,"identity":"5f488242-81d2-466f-a4a1-85da4d58d75b","order_by":7,"name":"Jingyan Tian","email":"","orcid":"","institution":"Department of urology, Lequn branch,The First Hospital of Jilin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingyan","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2023-09-25 14:44:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3385390/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3385390/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46029902,"identity":"1c628f09-5909-4c3e-a859-967d8dea0fba","added_by":"auto","created_at":"2023-11-07 17:51:30","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":231213,"visible":true,"origin":"","legend":"\u003cp\u003eChemotactic activity-related genes that differ in expression between BLCA tissues and healthy tissues. Chemotactic activity-related genes are shown on a volcano map, with red dots denoting high expression and green dots denoting low expression. B Chemotactic activity-related genes interact as seen by the PPI network (interaction score = 0.4). C Chemotactic activity-related gene mutation analyses in the TCGA cohort. D Heatmap showing genes associated with chemotactic activity that are differentially expressed. The barplot graph showing the TCGA's GO(E) and KEGG(F) enrichment analysis results.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3385390/v1/453b3adecc43fcb77744450c.jpeg"},{"id":46027855,"identity":"ce9923ba-897f-4097-bba6-f4167b7d41c4","added_by":"auto","created_at":"2023-11-07 17:43:30","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":158966,"visible":true,"origin":"","legend":"\u003cp\u003eBuilding a risk predictive model based on TCGA cohort genes associated to chemotactic activity. All chemotactic activity-related genes were subjected to a Univariate Cox regression analysis. A statistically significant P value was defined as 0.05. B OS comparing the chemotactic activity-related genes' high and low expression in BLCA patients. The OS-related genes were regressed using LASSO in C.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3385390/v1/c7bdd0296de28606c5a21bd9.jpeg"},{"id":46027852,"identity":"b4615abe-215b-4dea-86fc-8aeb29a9cdc0","added_by":"auto","created_at":"2023-11-07 17:43:30","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128430,"visible":true,"origin":"","legend":"\u003cp\u003eThe connection between the riskscore, clinical traits, and survival in BLCA patients. (A and B) The clinical-pathologic variables for each BLCA were shown on the heatmap in increasing order of the riskscore in the training and validation groups. (C and D)Patients in the high-risk category have shorter OS than patients in the low-risk group, according to the Kaplan-Meier curves.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3385390/v1/6e43a0d4ea8d0beeff3f1d3d.jpeg"},{"id":46029903,"identity":"c65cbd16-9ea7-443f-aa22-8fbbb628df66","added_by":"auto","created_at":"2023-11-07 17:51:30","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":213889,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram to forecast the survival probability of BLCA patients. A nomogram combines pathologic characteristics with a risk score. B–C In the training (B) and validation (C) groups, the calibration plots for 1-, 3-, and 5-year survival probabilities compared predicted with actual OS. D-E Analyzing the AUC in the ROC for the risk signature at 1, 3, and 5 years of survival in the training (D) and validation (E) groups.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3385390/v1/9d5f59b8deb161a6cddc6179.jpeg"},{"id":46027857,"identity":"09522c22-cfd8-4276-a4aa-02cba873a343","added_by":"auto","created_at":"2023-11-07 17:43:30","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":381092,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of chemotactic activity-related genes signature genes in the TCGA cohort. sig\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3385390/v1/5496bac6acf9c8957c6287ea.jpeg"},{"id":46027854,"identity":"405fd666-d680-49e3-aebf-9422e836ab16","added_by":"auto","created_at":"2023-11-07 17:43:30","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":371183,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the tumor microenvironment and immune cell invasion. The association between the risk score and the stromal score (A), estimate score (B), and TIS score (C) is shown in a violin plot. (D-E) Immune cell infiltration in high-risk and low-risk categories, in relation to each other. Low-risk category is shown by the color orange. Red denotes the group at most danger.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3385390/v1/21cc39993d81578e948268bc.jpeg"},{"id":46027860,"identity":"635a225e-20c3-44bf-a317-820c3ae77992","added_by":"auto","created_at":"2023-11-07 17:43:30","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":352962,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of three genes expression and infiltration levels of immune cells in BLCA tissues based on the TIMER database.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3385390/v1/b05ac8a2f2c4076bce77a96b.jpeg"},{"id":46027859,"identity":"410a622b-ca2c-4b48-acd7-f6b408a5fa8b","added_by":"auto","created_at":"2023-11-07 17:43:30","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":813476,"visible":true,"origin":"","legend":"\u003cp\u003eThe level of CXCL12(A), ACKR3(CXCR7, B), CXCL10(C) expression in different tumor types from the TCGA database in TIMER. Note: *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3385390/v1/66c2ef75984a02cdfb628d09.jpeg"},{"id":49867709,"identity":"a2f3bae1-dfef-4bcd-8f33-7e8bf1cee75b","added_by":"auto","created_at":"2024-01-19 10:52:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1752054,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3385390/v1/1de3744e-011d-480d-8c9a-22205203fc9a.pdf"},{"id":46027858,"identity":"9ae37a7c-1734-4771-afe6-9ceb99f864eb","added_by":"auto","created_at":"2023-11-07 17:43:30","extension":"doc","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":937472,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.doc","url":"https://assets-eu.researchsquare.com/files/rs-3385390/v1/b8536dd39cca4261e9d3165f.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel 3 chemotactic activity-related gene signature for Predicting prognosis of bladder Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBladder cancer is one of the most common malignant tumors of the urinary system, account for 500 000 new cases and 200 000 deaths worldwide each year \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Bladder urothelial carcinoma (BLCA) accounts for approximately 95% of bladder tumors. Histologically, approximately 75% of bladder cancers are classified as pure urothelial carcinoma, with histological variants accounting for the remaining 25%\u003csup\u003e2\u003c/sup\u003e, which poses a unique challenge for both clinical treatment and monitoring of tumor prognosis. The 5-year survival rate of advanced BLCA patients is only about 6%\u003csup\u003e3,4\u003c/sup\u003e, with 12\u0026ndash;14 months of the OS\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.Although some biomarkers are related to the tumorigenesis and prognosis of BLCA have been evaluated\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, the methods that could accurately predict the prognosis of BLCA patients remain limited. Therefore, it is an urgent need to develop predictive models for the improved prognosis and treatment of BLCA patients.\u003c/p\u003e \u003cp\u003eIn recent years, immunotherapy has become a hot topic in the treatment of various tumors, and chemokines and their receptors, which are important components of the immune system, have gradually attracted attention. Chemokines, a large family of cytokines with chemotactic activity, and their cognate receptors are expressed by both cancer and stromal cells. Their altered expression dictates immunocyte recruitment and activation, angiogenesis, cancer cell proliferation, and metastasis in all the stages of various cancers\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.In recent study showed that CXCR6 is critical for cytotoxic T lymphocyte(CTL) -mediated tumor control, and uncover the central role of the chemokine receptor CXCR6 in positioning tumor-infiltrating CTLs provides critical survival and proliferation signals to prolong survival in human cancer patients\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.In prostate cancer, CXCL12/CXCR4 are identified as important prognostic markers of specific vasculature alterations, and inhibition of CXCR4 has been shown to effectively reduce the proliferative capacity and inhibit migration of tumor endothelial cells\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, the same results were found for CXCR7(ACKR3) in breast and lung tumor\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e; Another study found that activation of MAPK pathway by CXCR7 through recruitment of ARRB2 is a mechanism of resistance to second generation anti-androgen therapy\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. HCC tumors derived IRF-1 activates immune cells to induce apoptosis of tumor cells through the CXCL10/CXCR3 axis in murine HCC tumor\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe importance of chemokines and their receptors in the cancerous process is becoming more and more clear from research. To effectively treat BLCA, it is crucial to thoroughly understand the function of bladder carcinogenesis and progression as well as to develop an accurate prognostic model of chemokines and their receptors. In this work, we explored the transcriptional profiles and clinical relevance of chemokines and their receptors in BLCA patients. We initially developed and validated the chemokines- and chemokine receptors-based prognostic risk model using 617 cases of BLCA from 3 independent cohorts.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition and preprocessing\u003c/h2\u003e \u003cp\u003eThe RNA sequencing (RNA-seq) data of 406 BLCA samples and 19 normal pancreatic samples with their clinical pathological parameters were downloaded from the cancer genome atlas (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases as the training cohort. The RNA-seq data and clinical pathological features of 219 BLCA samples were downloaded from Gene Expression Omnibus GSE13507 and GSE48276 datasets (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as the validation cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of differentially expressed chemotactic activity-related genes\u003c/h2\u003e \u003cp\u003eThe \u0026ldquo;limma\u0026rdquo; package was used to identify differentially expressed chemotactic activity-related genes in TCGA-BLCA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2 fold change (FC)|\u0026gt; 0.8 were set as cut-of values. Protein protein interactions (PPIs) were plotted by using string database, the minimum interaction score required for PPI analysis was set at 0.4 (medium confidence).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of prognostic genes\u003c/h2\u003e \u003cp\u003eTo investigate the relationship between the expression levels of chemotactic activity-related genes and OS of BLCA patients, we conducted a univariate Cox regression analysis using the SPSS software (IBM, v22.0, Chicago, IL). A significant filtering criterion was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for further analysis. We next eliminated gene collinearity and reduced the number of genes using LASSO Cox regression. LASSO-COX dimension reduction analysis was performed by glmnet and survival packages in R. The λ value corresponding to the minimum partial likelihood deviance was selected as the optimal λ in our study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of a prognostic model based on chemotactic activity-related genes\u003c/h2\u003e \u003cp\u003eThe risk score was calculated according to the standardized BLCA mRNA expression data in the training set. Risk score\u0026thinsp;=\u0026thinsp;expr\u003csub\u003eCXCL12\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;λ\u003csub\u003eCXCL12\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;expr\u003csub\u003eACKR3\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;λ\u003csub\u003eACKR3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;expr\u003csub\u003eCXCL10\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;λ\u003csub\u003eCXCL10\u003c/sub\u003e.where expr\u003csub\u003egene\u003c/sub\u003e was the expression level of the gene and λ\u003csub\u003egene\u003c/sub\u003e was the corresponding lambda value. BLCA patients were divided into high-risk and low-risk groups based on the median risk score, and the OS between these two groups was analyzed. To make the model more convincing, we utilized the BLCA cohort in the GEO(GSE13507 and GSE48276) database for validation. The expression of each chemotactic activity-related gene was also normalized, and the risk score was then calculated by the above formula. Next, to determine if risk score was an independent prognostic factor for OS in BLCA patients in the train set, univariate and multivariate Cox regression analyses were conducted. Covariates included age, gender, T, N and M.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eNomogram construction\u003c/h2\u003e \u003cp\u003eNomogram analysis was constructed in the training group by rms package in R. The upper part is the scoring system and the lower part is the prediction system. The 1-, 3-, and 5-year survival BLCA patients could exactly be predicted by total points, sum points of every factor. Verification of the prediction accuracy of OS was performed in patients of the validation group. Receiver operating characteristic (ROC) curves were produced by the timeROC package. Calibration curves and ROC values were used to show the accuracy of the survival prediction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were executed using R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/,v4.1.3\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/,v4.1.3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), SPSS software (IBM, v22.0, Chicago, IL), and GraphPad Prism (v9.0, La Jolla, CA). The prognostic value was evaluated by Kaplan-Meier analysis and COX analysis. GSEA analyses were implemented with GSEA package in java software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://software.broadinstitute.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"http://software.broadinstitute.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Gene Ontology (GO ) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID portal website ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/summary.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/summary.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). For all statistical methods, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered a significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of differentially expressed chemotactic activity-related genes in BLCA\u003c/h2\u003e \u003cp\u003eWe obtained 406 BLCA patients and 19 normal tissues from TCGA. A total of 11 chemotactic activity-related differentially expressed genes(DEGs) were identified based on the cutoff criteria of |log2 (fold change) |\u0026gt;0.8 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from 58 chemotactic activity-related genes. 8 chemotactic activity-related genes were considerably downregulated in BLCA patients, while 3 genes were upregulated, according to volcano plots and heatmaps (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, D). This differentially expressed chemotactic activity-related genes' protein-protein interaction network was displayed in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Furthermore, multiple mutations have been found in these genes associated with variable chemotactic activity in BLCA patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eTo explore the biological functions and pathways associated with the chemotactic activity-related DEGs, GO and KEGG functional enrichment analysis were performed. The analysis of GO enrichment revealed that apart from its role in the chemotactic activity, these DEGs were primarily associated with the killing of cells of other organism, inflammatory response, antimicrobial humoral immune response mediated by antimicrobial peptide, immune response and G-protein coupled receptor signaling pathway(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE); Additionally, KEGG analysis showed that these DEGs were connected to viral protein interactions with cytokines and cytokine receptors, the NF-kappa B signaling pathway, the TNF signaling pathway, human cytomegalovirus infection and rheumatoid arthritis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). This suggests that these genes are involved in biological processes other than chemotactic activity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a prognostic model based on chemotactic activity-related genes in the train set\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, we screened out 3 chemotactic activity-related genes with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 using univariate Cox regression analysis, including 2 potential risky genes (CXCL12, ACKR3) and one potential protective genes (CXCL10). We examined whether the expression of genes identified with prognostic chemotactic activity was associated with the prognosis of BLCA using the Kaplan-Meier plotter.Our results demonstrate that BLCA patients with elevated CXCL10 expression had a better prognosis, while BLCA patients with low CXCL12 and ACKR3 expression were found to have a better prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). We then conducted LASSO regression analysis based on the results of the multivariate Cox regression. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).The predictive chemotactic activity-related model was then created by LASSO regression using CXCL12, ACKR3, and CXCL10. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).For each cancer sample, we created a prognostic score using the following formula: Risk score\u0026thinsp;=\u0026thinsp;expr\u003csub\u003eCXCL12\u003c/sub\u003e\u0026times; 0.011159888\u0026thinsp;+\u0026thinsp;expr\u003csub\u003eACKR3\u003c/sub\u003e \u0026times; 0.002573076\u0026thinsp;+\u0026thinsp;expr\u003csub\u003eCXCL10\u003c/sub\u003e \u0026times; -0.001526840.We split the 398 patients into high-risk and low-risk groups based on the median risk score for the purpose to determine whether this chemotactic activity-related model could accurately forecast the prognosis of patients with BLCA. In comparison to the low-risk group, the high-risk group had a higher mortality rate and a shorter survival period. For BLCA patients, higher scores were linked to a worse outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).Patients in the high-risk group had a worse outcome, according to Kaplan Meier curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).The validation cohort also verified similar results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB,D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe correlation of risk score and characteristics\u003c/h2\u003e \u003cp\u003eTo investigate the clinical characterstics of the gene signature, we examined the association between the riskscore and clinic pathological information. First, we evaluated the connection between riskscore and T-staging and discovered that, in both datasets, the T3/T4 group is enriched for greater riskscore.( Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e A,C).In TCGA cohort, BLCA patients in the high risk group were older at diagnosis, ( Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e B).The risk score trended in the same direction in the validation cohort, though this difference was not statistically significant ( Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e D).However, the risk score did not correlate with N and M staging in either the training or validation databases ( Fig. File:S1 E,F,G,H).Overall, these suggesting that the signature predict malignant progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eindependent evaluation of the prognosis using clinical characteristics and riskscore\u003c/h2\u003e \u003cp\u003eWe carried out both univariate and multivariate independently prognosis analyses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to see whether the risk score and clinical features could function as separate indicators of prognosis. Age, T stage, N stage, and risk score were substantially linked with the OS of BLCA patients, according to the findings of a univariate independent prognostic analysis. An independent multivariable analysis of prognosis showed that risk score may be the most independent predictor (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eUnivariate and multivariate analysis of prognostic parameters in training group (OS)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003cp\u003e(1-1.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003cp\u003e(0.998\u0026ndash;1.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003egender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.617(0.354\u0026ndash;1.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003cp\u003e(0.406\u0026ndash;1.354)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTx\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.724(1.16\u0026ndash;2.562)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.525(0.979\u0026ndash;2.376)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.566\u003c/p\u003e \u003cp\u003e(1.192\u0026ndash;2.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.491\u003c/p\u003e \u003cp\u003e(1.085\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.069\u003c/p\u003e \u003cp\u003e(0.745\u0026ndash;5.746)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.907(0.286\u0026ndash;2.872)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRiskscore\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003cp\u003e(1.926\u0026ndash;6.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e༜0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.586\u003c/p\u003e \u003cp\u003e(1.805\u0026ndash;7.127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e༜0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and verification The individualized prediction model\u003c/h2\u003e \u003cp\u003eWe developed a nomogram incorporating age, gender, T, N, M, and risk score to make it easier for clinicians to apply this model to gain accurate prognostic information for each patient. The nomogram demonstrated that among many clinical criteria, the risk score was a significant contributor (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In the training and validation databases, the nomogram and actual observations in the calibration curve displayed an acceptable overlap, indicating great predictive accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C). The prognosis accuracy of OS was 0.715 at 1 year, 0.766 at 3 years, and 0.760 at 5 years, according to a time-dependent ROC analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Similarly, our model shows strong predictive accuracy in the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).These research' findings suggested that our model would be beneficial in forecasting BLCA prognostic, and the model we developed has a very high prognosis prediction accuracy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis for different risk groups\u003c/h2\u003e \u003cp\u003eNext, To learn more about the connection between signatures and biological functions and activities, GSEA analysis was carried out. The examination of the identifiable gene sets revealed that DNA_REPLICATION_PRE_INITIATION, CELLULAR_RESPONSE_TO_HYPOXIA, STABILIZATION_OF_P53, NEGATIVE_REGULATION_OF_NOTCH4_SIGNALING and CYTOPROTECTION_BY_HMOX1 were significantly enriched in low-risk group, and ION_HOMEOSTASIS and GLYCOSAMINOGLYCAN_METABOLISM was significantly enriched in high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The dysregulation of these pathways may be one of the factors affecting the prognosis of BLCA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Tumor Microenvironment and Immune cell infiltration\u003c/h2\u003e \u003cp\u003eThe development, progression, metastasis, and therapeutic response of tumors are all significantly influenced by the tumor microenvironment\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.In order to investigate the relationship between these scores and the risk score, we merged the stromal score with the ESTIMATE score and the TIS score. The findings revealed a strong correlation between risk score and stromal score (P\u0026thinsp;=\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) and estimate score (P\u0026thinsp;=\u0026thinsp;0.039, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Tumor inflammation signature (TIS) scores could well-predicted the cancers\u0026rsquo; responsiveness to checkpoint blockade\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.According to Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC of our study, patients in the lower risk group had higher TIS scores, which suggests that they would respond more favorably to immune checkpoint inhibitors. Using the CIBERSORT algorithm, the proportion of 22 different types of immune cells in BLCA patients was determined, and a cut-off value of P-value 0.05 was used (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The high-risk group had higher levels of B cell naive infiltration and mast cell resting than the low-risk group did. However, the low-risk group had higher levels of infiltration of T cells CD4 memory activated, NK cells dormant, T cells follicular helper, T cells CD8, and Macrophages M1 than the high-risk group did (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe association of the levels of chemotactic activity-related genes with the infiltration level of immune cells in BLCA\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe also discovered a negative correlation between the expression of all three genes in BLCA tissues and tumor purity. The infiltration rates of CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, B cells, macrophages, neutrophils, and DCs in BLCA were favorably correlated with the expression of CXCL12 mRNA. However, only B cells and macrophages showed a substantial and positive correlation with ACKR3 expression. In the end, we discovered that, with the exception of B cells, the CXCL10 was positively connected with the infiltration of CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, neutrophils, macrophages, and dendritic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.A-C). Inflammatory response and tumor immunity were both highly correlated with genes associated to chemotactic activity, as was already mentioned.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe chemokine superfamily, consisting of 48 chemokines and 23 receptors, is closely correlated with several hallmarks of cancer\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The four main classes of chemokines\u0026mdash;CC, CXC, C, and CX3C\u0026mdash;make up the biggest subfamily of cytokines\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. All leukocytes and many nonhematopoietic cells, including cancer cells, express chemokine receptors differently. These receptors can be categorized into two groups: Atypical chemokine receptors and G protein-coupled chemokine receptors\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Together they coordinate and control the migration and localization of all immune cells in the body\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Therefore, chemokine systems constitute highly relevant therapeutic targets for a wide range of immune-related and inflammatory diseases. In particular, the chemokine system is highly associated with cancer and is reported in almost all disease hallmarks, such as promoting angiogenesis, metastasis, and immunosuppression of the tumor microenvironment\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Although some chemokines, such as CXCL13, have been found to modulate the structural organization of mature TLS in human tumors to promote an effective anti-tumor immune response, the role of most chemotactic activity-related genes in BLCA progression has not been reported. Therefore, a comprehensive analysis and the molecular characterization of chemotactic activity-related genes in BLCA will advance our understanding of the antitumor immune response and prognosis of BLCA.\u003c/p\u003e \u003cp\u003eIn our current studies, we first analyzed the differentially expressed chemotactic activity-related genes by using the information from TCGA database,then, selected the top 11 chemotactic activity-related genes that exhibited significantly differential expression between normal and BLCA tissue for further investigation of their potential mechanisms and biological functions. As expected, these genes were predominantly enriched in chemokinetic functionally relevant gene sets and signaling pathways, in addition to being involved in the killing of cells of other organism, inflammatory response, antimicrobial humoral immune response mediated by antimicrobial peptide, immune response, G-protein coupled receptor signaling pathway(GO) and Viral protein interaction with cytokine and cytokine receptors, NF-kappa B signaling pathway, TNF signaling pathway, Human cytomegalovirus infection and Rheumatoid arthritis(KEGG).\u003c/p\u003e \u003cp\u003eUsing the use of univariate Cox and Lasso Cox regression analysis, we created a prognostic risk model based on 3 genes (CXCL12, ACKR3, and CXCL10). Meanwhile, we analyzed these three genes in pan-cancer and found significant differences in various tumors(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).In the previous study, CXCL12 is a CXC chemokine that traditionally has been classified as a homeostatic chemokine\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, It contributes to physiological processes such as hematopoiesis, cardiogenesis, vascular formation, neurogenesis, and lastly maintenance of tissue stem cells\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Six different splice variants have been identified in humans (CXCL12α to ϕ)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, only CXCL12 β mRNA levels were significantly elevated in bladder cancer compared to normal bladder tissue and were associated with recurrence, metastasis and poor survival, in addition,CXCL12-β mRNA levels in exfoliated cells are highly sensitive to bladder cancer and a potential predictor of future recurrence\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Margitta M et al reported CXCL12 induces actin polymerization in bladder cancer cells and promotes bladder cancer migration\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.ACKR3 is an atypical chemokine receptor first identified and was originally identified as orphan chemokine receptor RDC-1 / CXCR7 with high affinity for CXCL11 and CXCL12\u003csup\u003e16,24\u003c/sup\u003e. However, some studies indicate that ACKR3 does not couple or activate G proteins and does not trigger the typical chemokine receptor signaling pathway\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, thus it behaves more like an atypical chemokine receptor and has therefore been renamed ACKR3.Until now, most research has focused on the potential role of CXCR7 expression in cancer cells. ACKR3 is expressed on tumors of hematopoietic origin, such as lymphomas\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and of mesenchymal origin, such as sarcomas\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e as well as prostate and breast cancer\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In addition to increasing tumor cell proliferation and reducing trail-mediated apoptosis, it also plays pivotal parts in a plethora of physiological processes such as induces EGFR activation\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, leads to increased endothelial cell migration (angiogenesis)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, enriches the cancer stem cells (CSCs) sub-population\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.In vitro and in vivo studies of bladder cancer cell lines showed that alterations in ACKR3 expression were associated with proliferation, apoptosis, migration, invasion, angiogenesis and tumor growth activity. Furthermore, ACKR3 expression modulates the expression of pro-angiogenic factors IL-8 or VEGF, which may be involved in the regulation of tumor angiogenesis\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. CXCL10, also known as interferon -induced protein 10 (IP10), is a 10-kDa protein that is functionally classified as an \"inflammatory\" chemokine. Moreover, due to lacking ELR motif, CXCL10 suppresses neovascularization and functions as an \u0026ldquo;angiostatic factor\". In addition to inhibiting basal fibroblast growth factor (bFGF) and VEGF-induced angiogenesis, CXCL10 has potent anti-tumor activity against tumors by recruiting CTLs\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.CXCL10 binds to CXCR3 and regulates the immune response by activating and recruiting leukocytes, including T cells, eosinophils, monocytes and NK cells\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. For BC patients that responded to BCG therapy, CXCL10 exhibited a consistent pattern of stepwise increases, suggesting that CXCL10 expression may be related to the strength of the immune anti-tumor response\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further performed GSEA to determine the difference in signaling pathway enrichment between the high- and low-risk groups. Our results showed that the low-risk group was significantly enriched in stabilization of P53 and negative regulation of NOTCH4 signaling, while the high-risk group showed no significant enrichment. P53 tumor suppressor is the most frequently mutated gene in human cancer. The fundamental role of P53 in tumor suppression is unequivocal and involved in a variety of biological processes: cell cycle arrest, DNA repair, apoptosis, aging, autophagy, iron downfall, or metabolism\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.Unfortunately, TP53 is inactivated in most, if not all, human cancers. NOTCH is considered an ancient and highly conserved signaling pathway. Potential mechanisms of tumorigenesis involving NOTCH signaling include controlling of tumor-initiating cell phenotypes, regulation of known upstream or downstream tumor-associated signaling factors such as MYC or P53, promotion of angiogenesis or tumor invasion, and regulation of cell cycle\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The abnormalities of these pathways may be the cause for the difference in prognosis between high-risk and low-risk patients.\u003c/p\u003e \u003cp\u003eIn this study, a nomogram incorporating age, gender, T, N, M, and risk score was created. The risk value that was determined after the model was created is a trustworthy independent prognostic index. The risk score produced by the aforementioned three gene expressions can more accurately predict patient survival when compared to the traditional prognostic indexes such as \"age,\" \"gender,\" \"T,\" \"N,\" and \"M.\" This finding further supports the notion that the gene-based expression signal can reliably predict the prognosis of BC patients. The model's adequate predictive power is demonstrated by the calibration curves and time-dependent ROC curves.\u003c/p\u003e \u003cp\u003eHowever, our study has some limitations. Firstly, our study is a retrospective study based on an online database, the nature of the retrospective study design is one of the weaknesses of this study. In addition, the predictive power of this model in BC patients requires further validation by clinicians for better prognostic stratification and treatment management. Moreover, the biological function of above three genes requires further experimental validation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWith the use of CXCL12, ACKR3, and CXCL10, we have created a chemotactic activity-related predictive model in this study that may aid doctors in making conclusions regarding BLCA patients and provide useful information for tailored management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of our research data were obtained from a freely available website and did not include any information from human participants. All procedures were carried out in conformity with the pertinent rules and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn reasonable request, the corresponding author will provide access to all the data analyzed during the current study. In this work, publicly accessible datasets from Gene Expression Omnibus (GSE13507 and GSE48276) and The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) were evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this domain, the writers affirm their claim to had no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJingyan Tian and Xiaoqing Wang supervised this project and were responsible for designing the paper. Zhang Ming conducted data analysis and prepared all the figures and tables. Zhang Ming, Xing Dong, Weijie Yang, Mingyang Chang, Jianing Lv1 drafted the manuscript. Jingyan Tian, Xiaoqing Wang and Wu Qian edited and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLenis AT, Lec PM, Chamie K, Mshs MD. Bladder Cancer: A Review. JAMA Nov. 2020;17(19):1980\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2020.17598\u003c/span\u003e\u003cspan address=\"10.1001/jama.2020.17598\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLobo N, Shariat SF, Guo CC, Fernandez MI, Kassouf W, Choudhury A, et al. What Is the Significance of Variant Histology in Urothelial Carcinoma? 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Signal Transduct Target Ther Mar. 2022;24(1):95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41392-022-00934-y\u003c/span\u003e\u003cspan address=\"10.1038/s41392-022-00934-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"bladder cancer, chemokine, chemokine receptors, prognosis, immune infiltrates","lastPublishedDoi":"10.21203/rs.3.rs-3385390/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3385390/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBladder cancer is one of the most common malignant tumors of the urinary system. Both cancer and stromal cells, including bladder cancer, express chemokines and their corresponding receptors. Their altered expression controls angiogenesis, cancer cell proliferation, metastasis, and immune cell recruitment and activation in a variety of malignancies. Therefore, it is necessary to investigate the association between chemotactic activity-related genes and the prognosis of bladder cancer patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDownload the The Cancer Genome Atlas (TCGA) database's expression profiles for chemotactic activity-related genes and clinical information. Create a prognostic model by using the univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model. Additionally, the validation cohort for the GSE13507 and GSE48276 datasets is used to verify the signature's predictive power.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified 3 chemotactic activity-related genes related to BLCA patients\u0026rsquo; overall survival (OS) and established a prognostic model based on their expression. According on the findings of the LASSO regression analysis, patients were split into high-risk and low-risk groups during the study. The survival time of the low-risk group was significantly longer than that of the high-risk group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The riskscore and clinical prognostic indicators were combined to create a nomogram, which demonstrated strong predictive capacity in the training and validation groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWith the use of CXCL12, ACKR3, and CXCL10, we have created a chemotactic activity-related predictive model in this study that may aid doctors in making conclusions regarding BLCA patients and provide useful information for tailored management.\u003c/p\u003e","manuscriptTitle":"A Novel 3 chemotactic activity-related gene signature for Predicting prognosis of bladder Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-07 17:43:25","doi":"10.21203/rs.3.rs-3385390/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":"75b2809e-8bb0-4af0-bfb3-3ce73b115c1b","owner":[],"postedDate":"November 7th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-19T10:44:12+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-07 17:43:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3385390","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3385390","identity":"rs-3385390","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}