Identification of a pyroptosis-immune-related lncRNA signature for prognostic and immune landscape prediction in bladder cancer patients

Identification of a pyroptosis-immune-related lncRNA signature for prognostic and immune landscape prediction in bladder cancer patients | 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 Identification of a pyroptosis-immune-related lncRNA signature for prognostic and immune landscape prediction in bladder cancer patients Fuguang Zhao, Zhibo Jia, Hui Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3458227/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 May, 2024 Read the published version in Discover Oncology → Version 1 posted You are reading this latest preprint version Abstract Purpose Individualized medicine has become increasingly important in bladder cancer treatment, whereas useful biomarkers for prognostic prediction are still lacking. The current study, therefore, constructed a novel risk model based on pyroptosis- and immune-related long noncoding RNAs (Pyro-Imm lncRNAs) to evaluate the potential prognosis of bladder cancer. Methods Corresponding data of bladder cancer patients were downloaded from the Cancer Genome Atlas (TCGA) database. The univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis were employed to establish a predictive signature, which was evaluated by receiver operator characteristic (ROC) analysis and Kaplan–Meier analysis. Furthermore, the immune infiltration, immune checkpoints, and responses to chemotherapeutic drugs were analyzed with this model. Results Three Pyro-Imm lncRNAs (MAFG-DT, AC024060.1, AC116914.2) were finally identified. Patients in the low-risk group demonstrated a significant survival advantage. The area under the ROC curve (AUC) at 1, 3, and 5 years was 0.694, 0.709, and 0.736 respectively in the entire cohort. KEGG and GO analyses showed that the Wnt pathway plays a crucial role in the high-risk group. The risk score was significantly related to the degree of infiltration of different immune cells, the expression of multiple immune checkpoint genes, and the sensitivity of various chemotherapeutic drugs. Conclusion This novel signature provides a theoretical basis for cancer immunology and chemotherapy, which might help develop individualized therapy. pyroptosis lncRNA immune bladder cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction As a heterogeneous disease, bladder cancer accounts for approximately 573 000 new cases and 213 000 deaths per year, and it is the 6th most common cancer and the 9th leading cause of cancer death worldwide [ 1 ]. Current care options include surgery, cisplatin-based chemotherapy, radiation therapy, and immunotherapy, depending on the histology category, which is broadly classified into non-muscle-invasive bladder (NMIBC), muscle-invasive bladder cancer (MIBC), or metastatic bladder cancer [ 2 ]. However, lower initial response rates, side effects, and therapeutic resistance inevitably occur, leading to therapeutic failure or poor prognosis, particularly for those with advanced or metastatic disease [ 3 ]. Even effective biomarkers based on transcriptomic data have been established for prognostic prediction, the role of which in bladder cancer is still largely unknown, making it difficult to predict whether a certain treatment benefits bladder cancer patients [ 4 ]. Therefore, it is necessary to find reliable biomarkers to guide the clinical treatment and prognosis of bladder cancer. Pyroptosis was discovered as a form of lytic programmed cell death triggered by inflammasomes, which sense cytosolic invasive infection or danger signals [ 5 ]. Recent documents demonstrated that pyroptosis exerted a vital role in various cancer development [ 6 ]. In hepatocellular carcinoma, pyroptosis was strongly inhibited, activation of which could significantly suppress its progression [ 7 , 8 ]. The impairment of pyroptosis in triple-negative breast cancer could also remarkably promote cell proliferation potential through mitochondrial uncoupling protein 1 (UCP-1) [ 9 ]. Indeed, pyroptosis induction has become one of the mechanisms for many chemotherapy drugs to reduce tumor growth [ 10 ]. It should be noted that the function of pyroptosis was closely associated with cytotoxic lymphocytes such as natural-killer and CD8 + T lymphocytes, which indicates the process of pyroptosis is closely associated with the immune microenvironment [ 11 ]. Hence, the alteration of pyroptosis might be linked to the anti-tumor effect and the immune responses of cancer patients after immunotherapy. Long noncoding RNAs (lncRNAs) point to transcripts of more than 200 nucleotides that lack translational activities [ 12 ]. The lncRNAs have received widespread attention currently regarding their multiple functions in biological processes, including cell-cycle regulation, the establishment of cell identity, and pyroptosis [ 13 , 14 ]. Additionally, accumulated evidence demonstrates that aberrant expression of lncRNAs is involved in tumor development [ 15 ]. Moreover, the lncRNA has been proposed as relevant for cancer immunity regulation and tumor microenvironment, and many lncRNAs have been utilized as prognostic biomarkers for several types of cancers, such as kidney cancer, hepatocellular cancer, and lung cancer [ 16 – 19 ]. Therefore, lncRNAs might be of significant clinical relevance and useful biomarkers for survival outcomes prediction of bladder cancer. In the present study, a pyroptosis- and immune-related lncRNA signature was constructed to evaluate the prognosis prediction and the response to clinical treatment in patients with bladder cancer. Materials and methods Transcriptional and clinical information on bladder cancer acquisition. RNA-seq data of 412 tumor tissue samples and 19 paracancerous (normal) tissue samples of bladder cancer were retrieved from the TCGA database ( https://tcgadata.nci.nih.gov/tcga/ ). We obtained 412 clinical data of bladder cancer patients from TCGA. The patients with missing clinical features or overall survival (OS) of less than 30 days were excluded. The Supplementary table 1 showed all the clinical characteristics of bladder cancer patients. Acquisition of pyroptosis- and immune-related lncRNAs. A total of 404 pyroptosis-related genes were retrieved from GeneCards ( https://www.genecards.org/ ). From the ImmPort database ( http://www.immport.org ), a total of 2484 immune-related genes were downloaded. We obtained differentially expressed genes through the limma R package (thresholds of log fold change (FC) > 1 and false discovery rate (FDR) < 0.05) (version 3.53.10), respectively. The correlation between pyroptosis/immune-related genes and lncRNAs were calculated by limma R package. We identified the pyroptosis-related lncRNAs (PyrolncRNAs) and immune-related lncRNAs (ImmlncRNAs) by using the screening criteria (correlation coefficient > 0.3, p-value < 0.01). Pyroptosis- and immune-related lncRNAs (Pyro-Imm lncRNAs) were revealed by calculating the intersection of differentially expressed PyrolncRNAs and ImmlncRNAs with the Venn diagram ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ). Creation of pyroptosis- and immune-related lncRNA prognostic model. We used univariate Cox regression analysis to obtain the Pyro-Imm lncRNAs associated with prognosis (p < 0.05). After least absolute shrinkage and selection operator (LASSO) regression analysis, multivariate Cox regression analysis was carried out to screen the suitable Pyro-Imm lncRNAs to construct a predictive signature (p < 0.05). The risk score was calculated as follows: risk score = Ʃ[Coef(lncRNA) × Exp(lncRNA)]. Coef (lncRNA) and Exp(lncRNA) represent the regression coefficient of the multivariate Cox analysis for the Pyro-Imm lncRNAs and Pyro-Imm lncRNAs expression value, respectively. All analyses were performed using the “survival,” “glmnet,” “survminer,” “caret,” “pheatmap,” "ggplot2," "ggalluvial," “dplyr,” and “survivalROC” R packages. Cytoscape software was used to visualize the network between lncRNA and mRNA. Validation of pyroptosis- and immune-related lncRNAs prognostic risk model. To examine the difference in survival between the low- and high-risk groups, we performed a time-dependent receiver operator characteristic (ROC) analysis, Kaplan–Meier analysis, risk score analysis, and survival outcome analysis. Univariate and multivariate Cox regression analysis and multi-index ROC analysis were used to demonstrate whether this model could be an independent clinical prognostic predictor. Additionally, using the chi-square test, we assessed the relationship between the risk score and the clinical characteristics. The associations between the Pyro-Imm lncRNAs and clinical traits were also evaluated. For these analyses, we applied “survivalROC” “survminer,” “pheatmap,” “timeROC,” "beeswarm," and “survival” R packages. Nomogram establishment. Based on risk score and clinicopathological factors, including age, sex, tumor stage, T stage (the size and extent of the main tumor), and N stage (the number of nearby lymph nodes), a nomogram was constructed to predict the 1-, 3-, and 5-year survival of patients. The calibration curve was used to predict the accuracy of the established nomogram. The "rms" R package was performed for this analysis. Gene set enrichment analysis (GSEA). GSEA software (version 4.2.1) was used to conduct the analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) analysis were performed to evaluate the differential signaling pathways and biological processes as well as a molecular function between the low-risk group and the high-risk group (Nominal p-value < 0.05, FDR value < 0.05). Immune-infiltrated cells and immune checkpoints. The correlations between immune infiltration and risk score were analyzed using single-sample gene set enrichment analysis (ssGSEA). The relationships between immune checkpoint genes and risk score were further investigated. We employed the "GSEABase," "GSVA," "reshape2," "ggplot2," "ggpubr," and "limma" R packages for these analyses. The response of predictive signature in clinical treatment. Based on the half-maximal inhibitory concentration (IC50), the difference between the low- and high-risk groups with selected drug candidates was compared (P < 0.05) using the “pRRophetic” and “ggplot2” R packages. Statistical analysis. All analyses in this study were performed by R software (version 4.2.1), except for 1.8 (version 3.6.3). The Wilcoxon test was employed to compare gene expression levels between tumor and control groups. Kaplan-Meier survival curves and log-rank analysis were utilized to assess the OS between low- and high-risk groups. For all statistical tests, p < 0.05 was considered statistically significant. Results Identification of dysregulated pyroptosis- and immune-related lncRNAs. The transcriptome data downloaded from the TCGA database was divided into mRNA and lncRNA datasets. We identified 59 dysregulated pyroptosis-related genes and 316 dysregulated immune-related genes. We screened out 279 differentially expressed PyrolncRNAs (DE-Pyro lncRNAs) from 797 PyrolncRNAs (Fig. 1 A) and 379 differentially expressed immune-related lncRNAs (DE-Imm lncRNAs) from 1194 immune-related lncRNAs (Fig. 1 B). Then, Venn analysis between the DE-Pyro lncRNAs and DE-Imm lncRNAs was performed, generating 245 differentially expressed pyroptosis- and immune-related lncRNAs (Fig. 1 C). Prognostic model construction. 60 Pyro-Imm lncRNAs associated with the prognosis of bladder cancer patients through univariate Cox regression analysis were obtained (Fig. 2 A). Then, LASSO regression analysis screened seven Pyro-Imm lncRNAs (Fig. 2 B). Ultimately, Multivariate Cox regression revealed three Pyro-Imm lncRNAs (MAFG-DT, AC024060.1, AC116914.2) to construct a prognostic model. Risk score = (0.5185 × MAFG-DT expression value) + (-0.3744 × AC024060.1 expression value) + (-0.5498×AC116914.2 expression value). The lncRNA-mRNA co-expression network was visualized in Fig. 2 C. AC024060.1 and AC116914.2 were protective factors and MAFG-DT was a risk factor in the Sankey diagram (Fig. 2 D). Evaluation of the prognostic risk model. To evaluate the predictive signature, patients were randomly separated into two cohorts (first internal or second internal cohort) and divided into low- and high-risk groups based on their median risk score. Regardless of internal cohorts or overall dataset, Kaplan-Meier analysis revealed that the OS rate in the low-risk group was significantly higher than that in the high-risk group. Here, we take the result of the first internal cohort as a representative, and the result of the second internal cohort or overall cohort was presented in the supplementary Fig. 1. The risk score for the low-and high-risk groups in the first internal is shown in Fig. 3 A respectively. Figure 3 B displayed the survival stats of these cases. The Kaplan-Meier analysis revealed that the OS rate in the low-risk group was significantly higher than that in the high-risk group (Fig. 3 C). In the time-dependent ROC curve, the first internal cohort’s area under the curve (AUC) at 1, 3, and 5 years was 0.741, 0.74, and 0.805 (Fig. 3 D). Additionally, Cox regression analysis indicated that risk score and age were in-dependent prognostic factors of OS in the overall cohort (Supplementary Fig. 2A, B). Multi-index ROC analysis showed that the AUC of risk score was 0.731, higher than clinicopathological characteristics (age, sex, tumor stage, T stage, and N stage) (Supplementary Fig. 2C). Principal component analyses (PCA) showed that patients with different risk scores could be better distinguished based on the three Pyro-Imm lncRNAs (Supplementary Fig. 2F), compared with the whole genes (Supplementary Fig. 2D) and the overlapping 245 Pyro-Imm lncRNAs (Supplementary Fig. 2E). The risk model was associated with prognosis in different clinicopathological features. Bladder cancer patients were stratified by the clinicopathologic features, including age, sex, tumor stage, grade, T stage, and N stage. According to Kaplan–Meier survival analysis, the age, male, tumor stage III-IV, T stage, and N stage were associated with higher survival probability in the low-risk group compared with that in the high-risk group (Supplementary Fig. 3A). Relationships between Pyro-Imm lncRNAs and clinicopathological features and nomogram construction. In the risk prognosis model, the 3 Pyro-Imm lncRNAs were related to clinicopathological features. MAFG-DT was associated with fustat, age, and N stage (Supplementary Fig. 4A, B, C). AC024060.1 was related to fustat and grade (Supplementary Fig. 4D, E). AC116914.2 was connected to the fustat, grade, tumor, T, and N stages (Supplementary Fig. 4F-J). Besides, containing the risk score and clinicopathological factors, the nomogram could predict the 1-year, 3-year, and 5-year prognosis of bladder cancer patients (Supplementary Fig. 5, A). The predicted survival rates were consistent with the actual OS rates at the time of 1-year, 3-year, and 5-year, demonstrating the nomogram's strong predictive ability (Supplementary Fig. 5B). Gene Set Enrichment Analysis. GSEA revealed that KEGG pathways, including the WNT signaling pathway, cell cycle, DNA replication, focal adhesion, and ECM (extracellular matrix) receptor interaction were significantly enriched in the high-risk group, while no signaling pathways were enriched in the low-risk group (Fig. 4 A). The GO analysis showed that the high-risk group enriched in cell adhesion via plasma membrane adhesion molecules, cell adhesion mediator activity, positive regulation of cell cycle G2/M phase transition, positive regulation of cell division, and WNT protein binding, whereas no items were enriched in the low-risk group (Fig. 4 B). Immune infiltration and immune checkpoints. We investigated the correlations between immune cells and risk score. The results showed that macrophages, plasmacytoid dendritic cells (pDCs), T helper type 1 cells (Th1), and regulatory T cells (Treg) were significantly higher in the high-risk group, compared with the low-risk group (Fig. 5 A). We further evaluated the relationships between the expression value of immune checkpoint genes and the two risk groups. The immune checkpoint genes, including TNFRSF14, TNFRSF15, TNFRSF25, LGALS9, BTNL2, HHLA2, CD40, CD40LG, CD160, ADORA2A, TMIGD2, and IDO2 were highly expressed in low-risk group, while TNFRSF4, PDCD1LG2, NRP1, CD44, and CD276 were substantially expressed in high-risk group (Fig. 5 B). The results indicate that the two risk groups' immune responses varied and may react differently to immunotherapy. Relationship between the risk model and bladder cancer treatment. We analyzed the relationship between the risk model and the efficacy of general chemotherapeutic and targeted drug treatment for bladder cancer. The results showed that the IC50 values of cisplatin, docetaxel, paclitaxel, imatinib, and pazopanib were lower in the high-risk group, whereas the IC50 values of methotrexate, vinorelbine, and axitinib were higher in the high-risk group (Fig. 6 ). Discussion Given several PyrolncRNAs have been found for the potential bladder cancer prognosis biomarkers, there is a paucity of data on the combination of ImmlncRNAs and PyrolncRNAs in bladder cancer prognosis analysis [ 4 , 12 ]. Based on public databases (TCGA, GeneCards, and ImmPort), we integrated ImmlncRNAs into PyrolncRNAs research to achieve a novel and more comprehensive risk model for prognosis prediction. In the current study, three Pyro-Imm lncRNAs including MAFG-DT, AC024060.1, and AC116914.2 have been found and used for the bladder cancer risk model construction. Previous evidence showed that the MAFG-DT might be an indicator of infiltration of mononuclear immune cells and linked to the immunosuppressive phenotype of bladder cancer, which may explain why the MAFG-DT is a risk factor in our data [ 16 ]. The AC024060.1 was considered a protective factor for bladder cancer patients in our study, which is consistent with the analysis from other authors [ 20 ]. Regarding AC116914.2, no data on bladder cancer has been found yet. However, it has been applied as a risk model in clear cell renal cell carcinoma to predict survival status and tumor immune infiltration [ 21 ]. The risk score from the three Pyro-Imm lncRNAs model is an independent and better predictor for the prognosis of bladder cancer patients in comparison to other clinicopathological characteristics such as age, sex, tumor stage, T stage, and N stage. Patients being divided into a low-risk group according to the median risk score have higher overall survival rates than the patients in a high-risk group. Our analysis also shows that Treg cells have higher infiltration in the high-risk group relative to the low-risk group, and the high infiltration levels of Treg cells are generally associated with poor prognosis [ 22 ]. These results indicate that grouping due to the risk score is highly clinical relevance since urologists may use it to distinguish different risk patients and assist the decision-making of individualized drug medicine for more precise therapy. Pyroptosis is closely involved in immune cell infiltration. Tumor-infiltrating immune cells can trigger pyroptosis, affecting the prognosis of bladder cancer patients [ 22 ]. Indeed, increased CD11b + cells infiltrating in bladder cancer 5637 cells microenvironment delayed tumor growth, which is attributed to inflammasome activation and elevated level pyroptosis [ 23 ]. lncRNA signature of tumor-infiltrating B lymphocytes was identified to predict the prognosis and response to immunotherapy of bladder cancer [ 16 ]. Nevertheless, the role of pyroptosis in bladder cancer is not clear yet. GSDMB, a member of the gasdermin protein family participating in the provoking of pyroptosis, has been proven to promote the progression of bladder cancer by activating the signal transducer and activator of transcription 3 (STAT3) [ 24 ]. The controversial roles of pyroptosis are hard to explain. Oltra and co-authors demonstrated that GSDMB-mediated pyroptosis is detrimental to GSDMB cleavage, uncleaved of which promotes pro-tumor effects. Meanwhile, various GSDMB isoforms cleaved by specific proteases have different effects on pyroptosis regulation, and only GSDMB isoforms containing exon 6 translation can induce cancer cell pyroptosis [ 25 ]. It indicates that the successful induction of pyroptosis might be a key step for its anti-tumor function. Further investigations are also required for a definitive answer on whether pyroptosis or to what degree pyroptosis is beneficial for bladder cancer treatment. Both KEGG and GO enriched analysis revealed that the WNT signaling pathway emerged in the high-risk group based on the risk model, which means the aberrant WNT pathway is one of the probable mechanisms involved in a worse survival rate. Evidence shows that aberrant-activated this pathway could enhance bladder cancer cell proliferation, invasion, migration, and epithelial-to-mesenchymal transition (EMT) [ 26 ]. In contrast, inhibition of the canonical WNT/β-catenin pathway reversed the EMT process and suppressed the invasion and metastasis of bladder cancer cells [ 27 ]. Targeting this pathway, therefore, might be a promising therapeutic choice for a better survival outcome. However, these in vitro findings have not been verified by patient studies. Pertinent clinical trials about the therapeutic role of the WNT/β-catenin pathway inhibitor in bladder cancer patients should be further performed. It is worth noting that lncRNAs can participate in the development of bladder cancer through the WNT/β-catenin pathway. By activating this signaling pathway, lncRNA SNHG20 significantly promotes the malignant progression of bladder cancer [ 28 ]. Another lncRNA PVT1 has been proven to accelerate malignant phenotypes of bladder cancer cells via the overactivation of the WNT/β-catenin pathway [ 29 ]. Importantly, lncRNAs are also involved in the development of chemoresistance via regulation of the WNT/β-catenin signaling pathway. Overexpressed lncRNA NEAT1 results in cisplatin-resistance development in bladder cancer T24 cell line through WNT pathway activation [ 30 ]. lncRNA UCA1 increases the chemoresistance of cisplatin in bladder cancer treatment by activating the WNT signaling [ 31 ]. In addition, the lncRNA CDKN2B antisense RNA 1 gene can inhibit gemcitabine sensitivity via the induction of WNT signaling in bladder urothelial carcinoma [ 32 ]. It is not surprising that the high-risk group of our risk model is more sensitive to cisplatin (with a lower IC50 value), whereas the low-risk group is more sensitive to methotrexate (with a lower IC50 value). This suggests that urologists might give sensitive chemotherapeutic drugs to different patients based on our risk model. It is postulated that cisplatin plus gemcitabine combination therapy might be beneficial for the high-risk group, while the Methotrexate, Vinblastine, Doxorubicin, and Cisplatin (MAVC) regime may be beneficial for the low-risk group. However, this is purely speculated, and whether this postulation is clinically relevant needs further validation. With a greater understanding of molecular mechanisms, immunotherapy has revolutionized the approach to urothelial carcinoma. For example, immune checkpoint inhibitors targeting PD-1 or PD-L1 have been approved by the Federal Food and Drug Administration (FDA) for patients who progressed after cisplatin-based chemotherapy or were ineligible for cisplatin-based chemotherapy [ 33 ]. Nevertheless, the objective response rates for advanced bladder cancer patients only range from 15–31% [ 34 ]. This is probably because these unresponsive patients have no corresponding immune checkpoint gene expression limiting the application of immunotherapies. Identifying the expression of immune checkpoint genes in advanced cancer patients before immunotherapy is of great importance in improving immunotherapeutic efficiency. Our study reveals that the expression of immune checkpoint genes was significantly different between the high-risk and low-risk groups. The utilization of our risk model may offer useful information for individualized and precise immunotherapy in the future. Several limitations of our study should not be ignored. Our research data is based on public datasets, which might be limited due to insufficient sample size for certain analyses. Since we did not verify the reliability of our Pyro-Imm lncRNAs signature in another independent cohort, potential errors or deviation may inevitably occur. Moreover, in vitro, in vivo, or clinical studies are still lacking for further validating the practical use of this risk model. Declarations Supplementary material Supplementary data can be found at (xxx) Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions Conceptualization, F.G.Z.; Methodology, F.G.Z., H.X., and Z.B.J.; Formal Analysis, F.G.Z. and Z.B.J.; Visualization, F.G.Z.; Writing Original Draft Preparation, F.G.Z; Writing Review and Editing, H.X. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The datasets generated during the current study are available in the in TCGA database (https://tcgadata.nci.nih.gov/tcga/) References Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021, 71, 209-249. Lenis, A.T. et al. Bladder Cancer: A Review. Jama 2020, 324, 1980-1991. Xie, H. et al. Plant-Derived Sulforaphane Suppresses Growth and Proliferation of Drug-Sensitive and Drug-Resistant Bladder Cancer Cell Lines In Vitro. Cancers (Basel) 2022, 14, (19). Zhang, Q. et al. Pyroptosis-Related Signature Predicts Prognosis and Immunotherapy Efficacy in Muscle-Invasive Bladder Cancer. Front Immunol 2022, 13, 782982. Liu, X. et al. Channelling inflammation: Gasdermins in physiology and disease. Nat Rev Drug Discov 2021, 20, 384-405. Yu, P. et al. Pyroptosis: Mechanisms and diseases. Signal Transduct Target Ther 2021, 6, 128. Wei, Q. et al. Deregulation of the NLRP3 inflammasome in hepatic parenchymal cells during liver cancer progression. Lab Invest 2014, 94, 52-62. Wei, Q. et al. E2-Induced Activation of the NLRP3 Inflammasome Triggers Pyroptosis and Inhibits Autophagy in HCC Cells. Oncol Res 2019, 27, 827-834. Xia, J. et al. Mitochondrial Protein UCP1 Inhibits the Malignant Behaviors of Triple-negative Breast Cancer through Activation of Mitophagy and Pyroptosis. Int J Biol Sci 2022, 18, 2949-2961. Wang, Y. et al. Chemotherapy drugs induce pyroptosis through caspase-3 cleavage of a gasdermin. Nature 2017, 547, 99-103. Zhang, Z. et al. Gasdermin E suppresses tumour growth by activating anti-tumour immunity. Nature 2020, 579, 415-420. Lu, H. et al. Identifying a Novel Defined Pyroptosis-Associated Long Noncoding RNA Signature Contributes to Predicting Prognosis and Tumor Microenvironment of Bladder Cancer. Front Immunol 2022, 13, 803355. Ulitsky, I. et al. lincRNAs: Genomics, evolution, and mechanisms. Cell 2013, 154, 26-46. Zhang, Y. et al. Melatonin prevents endothelial cell pyroptosis via regulation of long noncoding RNA MEG3/miR-223/NLRP3 axis. J Pineal Res 2018, 64, (2). Zhan, Y. et al. Long non-coding RNA SOX2OT promotes the stemness phenotype of bladder cancer cells by modulating SOX2. Mol Cancer 2020, 19, 25. Zhou, M. et al. Computational recognition of lncRNA signature of tumor-infiltrating B lymphocytes with potential implications in prognosis and immunotherapy of bladder cancer. Brief Bioinform 2021, 22, (3). Ning, L. et al. LncRNA, NEAT1 is a prognosis biomarker and regulates cancer progression via epithelial-mesenchymal transition in clear cell renal cell carcinoma. Cancer Biomark 2017, 19, 75-83. Zhang, G. et al. A novel Cuproptosis-related LncRNA signature to predict prognosis in hepatocellular carcinoma. Sci Rep 2022, 12, 11325. Jiang, N. et al. Circulating lncRNA XLOC_009167 serves as a diagnostic biomarker to predict lung cancer. Clin Chim Acta 2018, 486, 26–33. Wang, J. et al. Identification and verification of an immune-related lncRNA signature for predicting the prognosis of patients with bladder cancer. Int Immunopharmacol 2021, 90, 107146. Wang, G. et al. Novel Prognosis and Therapeutic Response Model of Immune-Related lncRNA Pairs in Clear Cell Renal Cell Carcinoma. Vaccines (Basel) 2022, 10. Schneider, A.K. et al. The multifaceted immune regulation of bladder cancer. Nat Rev Urol 2019, 16, 613-630. Zhou, H. et al. AIM2 inflammasome activation benefits the therapeutic effect of BCG in bladder carcinoma. Front Pharmacol 2022, 13, 1050774. He, H. et al. USP24-GSDMB complex promotes bladder cancer proliferation via activation of the STAT3 pathway. Int J Biol Sci 2021, 17, 2417-2429. Oltra, S.S. et al. Distinct GSDMB protein isoforms and protease cleavage processes differentially control pyroptotic cell death and mitochondrial damage in cancer cells. Cell Death Differ 2023. Guo, J. et al. The lncRNA DLX6-AS1 promoted cell proliferation, invasion, migration and epithelial-to-mesenchymal transition in bladder cancer via modulating Wnt/β-catenin signaling pathway. Cancer Cell Int 2019, 19, 312. Zhang, M. et al. Thymoquinone suppresses invasion and metastasis in bladder cancer cells by reversing EMT through the Wnt/β-catenin signaling pathway. Chem Biol Interact 2020, 320, 109022. Zhao, Q. et al. Long non-coding RNA SNHG20 promotes bladder cancer via activating the Wnt/β-catenin signalling pathway. Int J Mol Med 2018, 42, 2839-2848. Chen, M. et al. LncRNA PVT1 accelerates malignant phenotypes of bladder cancer cells by modulating miR-194-5p/BCLAF1 axis as a ceRNA. Aging (Albany NY) 2020, 12, 22291-22312. Zhao, W. et al. Silencing long non-coding RNA NEAT1 enhances the suppression of cell growth, invasion, and apoptosis of bladder cancer cells under cisplatin chemotherapy. Int J Clin Exp Pathol 2019, 12, 549-558. Fan, Y. et al. Long non-coding RNA UCA1 increases chemoresistance of bladder cancer cells by regulating Wnt signaling. Febs j 2014, 281, 1750–1758. Xie, D. et al. Long non-coding RNA CDKN2B antisense RNA 1 gene inhibits Gemcitabine sensitivity in bladder urothelial carcinoma. J Cancer 2018, 9, 2160-2166. Zarrabi, K. et al. Emerging therapeutic agents for genitourinary cancers. J Hematol Oncol 2019, 12, 89. Pignot, G. et al. Effect of Immunotherapy on Local Treatment of Genitourinary Malignancies. Eur Urol Oncol 2019, 2, 355-364. Additional Declarations No competing interests reported. Supplementary Files supplementarydata.docx Cite Share Download PDF Status: Published Journal Publication published 02 May, 2024 Read the published version in Discover Oncology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3458227","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":240989722,"identity":"b3134fc0-7663-4589-9b08-137779e94f63","order_by":0,"name":"Fuguang Zhao","email":"","orcid":"","institution":"Chinese People’s Liberation Army (PLA) General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fuguang","middleName":"","lastName":"Zhao","suffix":""},{"id":240989723,"identity":"6c5aeca5-9e8d-4f3e-b3d9-45ee17a3daa2","order_by":1,"name":"Zhibo Jia","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhibo","middleName":"","lastName":"Jia","suffix":""},{"id":240989724,"identity":"2d7a88fb-c0a1-4a89-8406-6ac89f3ce5b7","order_by":2,"name":"Hui Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3RscrCMBDA8SsHcbnSNUWorxAJiEMfpkWoyzf4CIEOLvoAgg8hCM5KQCfpKyiCk4NdPhwcvOKolro55L8F8uO4BMDl+sUQBICSUQdxzcc+ADUjse6ORQKQyAYEKgJZagpSzYja4fZEI6u9nMqTf5NRQGvvWv59JmEuhpqUjVroLzUlUodTg+Fs9ZkESL02E57ir9pM0kUBAv0aIjD4r0hqLJ2bEZ4imGQVEU+yN/WEd+mF8+qRc6G784x3mWzy2l1UYc/ycuevDOzxcIljfrHB5lrWkHd55rv7LpfL5XrpARxMQifHTHCJAAAAAElFTkSuQmCC","orcid":"","institution":"the First Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2023-10-17 15:14:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3458227/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3458227/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12672-024-00998-y","type":"published","date":"2024-05-02T17:00:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":44951285,"identity":"82fcf79a-10ba-4176-a44f-a21638912d61","added_by":"auto","created_at":"2023-10-19 21:38:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84465,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of differentially expressed PyrolncRNAs (DE-Pyro lncRNAs) and immune-related lncRNAs (DE-Imm lncRNAs) in bladder cancer. (A) Volcano plot of DE-Pyro lncRNAs. Blue dots: down-regulation. Orange dots: up-regulation. (B) Volcano plot of DE-Imm lncRNAs. Blue dots: down-regulation. Orange dots: up-regulation. (C) Venn analysis between the DE-Pyro lncRNAs and DE-Imm lncRNAs.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3458227/v1/0fb3e82085c6c9651acb544d.png"},{"id":44951282,"identity":"ee186460-09ae-4e00-8014-7f65679733c6","added_by":"auto","created_at":"2023-10-19 21:38:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":244603,"visible":true,"origin":"","legend":"\u003cp\u003eEstablishment of a risk score model. (A, B) LASSO regression was performed based on 60 Pyro-Imm lncRNAs obtained by univariate Cox regression analysis. (C) The co-expression network of lncRNAs and mRNAs. Blue color: Pyro-Imm lncRNAs. Green color: Pyroptosis-related mRNAs. Purple color: immune-related mRNAs. (D) Sankey diagram showing the correlations between prognostic Pyro-Imm lncRNAs, mRNAs, and risk type.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3458227/v1/388e0fe6ed18ab5d55f99bea.png"},{"id":44952246,"identity":"ed91d738-e014-4735-b2e5-64fd1abb12ef","added_by":"auto","created_at":"2023-10-19 21:46:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96079,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the risk score model. Risk scores and survival status in the first internal cohort (A, B). Kaplan–Meier tests in the first internal cohort (C). Time-dependent ROC analysis of risk score at 1, 3, and 5 years in the first internal cohort (D). AUC area is under the curve.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3458227/v1/489ca0c6ddaa591714731dbb.png"},{"id":44951280,"identity":"256695ce-104b-4837-a722-7f61433ed8b9","added_by":"auto","created_at":"2023-10-19 21:38:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":158023,"visible":true,"origin":"","legend":"\u003cp\u003eGene Set Enrichment Analysis. (A) KEGG pathway analysis showed five pathways were enriched in the high-risk group. (B) Five GO items were enriched in the high-risk group. (ECM represents extracellular matrix).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3458227/v1/c8aa6e3664601040e3a7b8ab.png"},{"id":44951284,"identity":"290ec57b-01ce-4105-ac38-d9ba7cf5f849","added_by":"auto","created_at":"2023-10-19 21:38:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":100880,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration and immune checkpoints. (A) The infiltration levels of 16 immune cells in the low-risk and high-risk groups. (B) The expression value of 17 immune checkpoints in the low-risk and high-risk groups. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ns: not significant.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3458227/v1/953a8fe81b0cbc7d406dd7c4.png"},{"id":44951287,"identity":"9db2925a-3c99-49d5-bff9-62d130c46f06","added_by":"auto","created_at":"2023-10-19 21:38:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":112284,"visible":true,"origin":"","legend":"\u003cp\u003eThe efficacy of bladder cancer treatment in low-risk and high-risk groups. The IC50 of cisplatin (A), docetaxel (B), methotrexate (C), paclitaxel (D), vinorelbine (E), axitinib (F), imatinib (G), and pazopanib (H) were compared between low-risk and high-risk groups. (IC50 represents half-maximal inhibitory concentration).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3458227/v1/0786c72943c0e606afa86cca.png"},{"id":45410048,"identity":"96cec5b4-312e-4963-b12e-b77c1a49522c","added_by":"auto","created_at":"2023-10-29 18:52:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1180829,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3458227/v1/25c74d6b-5011-45f2-9b50-cbc4117b05ba.pdf"},{"id":44951286,"identity":"93c0bbce-23a0-4df0-8eaf-a471d1616fc9","added_by":"auto","created_at":"2023-10-19 21:38:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1292884,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-3458227/v1/62fd0afe6693634b3f5d4579.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of a pyroptosis-immune-related lncRNA signature for prognostic and immune landscape prediction in bladder cancer patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs a heterogeneous disease, bladder cancer accounts for approximately 573 000 new cases and 213 000 deaths per year, and it is the 6th most common cancer and the 9th leading cause of cancer death worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Current care options include surgery, cisplatin-based chemotherapy, radiation therapy, and immunotherapy, depending on the histology category, which is broadly classified into non-muscle-invasive bladder (NMIBC), muscle-invasive bladder cancer (MIBC), or metastatic bladder cancer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, lower initial response rates, side effects, and therapeutic resistance inevitably occur, leading to therapeutic failure or poor prognosis, particularly for those with advanced or metastatic disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Even effective biomarkers based on transcriptomic data have been established for prognostic prediction, the role of which in bladder cancer is still largely unknown, making it difficult to predict whether a certain treatment benefits bladder cancer patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, it is necessary to find reliable biomarkers to guide the clinical treatment and prognosis of bladder cancer.\u003c/p\u003e \u003cp\u003ePyroptosis was discovered as a form of lytic programmed cell death triggered by inflammasomes, which sense cytosolic invasive infection or danger signals [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recent documents demonstrated that pyroptosis exerted a vital role in various cancer development [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In hepatocellular carcinoma, pyroptosis was strongly inhibited, activation of which could significantly suppress its progression [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The impairment of pyroptosis in triple-negative breast cancer could also remarkably promote cell proliferation potential through mitochondrial uncoupling protein 1 (UCP-1) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Indeed, pyroptosis induction has become one of the mechanisms for many chemotherapy drugs to reduce tumor growth [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It should be noted that the function of pyroptosis was closely associated with cytotoxic lymphocytes such as natural-killer and CD8\u0026thinsp;+\u0026thinsp;T lymphocytes, which indicates the process of pyroptosis is closely associated with the immune microenvironment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Hence, the alteration of pyroptosis might be linked to the anti-tumor effect and the immune responses of cancer patients after immunotherapy.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eLong noncoding RNAs (lncRNAs) point to transcripts of more than 200 nucleotides that lack translational activities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The lncRNAs have received widespread attention currently regarding their multiple functions in biological processes, including cell-cycle regulation, the establishment of cell identity, and pyroptosis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, accumulated evidence demonstrates that aberrant expression of lncRNAs is involved in tumor development [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, the lncRNA has been proposed as relevant for cancer immunity regulation and tumor microenvironment, and many lncRNAs have been utilized as prognostic biomarkers for several types of cancers, such as kidney cancer, hepatocellular cancer, and lung cancer [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, lncRNAs might be of significant clinical relevance and useful biomarkers for survival outcomes prediction of bladder cancer. In the present study, a pyroptosis- and immune-related lncRNA signature was constructed to evaluate the prognosis prediction and the response to clinical treatment in patients with bladder cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eTranscriptional and clinical information on bladder cancer acquisition.\u003c/p\u003e \u003cp\u003eRNA-seq data of 412 tumor tissue samples and 19 paracancerous (normal) tissue samples of bladder cancer were retrieved from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcgadata.nci.nih.gov/tcga/\u003c/span\u003e\u003cspan address=\"https://tcgadata.nci.nih.gov/tcga/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We obtained 412 clinical data of bladder cancer patients from TCGA. The patients with missing clinical features or overall survival (OS) of less than 30 days were excluded. The Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed all the clinical characteristics of bladder cancer patients.\u003c/p\u003e \u003cp\u003eAcquisition of pyroptosis- and immune-related lncRNAs.\u003c/p\u003e \u003cp\u003eA total of 404 pyroptosis-related genes were retrieved from GeneCards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). From the ImmPort database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.immport.org\u003c/span\u003e\u003cspan address=\"http://www.immport.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a total of 2484 immune-related genes were downloaded. We obtained differentially expressed genes through the limma R package (thresholds of log fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;1 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (version 3.53.10), respectively. The correlation between pyroptosis/immune-related genes and lncRNAs were calculated by limma R package. We identified the pyroptosis-related lncRNAs (PyrolncRNAs) and immune-related lncRNAs (ImmlncRNAs) by using the screening criteria (correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.3, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Pyroptosis- and immune-related lncRNAs (Pyro-Imm lncRNAs) were revealed by calculating the intersection of differentially expressed PyrolncRNAs and ImmlncRNAs with the Venn diagram (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCreation of pyroptosis- and immune-related lncRNA prognostic model.\u003c/p\u003e \u003cp\u003eWe used univariate Cox regression analysis to obtain the Pyro-Imm lncRNAs associated with prognosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After least absolute shrinkage and selection operator (LASSO) regression analysis, multivariate Cox regression analysis was carried out to screen the suitable Pyro-Imm lncRNAs to construct a predictive signature (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The risk score was calculated as follows: risk score = Ʃ[Coef(lncRNA) \u0026times; Exp(lncRNA)]. Coef (lncRNA) and Exp(lncRNA) represent the regression coefficient of the multivariate Cox analysis for the Pyro-Imm lncRNAs and Pyro-Imm lncRNAs expression value, respectively. All analyses were performed using the \u0026ldquo;survival,\u0026rdquo; \u0026ldquo;glmnet,\u0026rdquo; \u0026ldquo;survminer,\u0026rdquo; \u0026ldquo;caret,\u0026rdquo; \u0026ldquo;pheatmap,\u0026rdquo; \"ggplot2,\" \"ggalluvial,\" \u0026ldquo;dplyr,\u0026rdquo; and \u0026ldquo;survivalROC\u0026rdquo; R packages. Cytoscape software was used to visualize the network between lncRNA and mRNA.\u003c/p\u003e \u003cp\u003eValidation of pyroptosis- and immune-related lncRNAs prognostic risk model.\u003c/p\u003e \u003cp\u003eTo examine the difference in survival between the low- and high-risk groups, we performed a time-dependent receiver operator characteristic (ROC) analysis, Kaplan\u0026ndash;Meier analysis, risk score analysis, and survival outcome analysis. Univariate and multivariate Cox regression analysis and multi-index ROC analysis were used to demonstrate whether this model could be an independent clinical prognostic predictor. Additionally, using the chi-square test, we assessed the relationship between the risk score and the clinical characteristics. The associations between the Pyro-Imm lncRNAs and clinical traits were also evaluated. For these analyses, we applied \u0026ldquo;survivalROC\u0026rdquo; \u0026ldquo;survminer,\u0026rdquo; \u0026ldquo;pheatmap,\u0026rdquo; \u0026ldquo;timeROC,\u0026rdquo; \"beeswarm,\" and \u0026ldquo;survival\u0026rdquo; R packages.\u003c/p\u003e \u003cp\u003eNomogram establishment.\u003c/p\u003e \u003cp\u003eBased on risk score and clinicopathological factors, including age, sex, tumor stage, T stage (the size and extent of the main tumor), and N stage (the number of nearby lymph nodes), a nomogram was constructed to predict the 1-, 3-, and 5-year survival of patients. The calibration curve was used to predict the accuracy of the established nomogram. The \"rms\" R package was performed for this analysis.\u003c/p\u003e \u003cp\u003eGene set enrichment analysis (GSEA).\u003c/p\u003e \u003cp\u003eGSEA software (version 4.2.1) was used to conduct the analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) analysis were performed to evaluate the differential signaling pathways and biological processes as well as a molecular function between the low-risk group and the high-risk group (Nominal p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eImmune-infiltrated cells and immune checkpoints.\u003c/p\u003e \u003cp\u003eThe correlations between immune infiltration and risk score were analyzed using single-sample gene set enrichment analysis (ssGSEA). The relationships between immune checkpoint genes and risk score were further investigated. We employed the \"GSEABase,\" \"GSVA,\" \"reshape2,\" \"ggplot2,\" \"ggpubr,\" and \"limma\" R packages for these analyses.\u003c/p\u003e \u003cp\u003eThe response of predictive signature in clinical treatment.\u003c/p\u003e \u003cp\u003eBased on the half-maximal inhibitory concentration (IC50), the difference between the low- and high-risk groups with selected drug candidates was compared (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) using the \u0026ldquo;pRRophetic\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo; R packages.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis.\u003c/h2\u003e \u003cp\u003eAll analyses in this study were performed by R software (version 4.2.1), except for 1.8 (version 3.6.3). The Wilcoxon test was employed to compare gene expression levels between tumor and control groups. Kaplan-Meier survival curves and log-rank analysis were utilized to assess the OS between low- and high-risk groups. For all statistical tests, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003eIdentification of dysregulated pyroptosis- and immune-related lncRNAs.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe transcriptome data downloaded from the TCGA database was divided into mRNA and lncRNA datasets. We identified 59 dysregulated pyroptosis-related genes and 316 dysregulated immune-related genes. We screened out 279 differentially expressed PyrolncRNAs (DE-Pyro lncRNAs) from 797 PyrolncRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and 379 differentially expressed immune-related lncRNAs (DE-Imm lncRNAs) from 1194 immune-related lncRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Then, Venn analysis between the DE-Pyro lncRNAs and DE-Imm lncRNAs was performed, generating 245 differentially expressed pyroptosis- and immune-related lncRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePrognostic model construction.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e60 Pyro-Imm lncRNAs associated with the prognosis of bladder cancer patients through univariate Cox regression analysis were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Then, LASSO regression analysis screened seven Pyro-Imm lncRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Ultimately, Multivariate Cox regression revealed three Pyro-Imm lncRNAs (MAFG-DT, AC024060.1, AC116914.2) to construct a prognostic model. Risk score = (0.5185 \u0026times; MAFG-DT expression value) + (-0.3744 \u0026times; AC024060.1 expression value) + (-0.5498\u0026times;AC116914.2 expression value). The lncRNA-mRNA co-expression network was visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC. AC024060.1 and AC116914.2 were protective factors and MAFG-DT was a risk factor in the Sankey diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eEvaluation of the prognostic risk model.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the predictive signature, patients were randomly separated into two cohorts (first internal or second internal cohort) and divided into low- and high-risk groups based on their median risk score. Regardless of internal cohorts or overall dataset, Kaplan-Meier analysis revealed that the OS rate in the low-risk group was significantly higher than that in the high-risk group. Here, we take the result of the first internal cohort as a representative, and the result of the second internal cohort or overall cohort was presented in the supplementary Fig.\u0026nbsp;1. The risk score for the low-and high-risk groups in the first internal is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB displayed the survival stats of these cases. The Kaplan-Meier analysis revealed that the OS rate in the low-risk group was significantly higher than that in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In the time-dependent ROC curve, the first internal cohort\u0026rsquo;s area under the curve (AUC) at 1, 3, and 5 years was 0.741, 0.74, and 0.805 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Additionally, Cox regression analysis indicated that risk score and age were in-dependent prognostic factors of OS in the overall cohort (Supplementary Fig.\u0026nbsp;2A, B). Multi-index ROC analysis showed that the AUC of risk score was 0.731, higher than clinicopathological characteristics (age, sex, tumor stage, T stage, and N stage) (Supplementary Fig.\u0026nbsp;2C). Principal component analyses (PCA) showed that patients with different risk scores could be better distinguished based on the three Pyro-Imm lncRNAs (Supplementary Fig.\u0026nbsp;2F), compared with the whole genes (Supplementary Fig.\u0026nbsp;2D) and the overlapping 245 Pyro-Imm lncRNAs (Supplementary Fig.\u0026nbsp;2E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe risk model was associated with prognosis in different clinicopathological features.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eBladder cancer patients were stratified by the clinicopathologic features, including age, sex, tumor stage, grade, T stage, and N stage. According to Kaplan\u0026ndash;Meier survival analysis, the age, male, tumor stage III-IV, T stage, and N stage were associated with higher survival probability in the low-risk group compared with that in the high-risk group (Supplementary Fig.\u0026nbsp;3A).\u003c/p\u003e \u003cp\u003e \u003cem\u003eRelationships between Pyro-Imm lncRNAs and clinicopathological features and nomogram construction.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn the risk prognosis model, the 3 Pyro-Imm lncRNAs were related to clinicopathological features. MAFG-DT was associated with fustat, age, and N stage (Supplementary Fig.\u0026nbsp;4A, B, C). AC024060.1 was related to fustat and grade (Supplementary Fig.\u0026nbsp;4D, E). AC116914.2 was connected to the fustat, grade, tumor, T, and N stages (Supplementary Fig.\u0026nbsp;4F-J). Besides, containing the risk score and clinicopathological factors, the nomogram could predict the 1-year, 3-year, and 5-year prognosis of bladder cancer patients (Supplementary Fig.\u0026nbsp;5, A). The predicted survival rates were consistent with the actual OS rates at the time of 1-year, 3-year, and 5-year, demonstrating the nomogram's strong predictive ability (Supplementary Fig.\u0026nbsp;5B).\u003c/p\u003e \u003cp\u003e \u003cem\u003eGene Set Enrichment Analysis.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eGSEA revealed that KEGG pathways, including the WNT signaling pathway, cell cycle, DNA replication, focal adhesion, and ECM (extracellular matrix) receptor interaction were significantly enriched in the high-risk group, while no signaling pathways were enriched in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The GO analysis showed that the high-risk group enriched in cell adhesion via plasma membrane adhesion molecules, cell adhesion mediator activity, positive regulation of cell cycle G2/M phase transition, positive regulation of cell division, and WNT protein binding, whereas no items were enriched in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eImmune infiltration and immune checkpoints.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe investigated the correlations between immune cells and risk score. The results showed that macrophages, plasmacytoid dendritic cells (pDCs), T helper type 1 cells (Th1), and regulatory T cells (Treg) were significantly higher in the high-risk group, compared with the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). We further evaluated the relationships between the expression value of immune checkpoint genes and the two risk groups. The immune checkpoint genes, including TNFRSF14, TNFRSF15, TNFRSF25, LGALS9, BTNL2, HHLA2, CD40, CD40LG, CD160, ADORA2A, TMIGD2, and IDO2 were highly expressed in low-risk group, while TNFRSF4, PDCD1LG2, NRP1, CD44, and CD276 were substantially expressed in high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The results indicate that the two risk groups' immune responses varied and may react differently to immunotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRelationship between the risk model and bladder cancer treatment.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe analyzed the relationship between the risk model and the efficacy of general chemotherapeutic and targeted drug treatment for bladder cancer. The results showed that the IC50 values of cisplatin, docetaxel, paclitaxel, imatinib, and pazopanib were lower in the high-risk group, whereas the IC50 values of methotrexate, vinorelbine, and axitinib were higher in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGiven several PyrolncRNAs have been found for the potential bladder cancer prognosis biomarkers, there is a paucity of data on the combination of ImmlncRNAs and PyrolncRNAs in bladder cancer prognosis analysis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Based on public databases (TCGA, GeneCards, and ImmPort), we integrated ImmlncRNAs into PyrolncRNAs research to achieve a novel and more comprehensive risk model for prognosis prediction. In the current study, three Pyro-Imm lncRNAs including MAFG-DT, AC024060.1, and AC116914.2 have been found and used for the bladder cancer risk model construction. Previous evidence showed that the MAFG-DT might be an indicator of infiltration of mononuclear immune cells and linked to the immunosuppressive phenotype of bladder cancer, which may explain why the MAFG-DT is a risk factor in our data [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The AC024060.1 was considered a protective factor for bladder cancer patients in our study, which is consistent with the analysis from other authors [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Regarding AC116914.2, no data on bladder cancer has been found yet. However, it has been applied as a risk model in clear cell renal cell carcinoma to predict survival status and tumor immune infiltration [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe risk score from the three Pyro-Imm lncRNAs model is an independent and better predictor for the prognosis of bladder cancer patients in comparison to other clinicopathological characteristics such as age, sex, tumor stage, T stage, and N stage. Patients being divided into a low-risk group according to the median risk score have higher overall survival rates than the patients in a high-risk group. Our analysis also shows that Treg cells have higher infiltration in the high-risk group relative to the low-risk group, and the high infiltration levels of Treg cells are generally associated with poor prognosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These results indicate that grouping due to the risk score is highly clinical relevance since urologists may use it to distinguish different risk patients and assist the decision-making of individualized drug medicine for more precise therapy.\u003c/p\u003e \u003cp\u003ePyroptosis is closely involved in immune cell infiltration. Tumor-infiltrating immune cells can trigger pyroptosis, affecting the prognosis of bladder cancer patients [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Indeed, increased CD11b\u0026thinsp;+\u0026thinsp;cells infiltrating in bladder cancer 5637 cells microenvironment delayed tumor growth, which is attributed to inflammasome activation and elevated level pyroptosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. lncRNA signature of tumor-infiltrating B lymphocytes was identified to predict the prognosis and response to immunotherapy of bladder cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Nevertheless, the role of pyroptosis in bladder cancer is not clear yet. GSDMB, a member of the gasdermin protein family participating in the provoking of pyroptosis, has been proven to promote the progression of bladder cancer by activating the signal transducer and activator of transcription 3 (STAT3) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The controversial roles of pyroptosis are hard to explain. Oltra and co-authors demonstrated that GSDMB-mediated pyroptosis is detrimental to GSDMB cleavage, uncleaved of which promotes pro-tumor effects. Meanwhile, various GSDMB isoforms cleaved by specific proteases have different effects on pyroptosis regulation, and only GSDMB isoforms containing exon 6 translation can induce cancer cell pyroptosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It indicates that the successful induction of pyroptosis might be a key step for its anti-tumor function. Further investigations are also required for a definitive answer on whether pyroptosis or to what degree pyroptosis is beneficial for bladder cancer treatment.\u003c/p\u003e \u003cp\u003eBoth KEGG and GO enriched analysis revealed that the WNT signaling pathway emerged in the high-risk group based on the risk model, which means the aberrant WNT pathway is one of the probable mechanisms involved in a worse survival rate. Evidence shows that aberrant-activated this pathway could enhance bladder cancer cell proliferation, invasion, migration, and epithelial-to-mesenchymal transition (EMT) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In contrast, inhibition of the canonical WNT/β-catenin pathway reversed the EMT process and suppressed the invasion and metastasis of bladder cancer cells [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Targeting this pathway, therefore, might be a promising therapeutic choice for a better survival outcome. However, these in vitro findings have not been verified by patient studies. Pertinent clinical trials about the therapeutic role of the WNT/β-catenin pathway inhibitor in bladder cancer patients should be further performed.\u003c/p\u003e \u003cp\u003eIt is worth noting that lncRNAs can participate in the development of bladder cancer through the WNT/β-catenin pathway. By activating this signaling pathway, lncRNA SNHG20 significantly promotes the malignant progression of bladder cancer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Another lncRNA PVT1 has been proven to accelerate malignant phenotypes of bladder cancer cells via the overactivation of the WNT/β-catenin pathway [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Importantly, lncRNAs are also involved in the development of chemoresistance via regulation of the WNT/β-catenin signaling pathway. Overexpressed lncRNA NEAT1 results in cisplatin-resistance development in bladder cancer T24 cell line through WNT pathway activation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. lncRNA UCA1 increases the chemoresistance of cisplatin in bladder cancer treatment by activating the WNT signaling [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, the lncRNA CDKN2B antisense RNA 1 gene can inhibit gemcitabine sensitivity via the induction of WNT signaling in bladder urothelial carcinoma [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. It is not surprising that the high-risk group of our risk model is more sensitive to cisplatin (with a lower IC50 value), whereas the low-risk group is more sensitive to methotrexate (with a lower IC50 value). This suggests that urologists might give sensitive chemotherapeutic drugs to different patients based on our risk model. It is postulated that cisplatin plus gemcitabine combination therapy might be beneficial for the high-risk group, while the Methotrexate, Vinblastine, Doxorubicin, and Cisplatin (MAVC) regime may be beneficial for the low-risk group. However, this is purely speculated, and whether this postulation is clinically relevant needs further validation.\u003c/p\u003e \u003cp\u003eWith a greater understanding of molecular mechanisms, immunotherapy has revolutionized the approach to urothelial carcinoma. For example, immune checkpoint inhibitors targeting PD-1 or PD-L1 have been approved by the Federal Food and Drug Administration (FDA) for patients who progressed after cisplatin-based chemotherapy or were ineligible for cisplatin-based chemotherapy [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Nevertheless, the objective response rates for advanced bladder cancer patients only range from 15\u0026ndash;31% [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This is probably because these unresponsive patients have no corresponding immune checkpoint gene expression limiting the application of immunotherapies. Identifying the expression of immune checkpoint genes in advanced cancer patients before immunotherapy is of great importance in improving immunotherapeutic efficiency. Our study reveals that the expression of immune checkpoint genes was significantly different between the high-risk and low-risk groups. The utilization of our risk model may offer useful information for individualized and precise immunotherapy in the future.\u003c/p\u003e \u003cp\u003eSeveral limitations of our study should not be ignored. Our research data is based on public datasets, which might be limited due to insufficient sample size for certain analyses. Since we did not verify the reliability of our Pyro-Imm lncRNAs signature in another independent cohort, potential errors or deviation may inevitably occur. Moreover, in vitro, in vivo, or clinical studies are still lacking for further validating the practical use of this risk model.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data can be found at (xxx)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr\u003e \u003c/strong\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, F.G.Z.; Methodology, F.G.Z., H.X., and Z.B.J.; Formal Analysis, F.G.Z. and Z.B.J.; Visualization, F.G.Z.; Writing Original Draft Preparation, F.G.Z; Writing Review and Editing, H.X. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available in the in TCGA database (https://tcgadata.nci.nih.gov/tcga/)\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021, 71, 209-249.\u003c/li\u003e\n\u003cli\u003eLenis, A.T. et al. Bladder Cancer: A Review. Jama 2020, 324, 1980-1991.\u003c/li\u003e\n\u003cli\u003eXie, H. et al. Plant-Derived Sulforaphane Suppresses Growth and Proliferation of Drug-Sensitive and Drug-Resistant Bladder Cancer Cell Lines In Vitro. Cancers (Basel) 2022, 14, (19).\u003c/li\u003e\n\u003cli\u003eZhang, Q. et al. Pyroptosis-Related Signature Predicts Prognosis and Immunotherapy Efficacy in Muscle-Invasive Bladder Cancer. Front Immunol 2022, 13, 782982.\u003c/li\u003e\n\u003cli\u003eLiu, X. et al. Channelling inflammation: Gasdermins in physiology and disease. Nat Rev Drug Discov 2021, 20, 384-405.\u003c/li\u003e\n\u003cli\u003eYu, P. et al. Pyroptosis: Mechanisms and diseases. Signal Transduct Target Ther 2021, 6, 128.\u003c/li\u003e\n\u003cli\u003eWei, Q. et al. Deregulation of the NLRP3 inflammasome in hepatic parenchymal cells during liver cancer progression. Lab Invest 2014, 94, 52-62.\u003c/li\u003e\n\u003cli\u003eWei, Q. et al. E2-Induced Activation of the NLRP3 Inflammasome Triggers Pyroptosis and Inhibits Autophagy in HCC Cells. Oncol Res 2019, 27, 827-834.\u003c/li\u003e\n\u003cli\u003eXia, J. et al. Mitochondrial Protein UCP1 Inhibits the Malignant Behaviors of Triple-negative Breast Cancer through Activation of Mitophagy and Pyroptosis. Int J Biol Sci 2022, 18, 2949-2961.\u003c/li\u003e\n\u003cli\u003eWang, Y. et al. Chemotherapy drugs induce pyroptosis through caspase-3 cleavage of a gasdermin. Nature 2017, 547, 99-103.\u003c/li\u003e\n\u003cli\u003eZhang, Z. et al. Gasdermin E suppresses tumour growth by activating anti-tumour immunity. Nature 2020, 579, 415-420.\u003c/li\u003e\n\u003cli\u003eLu, H. et al. Identifying a Novel Defined Pyroptosis-Associated Long Noncoding RNA Signature Contributes to Predicting Prognosis and Tumor Microenvironment of Bladder Cancer. Front Immunol 2022, 13, 803355.\u003c/li\u003e\n\u003cli\u003eUlitsky, I. et al. lincRNAs: Genomics, evolution, and mechanisms. Cell 2013, 154, 26-46.\u003c/li\u003e\n\u003cli\u003eZhang, Y. et al. Melatonin prevents endothelial cell pyroptosis via regulation of long noncoding RNA MEG3/miR-223/NLRP3 axis. J Pineal Res 2018, 64, (2).\u003c/li\u003e\n\u003cli\u003eZhan, Y. et al. Long non-coding RNA SOX2OT promotes the stemness phenotype of bladder cancer cells by modulating SOX2. Mol Cancer 2020, 19, 25.\u003c/li\u003e\n\u003cli\u003eZhou, M. et al. Computational recognition of lncRNA signature of tumor-infiltrating B lymphocytes with potential implications in prognosis and immunotherapy of bladder cancer. Brief Bioinform 2021, 22, (3).\u003c/li\u003e\n\u003cli\u003eNing, L. et al. LncRNA, NEAT1 is a prognosis biomarker and regulates cancer progression via epithelial-mesenchymal transition in clear cell renal cell carcinoma. Cancer Biomark 2017, 19, 75-83.\u003c/li\u003e\n\u003cli\u003eZhang, G. et al. A novel Cuproptosis-related LncRNA signature to predict prognosis in hepatocellular carcinoma. Sci Rep 2022, 12, 11325.\u003c/li\u003e\n\u003cli\u003eJiang, N. et al. Circulating lncRNA XLOC_009167 serves as a diagnostic biomarker to predict lung cancer. Clin Chim Acta 2018, 486, 26\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eWang, J. et al. Identification and verification of an immune-related lncRNA signature for predicting the prognosis of patients with bladder cancer. Int Immunopharmacol 2021, 90, 107146.\u003c/li\u003e\n\u003cli\u003eWang, G. et al. Novel Prognosis and Therapeutic Response Model of Immune-Related lncRNA Pairs in Clear Cell Renal Cell Carcinoma. Vaccines (Basel) 2022, 10.\u003c/li\u003e\n\u003cli\u003eSchneider, A.K. et al. The multifaceted immune regulation of bladder cancer. Nat Rev Urol 2019, 16, 613-630.\u003c/li\u003e\n\u003cli\u003eZhou, H. et al. AIM2 inflammasome activation benefits the therapeutic effect of BCG in bladder carcinoma. Front Pharmacol 2022, 13, 1050774.\u003c/li\u003e\n\u003cli\u003eHe, H. et al. USP24-GSDMB complex promotes bladder cancer proliferation via activation of the STAT3 pathway. Int J Biol Sci 2021, 17, 2417-2429.\u003c/li\u003e\n\u003cli\u003eOltra, S.S. et al. Distinct GSDMB protein isoforms and protease cleavage processes differentially control pyroptotic cell death and mitochondrial damage in cancer cells. Cell Death Differ 2023.\u003c/li\u003e\n\u003cli\u003eGuo, J. et al. The lncRNA DLX6-AS1 promoted cell proliferation, invasion, migration and epithelial-to-mesenchymal transition in bladder cancer via modulating Wnt/\u0026beta;-catenin signaling pathway. Cancer Cell Int 2019, 19, 312.\u003c/li\u003e\n\u003cli\u003eZhang, M. et al. Thymoquinone suppresses invasion and metastasis in bladder cancer cells by reversing EMT through the Wnt/\u0026beta;-catenin signaling pathway. Chem Biol Interact 2020, 320, 109022.\u003c/li\u003e\n\u003cli\u003eZhao, Q. et al. Long non-coding RNA SNHG20 promotes bladder cancer via activating the Wnt/\u0026beta;-catenin signalling pathway. Int J Mol Med 2018, 42, 2839-2848.\u003c/li\u003e\n\u003cli\u003eChen, M. et al. LncRNA PVT1 accelerates malignant phenotypes of bladder cancer cells by modulating miR-194-5p/BCLAF1 axis as a ceRNA. Aging (Albany NY) 2020, 12, 22291-22312.\u003c/li\u003e\n\u003cli\u003eZhao, W. et al. Silencing long non-coding RNA NEAT1 enhances the suppression of cell growth, invasion, and apoptosis of bladder cancer cells under cisplatin chemotherapy. Int J Clin Exp Pathol 2019, 12, 549-558.\u003c/li\u003e\n\u003cli\u003eFan, Y. et al. Long non-coding RNA UCA1 increases chemoresistance of bladder cancer cells by regulating Wnt signaling. Febs j 2014, 281, 1750\u0026ndash;1758.\u003c/li\u003e\n\u003cli\u003eXie, D. et al. Long non-coding RNA CDKN2B antisense RNA 1 gene inhibits Gemcitabine sensitivity in bladder urothelial carcinoma. J Cancer 2018, 9, 2160-2166.\u003c/li\u003e\n\u003cli\u003eZarrabi, K. et al. Emerging therapeutic agents for genitourinary cancers. J Hematol Oncol 2019, 12, 89.\u003c/li\u003e\n\u003cli\u003ePignot, G. et al. Effect of Immunotherapy on Local Treatment of Genitourinary Malignancies. Eur Urol Oncol 2019, 2, 355-364.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"pyroptosis, lncRNA, immune, bladder cancer","lastPublishedDoi":"10.21203/rs.3.rs-3458227/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3458227/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eIndividualized medicine has become increasingly important in bladder cancer treatment, whereas useful biomarkers for prognostic prediction are still lacking. The current study, therefore, constructed a novel risk model based on pyroptosis- and immune-related long noncoding RNAs (Pyro-Imm lncRNAs) to evaluate the potential prognosis of bladder cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCorresponding data of bladder cancer patients were downloaded from the Cancer Genome Atlas (TCGA) database. The univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis were employed to establish a predictive signature, which was evaluated by receiver operator characteristic (ROC) analysis and Kaplan\u0026ndash;Meier analysis. Furthermore, the immune infiltration, immune checkpoints, and responses to chemotherapeutic drugs were analyzed with this model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThree Pyro-Imm lncRNAs (MAFG-DT, AC024060.1, AC116914.2) were finally identified. Patients in the low-risk group demonstrated a significant survival advantage. The area under the ROC curve (AUC) at 1, 3, and 5 years was 0.694, 0.709, and 0.736 respectively in the entire cohort. KEGG and GO analyses showed that the Wnt pathway plays a crucial role in the high-risk group. The risk score was significantly related to the degree of infiltration of different immune cells, the expression of multiple immune checkpoint genes, and the sensitivity of various chemotherapeutic drugs.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis novel signature provides a theoretical basis for cancer immunology and chemotherapy, which might help develop individualized therapy.\u003c/p\u003e","manuscriptTitle":"Identification of a pyroptosis-immune-related lncRNA signature for prognostic and immune landscape prediction in bladder cancer patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-19 21:38:20","doi":"10.21203/rs.3.rs-3458227/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":"a097ff62-d303-49fa-a044-df9e6b3965b9","owner":[],"postedDate":"October 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-09T17:00:52+00:00","versionOfRecord":{"articleIdentity":"rs-3458227","link":"https://doi.org/10.1007/s12672-024-00998-y","journal":{"identity":"discover-oncology","isVorOnly":false,"title":"Discover Oncology"},"publishedOn":"2024-05-02 17:00:52","publishedOnDateReadable":"May 2nd, 2024"},"versionCreatedAt":"2023-10-19 21:38:20","video":"","vorDoi":"10.1007/s12672-024-00998-y","vorDoiUrl":"https://doi.org/10.1007/s12672-024-00998-y","workflowStages":[]},"version":"v1","identity":"rs-3458227","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3458227","identity":"rs-3458227","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}