Computational Insights into Captopril's Inhibitory Potential Against MMP9 and LCN2 in Bladder Cancer: Implications for Therapeutic Application.

Objectives: Captopril is a commonly used therapeutic agent in the management of renovascular hypertension (high blood pressure), congestive heart failure, left ventricular dysfunction following myocardial infarction, and nephropathy. Captopril has been found to interact with proteins that are significantly associated with bladder cancer (BLCA), suggesting that it could be a potential medication for BLCA patients with concurrent hypertension. Methods: DrugBank 5.0 was utilized to identify the direct protein targets (DPTs) of captopril. STRING was used to analyze the multiple protein interactions. TNMPlot was used for comparing gene expression in normal, tumor, and metastatic tissue. Then, docking with target proteins was done using Autodock. Molecular dynamics simulations were applied for estimate the diffusion coefficients and mean-square displacements in materials. Results: Among all these proteins, MMP9 is observed to be an overexpressed gene in BLCA and its increased expression is linked to reduced survival in patients. Our findings indicate that captopril effectively inhibits both the wild type and common mutated forms of MMP9 in BLCA. Furthermore, the LCN2 gene, which is also overexpressed in BLCA, interacts with captopril-associated proteins. The overexpression of LCN2 is similarly associated with reduced survival in BLCA. Through molecular docking analysis, we have identified specific amino acid residues (Tyr179, Pro421, Tyr423, and Lys603) at the active pocket of MMP9, as well as Tyr78, Tyr106, Phe145, Lys147, and Lys156 at the active pocket of LCN2, with which captopril interacts. Thus, our data provide compelling evidence for the inhibitory potential of captopril against human proteins MMP9 and LCN2, both of which play crucial roles in BLCA. Conclusion: These discoveries present promising prospects for conducting subsequent validation studies both in vitro and in vivo, with the aim of assessing the suitability of captopril for treating BLCA patients, irrespective of their hypertension status, who exhibit elevated levels of MMP9 and LCN2 expression.

APOL6 predicts immunotherapy efficacy of bladder cancer by ferroptosis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are rapidly evolving in the management of bladder cancer (BLCA). Nevertheless, effective biomarkers for predicting immunotherapeutic outcomes in BLCA are still insufficient. Ferroptosis, a form of immunogenic cell death, has been found to enhance patient sensitivity to ICIs. However, the underlying mechanisms of ferroptosis in promoting immunotherapy efficacy in BLCA remain obscure. METHODS: Our analysis of The Cancer Genome Atlas (TCGA) mRNA data using single sample Gene Set Enrichment Analysis (ssGSEA) revealed two immunologically distinct subtypes. Based on these subtypes and various other public cohorts, we identified Apolipoprotein L6 (APOL6) as a biomarker predicting the efficacy of ICIs and explored its immunological correlation and predictive value for treatment. Furthermore, the role of APOL6 in promoting ferroptosis and its mechanism in regulating this process were experimentally validated. RESULTS: The results indicate that APOL6 has significant immunological relevance and is indicative of immunologically hot tumors in BLCA and many other cancers. APOL6, interacting with acyl-coenzyme A synthetase long-chain family member 4 (ACSL4), mediates immunotherapy efficacy by ferroptosis. Additionally, APOL6 is regulated by signal transducer and activator of transcription 1 (STAT1). CONCLUSIONS: To conclude, our findings indicate APOL6 has potential as a predictive biomarker for immunotherapy treatment success estimation and reveal the STAT1/APOL6/GPX4 axis as a critical regulatory mechanism in BLCA.

Association between CYP1A2 gene variants -163 C/A (rs762551) and -3860 G/A (rs2069514) and bladder cancer susceptibility.

BACKGROUND: Bladder cancer (BLCA) poses a significant global health challenge due to its high incidence, poor prognosis, and limited treatment options. AIMS AND OBJECTIVES: This study aims to investigate the association between two specific polymorphisms, CYP1A2-163 C/A and CYP1A2-3860G/A, within the Cytochrome P450 1A2 (CYP1A2) gene and susceptibility to BLCA. METHODS: The study employed a case-control design, genotyping 340 individuals using Polymerase Chain Reaction-High-Resolution Melting Curve (PCR-HRM). Various genetic models were applied to evaluate allele and genotype frequencies. Genetic linkage analysis was facilitated using R packages. RESULTS: The study reveals a significant association with the - 163 C/A allele, particularly in the additive model. Odds ratio (OR) analysis links CYP1A2-163 C/A (rs762551) and CYP1A2-3860G/A(rs2069514) polymorphisms to BLCA susceptibility. The rs762551 C/A genotype is prevalent in 55% of BLCA cases and exhibits an OR of 2.21. The A/A genotype has an OR of 1.54. Regarding CYP1A2-3860G/A, the G/A genotype has an OR of 1.54, and the A/A genotype has an OR of 2.08. Haplotype analysis shows a predominant C-C haplotype at 38.2%, followed by a C-A haplotype at 54.7%, and a less frequent A-A haplotype at 7.1%. This study underscores associations between CYP1A2 gene variants, particularly rs762551 (CYP1A2-163 C/A), and an increased susceptibility to BLCA. Haplotype analysis of 340 individuals reveals a predominant C-C haplotype at 38.2%, followed by a C-A haplotype at 54.7%, and a less frequent A-A haplotype at 7.1%. CONCLUSION: In conclusion, the - 163 C/A allele, C/A genotype of rs762551, and G/A genotype of rs2069514 emerge as potential genetic markers associated with elevated BLCA risk.

Nicotinamide phosphoribosyltransferase modulates PD-L1 in bladder cancer and enhances immunotherapeutic sensitivity.

Bladder cancer (BLCA) is one of the most prevalent malignancies worldwide with a high mortality rate and poor response to immunotherapy in patients expressing lower programmed death ligand 1 (PD-L1) levels. Nicotinamide phosphoribosyltransferase (NAMPT), a rate-limiting enzyme responsible for the biosynthesis of nicotinamide adenine dinucleotide (NAD+) from nicotinamide was reported to be overexpressed in various cancers; however, the role of NAMPT in BLCA is obscure. Immunohistochemistry of tissue microarrays, a real-time polymerase chain reaction, Western blotting, proliferation assay, NAD+ quantification, transwell-migration assay, and colony-formation assay were performed to measure NAMPT and PD-L1 expression levels in patients and the effect of NAMPT inhibition on T24 cells. Our study revealed that NAMPT expression was upregulated in BLCA patients with different grades and associated with poor T-cell infiltration. Notably, FK866-mediated NAMPT inhibition decreased cell viability by depleting NAD+, and reducing the migration ability and colony-formation ability of T24 cells. Interestingly, NAMPT negatively regulated PD-L1 under an interferon (IFN)-γ-mediated microenvironment. However, exogenous NAMPT activator has no effect on PD-L1. NAD+ supplementation also only increased PD-L1 in the absence of IFN-γ. Conclusively, NAMPT is crucial for BLCA tumorigenic properties, and it regulates expression of the PD-L1 immune checkpoint protein. NAMPT could be considered a target for modulating sensitivity to immunotherapy.

Acyl-coenzyme A: cholesterol acyltransferase inhibitor avasimibe suppresses tumorigenesis and induces G1-phase cell-cycle arrest by activating PPARγ signaling pathway in bladder cancer.

Reprogramming of energy metabolism is one of the most important characteristics of tumors. Bladder cancer (BLCA) cells contain higher levels of cholesterol content compared to normal cells, and acyl-coenzyme A (CoA): cholesterol acyltransferase-1 (ACAT1) plays a crucial role in the esterification of cholesterol. Avasimibe is a drug that has been used in the treatment of atherosclerosis, and it can effectively inhibit ACAT1. We observed that ACAT1 was significantly up-regulated in BLCA and positively correlated with tumor grade. By avasimibe administration, the proliferation and migration ability of BLCA cells were reduced, while the production of ROS was strongly increased, accompanied by the up-regulated expression of ROS metabolism-related proteins SOD2 and catalase. Furthermore, BLCA cell cycle was arrested at the G1 phase, accompanied by the downregulation of cell cycle-related proteins (CCNA1/2, CCND1, CDK2 and CDK4), while the PPARγ was found to be up-regulated at both transcriptional and protein levels after avasimibe treatment. Then we found that the PPARγ antagonist GW9662 could reverse the effect of avasimibe on the cell cycle. Moreover, xenograft and pulmonary metastasis models further demonstrated that avasimibe could inhibit tumor cell growth and metastasis in vivo. Taken together, our results for the first time revealed that avasimibe can inhibit BLCA progression and metastasis, and PPARγ signaling pathway may play a key role in regulation of cell cycle distribution induced by avasimibe.

Establishing a prognostic model with ferroptosis-related long non-coding RNAs in bladder cancer.

Background: Ferroptosis is a distinct form of cell death that has the potential to supersede the drug resistance that is commonly observed with current chemotherapeutic agents. As a result, ferroptosis presents a new and innovative therapeutic pathway for cancer treatment. The current understanding regarding the expression of genes associated with ferroptosis in bladder cancer (BLCA) and their prognostic implications remains unclear. Consequently, this study aimed to examine the potential prognostic value of ferroptosis-associated long non-coding RNAs (lncRNAs) in BLCA. Methods: The Cancer Genome Atlas (TCGA) was accessed to download RNA sequencing data and clinicopathological features of BLCA while accessing the FerrDb database to download ferroptosis-associated genes. The study calculated risk scores for ferroptosis-associated lncRNAs, and subsequently divided patients with BLCA into two groups, namely high- and low-risk, on the basis of the median risk score. Moreover, Kaplan-Meier (K-M) curves, Cox regression analysis, and column plots were utilized for evaluating the risk score prognostic value. Subsequently, the involvement of ferroptosis-associated mRNA, N6-methyladenosine (m6A) mRNA status, and immune responses was investigated for BLCA prognosis. Results: Thirty-six lncRNAs were identified to be differently expressed and linked to the prognosis of BLCA. The findings from the K-M curve analysis indicated a significant association between a high-risk lncRNA profile and poor BLCA prognosis. The area under curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.810. The risk assessment model exhibited superior performance in predicting prognosis for BLCA compared to conventional clinicopathological features. Conclusions: Thirty-six lncRNAs were found to be linked to ferroptosis for the prognosis of patients with BLCA, and these results may provide new insights for treating BLCA.

Prognostic analysis and validation of lncRNAs in bladder cancer on the basis of neutrophil extracellular traps.

BACKGROUND: Complex interactions in the tumor microenvironment (TME) between bladder cancer (BLCA) and immune cells are critical for cancer progression. However, studies of neutrophil extracellular trap-associated long non-coding RNAs (NET-lncRNAs) in the TME of BLCA have not been reported. This study aims to screen for NET-lncRNAs in BLCA and to preliminarily explore their effects on BLCA development. METHODS: The correlation of NET-related gene sets, which were identified from the cancer genome atlas (TCGA) BLCA datasets, with lncRNAs was analyzed and the prognosis-related genes were identified through random forest analysis. The least absolute shrinkage and selection operator (LASSO) model was utilized to obtain prognostic risk scores for NET-lncRNAs (NET-Score). We collected clinical BLCA samples, as well as SV-HUC-1 and BLCA cells, to validate the expression of NET-lncRNAs. Survival and independent prognostic analysis were performed. In J82 and UM-UC-3 cells, after NKILA expression was inhibited, cell proliferation and apoptosis levels were detected. RESULTS: NET-related gene sets mainly included CREB5, MMP9, PADI4, CRISPLD2, CD93, DYSF, MAPK3, TECPR2, MAPK1 and PIK3CA. Then, four NET-lncRNAs, MAP 3 K4-AS1, MIR100HG, NKILA and THY1-AS1, were identified. NET-Score had the highest hazard ratio for BLCA. An elevated NET-Score was linked to a significant increase in immune cell infiltration and copy number variation, as well as a notable decrease in survival rate and drug sensitivity. NET-lncRNA-related genes were mainly enriched in the pathways of angiogenesis, immune response, cell cycle and T cell activation. MAP 3 K4-AS1, MIR100HG, NKILA and THY1-AS1 expressions were significantly increased in BLCA tissues. Compared with SV-HUC-1 cells, NKILA expression was elevated in J82 and UM-UC-3 cells. Inhibition of NKILA expression inhibited the proliferation and promoted apoptosis of J82 and UM-UC-3 cells. CONCLUSIONS: Several NET-lncRNAs, including MAP 3 K4-AS1, MIR100HG, NKILA and THY1-AS1, were successfully screened in the BLCA. The NET-Score was an independent prognostic factor for BLCA. In addition, inhibition of NKILA expression suppressed BLCA cell development. The above NET-lncRNAs could serve as potential prognostic markers and targets in BLCA.

Comprehensive analysis of cuproptosis-related lncRNAs in the prognosis and therapy response of patients with bladder cancer.

Background: Cuproptosis is the recently defined regulatory cell death (RCD) that plays essential roles in tumorigenesis and progression. Long noncoding RNAs (lncRNAs) regulate the gene expression through various means. However, the clinical value of cuproptosis-related lncRNAs in bladder cancer (BLCA) remains poorly described. Methods: We downloaded the transcriptome sequencing data and clinical information from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and lasso Cox regression analyses were performed to construct the prognostic risk signature, the predictive accuracy of which was validated in the subsequent independence and stratification analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the underlying molecular mechanisms involved in the signature to explore therapeutic vulnerabilities and potential targets in BLCA. Tumor mutational burden (TMB) and tumor immune dysfunction and exclusion (TIDE) were used to estimate the response to immune checkpoint inhibitors (ICIs). We further explored the potential new drug-target candidates based on the half maximal inhibitory concentration for this patient population. Results: Fifteen cuproptosis-related lncRNAs significantly associated with survival were identified to construct the risk signature based on the normalized expression level and regression coefficient of each gene. The patients with BLCA and high-risk scores defined by the signature were associated with worse survival outcomes. The differentially expressed genes (DEGs) between the 2 risk groups had different biological activity. Furthermore, the patients in the low-risk group exhibited a higher TMB index and a lower TIDE score. The sensitivity of multiple antitumor drugs was negatively related to risk score, including AR-42, AS605240, FK866, TAK-715, and tubastatin A, while the sensitivity of some antitumor drugs, such as AMG-706, BX-795, and RO-3306, were positively correlated with risk score. Conclusions: Our study established and verified a novel clinical risk signature with cuproptosis-related lncRNAs that may predict therapy response and prognosis with robust and stable accuracy in patients with BLCA and enhance the personalized management of this patient population.

A reporting quality evaluation of the clinical practice guidelines for bladder cancer based on the RIGHT checklist.

Background: The Reporting Items for Practice Guidelines in Healthcare (RIGHT) checklist was developed to improve the reporting quality in clinical practice guidelines (CPGs). CPGs could provide the recommendations for key clinical issues with alternative care options and adherence to them could improve the outcomes. And, high reporting quality CPGs can assist health workers to incorporate the best evidence into the individual practice. There is no evaluation study on the reporting quality of CPGs in bladder cancer (BLCA). This study assessed the reporting quality of CPGs on BLCA and provided new insights for the development of CPGs in this disease. Methods: We conducted a systematic search in multiple literature databases, including PubMed, Wanfang, China National Knowledge Infrastructure (CNKI) and China Biology Medicine (CBM) as well as the medical associations and websites of guideline development organizations. Relevant CPGs published between January 2017 and December 2021 were identified. Four trained investigators independently screened the extracted documents to include all eligible CPGs and evaluated whether the items in the RIGHT checklist were reported in each CPG. Subsequently, the reporting rate of each CPG and item, as well as the mean reporting rate of each domain in the RIGHT checklist was calculated. Results: A total of 23 CPGs related to BLCA were finally included, of which, 22 guidelines were written in English and 1 was published in Chinese. The mean reporting rate of the included CPGs was approximately 65%. The reporting rates of the items in each RIGHT domain were 77% for basic information domain, 75% for recommendations domain, 72% for evidence domain, 69% for background domain, 43% for funding and declaration and management of interest domain, 35% for review and quality assurance domain, and 41% for other information domain. The reporting rate was determined as the mean value in Office Excel 2019. Conclusions: The reporting quality of BLCA CPGs related to the domains of funding and declaration and management of interest domain, review and quality assurance domain, and other information domain is poor and warrants improvement in the future.

Prognosis Risk Model Based on Necroptosis-Related Signature for Bladder Cancer.

BACKGROUND: Bladder cancer(BLCA) is the ninth most common cancer. In recent years, necroptosis was found to be related to the occurrence and development of tumors. In this study, we aimed to construct a model based on a necroptosis-related signature to evaluate the potential prognostic application in BLCA. METHODS: A total of 67 necroptosis-related genes were used to select the ideal cluster numbers, and it was found that there were four necroptosis-related patterns. Then, we compared the gene expression levels among all of the groups and established a necroptosis-related prognostic model. We made the following enrichment analysis of function and built a novel scoring system, the NEC score, to evaluate the state of necroptosis according to the expression level of necroptosis-related genes. RESULTS: A total of 67 necroptosis-related genes were used to define four distinct necroptosis-related patterns: NEC cluster1-4. Each NEC cluster exhibited different patterns of survival and immune infiltration. Based on univariate Cox regression analyses and least absolute shrinkage and selection operator (Lasso) regression, 14 necroptosis-related genes were established to develop the NEC score. Patients were divided into two groups based on the NEC score. Patients in the high NEC score group had a significantly poorer overall survival than those in the low NEC score group. We further confirmed the correlation of clinical characteristics, as well as the immunotherapy outcome, with the NEC score, and confirmed the potential of the NEC score to be an independent prognostic factor. Furthermore, we compared the expression levels of eight potential biomarker genes between our own BLCA tissues and para-carcinoma tissue. CONCLUSION: We developed a novel NEC score that has a potential prognostic value in BLCA patients and may help personalized immunotherapy counselling.

An immune-related gene signature for the prognosis of human bladder cancer based on WGCNA.

The innovation of immunotherapy was a milestone in the treatment of bladder cancer (BLCA). However, the treatment benefits varied by individual thus promoting the investigation of the biomarker of the patients. Unfortunately, there were not many effective predictive models, which were desired by clinicians, for BLCA that can predict the prognosis and benefit of immunotherapy. We constructed a three genes prognosis prediction model termed RiskScore based on the result of weighted correlation network analysis (WGCNA) from The Cancer Genome Atlas (TCGA) cohort (n = 406). We then validated the prediction accuracy with three validation cohort(GSE13507 (n = 165), GSE48075(n = 73), GSE32894(n = 224)). We compared the differences in gene expression, immune relate function, and immune infiltration between two groups divided by RiskScore. We further discovered the potential drug target and suitable compounds for high-risk groups. Our results suggested that the low-risk group may be more potential for immunotherapy for they have higher B cell infiltration, higher expression of immune checkpoints(PDCD1, CTLA4), and much more active immune-related pathways(B cell and T cell receptor signaling pathway). The RiskScore showed a well predictive accuracy for the prognosis of BLCA. After Spearman analysis, we found the suitable drug target and compounds for the patients in the high-risk group. The model we constructed is able to predict the prognosis of BLCA patients with ease and accuracy. PLK1 and gefitinib may be utilized for further treatment of BLCA patients.

m6A-immune-related lncRNA prognostic signature for predicting immune landscape and prognosis of bladder cancer.

BACKGROUND: N6-methyladenosine (m6A) related long noncoding RNAs (lncRNAs) may have prognostic value in bladder cancer for their key role in tumorigenesis and innate immunity. METHODS: Bladder cancer transcriptome data and the corresponding clinical data were acquired from the Cancer Genome Atlas (TCGA) database. The m6A-immune-related lncRNAs were identified using univariate Cox regression analysis and Pearson correlation analysis. A risk model was established using least absolute shrinkage and selection operator (LASSO) Cox regression analyses, and analyzed using nomogram, time-dependent receiver operating characteristics (ROC) and Kaplan-Meier survival analysis. The differences in infiltration scores, clinical features, and sensitivity to Talazoparib of various immune cells between low- and high-risk groups were investigated. RESULTS: Totally 618 m6A-immune-related lncRNAs and 490 immune-related lncRNAs were identified from TCGA, and 47 lncRNAs of their intersection demonstrated prognostic values. A risk model with 11 lncRNAs was established by Lasso Cox regression, and can predict the prognosis of bladder cancer patients as demonstrated by time-dependent ROC and Kaplan-Meier analysis. Significant correlations were determined between risk score and tumor malignancy or immune cell infiltration. Meanwhile, significant differences were observed in tumor mutation burden and stemness-score between the low-risk group and high-risk group. Moreover, high-risk group patients were more responsive to Talazoparib. CONCLUSIONS: An m6A-immune-related lncRNA risk model was established in this study, which can be applied to predict prognosis, immune landscape and chemotherapeutic response in bladder cancer.

A novel prognostic model based on three integrin subunit genes-related signature for bladder cancer.

Background: Presently, a comprehensive analysis of integrin subunit genes (ITGs) in bladder cancer (BLCA) is absent. This study endeavored to thoroughly analyze the utility of ITGs in BLCA through computer algorithm-based bioinformatics. Methods: BLCA-related materials were sourced from reputable databases, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). R software-based bioinformatics analyses included limma-differential expression analysis, survival-Cox analysis, glmnet-Least absolute shrinkage and selection operator (LASSO), clusterProfiler-functional annotation, and gsva-estimate-immune landscape analysis. The expression difference of key genes was verified by quantitative real-time polymerase chain reaction (qRT-PCR). Results: Among the 11 ITGs that were abnormally expressed in BLCA, ITGA7, ITGA5, and ITGB6 were categorized as the optimal variables for structuring the risk model. The high-risk subcategories were typified by brief survival, abysmal prognosis, prominent immune and stromal markers, and depressed tumor purity. The risk model was also an isolated indicator of the impact of clinical outcomes in BLCA patients. Moreover, the risk model, specifically the high-risk subcategory with inferior prognosis, became heavily interlinked with the immune-inflammatory response and smooth muscle contraction and relaxation. Conclusion: This study determined three ITGs with prognostic values (ITGA7, ITGA5, and ITGB6), composed a novel (ITG-associated) prognostic gene signature, and preliminarily probed the latent molecular mechanisms of the model.

Inhibition of PP2A by LB100 sensitizes bladder cancer cells to chemotherapy by inducing p21 degradation.

PURPOSE: Bladder carcinoma (BLCA) is the most common urinary tract malignancy and exhibits a poor response to chemotherapy. Protein phosphatase 2A (PP2A) is a serine/threonine phosphatase involved in a wide variety of regulatory cellular processes, including apoptosis and the DNA-damage response (DDR). LB100, a small molecule inhibitor of PP2A, has been shown to act as a chemo-sensitizer in multiple types of cancer. However, the anti-tumor effect and mode of action of LB100 in BLCA have yet to be identified. METHODS: In vitro and in vivo experiments were performed to assess the anti-tumor effect of LB100 alone or in combination with gemcitabine. Mass spectrometry (MS)-based phosphoproteomics analysis was used to identify the downstream substrates of PP2A and to explore the mechanism underlying LB100-induced DNA damage and apoptosis. In addition, we established a chemo-resistant BLCA cell line (RT-112-R) by prolonged drug exposure and determined the effect of LB100 in enhancing genotoxicity in BLCA cell lines and xenograft mouse models. RESULTS: We found that LB100 is sufficient to induce an anti-tumor response in BLCA cells by inducing DNA damage and apoptosis both in vitro and in vivo. Furthermore, we found that PP2A potentially dephosphorylates p-p21-ser130 to stabilize p21. Inhibition of PP2A by LB100 increased the level of p-p21-ser130, subsequently leading to a reduction in p21 level in a dose-dependent manner. In addition, we found that treatment of LB100 abrogated the G1/S cell cycle checkpoint, resulting in increased phosphorylation of γH2AX in BLCA cells. Moreover, LB100 enhanced genotoxicity in chemo-resistant BLCA cells by inducing DNA damage and apoptosis in vitro and in vivo. CONCLUSION: Our findings indicate that PP2A may serve as a potential therapeutic target in BLCA through regulating p21 stability.

Identification of a pyroptosis related gene signature for predicting prognosis and estimating tumor immune microenvironment in bladder cancer.

Background: Recently, there are growing evidence indicated that pyroptosis play a critical role in the incidence of many diseases. Here, we aimed to identify the specific function and prognosis predictive of pyroptosis-related genes (PRGs) in bladder cancer (BLCA) patients. Methods: The gene expression and corresponding clinical data of BLCA patients were obtained from The Cancer Genome Atlas (TCGA), and the expression level of PRGs was identified between normal and tumor tissues. Furthermore, univariate Cox regression was conducted to filter the PRGs related to overall survival, and LASSO Cox regression was subsequently conducted to establish the PRGs risk model. Besides, the correlation of risk score with patients' clinical features, tumor mutational burden (TMB) as well as tumor microenvironment (TME) was also investigated. Results: A total of 6 PRGs was used to establish the risk prognostic model. According the median value of risk score, the patients were classified into low- and high-risk subgroup. Kaplan-Meier survival analysis revealed that the BLCA patients in low-risk group exhibited a better survival prognosis compared with high-risk group. More important, after adjusting for age, gender, tumor grade, and clinical stage, the risk score resulted as an independent factor affecting the clinical prognosis of BLCA patients. In addition, the PRGs risk signature was also correlated with immune cell infiltration, TMB and TME. Conclusions: The present study offered a novel PRGs risk model to access the clinical prognosis of BLCA and provided new insight for future study to improve overall survival and responses to cancer therapy targeting pyroptosis.

Identification of hub genes associated with bladder cancer using bioinformatic analyses.

Background: Bladder cancer (BLCA) is the ninth most common cancer worldwide, with high mortality and recurrence rates. Studies have increasingly reported that molecular diagnosis contributes to the early diagnosis and prognostic assessment of diseases. Thus, this study aims to find new biomarkers for the diagnosis and prognosis of BLCA. Methods: The microarray datasets GSE147983 and The Cancer Genome Atlas (TCGA)-BLCA mRNA were obtained from the Gene Expression Omnibus (GEO) and TCGA. Differentially expressed genes (DEGs) were screened using the R "Limma" package. The "ClusterProfiler" package was used to conduct Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs. A DEG protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape. The functional module was reanalyzed using Cytoscape's Molecular Complex Detection ("MCODE") plugin, and key genes related to BLCA were identified via the "cytoHubba" plugin. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and the Tumor Immune Estimation Resource (TIMER) were used to verify the correlation between hub gene expression and immunity. A survival analysis of hub genes was performed using the Kaplan-Meier Plotter online tool. Results: A total of 355 DEGs were screened out, including 236 upregulated and 119 downregulated DEGs. Some of the GO terms and pathways, such as chromosome separation, cell cycle, and cell senescence, were found to be significantly enriched in the DEGs. The key genes were kinesin family member 11 (KIF11), DLG associated protein 5 (DLGAP5), non-SMC condensin I complex subunit G (NCAPG), cell division cycle 20 (CDC20), cyclin B2 (CCNB2), BUB1 mitotic checkpoint serine (BUB1B), TPX2 microtubule nucleation factor (TPX2), NUF2 component of NDC80 kinetochore complex (NUF2), kinesin family member 2C (KIF2C), and cyclin B1 (CCNB1). Nine of them were immune-related, including KIF11, DLGAP5, NCAPG, CDC20, CCNB2, BUB1B, NUF2, KIF2C, and CCNB1. Survival analysis showed that the overexpression of BUB1B, CCNB1, CDC20, and DLGAP5 significantly reduced overall survival (OS) in patients with BLCA. Conclusions: This study provided a theoretical basis for elucidating the pathogenesis and evaluating the prognosis of BLCA by screening potential biomarkers of BLCA.

Corrigendum: Identification of Signature Genes Associated With Invasiveness and the Construction of a Prognostic Model That Predicts the Overall Survival of Bladder Cancer.

[This corrects the article DOI: 10.3389/fgene.2021.694777.].

A tumor microenvironment preoperative nomogram for prediction of lymph node metastasis in bladder cancer.

Background: Growing evidence suggests that tumor metastasis necessitates multi-step microenvironmental regulation. Lymph node metastasis (LNM) influences both pre- and post-operative bladder cancer (BLCA) treatment strategies. Given that current LNM diagnosis methods are still insufficient, we intend to investigate the microenvironmental changes in BLCA with and without LNM and develop a prediction model to confirm LNM status. Method: "Estimation of Stromal and Immune cells in Malignant Tumors using Expression data" (ESTIMATE) algorithm was used to characterize the tumor microenvironment pattern of TCGA-BLCA cohort, and dimension reduction, feature selection, and StrLNM signature construction were accomplished using least absolute shrinkage and selection operator (LASSO) regression. StrLNM signature was combined with the genomic mutation to establish an LNM nomogram by using multivariable logistic regression. The performance of the nomogram was evaluated in terms of calibration, discrimination, and clinical utility. The testing set from the TCGA-BLCA cohort was used for internal validation. Moreover, three independent cohorts were used for external validation, and BLCA patients from our cohort were also used for further validation. Results: The StrLNM signature, consisting of 22 selected features, could accurately predict LNM status in the TCGA-BLCA cohort and several independent cohorts. The nomogram performed well in discriminating LNM status, with the area under curve (AUC) of 75.1% and 65.4% in training and testing datasets from the TCGA-BLCA cohort. Furthermore, the StrLNM nomogram demonstrated good calibration with p >0.05 in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis (DCA) revealed that the StrLNM nomogram had a high potential for clinical utility. Additionally, 14 of 22 stably expressed genes were identified by survival analysis and confirmed by qPCR in BLCA patient samples in our cohort. Conclusion: In summary, we developed a nomogram that included an StrLNM signature and facilitated the preoperative prediction of LNM status in BLCA patients.

Bioinformatics analysis to screen DNA methylation-driven genes for prognosis of patients with bladder cancer.

BACKGROUND: Bladder cancer (BLCA) is the most prevalent tumor affecting the urinary system, and has contributed to a rise in morbidity and mortality rates. Herein, we sought to identify the methylation-driven genes (MDGs)of BLCA in an effort to develop prognostic biomarkers suitable for the individualized assessment of patients with this particular cancer. METHODS: The Cancer Genome Atlas (TCGA) dataset was distributed into training set (n=272) and testing set(n=117). The ConsensusClusterPluspackage was used to identify BLCA subtypes. The ChAMP package was used to analyze differential methylation probe (DMP) and differential methylation region (DMR). The differentially expressed genes (DEGs) were detected using DESeq2. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were utilized to identify the pathways enriched of DEGs. Correlation analysis between 5'-C-phosphate-G-3's (CpGs) and DEGs was employed to identify the MDGs. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) was used to build the protein-protein interaction (PPI) network of MDGs. Screening for BLCA prognosis-related MDGs and clinical features was conducted via the Cox regression model. A prognosis-related nomogram was developed and validated for prediction of the BLCA patients' survival. RESULTS: We identified 2 BLCA clusters. Differential methylations at CpGs sites (dm-CpGs) were observed between cluster2 and cluster1, with 14,189 of them hypermethylated and 878 hypomethylated, predominantly in the CpG islands. In addition, a total 4,234 DEGs were identified between cluster2 and cluster1. The KEGG pathway and GO term enrichment analyses found that some DEGs were significantly enriched in multiple cancer-related pathways. A total of 33 MDGs were detected from correlation analysis between CpGs and DEGs. We selected BLCA-specific prognostic DMGs signatures for risk model development. The nomogram comprised a risk model to predict survival for BLCA patients. The efficiency of the prognostic prediction model was validated in the training and testing set. CONCLUSIONS: This study discovered differential methylation patterns and MDGs in BLCA patients, which provided a bioinformatics basis for guiding BLCA early diagnosis and prognosis analyses.

The construction and validation of an RNA binding protein-related prognostic model for bladder cancer.

BACKGROUND: RNA-binding proteins (RBPs) play crucial and multifaceted roles in post-transcriptional regulation. While RBPs dysregulation is involved in tumorigenesis and progression, little is known about the role of RBPs in bladder cancer (BLCA) prognosis. This study aimed to establish a prognostic model based on the prognosis-related RBPs to predict the survival of BLCA patients. METHODS: We downloaded BLCA RNA sequence data from The Cancer Genome Atlas (TCGA) database and identified RBPs differentially expressed between tumour and normal tissues. Then, functional enrichment analysis of these differentially expressed RBPs was conducted. Independent prognosis-associated RBPs were identified by univariable and multivariable Cox regression analyses to construct a risk score model. Subsequently, Kaplan-Meier and receiver operating characteristic curves were plotted to assess the performance of this prognostic model. Finally, a nomogram was established followed by the validation of its prognostic value and expression of the hub RBPs. RESULTS: The 385 differentially expressed RBPs were identified included 218 and 167 upregulated and downregulated RBPs, respectively. The eight independent prognosis-associated RBPs (EFTUD2, GEMIN7, OAS1, APOBEC3H, TRIM71, DARS2, YTHDC1, and RBMS3) were then used to construct a prognostic prediction model. An in-depth analysis showed lower overall survival (OS) in patients in the high-risk subgroup compared to that in patients in the low-risk subgroup according to the prognostic model. The area under the curve of the time-dependent receiver operator characteristic (ROC) curve were 0.795 and 0.669 for the TCGA training and test datasets, respectively, showing a moderate predictive discrimination of the prognostic model. A nomogram was established, which showed a favourable predictive value for the prognosis of BLCA. CONCLUSIONS: We developed and validated the performance of a prognostic model for BLCA that might facilitate the development of new biomarkers for the prognostic assessment of BLCA patients.