Survival In Bladder Cancer Patients Research Articles

Survival Prediction of Patients with Bladder Cancer after Cystectomy Based on Clinical, Radiomics, and Deep-Learning Descriptors.

Accurate survival prediction for bladder cancer patients who have undergone radical cystectomy can improve their treatment management. However, the existing predictive models do not take advantage of both clinical and radiological imaging data. This study aimed to fill this gap by developing an approach that leverages the strengths of clinical (C), radiomics (R), and deep-learning (D) descriptors to improve survival prediction. The dataset comprised 163 patients, including clinical, histopathological information, and CT urography scans. The data were divided by patient into training, validation, and test sets. We analyzed the clinical data by a nomogram and the image data by radiomics and deep-learning models. The descriptors were input into a BPNN model for survival prediction. The AUCs on the test set were (C): 0.82 ± 0.06, (R): 0.73 ± 0.07, (D): 0.71 ± 0.07, (CR): 0.86 ± 0.05, (CD): 0.86 ± 0.05, and (CRD): 0.87 ± 0.05. The predictions based on D and CRD descriptors showed a significant difference (p = 0.007). For Kaplan-Meier survival analysis, the deceased and alive groups were stratified successfully by C (p < 0.001) and CRD (p < 0.001), with CRD predicting the alive group more accurately. The results highlight the potential of combining C, R, and D descriptors to accurately predict the survival of bladder cancer patients after cystectomy.

Comprehensive analysis of N1-methylandenosine regulators and m1A-related mRNAs and lncRNAs as prognostic factors in bladder cancer

Bladder cancer (BC) is a lethal malignancy and recurs frequently. m1A plays a vital role in maintaining the biological functions of non-coding RNAs. The Cancer Genome Atlas (TCGA) is a free website from where transcriptome data of BC were obtained. We chose m1A methylation regulators for this study. Six m1A methylation regulator genes have a higher expression in BC tissue compared to normal tissue. The aberrant expression of those m1A regulator genes was remarkably related to BC prognosis and clinicopathological features. First, m1A-related mRNAs and long noncoding RNAs (lncRNAs) were identified. Next, univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were performed to get the optimum RNAs for the development of prognostic signatures. Also, a nomogram with T status, lncRNA risk scores and mRNA risk scores was constructed. It revealed an adequate capacity to predict the overall survival of BC cases in the training set as well as in the testing set and in the total TCGA cohort. In conclusion, m1A methylation regulator genes played an important role in predicting the overall survival of BC patients. In addition, m1A-related lncRNAs and mRNAs illustrated underlying mechanisms of tumorigenesis and development of BC.

Homologous recombination mRNAs (RAD21, RAD50 and BARD1) have a potentially poor prognostic role in ERBB2-low bladder cancer patients

Human epidermal growth factor receptor 2 (HER2/ERBB2) factor is known to be implicated in many malignancies and the potential of it as a prognostic biomarker was reported years ago. Molecular subtypes of HER2/ERBB2 negative and positive with distinct clinical outcomes have been identified in recent years; however, it is still under investigation for bladder cancer. This study evaluates the biological and prognostic significance of RAD21, RAD50 and BARD1 (homologous recombination biomarkers) mRNA levels with ERBB2 low and high expression to explore their impact on bladder cancer patient survival and cancer aggressiveness. The expression of ERBB2, RAD21, RAD50 and BARD1 mRNA levels was assessed in The Cancer Genome Atlas (TCGA) bladder cancer dataset along with four validation cohorts. Outcome analysis was evaluated using disease-free survival (DFS) and overall survival (OS). Univariate and multivariate analysis were used to evaluate the relationship between RAD21, RAD50, BARD1 and ERBB2 expression and clinicopathological variables. A significant increase in mRNA expression levels of RAD21, RAD50 and BARD1 was noticed in ERBB2-low patients compared to ERBB2-high patients. This overexpression of the homologous recombination repair transcripts was associated with poor outcome in ERBB2-low tumors, not in ERBB2-high tumors. Furthermore, the combined expression of high RAD21/RAD50, high RAD21/BARD1 or high RAD50/BARD1 were significantly associated with worse DFS and a better outcome for those with low co-expression in the ERBB2-low cohort. High expression of either RAD21/RAD50 or RAD21/BARD1 in ERBB2-low cohort associated with higher chance of metastasis. In addition, gene expression of BARD1 alone or in combination with RAD50 acted as an independent prognostic factor for worst survival. The data presented in this study reveal a connection between RAD21, RAD50, BARD1 and ERBB2 and patient survival. Importantly, it provided novel findings and potential prognostic markers, particularly in ERBB2-low bladder cancer.

Impact of postoperative complications on long-term survival in bladder cancer patients.

To determine the impact of postoperative complications on long-term survival outcomes in patients with bladder cancer undergoing radical cystectomy. This retrospective multi-institutional study included 766bladder cancer patients who underwent radical cystectomy between 2011 and 2017. Patient characteristics, perioperative outcomes, all complications within 90days after surgery and survival outcomes were collected. Each complication was graded based on the Clavien-Dindo system, and grouped using a standardized grouping method. The Comprehensive Complication Index, which incorporates all complications into a single formula weighted by their severity, was utilized. Overall survival and recurrence-free survival (local, distant or urothelial recurrences) were stratified by Comprehensive Complication Index (high: ≥26.2; low: <26.2). A multivariate model was utilized to identify independent prognostic factors. The incidence of any and major complications (≥Clavien-Dindo grade III) was 70 and 24%, respectively. In terms of Comprehensive Complication Index, 34% (261/766) of the patients had ≥26.2. Patients with Comprehensive Complication Index≥26.2 had shorter overall survival (4-year, 59.5 vs. 69.8%, respectively, log-rank test, P=0.0037) and recurrence free survival (51.9 vs. 60.1%, respectively, P=0.0234), than those with Comprehensive Complication Index<26.2. The Cox multivariate model identified the age, performance status, pT-stage, pN-stage and higher CCI (overall survival: HR=1.35, P=0.0174, recurrence-free survival: HR=1.26, P=0.0443) as independent predictors of both overall survivial and recurrence-free survival. Postoperative complications assessed by Comprehensive Complication Index had adverse effects on long-term survival outcomes. Physicians should be aware that major postoperative complications can adversely affect long-term disease control.

Predictive role of pretreatment skeletal muscle mass index for long-term survival of bladder cancer patients: A meta-analysis.

To identify the predictive role of pretreatment skeletal muscle mass index (SMI) for long-term survival of bladder cancer patients. Several databases were searched for studies investigating the relationship between pretreatment SMI and prognosis in bladder cancer. The overall survival (OS) and cancer-specific survival (CSS) were defined as primary and secondary outcomes, respectively. Hazard ratios (HRs) and 95% confidence intervals (CIs) were combined. Nine studies involving 1476 cases were included. The results demonstrated that a lower pretreatment SMI was significantly related to poorer OS (HR = 1.56, 95% CI: 1.33-1.82, P<0.001) and subgroup analysis based on thresholds of SMI revealed similar results. Besides, pretreatment SMI was also obviously related to CSS (HR = 1.75, 95% CI: 1.36-2.25, P<0.001). Lower pretreatment SMI was associated with worse long-term survival of bladder cancer patients.

Leveraging a disulfidptosis-based signature to improve the survival and drug sensitivity of bladder cancer patients.

Disulfidptosis is a recently discovered form of cell death. However, its biological mechanisms in bladder cancer (BCa) are yet to be understood. Disulfidptosis-related clusters were identified by consensus clustering. A disulfidptosis-related gene (DRG) prognostic model was established and verified in various datasets. A series of experiments including qRT-PCR, immunoblotting, IHC, CCK-8, EdU, wound-healing, transwell, dual-luciferase reporter, and ChIP assays were used to study the biological functions. We identified two DRG clusters, which exhibited distinct clinicopathological features, prognosis, and tumor immune microenvironment (TIME) landscapes. A DRG prognostic model with ten features (DCBLD2, JAM3, CSPG4, SCEL, GOLGA8A, CNTN1, APLP1, PTPRR, POU5F1, CTSE) was established and verified in several external datasets in terms of prognosis and immunotherapy response prediction. BCa patients with high DRG scores may be characterized by declined survival, inflamed TIME, and elevated tumor mutation burden. Besides, the correlation between DRG score and immune checkpoint genes and chemoradiotherapy-related genes indicated the implication of the model in personalized therapy. Furthermore, random survival forest analysis was performed to select the top important features within the model: POU5F1 and CTSE. qRT-PCR, immunoblotting, and immunohistochemistry assays showed the enhanced expression of CTSE in BCa tumor tissues. A series of phenotypic assays revealed the oncogenetic roles of CTSE in BCa cells. Mechanically, POU5F1 can transactivate CTSE, promoting BCa cell proliferation and metastasis. Our study highlighted the disulfidptosis in the regulation of tumor progression, sensitivity to therapy, and survival of BCa patients. POU5F1 and CTSE may be potential therapeutic targets for the clinical treatment of BCa.

Prognostic nutritional index combined with NLR to construct a survival prediction model and decision analysis of patients with muscle-invasive bladder cancer after surgery.

To build a nomogram prediction model, assess its predictive ability, and perform a survival decision analysis on patients with muscle-invasive bladder cancer (MIBC) to study risk factors affecting overall survival (OS). A retrospective analysis was performed on the clinical information of 262 patients with MIBC who underwent radical cystectomy (RC) at the Urology Department of the Second Affiliated Hospital of Kunming Medical University between July 2015 and August 2021. The final model variables that were included were chosen using single-factor stepwise Cox regression, optimal subset regression, and LASSO regression + cross-validation with the minimum AIC value. The next step was to do a multivariate Cox regression analysis. The establishment of a nomogram model by fitting and the screening out of independent risk factors impacting the survival of patients with MIBC having radical resection. Receiver Activity Characteristic curves, C-index, and a calibration plot evaluated the prediction accuracy, validity, and clinical benefit of the model. The 1-, 3-, and 5-year survival rates were then computed for each risk factor using a Kaplan-Meier survival analysis. 262 eligible patients in total were enrolled. With a median follow-up of 32 months, the follow-up period ranged from 2 to 83 months. 171 cases (65.27%) survived while 91 cases (34.73%) perished. Age (HR = 1.06 [1.04; 1.08], p = 0.001), preoperative hydronephrosis (HR = 0.69 [0.46, 1.05], p = 0.087), T stage (HR = 2.06 [1.09, 3.93], p = 0.027), lymphovascular invasion (LVI, HR = 1.73 [1.12, 2.67], p = 0.013), prognostic nutritional index (PNI, HR = 1.70 [1.09, 2.63], p = 0.018), and neutrophil-to-lymphocyte ratio (NLR, HR = 0.52 [0.29, 0.93)], p = 0.026) were independent risk factor for the survival of bladder cancer patients. Create a nomogram based on the aforementioned findings, and then draw the 1-year, 3-year, and 5-year OS receiver operating characteristic curves by the nomogram. The AUC values were 0.811 (95% CI [0.752, 0.869]), 0.814 (95% CI [0.755, 0.873]), and 0.787 (95% CI [0.708, 0.865]), respectively, and the calibration plot matched the predicted value well. The 1-year, 3-year, and 5-year decision curve analyses were higher than the ALL line and None line at threshold values of >5%, 5%-70%, and 20%-70% indicating that the model has good clinical applicability. The calibration plot for the Bootstrap 1000-time resampled validation model was similar to the actual value. Patients with preoperative combination hydronephrosis, higher T-stage, combined LVI, low PNI, and high NLR had worse survival, according to Kaplan-Meier survival analysis for each variable. This study might conclude that PNI and NLR were separate risk factors that affect a patient's OS after RC for MIBC. The prognosis of bladder cancer may be predicted by PNI and NLR, but additional confirmation in randomized controlled trials is required.

Screening of immunotherapy-related genes in bladder cancer based on GEO datasets.

As one of the most prevalent genitourinary cancers, bladder cancer (BLCA) is associated with high morbidity and mortality. Currently, limited indicators are available for early detection and diagnosis of bladder cancer, and there is a lack of specific biomarkers for evaluating the prognosis of BLCA patients. This study aims to identify critical genes that affect bladder cancer immunity to improve the diagnosis and prognosis of bladder cancer and to identify new biomarkers and targets for immunotherapy. Two GEO datasets were used to screen differentially expressed genes (DEGs). The STRING database was used to construct a protein-protein interaction network of DEGs, and plug-in APP CytoHubba in Cytoscape was used to identify critical genes in the network. GO and KEGG analyses explored the functions and pathways of differential gene enrichment. We used GEPIA to validate the expression of differential genes, their impact on patient survival, and their relationship to clinicopathological parameters. Additionally, hub genes were verified using qRT-PCR and Western blotting. Immune infiltration analysis and multiple immunohistochemistry reveal the impact of Hub genes on the tumor microenvironment. We screened out 259 differential genes, and identified 10 key hub genes by the degree algorithm. Four genes (ACTA2, FLNA, TAGLN, and TPM1) were associated with overall or disease-free survival in BLCA patients and were significantly associated with clinical parameters. We experimentally confirmed that the mRNA and protein levels of these four genes were significantly decreased in bladder cancer cells. Immunoassays revealed that these four genes affect immune cell infiltration in the tumor microenvironment; they increased the polarization of M2 macrophages. These four genes affect the tumor microenvironment of bladder cancer, provide a new direction for tumor immunotherapy, and have significant potential in the diagnosis and prognosis of bladder cancer.

LDL receptor related protein 1 is an adverse prognostic biomarker that correlates with stromal remodeling and macrophages infiltration in bladder cancer.

Bladder cancer (BLCA) is a highly heterogeneous disease influenced by the tumor microenvironment, which may affect patients' response to immune checkpoint blockade therapy. Therefore, identifying molecular markers and therapeutic targets to improve treatment is essential. In this study, we aimed to investigate the prognostic significance of LRP1 in BLCA. We analyzed TCGA and IMvigor210 cohorts to investigate the relationship of LRP1 with BLCA prognosis. We utilized gene mutation analysis and enrichment to identify LRP1-associated mutated genes and biological processes. Deconvolution algorithms and single-cell analysis were used to understand the tumor-infiltrated cells and biological pathways associated with LRP1 expression. Immunohistochemistry was conducted to validate the bioinformatics analysis. Our study revealed that LRP1 was an independent risk factor for overall survival in BLCA patients and was associated with clinicopathological features and FGFR3 mutation frequency. Enrichment analysis demonstrated that LRP1 was involved in extracellular matrix remodeling and tumor metabolic processes. Furthermore, the ssGSEA algorithm revealed that LRP1 was positively correlated with the activities of tumor-associated pathways. Our study also found that high LRP1 expression impaired patients' responsiveness to ICB therapy in BLCA, which was predicted by TIDE prediction and validated by IMvigor210 cohort. Immunohistochemistry confirmed the expression of LRP1 in Cancer-Associated Fibroblasts (CAFs) and macrophages in the tumor microenvironment of BLCA. Our study suggests that LRP1 may be a potential prognostic biomarker and therapeutic target in BLCA. Further research on LRP1 may improve BLCA precision medicine and enhance the efficacy of immune checkpoint blockade therapy.

Abstract 2219: Urine cell-free DNA multi-omics to detect molecular residual disease and predict survival in bladder cancer patients

Abstract Background: Circulating cell-free tumor DNA (ctDNA) analysis for minimal residual disease (MRD) detection is transforming cancer care. However, the sensitivity of these approaches remains subpar and there is significant risk of false negative results. We analyzed the most proximal biofluid (urine) in localized muscle-invasive bladder cancer (MIBC) patients, and performed two orthogonal ctDNA analytical techniques, one focusing on driver mutation detection, and another focusing on genome-wide copy number alterations. The primary objective of this combinatorial approach was to sensitively detect ctDNA MRD, predict pathologic complete response (pCR), and improve patient risk-stratification. Methods: Urine samples from 74 localized bladder cancer patients were collected preoperatively on the day of curative-intent radical cystectomy (RC) to assess urine tumor DNA (utDNA). We performed ultra-low pass whole genome sequencing (ULP-WGS) of urine cfDNA from all 74 patients as well as 15 healthy adults. Tumor fraction (TFx) level based on genome-wide copy number alterations was estimated using ichorCNA. Variant allele frequency (VAF) based on single-nucleotide variants (SNVs) was estimated by uCAPP-Seq. We also noninvasively inferred tumor mutation burden (iTMB). pCR was determined by surgical pathology. A random forest (RF) model with leave-one-out cross-validation (LOOCV) was utilized to predict disease status. Kaplan-Meier (KM) and Cox proportional hazards model survival analyses were performed to assess overall survival (OS) and progression-free survival (PFS). Results: Our study cohort consisted of 74 patients, of which 58 (78%) harbored localized MIBC, and 16 (22%) harbored treatment-refractory high-risk localized NMIBC. Among MIBC patients, 64% (37/58) received neo-adjuvant chemotherapy. VAF, iTMB and TFx levels significantly predicted pCR status. Our RF model incorporating these three utDNA parameters achieved a sensitivity of 87%, a negative predictive value of 77%, and a positive predictive value of 65% for predicting residual disease. AUC for the model was 0.80 (p&amp;lt;0.0001). KM analysis revealed that both PFS (HR = 3.00, P = 0.01) and OS (HR = 4.81, P = 0.009) were significantly worse for patients predicted by the model to have residual disease. Univariate and multivariate Cox proportional hazards models confirmed the significance of our MRD predictions. Survival analyses performed on MIBC, neoadjuvant chemotherapy, and held-out validation subgroups corroborated these findings. Conclusions: Integration of ULP-WGS with uCAPP-Seq enabled robust detection of residual urine tumor DNA with high sensitivity and predicted survival in localized bladder cancer patients. In the future, this type of multimodal urine-based genomic analysis may lead to more precise risk stratification and nonoperative clinical decision-making for bladder cancer patients. Citation Format: Pradeep Singh Chauhan, Alexander L. Shiang, Irfan Alahi, R. Taylor Sundby, Wenjia Feng, Bilge Gungoren, Cayce Nawaf, Kevin Chen, Ramandeep K. Babbra, Peter K. Harris, Faridi Qaium, Casey Hatscher, Anna Antiporda, Lindsey Brunt, Lindsey R. Mayer, Jack F. Shern, Brian C. Baumann, Eric H. Kim, Melissa A. Reimers, Zachary L. Smith, Aadel A. Chaudhuri. Urine cell-free DNA multi-omics to detect molecular residual disease and predict survival in bladder cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2219.

High expression of ABCF1 is an independent predictor of poor prognosis in bladder cancer

ABCF1, a member of the ATP-binding cassette (ABC) transporter family, is involved in the malignant progression of tumors. However, the role of ABCF1 in bladder cancer is poorly understood. In our study, we explored the differential expression of ABCF1 in bladder cancer and normal bladder tissues based on bioinformatic analysis and immunohistochemical results. GSEA was performed to ascertain the potential related signaling pathways of ABCF1. The relationship between ABCF1 expression and bladder cancer progression was analyzed using the GSE13507 dataset. In addition, the differential expression of ABCF1 in the cell lines was verified by quantitative real-time polymerase chain reaction (qRT‒PCR) and Western blotting. ABCF1 was upregulated in bladder cancer, and the high expression of ABCF1 was closely related to sex (P = 0.00056), grade (P = 0.00049), T stage (P = 0.00007), and N stage (P = 0.0076). High expression of ABCF1 was correlated with poor overall survival in bladder cancer patients (P < 0.001). In addition, univariate and multivariate Cox regression analyses showed that high ABCF1 expression was an independent factor for poor prognosis in bladder cancer patients. Therefore, ABCF1 expression is closely related to the progression of bladder cancer and can be used as a potential indicator of poor prognosis and a therapeutic target for bladder cancer.

SNHG18 inhibits bladder cancer cell proliferation by increasing p21 transcription through destabilizing c-Myc protein

BackgroundLong non-coding RNAs (lncRNAs) have been confirmed to play important roles in various cancers including bladder cancer (BC). The precise expression pattern of lncRNA small nucleolar RNA host gene 18 (SNHG18) in BC and its mechanisms of action have not been fully explored.Materials and methodsThe expression of SNHG18 was evaluated by RT-qPCR in bladder cancer clinical samples and human bladder cancer cell lines, and stable cell lines overexpressing SNHG18 were constructed. The effect of SNHG18 on the proliferation of bladder cancer cells was detected by soft agar colony formation test, ATP activity test and subcutaneous tumorigenesis model in nude mice. The specific mechanism of SNHG18 inhibition of bladder cancer proliferation was studied by flow cytometry, western blotting, dual luciferase reporter gene assay and protein degradation assay.ResultsWe found that SNHG18 is significantly downregulated in BC tissues and cell lines. Kaplan–Meier analysis showed that SNHG18 expression is positively correlated with survival in BC patients. Ectopic overexpression of SNHG18 significantly inhibited the proliferation of BC cells in vitro and in vivo. Further mechanistic investigations demonstrated that SNHG18 inhibited c-Myc expression by modulating the ubiquitination-proteasome pathway and that c-Myc is the critical transcription factor that mediates SNHG18 inhibition of BC growth by directly binding to the p21 promoter, which was attributed with significant p21 accumulation.ConclusionsSNHG18 promotes the transcription and expression of p21 by inhibiting c-Myc expression, leading to G0-G1 arrest and inhibiting the proliferation of bladder cancer cells. These findings highlight a novel cell cycle regulatory mechanism involving the SNHG18/c-Myc/p21 pathway in BC pathogenesis and could potentially lead to new lncRNA-based diagnostics and/or therapeutics for BC.

Prediction of immune infiltration and prognosis for patients with urothelial bladder cancer based on the DNA damage repair-related genes signature

ObjectivesTo analyze the correlations between the expression and effect of DNA damage repair genes and the immune status and clinical outcomes of urothelial bladder cancer (BLCA) patients. In addition, we evaluate the efficacy and value of utilizing the DNA damage repair genes signature as a prognosis model for BLCA. MethodsTwo subtype groups (C1 and C2) were produced based on the varied expression of DNA damage repair genes. Significantly differentiated genes and predicted enriched gene pathways were obtained between the two subtypes. Seven key genes were obtained from the DNA damage repair-related genes and a 7-gene signature prognosis model was established based on the key genes. The efficacy and accuracy of this model in prognosis prediction was evaluated and verified in two independent databases. Also, the difference in biological functions, drug sensitivity, immune infiltration and affinity between the high-risk group and low-risk group was analyzed. ResultsThe DNA damage repair gene signature could significantly differentiate the BLCA into two molecular subgroups with varied genetic expression and enriched gene pathways. Seven key genes were screened out from the 232 candidate genes for prognosis prediction and a 7-gene signature prognosis model was established based on them. Two independent patient cohorts (TCGA cohort and GEO cohort) were utilized to validate the efficacy of the prognosis model, which demonstrated an effective capability to differentiate and predict the overall survival of BLCA patients. Also, the high-risk group and low-risk group derived from the 7-gene model exhibited significantly differences in drug sensitivity, immune infiltration status and biological pathways enrichment. ConclusionsOur established 7-gene signature model based on the DNA damage repair genes could serve as a novel prognosis predictive tool for BLCA. The differentiation of BLCA patients based on the 7-gene signature model may be of great value for the appropriate selection of specific chemotherapy agents and immune-checkpoint blockade therapy administration.

Development and Validation of Nomograms Predicting the 5- and 8-Year Overall and Cancer-Specific Survival of Bladder Cancer Patients Based on SEER Program.

Bladder cancer is often prone to recurrence and metastasis. We sought to construct nomogram models to predict the overall survival (OS) and cancer-specific survival (CSS) of bladder cancer patients. A reliable random split-sample approach was used to divide patients into two groups: modeling and validation cohorts. Uni-variate and multivariate survival analyses were used to obtain the independent prognostic risk factors based on the modeling cohort. A nomogram was constructed using the R package, "rms". Harrell's concordance index (C-index), calibration curves and receiver operating characteristic (ROC) curves were applied to evaluate the discrimination, sensitivity and specificity of the nomograms using the R packages "hmisc", "rms" and "timeROC". A decision curve analysis (DCA) was used to evaluate the clinical value of the nomograms via R package "stdca.R". 10,478 and 10,379 patients were assigned into nomogram modeling and validation cohorts, respectively (split ratio ≈ 1:1). For OS and CSS, the C-index values for internal validation were 0.738 and 0.780, respectively, and the C-index values for external validation were 0.739 and 0.784, respectively. The area under the ROC curve (AUC) values for 5- and 8-year OS and CSS were all greater than 0.7. The calibration curves show that the predicted probability values of 5- and 8-year OS and CSS are close to the actual OS and CSS. The decision curve analysis revealed that the two nomograms have a positive clinical benefit. We successfully constructed two nomograms to forecast OS and CSS for bladder cancer patients. This information can help clinicians conduct prognostic evaluations in an individualized manner and tailor personalized treatment plans.

A model for predicting overall survival in bladder cancer patients with signet ring cell carcinoma: a population-based study

IntroductionThis study is to examine the predictors of survival and to construct a nomogram for predicting the overall survival (OS) of primary bladder signet ring cell carcinoma (SRCC) patients based on the analysis of the Surveillance, Epidemiology, and End Results (SEER) database.MethodsA total of 219 eligible patients diagnosed with SRCC were analyzed using the 2004–2015 data from SEER database. Univariate and multivariate Cox regression were used to determine independent prognostic factors, followed by development of a nomogram based on the multivariate Cox regression models. The consistency index (C-index), receiver operating characteristic (ROC) curve, and calibration curve were used to validate the prognostic nomogram.ResultsThe nomograms indicated appreciable accuracy in predicting the OS, with C-index of 0.771 and 0.715, respectively. The area under the curve (AUC) of the nomogram was 0.713 for 1 year, 0.742 for 3 years, and 0.776 for 5 years in the training set, while was 0.730 for 1 year, 0.727 for 3 years, and 0.697 for 5 years in the validation set. The calibration curves revealed satisfactory consistency between the prediction of deviation correction and ideal reference line.ConclusionsThe prognostic nomogram developed in the analytical data of SEER it provided high accuracy and reliability in predicting the survival outcomes of primary bladder SRCC patients and could be used to comprehensively assess the risk of SRCC. Moreover, they could enable clinicians to make more precise treatment decisions for primary bladder SRCC patients.

Identification and validation of a novel prognostic model based on platinum resistance-related genes in bladder cancer

The depth of response to platinum in urothelial neoplasm tissues varies greatly. Biomarkers that have practical value in prognosis stratification are increasingly needed. Our study aimed to select a set of BC (bladder cancer)-related genes involved in both platinum resistance and survival, then use these genes to establish the prognostic model. Platinum resistance-related DEGs (differentially expressed genes) and tumorigenesis-related DEGs were identified. Ten most predictive co-DEGs were acquired followed by building a risk score model. Survival analysis and ROC (receiver operating characteristic) plot were used to evaluate the predictive accuracy. Combined with age and tumor stages, a nomogram was generated to create a graphical representation of survival rates at 1-, 3-, 5-, and 8-year in BC patients. The prognostic performance was validated in three independent BC datasets with platinum-based chemotherapy. The potential mechanism was explored by enrichment analysis. PPP2R2B, TSPAN7, ATAD3C, SYT15, SAPCD1, AKR1B1, TCHH, AKAP12, AGLN3, and IGF2 were selected for our prognostic model. Patients in high- and low-risk groups exhibited a significant survival difference with HR (hazard ratio) = 2.7 (p < 0.0001). The prognostic nomogram of predicting 3-year OS (overall survival) for BC patients could yield an AUC (area under the curve) of 0.819. In the external validation dataset, the risk score also has a robust predictive ability. A prognostic model derived from platinum resistance-related genes was constructed, we confirmed its value in predicting platinum-based chemotherapy benefits and overall survival for BC patients. The model might assist in therapeutic decisions for bladder malignancy.

Construction and validation of a bladder cancer risk model based on autophagy-related genes.

Autophagy has an important association with tumorigenesis, progression, and prognosis. However, the mechanism of autophagy-regulated genes on the risk prognosis of bladder cancer (BC) patients has not been fully elucidated yet. In this study, we created a prognostic model of BC risk based on autophagy-related genes, which further illustrates the value of genes associated with autophagy in the treatment of BC. We first downloaded human autophagy-associated genes and BC datasets from Human Autophagy Database and The Cancer Genome Atlas (TCGA) database, and finally obtained differential prognosis-associated genes for autophagy by univariate regression analysis and differential analysis of cancer versus normal tissues. Subsequently, we downloaded two datasets from Gene Expression Omnibus (GEO), GSE31684 and GSE15307, to expand the total number of samples. Based on these genes, we distinguished the molecular subtypes (C1, C2) and gene classes (A, B) of BC by consistent clustering analysis. Using the genes merged from TCGA and the two GEO datasets, we conducted least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to obtain risk genes and construct autophagy-related risk prediction models. The accuracy of this risk prediction model was assessed by receiver operating characteristic (ROC) and calibration curves, and then nomograms were constructed to predict the survival of bladder cancer patients at 1, 3, and 5years, respectively. According to the median value of the risk score, we divided BC samples into the high- and low-risk groups. Kaplan-Meier (K-M) survival analysis was performed to compare survival differences between subgroups. Then, we used single sample gene set enrichment analysis (ssGSEA) for immune cell infiltration abundance, immune checkpoint genes, immunotherapy response, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and tumor mutation burden (TMB) analysis for different subgroups. We also applied quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry (IHC) techniques to verify the expression of these six genes in the model. Finally, we chose the IMvigor210 dataset for external validation. Six risk genes associated with autophagy (SPOCD1, FKBP10, NAT8B, LDLR, STMN3, and ANXA2) were finally screened by LASSO regression algorithm and multivariate Cox regression analysis. ROC and calibration curves showed that the model established was accurate and reliable. Univariate and multivariate regression analyses were used to verify that the risk model was an independent predictor. K-M survival analysis indicated that patients in the high-risk group had significantly worse overall survival than those in the low-risk group. Analysis by algorithms such as correlation analysis, gene set variation analysis (GSVA), and ssGSEA showed that differences in immune microenvironment, enrichment of multiple biologically active pathways, TMB, immune checkpoint genes, and human leukocyte antigens (HLAs) were observed in the different risk groups. Then, we constructed nomograms that predicted the 1-, 3-, and 5-year survival rates of different BC patients. In addition, we screened nine sensitive chemotherapeutic drugs using the correlation between the obtained expression status of risk genes and drug sensitivity results. Finally, the external dataset IMvigor210 verified that the model is reliable and efficient. We established an autophagy-related risk prognostic model that is accurate and reliable, which lays the foundation for future personalized treatment of bladder cancer.

Incidental Diagnosis of Urothelial Bladder Cancer: Associations with Overall Survival.

We investigated whether an incidental diagnosis (ID) of bladder cancer (BC) was associated with improved survival. We retrospectively reviewed data of consecutive patients with no prior diagnosis of urothelial cancer who underwent a primary transurethral resection of bladder tumor (pTURBT) between January 2013 and February 2021 and were subsequently diagnosed with urothelial BC. The type of diagnosis (incidental or non-incidental) was identified. Overall, relative, recurrence-free, and progression-free survival rates (OS, RS, RFS, and PFS) after pTURBT were evaluated using the Kaplan-Meier curves and long-rank tests. A multivariable Cox regression model for the overall mortality was developed. A total of 435 patients were enrolled. The median follow-up was 2.7 years. ID cases were more likely to be low-grade (LG) and non-muscle-invasive. ID vs. non-ID was associated with a trend toward an improved 7-year OS (66% vs. 49%, p = 0.092) and a significantly improved 7-year OS, if incidental cases were limited to ultrasound-detected tumors (75% vs. 49%, p = 0.013). ID was associated with improved survival among muscle-invasive BC (MIBC) patients (3-year RS: 97% vs. 23%, p < 0.001), but not among other subgroups stratified according to disease stage or grade. In multivariable analysis, only age, MIBC, and high-grade (HG) cancer demonstrated an association with mortality. PFS and RFS among non-MIBC patients did not differ in regard to the type of diagnosis. Incidental diagnosis may contribute to an improved survival in BC patients, most probably in the mechanism of the relative downgrading of the disease, including the possible overdiagnosis of LG tumors. Nevertheless, in the subgroup analyses, we noted marked survival benefits in MIBC cases. Further prospective studies are warranted to gain a deeper understanding of the observed associations.

Urine cell-free DNA multi-omics to detect MRD and predict survival in bladder cancer patients

Circulating tumor DNA (ctDNA) sensitivity remains subpar for molecular residual disease (MRD) detection in bladder cancer patients. To remedy this problem, we focused on the biofluid most proximal to the disease, urine, and analyzed urine tumor DNA in 74 localized bladder cancer patients. We integrated ultra-low-pass whole genome sequencing (ULP-WGS) with urine cancer personalized profiling by deep sequencing (uCAPP-Seq) to achieve sensitive MRD detection and predict overall survival. Variant allele frequency, inferred tumor mutational burden, and copy number-derived tumor fraction levels in urine cell-free DNA (cfDNA) significantly predicted pathologic complete response status, far better than plasma ctDNA was able to. A random forest model incorporating these urine cfDNA-derived factors with leave-one-out cross-validation was 87% sensitive for predicting residual disease in reference to gold-standard surgical pathology. Both progression-free survival (HR = 3.00, p = 0.01) and overall survival (HR = 4.81, p = 0.009) were dramatically worse by Kaplan–Meier analysis for patients predicted by the model to have MRD, which was corroborated by Cox regression analysis. Additional survival analyses performed on muscle-invasive, neoadjuvant chemotherapy, and held-out validation subgroups corroborated these findings. In summary, we profiled urine samples from 74 patients with localized bladder cancer and used urine cfDNA multi-omics to detect MRD sensitively and predict survival accurately.

Web-Based Nomograms for Overall Survival and Cancer-Specific Survival of Bladder Cancer Patients with Bone Metastasis: A Retrospective Cohort Study from SEER Database.

Our study aimed to explore the prognostic factors of bladder cancer with bone metastasis (BCBM) and develop prediction models to predict the overall survival (OS) and cancer-specific survival (CSS) of BCBM patients. A total of 1438 patients with BCBM were obtained from the SEER database. Patients from 2010 to 2016 were randomly divided into training and validation datasets (7:3), while patients from 2017 were divided for external testing. Nomograms were established using prognostic factors identified through Cox regression analyses and validated internally and externally. The concordance index (C-index), calibration plots, and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the discrimination and calibration of nomogram models, while decision curve analyses (DCA) and Kaplan-Meier (KM) curves were used to estimate the clinical applicability. Marital status, tumor metastasis (brain, liver, and lung), primary site surgery, and chemotherapy were indicated as independent prognostic factors for OS and CSS. Calibration plots and the overall C-index showed a novel agreement between the observed and predicted outcomes. Nomograms revealed significant advantages in OS and CSS predictions. AUCs for internal and external validation were listed as follows: for OS, 3-month AUCs were 0.853 and 0.849; 6-month AUCs were 0.873 and 0.832; 12-month AUCs were 0.825 and 0.805; for CSS, 3-month AUCs were 0.849 and 0.847; 6-month AUCs were 0.870 and 0.824; 12-month AUCs were 0.815 and 0.797, respectively. DCA curves demonstrated good clinical benefit, and KM curves showed distinct stratification performance. The nomograms as web-based tools were proved to be accurate, efficient, and clinically beneficial, which might help in patient management and clinical decision-making for BCBM patients.