A panel of systemic inflammatory response biomarkers for outcome prediction in patients treated with radical cystectomy for urothelial carcinoma

Development and validation of a recurrence risk assessment model for high-grade bladder cancer based on TCGA and GEO

Background

Bladder cancer is one of the most commonly diagnosed urinary cancers worldwide. Although muscle-invasive bladder cancer (MIBC) accounts for only 25% of bladder cancer cases, it has a high recurrence rate and poor prognosis, especially among high-grade cases. Despite the existence of some molecular markers, there is a clear clinical need for a robust recurrence prediction model that can assist in patient management and therapeutic decision-making. Therefore, we aimed to use public databases to develop such an effective assessment model.

Methods

We developed a recurrence risk assessment model for high-grade bladder cancer based on the clinical information of 217 cases from The Cancer Genome Atlas (TCGA) and profiles of 87 samples from GSE31684 in the Gene Expression Omnibus (GEO) database. Edge R was used to analyze differences between RNAs of bladder cancer in the TCGA database, with thresholds of P<0.05 and |log2(fold change)| >1; least absolute shrinkage and selection operator (LASSO) Cox regression models were used to screen the RNAs significantly related to recurrence with minimum λ. Survival receiver operating characteristic (ROC) and area under the curve (AUC) was used to assess the predictive accuracy of the model in the training and validation sets of GSE31684.

Results

There were 2,876 differential RNAs obtained from TCGA data. Among a total of 284 RNAs identified as significantly related to recurrence of bladder cancer, 49 were obtained by LASSO regression, and 30 were finally obtained by multifactor risk regression to construct a risk assessment model. The model was found to predict the prognosis of bladder cancer recurrence well, with an AUC of 0.911 in the TCGA training set and an adjusted AUC value of 0.839 in the GEO validation set.

Conclusions

The recurrence assessment model is a relatively accurate recurrence prediction tool for high-grade bladder cancer and could provide a guidance for the treatment of bladder cancer.

A nomogram model for the occurrence of bladder spasm after TURP in patients with prostate enlargement based on serum prostacyclin and 5-hydroxytryptamine and clinical characteristics

OBJECTIVE

With the development of analytical methods, mathematical models based on humoral biomarkers have become more widely used in the medical field. This study aims to investigate the risk factors associated with the occurrence of bladder spasm after transurethral resection of the prostate (TURP) in patients with prostate enlargement, and then construct a nomogram model.

MATERIALS AND METHODS

Two hundred and forty-two patients with prostate enlargement who underwent TURP were included. Patients were divided into Spasm group (n=65) and non-spasm group (n=177) according to whether they had bladder spasm after surgery. Serum prostacyclin (PGI2) and 5-hydroxytryptamine (5-HT) levels were measured by enzyme-linked immunoassay. Univariate and multivariate logistic regression were used to analyze the risk factors.

RESULTS

Postoperative serum PGI2 and 5-HT levels were higher in patients in the Spasm group compared with the Non-spasm group (P<0.05). Preoperative anxiety, drainage tube obstruction, and elevated postoperative levels of PGI2 and 5-HT were independent risk factors for bladder spasm after TURP (P<0.05). The C-index of the model was 0.978 (0.959-0.997), with a χ2 = 4.438 (p = 0.816) for Hosmer-Lemeshow goodness-of-fit test. The ROC curve to assess the discrimination of the nomogram model showed an AUC of 0.978 (0.959-0.997).

CONCLUSION

Preoperative anxiety, drainage tube obstruction, and elevated postoperative serum PGI2 and 5-HT levels are independent risk factors for bladder spasm after TURP. The nomogram model based on the aforementioned independent risk factors had good discrimination and predictive abilities, which may provide a high guidance value for predicting the occurrence of bladder spasm in clinical practice.

Non-linear relationship between preoperative albumin-globulin ratio and postoperative pneumonia in patients with hip fracture

BACKGROUND AND OBJECTIVE

Postoperative pneumonia (POP) is the leading cause of death among patients with hip fractures. Simple and cost-effective markers can be used to assess the risk of these patients. This study aims to investigate the association between POP and preoperative albumin-globulin ratio (AGR) in patients with hip fractures.

METHODS

A retrospective analysis was conducted on data from 1417 hip fracture patients admitted to the Department of Orthopaedics at the hospital. Generalized additive and logistic regression models were used to determine both linear and non-linear associations between preoperative AGR and POP. A two-piece regression model was employed to determine the threshold effect.

RESULTS

The study included 1417 participants, with a mean age of 77.57 (8.53) years and 26.96% (382/1417) male patients. The prevalence of POP was 6.21%. Following full covariate adjustment, each unit increase in AGR was associated with a 79% reduction in the incidence of POP (OR, 0.23; 95% CI: 0.08-0.63; P = 0.0046). The inflection point was found to be 1.33 using a two-piecewise regression model. For each unit increase in AGR on the left side of the inflection point, the incidence of POP decreased by 93% (OR, 0.07; 95%CI: 0.02-0.34; P = 0.0010). However, there was no statistically significant correlation on the right side of the inflection point (OR, 0.84; 95% CI: 0.17-4.10; P = 0.8287).

CONCLUSION

There exists a non-linear association between preoperative AGR and the incidence of POP in elderly hip fracture patients. When AGR is less than 1.33, the incidence of POP is negatively correlated with AGR. However, there is no correlation when AGR is greater than 1.33.

Preoperative low plasma creatine kinase levels predict worse survival outcomes in bladder cancer after radical cystectomy

BACKGROUND AND AIMS

To evaluate the prognostic significance of preoperative creatine kinase (CK) levels in bladder cancer (BCa) patients who underwent radical cystectomy (RC).

MATERIALS AND METHODS

570 BCa patients with RC were identified between 2010 and 2020. 108.5 U/L of CK levels were defined as the cutoff value. Logistic regression analysis and Cox regression models were performed to evaluate the association between CK levels and oncologic outcomes. Subgroup analyses were performed to address cofounding factors.

RESULTS

Preoperative low CK levels were associated with worse recurrence-free survival (RFS, log-rank P = 0.001) and overall survival (OS, log-rank P = 0.002). Multivariate analysis revealed that preoperative low CK levels were an independent predictor for worse RFS (hazard ratio [HR]: 1.683; P < 0.001) and OS (HR: 1.567; P = 0.002).

CONCLUSIONS

The preoperative low CK level independently predicts worse survival outcomes in BCa after RC. Incorporating it into prediction models might be valuable to assist risk stratification.

Using machine learning to develop preoperative model for lymph node metastasis in patients with bladder urothelial carcinoma

BACKGROUND

Lymph node metastasis (LNM) is associated with worse prognosis in bladder urothelial carcinoma (BUC) patients. This study aimed to develop and validate machine learning (ML) models to preoperatively predict LNM in BUC patients treated with radical cystectomy (RC).

METHODS

We retrospectively collected demographic, pathological, imaging, and laboratory information of BUC patients who underwent RC and bilateral lymphadenectomy in our institution. Patients were randomly categorized into training set and testing set. Five ML algorithms were utilized to establish prediction models. The performance of each model was assessed by the area under the receiver operating characteristic curve (AUC) and accuracy. Finally, we calculated the corresponding variable coefficients based on the optimal model to reveal the contribution of each variable to LNM.

RESULTS

A total of 524 and 131 BUC patients were finally enrolled into training set and testing set, respectively. We identified that the support vector machine (SVM) model had the best prediction ability with an AUC of 0.934 (95% confidence interval [CI]: 0.903-0.964) and accuracy of 0.916 in the training set, and an AUC of 0.855 (95%CI: 0.777-0.933) and accuracy of 0.809 in the testing set. The SVM model contained 14 predictors, and positive lymph node in imaging contributed the most to the prediction of LNM in BUC patients.

CONCLUSIONS

We developed and validated the ML models to preoperatively predict LNM in BUC patients treated with RC, and identified that the SVM model with 14 variables had the best performance and high levels of clinical applicability.

Impact of Preoperative Plasma Potassium Levels on Oncological Outcomes, Major Complications, and 30-Day Mortality in Bladder Cancer Patients Undergoing Radical Cystectomy

INTRODUCTION AND OBJECTIVES

We examined the impact of preoperative plasma potassium levels (PPLs) on outcomes in patients undergoing radical cystectomy (RC) for urothelial carcinoma of the bladder (UCB), hypothesizing that potassium imbalances might influence outcomes.

PATIENTS AND METHODS

In this retrospective study, 501 UCB patients undergoing RC from 2009 to 2017 at a tertiary center were analyzed. Blood samples collected a week prior to surgery defined normal and abnormal PPL based on institutional standards. We assessed overall survival (OS), cancer-specific survival (CSS), recurrence-free survival (RFS), postoperative complications, 30-day mortality, and non-organ confined disease. Kaplan-Meier estimates, Cox proportional hazards, logistic regression, and decision curve analyses (DCA) were employed.

RESULTS

63 (13%) patients had abnormal preoperative PPLs, with 50 (10%) elevated and 13 (2.5%) decreased. In a 59 months median follow-up, 152 (31%) had disease recurrence, 197 (39%) died from any cause, and 119 (24%) from UCB. Multivariable cox regression analyses adjusting for perioperative parameters demonstrated abnormal PPL was associated with worse OS (HR=1.9, P=0.009), CSS (HR=2.8, P<0.001) and RFS (HR=2.1; P=0.007). Elevated preoperative PPLs also demonstrated significant associations with adverse outcomes in OS, CSS, and RFS (all P<0.05). In multivariable logistic regression analyses, abnormal and elevated PPLs were not associated with 30-day mortality, major 30-day postoperative complications, positive nodal disease, pT3/4 stage, and non-organ confined disease (all P>0.05).

CONCLUSION

Abnormal and elevated preoperative PPLs correlate with adverse oncologic outcomes in UCB patients treated with RC. Pending external validation, preoperative PPLs might be a cost-effective, easily obtainable supplemental biomarker for enriching accuracy of outcome prediction in this highly variable maladie.

Bladder Cancer and Artificial Intelligence: Emerging Applications

Bladder cancer is a common and heterogeneous disease that poses a significant burden to the patient and health care system. Major unmet needs include effective early detection strategy, imprecision of risk stratification, and treatment-associated morbidities. The existing clinical paradigm is imprecise, which results in missed tumors, suboptimal therapy, and disease progression. Artificial intelligence holds immense potential to address many unmet needs in bladder cancer, including early detection, risk stratification, treatment planning, quality assessment, and outcome prediction. Despite recent advances, extensive work remains to affirm the efficacy of artificial intelligence as a decision-making tool for bladder cancer management.

Association between platelet‑to‑lymphocyte ratio and serum prostate specific antigen

There is evidence that the systemic inflammatory response may have an impact on prostate-specific antigen (PSA) levels. However, the relationship between the platelet-to-lymphocyte ratio (PLR) and PSA remains unclear. As a result, the relationship between PLR and PSA using the National Health and Nutrition Examination Survey (NHANES) database was examined. After the screening, 6,638 participants out of 52,186 in the NHANES survey conducted between 2001 to 2010 were suitable for the present study. The PLR was the independent variable in the present study, and PSA was the dependent variable. The selected subjects in the present study had an average age of 58.563±11.848 years. After controlling for covariates, the results showed that with every increase in PLR, the PSA concentration increased by 0.004 ng/ml (0.001, 0.007). This difference was statistically significant. Furthermore, a smoothing curve based on a fully adjusted model was created to investigate the possibility of a linear relationship between PLR and PSA concentration in men from USA. In men from USA, an independent and positive correlation between PLR and PSA was identified, which could potentially result in overdiagnosis of asymptomatic prostate cancer in populations with higher PLR levels.

Existing trends and applications of artificial intelligence in urothelial cancer A scoping review

INTRODUCTION

The use of artificial intelligence (AI) in urology is gaining significant traction. While previous reviews of AI applications in urology exist, there have been few attempts to synthesize existing literature on urothelial cancer (UC).

METHODS

Comprehensive searches based on the concepts of "AI" and "urothelial cancer" were conducted in MEDLINE , EMBASE , Web of Science, and Scopus. Study selection and data abstraction were conducted by two independent reviewers. Two independent raters assessed study quality in a random sample of 25 studies with the prediction model risk of bias assessment tool (PROBAST) and the standardized reporting of machine learning applications in urology (STREAM-URO) framework.

RESULTS

From a database search of 4581 studies, 227 were included. By area of research, 33% focused on image analysis, 26% on genomics, 16% on radiomics, and 15% on clinicopathology. Thematic content analysis identified qualitative trends in AI models employed and variables for feature extraction. Only 19% of studies compared performance of AI models to non-AI methods. All selected studies demonstrated high risk of bias for analysis and overall concern with Cohen's kappa (k)=0.68. Selected studies met 66% of STREAM-URO items, with k=0.76.

CONCLUSIONS

The use of AI in UC is a topic of increasing importance; however, there is a need for improved standardized reporting, as evidenced by the high risk of bias and low methodologic quality identified in the included studies.

Development and validation of a preoperative nomogram to predict lymph node metastasis in patients with bladder urothelial carcinoma

PURPOSE

Predicting lymph node metastasis (LNM) in patients with bladder urothelial carcinoma (BUC) before radical cystectomy aids clinical decision making. Here, we aimed to develop and validate a nomogram to preoperatively predict LNM in BUC patients.

METHODS

Patients with histologically confirmed BUC, who underwent radical cystectomy and bilateral lymphadenectomy, were retrospectively recruited from two institutions. Patients from one institution were enrolled in the primary cohort, while those from the other were enrolled in the external validation cohort. Patient demographic, pathological (using transurethral resection of the bladder tumor specimens), imaging, and laboratory data were recorded. Univariate and multivariate logistic regression analyses were performed to explore the independent preoperative risk factors and develop the nomogram. Internal and external validation was conducted to assess nomogram performance.

RESULTS

522 and 215 BUC patients were enrolled in the primary and external validation cohorts, respectively. We identified tumor grade, infiltration, extravesical invasion, LNM on imaging, tumor size, and serum creatinine levels as independent preoperative risk factors, which were subsequently used to develop the nomogram. The nomogram showed a good predictive accuracy, with area under the receiver operator characteristic curve values of 0.817 and 0.825 for the primary and external validation cohorts, respectively. The corrected C-indexes, calibration curves (after 1000 bootstrap resampling), decision curve analysis results, and clinical impact curves demonstrated that the nomogram performed well in both cohorts and was highly clinically applicable.

CONCLUSION

We developed a nomogram to preoperatively predict LNM in BUC, which was highly accurate, reliable, and clinically applicable.

Predictive and Prognostic Role of the Neutrophil-to-Lymphocyte Ratio in Muscle Invasive Bladder Cancer Treated With Neoadjuvant Chemotherapy and Radical Cystectomy

INTRODUCTION

There is a persistent lack of validated biomarkers that identify patients most likely to benefit from neoadjuvant chemotherapy (NAC) in urothelial carcinoma of the bladder (UCB). Therefore, the purpose of this study was to investigate the predictive and prognostic impact of the pretreatment neutrophil-to-lymphocyte ratio (NLR) in UCB patients treated with NAC and radical cystectomy (RC).

PATIENTS AND METHODS

We conducted a retrospective analysis of an international-multicenter database comprising 404 UCB patients staged cT2-4N0-3M0. The cohort was split into low and high NLR using an optimal cutoff value determined by maximizing Youden's index. Logistic and Cox regression analyses were performed with respect to several clinical endpoints. The discriminative ability of the models and the additive discriminative value of NLR was assessed by calculating the area under receiver operating characteristics curves, C-index, and decision curve analysis (DCA).

RESULTS

A total of 169 patients (41.8%) had a high NLR, which was associated with a decreased probability of complete response (CR, OR: 0.24 [95% CI, 0.13-0.42], P < .001) and/or partial response (PR, OR: 0.33 [95% CI, 0.21-0.49], P < .001). Adding the NLR to predictive reference models significantly improved their accuracy for the prediction of both CR and PR. A high NLR was associated with poor survival outcomes in the pretreatment setting, however, it didn't meaningfully change the C-index based on the model.

CONCLUSION

We confirmed that an elevated NLR is an independent and clinically significant predictor of response to NAC and adverse pathological features in UCB treated with NAC plus RC. The accuracy of this biomarker in the age of immunotherapy warrants further evaluation.

Use of machine learning to predict bladder cancer survival outcomes: a systematic literature review

INTRODUCTION

The objective of this systematic review is to summarize the use of machine learning (ML) in predicting overall survival (OS) in patients with bladder cancer.

METHODS

Search terms for bladder cancer, ML algorithms, and mortality were used to identify studies in PubMed and Web of Science as of February 2022. Notable inclusion/exclusion criteria contained the inclusion of studies that utilized patient-level datasets and exclusion of primary gene expression-related dataset studies. Study quality and bias were assessed using the International Journal of Medical Informatics (IJMEDI) checklist.

RESULTS

Of the 14 included studies, the most common algorithms were artificial neural networks (n = 8) and logistic regression (n = 4). Nine articles described missing data handling, with five articles removing patients with missing data entirely. With respect to feature selection, the most common sociodemographic variables were age (n = 9), gender (n = 9), and smoking status (n = 3), with clinical variables most commonly including tumor stage (n = 8), grade (n = 7), and lymph node involvement (n = 6). Most studies (n = 10) were of medium IJMEDI quality, with common areas of improvement being the descriptions of data preparation and deployment.

CONCLUSIONS

ML holds promise for optimizing bladder cancer care through accurate OS predictions, but challenges related to data processing, feature selection, and data source quality must be resolved to develop robust models. While this review is limited by its inability to compare models across studies, this systematic review will inform decision-making by various stakeholders to improve understanding of ML-based OS prediction in bladder cancer and foster interpretability of future models.

Clinical value of cholinesterase in patients treated with radical nephroureterectomy for upper urinary tract carcinoma

PURPOSE

To evaluate the prognostic value and the clinical impact of preoperative serum cholinesterase (ChoE) levels on decision-making in patients treated with radical nephroureterectomy (RNU) for clinically non-metastatic upper tract urothelial cancer (UTUC).

METHODS

A retrospective review of an established multi-institutional UTUC database was performed. We evaluated preoperative ChoE as a continuous and dichotomized variable using a visual assessment of the functional form of the association of ChoE with cancer-specific survival (CSS). We used univariable and multivariable Cox regression models to establish its association with recurrence-free survival (RFS), CSS, and overall survival (OS). Discrimination was evaluated using Harrell's concordance index. Decision curve analysis (DCA) was used to assess the impact on clinical decision-making of preoperative ChoE.

RESULTS

A total of 748 patients were available for analysis. Within a median follow-up of 34 months (IQR 15-64), 191 patients experienced disease recurrence, and 257 died, with 165 dying of UTUC. The optimal ChoE cutoff identified was 5.8 U/l. ChoE as continuous variable was significantly associated with RFS (p < 0.001), OS (p < 0.001), and CSS (p < 0.001) on univariable and multivariable analyses. The concordance index improved by 8%, 4.4%, and 7% for RFS, OS, and CSS, respectively. On DCA, including ChoE did not improve the net benefit of standard prognostic models.

CONCLUSION

Despite its independent association with RFS, OS, and CSS, preoperative serum ChoE has no impact on clinical decision-making. In future studies, ChoE should be investigated as part of the tumor microenvironment and assessed as part of predictive and prognostic models, specifically in the setting of immune checkpoint-inhibitor therapy.

Blood-Based Biomarkers as Prognostic Factors of Recurrent Disease after Radical Cystectomy: A Systematic Review and Meta-Analysis

Survival outcomes after radical cystectomy (RC) for bladder cancer (BCa) have not improved in recent decades; nevertheless, RC remains the standard treatment for patients with localized muscle-invasive BCa. Identification of the patients most likely to benefit from RC only versus a combination with systemic therapy versus systemic therapy first/only and bladder-sparing is needed. This systematic review and meta-analysis pools the data from published studies on blood-based biomarkers to help prognosticate disease recurrence after RC. A literature search on PubMed and Scopus was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Articles published before November 2022 were screened for eligibility. A meta-analysis was performed on studies investigating the association of the neutrophil-to-lymphocyte ratio (NLR), the only biomarker with sufficient data, with recurrence-free survival. The systematic review identified 33 studies, and 7 articles were included in the meta-analysis. Our results demonstrated a statistically significant correlation between elevated NLR and an increased risk of disease recurrence (HR 1.26; 95% CI 1.09, 1.45; p = 0.002) after RC. The systematic review identified various other inflammatory biomarkers, such as interleukin-6 or the albumin-to-globulin ratio, which have been reported to have a prognostic impact on recurrence after RC. Besides that, the nutritional status, factors of angiogenesis and circulating tumor cells, and DNA seem to be promising tools for the prognostication of recurrence after RC. Due to the high heterogeneity between the studies and the different cut-off values of biomarkers, prospective and validation trials with larger sample sizes and standardized cut-off values should be conducted to strengthen the approach in using biomarkers as a tool for risk stratification in clinical decision-making for patients with localized muscle-invasive BCa.

Mortality prediction models after radical cystectomy for bladder tumour: A systematic review and critical appraisal

INTRODUCTION

To identify risk-predictive models for bladder-specific cancer mortality in patients undergoing radical cystectomy and assess their clinical utility and risk of bias.

METHODS

Systematic review (CRD42021224626:PROSPERO) in Medline and EMBASE (from their creation until 31/10/2021) was screened to include articles focused on the development and internal validation of a predictive model of specific cancer mortality in patients undergoing radical cystectomy. CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and Prediction model Risk Of Bias ASsessment Tool (PROBAST) were applied.

RESULTS

Nineteen observational studies were included. The main predictors were sociodemographic variables, such as age (18 studies, 94.7%) and sex (17, 89.5% studies), tumour characteristics (TNM stage (18 studies, 94.7%), histological subtype/grade (15 studies, 78.9%), lymphovascular invasion (10 studies, 52.6%) and treatment with chemotherapy (13 studies, 68.4%). C-index values were presented in 14 studies. The overall risk of bias assessed using PROBAST led to 100% of studies being classified as high risk (the analysis domain was rated to be at high risk of bias in all the studies), and 52.6% showed low applicability. Only 5 studies (26.3%) included an external validation and 2 (10.5%) included a prospective study design.

CONCLUSIONS

Using clinical predictors to assess the risk of bladder-specific cancer mortality is a feasibility alternative. However, the studies showed a high risk of bias and their applicability is uncertain. Studies should improve the conducting and reporting, and subsequent external validation studies should be developed.

Prognostic impact of insulin‐like growth factor‐I and its binding proteins, insulin‐like growth factor‐I binding protein‐2 and ‐3, on adverse histopathological features and survival outcomes after radical cystectomy

Objectives

Insulin‐like growth factor‐I and its binding proteins are involved in cancer development, progression, and metastasis. In urothelial carcinoma, the impact of this pathway is still poorly investigated. The present large cohort study aimed to evaluate the association of preoperative circulating levels of insulin‐like growth factor‐I, insulin‐like growth factor‐I binding protein‐2 and ‐3 on outcomes after radical cystectomy.

Methods

A retrospective cohort study of the plasma specimens from 1036 consecutive urothelial carcinoma patients who were treated with radical cystectomy. The primary and secondary outcomes were adverse histopathological features and survival outcomes. Binominal logistic regression and multivariable Cox regression analyses were performed to assess the association of plasma levels of insulin‐like growth factor‐I, insulin‐like growth factor‐I binding protein‐2 and ‐3 with outcomes.

Results

On multivariable analysis adjusting for the effects of preoperative variables, lower insulin‐like growth factor‐I binding protein‐2 levels were associated with an increased risk of lymph node metastasis and (any non‐organ confined disease) any non‐organ confined disease. Insulin‐like growth factor‐I binding protein‐3 levels were also inversely independently associated with lymph node metastasis. Receiver operating characteristic curve analysis showed that the addition of insulin‐like growth factor‐I binding proteins biomarkers to a reference model significantly improved the discriminating ability for the prediction of lymph node metastasis (+10.0%, P < 0.001). On multivariable Cox regression models, lower levels of both insulin‐like growth factor‐I binding protein‐2 and ‐3 plasma levels were associated with recurrence‐free survival, cancer‐specific survival, and overall survival. insulin‐like growth factor‐I binding protein‐2 and ‐3 levels and improved the discrimination of a standard reference model for the prediction of recurrence‐free survival, cancer‐specific survival, and overall survival (+4.9%, 4.9%, 2.3%, respectively).

Conclusions

Preoperative insulin‐like growth factor‐I binding protein‐2 and ‐3 are significantly associated with features of biologically and clinically aggressive urothelial carcinoma. These biomarkers improved prognostic urothelial carcinoma models.

Modified Glasgow Prognostic Score as a Predictor of Recurrence in Patients with High Grade Non-Muscle Invasive Bladder Cancer Undergoing Intravesical Bacillus Calmette–Guerin Immunotherapy

Background: A systemic inflammatory marker, the modified Glasgow prognostic score (mGPS), could predict outcomes in non-muscle-invasive bladder cancer (NIMBC). We aimed to investigate the predictive power of mGPS in oncological outcomes in HG/G3 T1 NMIBC patients undergoing Bacillus Calmette–Guérin (BCG) therapy. Methods: We retrospectively reviewed patient’s medical data from multicenter institutions. A total of 1382 patients with HG/G3 T1 NMIBC have been administered adjuvant intravesical BCG therapy, every week for 3 weeks given at 3, 6, 12, 18, 24, 30 and 36 months. The analysis of mGPS for recurrence and progression was performed using multivariable and univariable Cox regression models. Results: During follow-up, 659 patients (47.68%) suffered recurrence, 441 (31.91%) suffered progression, 156 (11.28%) died of all causes, and 67 (4.84%) died of bladder cancer. At multivariable analysis, neutrophil to lymphocyte ratio [hazard ratio (HR): 7.471; p = 0.0001] and erythrocyte sedimentation rate (ESR) (HR: 0.706; p = 0.006 were significantly associated with recurrence. mGPS has no statistical significance for progression (p = 0.076). Kaplan–Meier survival analysis showed a significant difference in survival among patients from different mGPS subgroups. Five-year OS was 93% (CI 95% 92–94), in patients with mGPS 0, 82.2% (CI 95% 78.9–85.5) in patients with mGPS 1 and 78.1% (CI 95% 60.4–70) in mGPS 2 patients. Five-year CSS was 98% (CI 95% 97–99) in patients with mGPS 0, 90% (CI 95% 87–94) in patients with mGPS 1, and 100% in mGPS 2 patients. Limitations are applicable to a retrospective study. Conclusions: mGPS may have the potential to predict recurrence in HG/G3 T1 NMIBC patients, but more prospective, with large cohorts, studies are needed to study the influence of systemic inflammatory markers in prediction of outcomes in NMIBC for a definitive conclusion.

Impact of preoperative systemic immune-inflammation Index on oncologic outcomes in bladder cancer patients treated with radical cystectomy

PURPOSE

To investigate the predictive and prognostic value of the preoperative systemic immune-inflammation index (SII) in patients undergoing radical cystectomy (RC) for clinically non-metastatic urothelial cancer of the bladder (UCB).

METHODS

Overall, 4,335 patients were included, and the cohort was stratified in two groups according to SII using an optimal cut-off determined by the Youden index. Uni- and multivariable logistic and Cox regression analyses were performed, and the discriminatory ability by adding SII to a reference model based on available clinicopathologic variables was assessed by area under receiver operating characteristics curves (AUC) and concordance-indices. The additional clinical net-benefit was assessed using decision curve analysis (DCA).

RESULTS

High SII was observed in 1879 (43%) patients. On multivariable preoperative logistic regression, high SII was associated with lymph node involvement (LNI; P = 0.004), pT3/4 disease (P <0.001), and non-organ confined disease (NOCD; P <0.001) with improvement of AUCs for predicting LNI (P = 0.01) and pT3/4 disease (P = 0.01). On multivariable Cox regression including preoperative available clinicopathologic values, high SII was associated with recurrence-free survival (P = 0.028), cancer-specific survival (P = 0.005), and overall survival (P = 0.006), without improvement of concordance-indices. On DCAs, the inclusion of SII did not meaningfully improve the net-benefit for clinical decision-making in all models.

CONCLUSION

High preoperative SII is independently associated with pathologic features of aggressive disease and worse survival outcomes. However, it did not improve the discriminatory margin of a prediction model beyond established clinicopathologic features and failed to add clinical benefit for decision making. The implementation of SII as a part of a panel of biomarkers in future studies might improve decision-making.

Controlling Nutritional Status Score Before Receiving Treatment as a Prognostic Indicator for Patients With Urothelial Cancer: An Exploration Evaluation Methods

Introduction

This meta-analysis aims to assess whether the Controlling nutritional status (CONUT) score before treatment can be an independent predictor of the prognosis of patients with urothelial cancer (UC).

Methods

The system searches Web of Science, PubMed, MEDLINE, China National Knowledge Infrastructure (CNKI), and Cochrane Library, and the search time is up to April 2021. Use STATA 16.0 and Engauge Digitizer 4.1 software for data processing and statistical analysis.

Results

A total of 8 studies were included in this meta-analysis. The meta-analysis results show that compared with the low CONUT group, the high CONUT group has worse over survival (OS) [HR=1.58, 95%CI (1.34, 1.86), P=0.001], cancer-specific survival (CSS) [HR=2.03, 95%CI (1.25-3.29), P=0.04] and recurrence-free survival (RFS) [HR=1.97, 95%CI (1.15, 3.40), P=0.014]; for progression-free survival (PFS), or disease-free survival (DFS), the difference between the two groups was not statistically significant [HR=2.30, 95%CI (0.72, 7.32), P=0.158]. According to different carcinoma types, cut-off value, and region, subgroup analysis of OS was performed, and similar results were obtained.

Conclusions

Based on current evidence, this meta-analysis proves that the CONUT score of UC patients before treatment is an independent prognostic predictor. It performs well on OS, CSS, and RFS, but the conclusions on DFS/PFS need to be treated with caution.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021251890, identifier CRD42021251890.

Development and Validation of Prognostic Model in Transitional Bladder Cancer Based on Inflammatory Response-Associated Genes

Background

Bladder cancer is a common malignant type in the world, and over 90% are transitional cell carcinoma. While the impact of inflammatory response on cancer progression has been reported, the role of inflammatory response-associated genes (IRAGs) in transitional bladder cancer still needs to be understood.

Methods

In this study, IRAGs were download from Molecular Signature Database (MSigDB). The transcriptional expression and matched clinicopathological data were separately obtained from public databases. The TCGA-BLCA cohort was used to identify the differentially expressed IRAGs, and prognostic IRAGs were filtrated by univariate survival analysis. The intersection between them was displayed by Venn diagram. Based on least absolute shrinkage and selection operator (LASSO) regression analysis method, the TCGA-BLCA cohort was used to construct a risk signature. Survival analysis was conducted to calculate the overall survival (OS) in TCGA and GSE13507 cohort between two groups. We then conducted univariate and multivariate survival analyses to identify independently significant indicators for prognosis. Relationships between the risk scores and age, grade, stage, immune cell infiltration, immune function, and drug sensitivity were demonstrated by correlation analysis. The expression level of prognostic genes in vivo and in vitro were determined by qRT-PCR assay.

Results

Comparing with normal tissues, there were 49 differentially expressed IRAGs in cancer tissues, and 12 of them were markedly related to the prognosis in TCGA cohort for transitional bladder cancer patients. Based on LASSO regression analysis, a risk model consists of 10 IRAGs was established. Comparing with high-risk groups, survival analysis showed that patients in low-risk groups were more likely to have a better survival time in TCGA and GSE13507 cohorts. Besides, the accuracy of the model in predicting prognosis is acceptable, which is demonstrated by receiver operating characteristic curve (ROC) analysis. Age, stage, and risk scores variables were identified as the independently significant indicators for survival in transitional bladder cancer. Correlation analysis represented that the risk score was identified to be significantly related to the above variables except gender variable. Moreover, the expression level of prognostic genes in vivo and in vitro was markedly upregulated for transitional bladder cancer.

Conclusions

A novel model based on the 10 IRAGs that can be used to predict survival time for transitional bladder cancer. In addition, this study may provide treatment strategies according to the drug sensitivity in the future.

Preoperative plasma level of endoglin as a predictor for disease outcomes after radical cystectomy for nonmetastatic urothelial carcinoma of the bladder

Elevated preoperative plasma level of endoglin has been associated with worse oncologic outcomes in various malignancies. The present large-scale study aimed to determine the predictive and prognostic values of preoperative endoglin with regard to clinicopathologic and survival outcomes in patients treated with radical cystectomy (RC) for nonmetastatic urothelial carcinoma of the bladder (UCB). We prospectively collected preoperative blood samples from 1036 consecutive patients treated with RC for UCB. Logistic and Cox regression analyses were undertaken to assess the correlation of endoglin levels with pathologic and survival outcomes, respectively. The AUC and C-index were used to assess the discrimination. Patients with adverse pathologic features had significantly higher median preoperative endoglin plasma levels than their counterparts. Higher preoperative endoglin level was independently associated with an increased risk for lymph node metastasis, ≥pT3 disease, and nonorgan confined disease (NOCD; all p < 0.001). Plasma endoglin level was also independently associated with cancer-specific and overall survival in both pre- and postoperative models (all p < 0.05), as well as with recurrence-free survival (RFS) in the preoperative model (p < 0.001). The addition of endoglin to the preoperative standard model improved its discrimination for prediction of lymph node metastasis, ≥pT3 disease, NOCD, and RFS (differential increases in C-indices: 10%, 5%, 5.8%, and 4%, respectively). Preoperative plasma endoglin is associated with features of biologically and clinically aggressive UCB as well as survival outcomes. Therefore, it seems to hold the potential of identifying UCB patients who may benefit from intensified therapy in addition to RC such as extended lymphadenectomy or/and preoperative systemic therapy.