Dataset Information

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

ABSTRACT:

Objectives

To determine the predictive and prognostic value of a panel of systemic inflammatory response (SIR) biomarkers relative to established clinicopathological variables in order to improve patient selection and facilitate more efficient delivery of peri-operative systemic therapy.

Materials and methods

The preoperative serum levels of a panel of SIR biomarkers, including albumin-globulin ratio, neutrophil-lymphocyte ratio, De Ritis ratio, monocyte-lymphocyte ratio and modified Glasgow prognostic score were assessed in 4199 patients treated with radical cystectomy for clinically non-metastatic urothelial carcinoma of the bladder. Patients were randomly divided into a training and a testing cohort. A machine-learning-based variable selection approach (least absolute shrinkage and selection operator regression) was used for the fitting of several multivariable predictive and prognostic models. The outcomes of interest included prediction of upstaging to carcinoma invading bladder muscle (MIBC), lymph node involvement, pT3/4 disease, cancer-specific survival (CSS) and recurrence-free survival (RFS). The discriminatory ability of each model was either quantified by area under the receiver-operating curves or by the C-index. After validation and calibration of each model, a nomogram was created and decision-curve analysis was used to evaluate the clinical net benefit.

Results

For all outcome variables, at least one SIR biomarker was selected by the machine-learning process to be of high discriminative power during the fitting of the models. In the testing cohort, model performance evaluation for preoperative prediction of lymph node metastasis, ≥pT3 disease and upstaging to MIBC showed a 200-fold bootstrap-corrected area under the curve of 67.3%, 73% and 65.8%, respectively. For postoperative prognosis of CSS and RFS, a 200-fold bootstrap corrected C-index of 73.3% and 72.2%, respectively, was found. However, even the most predictive combinations of SIR biomarkers only marginally increased the discriminative ability of the respective model in comparison to established clinicopathological variables.

Conclusion

While our machine-learning approach for fitting of the models with the highest discriminative ability incorporated several previously validated SIR biomarkers, these failed to improve the discriminative ability of the models to a clinically meaningful degree. While the prognostic and predictive value of such cheap and readily available biomarkers warrants further evaluation in the age of immunotherapy, additional novel biomarkers are still needed to improve risk stratification.

SUBMITTER: Schuettfort VM

PROVIDER: S-EPMC9291893 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGrowing evidence shows that the preoperative lymphocyte-related systemic inflammatory biomarkers are associated with the prognosis of patients with upper tract urothelial carcinoma (UTUC). These markers include neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR). However, these findings are inconsistent, and the prognostic significance of these biomarkers is unclear. Moreover, the currently available prognostic indicators do not precisely predict the outcome of UTUC patients. This motivated us to investigate the prognostic values of NLR, PLR, and MLR in UTUC patients treated with radical nephroureterectomy (RNU).MethodsWe prospectively registered this in PROSPERO (CRD42020186531). We performed a comprehensive literature search of the PubMed, Web of Science, EMBASE, and Cochrane Library databases to identify the eligible studies evaluating the prognostic values of preoperative NLR, PLR, and MLR. Hazard ratios with 95% confidence intervals of overall survival (OS), cancer-specific survival (CSS), disease-free survival (DFS), recurrence-free survival (RFS), metastasis-free survival (MFS), and progression-free survival (PFS) were extracted from the multivariate analyses and analyzed with fixed or random effects models when applicable. Heterogeneity among the studies was evaluated using Cochran's Q test and I2 statistic. Sensitivity and subgroup analyses were conducted to explore the origin of heterogeneity. The Newcastle-Ottawa Scale (NOS) was applied to assess the quality of each enrolled study. Publication bias was determined using funnel plots together with Egger's tests. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used to evaluate the quality of the evidence.ResultsOverall, we included 10,339 UTUC patients from twenty-five retrospective studies. The results indicated that elevated preoperative NLR, PLR, and MLR were significantly associated with worse OS, CSS, DFS/RFS/MFS, and PFS in the UTUC patients undergoing RNU. Furthermore, the results of sensitivity and subgroup analyses demonstrated the rationality and reliability of the results.ConclusionsThe present meta-analysis demonstrated a significant association between elevated preoperative NLR, PLR, and MLR and poor prognosis in patients with surgically treated UTUC. Hence, lymphocyte-related systemic inflammatory biomarkers, in conjunction with clinicopathological factors, molecular markers, and other prognostic indicators, could be helpful to determine the primary treatment strategies and to design individualized follow-up plans for UTUC patients.

Project description:PurposeThe aim of this study was to evaluate the prognostic significance of the systemic inflammatory response index (SIRI) in patients with bladder cancer (BCa) treated with radical cystectomy (RC) and develop a survival predictive model through establishing a nomogram.Materials and methodsA total of 203 BCa patients who underwent RC were included in this study. The relationship between the SIRI and overall survival (OS), disease-free survival (DFS), and clinicopathological features were evaluated. Cox regression analysis was performed to investigate the effect of the factors on the OS and DFS. The results were applied in the establishment of a nomogram. Receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves, and calibration curves were performed to assess the predictive performance and accuracy of the nomogram, respectively.ResultsAccording to the classification of the SIRI, 81 patients (39.9%) were assigned to SIRI grade 1, 94 patients (46.3%) to SIRI grade 2, and the remaining 28 patients (13.8%) to SIRI grade 3. Multivariate Cox regression revealed that a higher SIRI grade was significantly associated with a poor prognosis and served as an independent prognostic factor for the OS [Grade 2 vs Grade 1, odds ratio = 2.54, 95% confidence interval (CI),1.39-4.64, P = 0.002; Grade 3 vs Grade 1, odds ratio = 4.79, 95%CI: 2.41-9.50, P < 0.001] and DFS [Grade 2 vs Grade 1, odds ratio = 2.19, 95% CI, 1.12-4.31, P = 0.023; Grade 3 vs Grade 2, odds ratio = 3.36, 95%CI, 1.53-7.35, P = 0.002]. The ROC and DCA analysis indicated that the nomogram based on the SIRI contained a better predictive performance compared with the TNM stage (AUC = 0.750 and 0.791; all P < 0.05). The ROC analysis showed that nomograms can better predict the 3- and 5-year OS and DFS. The calibration curves exhibited a significant agreement between the nomogram and the actual observation.ConclusionSIRI as a novel independent prognostic index and potential prognostic biomarker can effectively improve the traditional clinicopathological analysis and optimize individualized clinical treatments for BCa patients after RC.