Identification of a gene signature for the prediction of recurrence and progression in non-muscle-invasive bladder cancer.
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ABSTRACT:
SUBMITTER: Dalla E
PROVIDER: S-EPMC8921352 | biostudies-literature | 2022 Mar
REPOSITORIES: biostudies-literature
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Identification of a gene signature for the prediction of recurrence and progression in non-muscle-invasive bladder cancer.
Dalla Emiliano E Picco Raffaella R Novara Giacomo G Moro Fabrizio Dal FD Brancolini Claudio C
Molecular biomedicine 20220315 1
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Project description:Purpose: Most bladder cancers are early-stage tumors known as papillary non-muscle-invasive bladder cancer (NMIBC). After resection, up to 70% of NMIBCs recur locally, and up to 20% of these recurrences progress to muscle invasion. There is an unmet need for additional biomarkers for stratifying tumors based on their risk of recurrence and progression. We previously identified STAG2 as among the most commonly mutated genes in NMIBC and provided initial evidence in a pilot cohort that STAG2-mutant tumors recurred less frequently than STAG2 wild-type tumors. Here, we report a STAG2 biomarker validation study using two independent cohorts of clinically annotated papillary NMIBC tumors from the United States and Europe.Experimental Design: The value of STAG2 immunostaining for prediction of recurrence was initially evaluated in a cohort of 82 patients with papillary NMIBC ("Georgetown cohort"). Next, the value of STAG2 immunostaining for prediction of progression to muscle invasion was evaluated in a progressor-enriched cohort of 253 patients with papillary NMIBC ("Aarhus cohort").Results: In the Georgetown cohort, 52% of NMIBC tumors with intact STAG2 expression recurred, whereas 25% of STAG2-deficient tumors recurred (P = 0.02). Multivariable analysis identified intact STAG2 expression as an independent predictor of recurrence (HR = 2.4; P = 0.05). In the progressor-enriched Aarhus cohort, 38% of tumors with intact STAG2 expression progressed within 5 years, versus 16% of STAG2-deficient tumors (P < 0.01). Multivariable analysis identified intact STAG2 expression as an independent predictor of progression (HR = 1.86; P = 0.05).Conclusions: STAG2 IHC is a simple, binary, new assay for risk stratification in papillary NMIBC. Clin Cancer Res; 24(17); 4145-53. ©2018 AACR.
| S-EPMC6125225 | biostudies-literature
Project description:Although non muscle-invasive bladder cancer can be treated successfully by surgical resection, there is a high rate of recurrence, which frequently develops into an invasive form of cancer. Due to the lack of marker molecules to predict recurrence, it is currently recommended that after resection each patient is screened via cystoscopy at least twice a year. This results in a psychic burden to the patient and substantial economic costs to the health care system. Using antibody microarrays targeting 724 different cancer-related proteins, we studied protein profiles of patients with and without recurrence. The analysis revealed 255 proteins of differential abundance. Most are involved in the regulation and execution of apoptosis and cell proliferation. For prognosis, a signature of 20 proteins was determined that predicts bladder cancer recurrence with 100% sensitivity and 80% specificity. As a measure of overall accuracy, the area under the curve (AUC) value was found to be 90%. This is well within a clinically relevant window of quality and should support decision making about the stringency of surveillance or even different treatment options.
2014-04-07 | E-MEXP-3625 | biostudies-arrayexpress
Project description:This study evaluated the prognostic value of a panel of 29 oncogenes derived from the analysis of The Cancer Genome Atlas (TCGA data) or from the recent literature on bladder tumors on a well-characterized series of muscle-invasive bladder cancer (MIBC) and non-MIBC (NMIBC) samples and tried to identify molecular prognostic markers. Mutations of HRAS, FGFR3, PIK3CA and TERT were found in 2.9%, 27.2%, 14.9% and 76.7% of tumor samples, respectively. Concerning NMIBC, on multivariate analysis, RXRA and FGFR3 levels were associated with recurrence-free survival (RFS) (p = 0.0022 and p = 0.0069) and RXRA level was associated with progression to muscle-invasive disease (p = 0.0068). We identified a 3-gene molecular signature associated with NMIBC prognosis. FGFR3 overexpression was associated with reduced response to Bacillus Calmette-Guerin treatment (p = 0.037). As regards MIBC, on multivariate analysis, ERCC2 overexpression was associated with RFS (p = 0.0011) and E2F3 and EGFR overexpression were associated with overall survival (p = 0.014 and p = 0.035). RT-PCR findings were confirmed by IHC for FGFR3. Genomic alterations in MIBC revealed in TCGA data also concern NMIBC and seem to be associated with prognosis in terms of recurrence and progression. Correcting these alterations by targeted therapies seems a promising pharmacological approach.
| S-EPMC7538919 | biostudies-literature
Project description:BACKGROUND: S100 calcium binding protein A8 (S100A8) has been implicated as a prognostic indicator in several types of cancer. However, previous studies are limited in their ability to predict the clinical behavior of the cancer. Here, we sought to identify a molecular signature based on S100A8 expression and to assess its usefulness as a prognostic indicator of disease progression in non-muscle invasive bladder cancer (NMIBC). METHODS: We used 103 primary NMIBC specimens for microarray gene expression profiling. The median follow-up period for all patients was 57.6 months (range: 3.2 to 137.0 months). Various statistical methods, including the leave-one-out cross validation method, were applied to identify a gene expression signature able to predict the likelihood of progression. The prognostic value of the gene expression signature was validated in an independent cohort (n = 302). RESULTS: Kaplan-Meier estimates revealed significant differences in disease progression associated with the expression signature of S100A8-correlated genes (log-rank test, P < 0.001). Multivariate Cox regression analysis revealed that the expression signature of S100A8-correlated genes was a strong predictor of disease progression (hazard ratio = 15.225, 95% confidence interval = 1.746 to 133.52, P = 0.014). We validated our results in an independent cohort and confirmed that this signature produced consistent prediction patterns. Finally, gene network analyses of the signature revealed that S100A8, IL1B, and S100A9 could be important mediators of the progression of NMIBC. CONCLUSIONS: The prognostic molecular signature defined by S100A8-correlated genes represents a promising diagnostic tool for the identification of NMIBC patients that have a high risk of progression to muscle invasive bladder cancer.
| S-EPMC2828413 | biostudies-literature
Project description:There are no reliable criteria to handle disease progression of muscle invasive bladder cancer (MIBC), which strongly influences patient survival. Therefore, an accurate predicting method to identify progressive MIBC patients is greatly needed. The aim of this study was to identify a genetic signature associated with disease progression in MIBC. To address this issue, we analyzed three independent cohorts (a training set, test set 1 and test set 2) comprising a total of 128 MIBC patients. Microarray gene expression profiling, including gene network analysis, was performed in the training set to identify a gene expression signature associated with disease progression. The prognostic value of the signature was validated in test set 1 and test set 2 by microarray and real-time reverse transcriptase polymerase chain reaction (RT-PCR), respectively. The determination of gene expression patterns by microarray data analysis identified 1,320 genes associated with disease progression. Gene network analysis of the 1,320 genes suggested that IL1B, S100A8, S100A9 and EGFR were important mediators of MIBC progression. We validated this putative four-gene signature in two independent cohorts (log-rank test, P < 0.05 each, respectively) and estimated the predictive value of the signature by multivariate Cox regression analysis (hazard ratio [HR], 6.24; 95% confidence interval [CI], 1.58-24.61; P = 0.009). Finally, signature-based stratification demonstrated that the four-gene signature was an independent predictor of MIBC progression. In conclusion, a molecular signature defined by four genes represents a promising diagnostic tool for the identification of MIBC patients at high risk of progression.
| S-EPMC3105150 | biostudies-literature
Project description:ObjectiveWe aimed to determine the validity of two risk scores for patients with non-muscle invasive bladder cancer in different European settings, in patients with primary tumours.MethodsWe included 1,892 patients with primary stage Ta or T1 non-muscle invasive bladder cancer who underwent a transurethral resection in Spain (n = 973), the Netherlands (n = 639), or Denmark (n = 280). We evaluated recurrence-free survival and progression-free survival according to the European Organisation for Research and Treatment of Cancer (EORTC) and the Spanish Urological Club for Oncological Treatment (CUETO) risk scores for each patient and used the concordance index (c-index) to indicate discriminative ability.ResultsThe 3 cohorts were comparable according to age and sex, but patients from Denmark had a larger proportion of patients with the high stage and grade at diagnosis (p<0.01). At least one recurrence occurred in 839 (44%) patients and 258 (14%) patients had a progression during a median follow-up of 74 months. Patients from Denmark had the highest 10-year recurrence and progression rates (75% and 24%, respectively), whereas patients from Spain had the lowest rates (34% and 10%, respectively). The EORTC and CUETO risk scores both predicted progression better than recurrence with c-indices ranging from 0.72 to 0.82 while for recurrence, those ranged from 0.55 to 0.61.ConclusionThe EORTC and CUETO risk scores can reasonably predict progression, while prediction of recurrence is more difficult. New prognostic markers are needed to better predict recurrence of tumours in primary non-muscle invasive bladder cancer patients.
| S-EPMC4048166 | biostudies-literature
Project description:We aimed to build a deep learning-based pathomics model to predict the early recurrence of non-muscle-infiltrating bladder cancer (NMIBC) in this work. A total of 147 patients from Xuzhou Central Hospital were enrolled as the training cohort, and 63 patients from Suqian Affiliated Hospital of Xuzhou Medical University were enrolled as the test cohort. Based on two consecutive phases of patch level prediction and WSI-level predictione, we built a pathomics model, with the initial model developed in the training cohort and subjected to transfer learning, and then the test cohort was validated for generalization. The features extracted from the visualization model were used for model interpretation. After migration learning, the area under the receiver operating characteristic curve for the deep learning-based pathomics model in the test cohort was 0.860 (95% CI 0.752-0.969), with good agreement between the migration training cohort and the test cohort in predicting recurrence, and the predicted values matched well with the observed values, with p values of 0.667766 and 0.140233 for the Hosmer-Lemeshow test, respectively. The good clinical application was observed using a decision curve analysis method. We developed a deep learning-based pathomics model showed promising performance in predicting recurrence within one year in NMIBC patients. Including 10 state prediction NMIBC recurrence group pathology features be visualized, which may be used to facilitate personalized management of NMIBC patients to avoid ineffective or unnecessary treatment for the benefit of patients.
| S-EPMC11327297 | biostudies-literature
Project description:Purpose: Bladder cancer (BCa) is generally considered one of the most prevalent deadly diseases worldwide. Patients suffering from muscle-invasive bladder cancer (MIBC) possess dismal prognoses, while those with non-muscle-invasive bladder cancer (NMIBC) generally have a favorable outcome after local treatment. However, some NMIBCs relapse and progress to MIBC, with an unclarified mechanism. Hence, insight into the genetic drivers of BCa progression has tremendous potential benefits for precision therapeutics, risk stratification, and molecular diagnosis. Methods: In this study, three cohorts profile datasets (GSE13507, GSE32584, and GSE89) consisting of NMIBC and MIBC samples were integrated to address the differently expressed genes (DEGs). Subsequently, the protein-protein interaction (PPI) network and pathway enrichment analysis of DGEs were performed. Results: Six collagen members (COL1A1, COL1A2, COL5A2, COL6A1, COL6A2, and COL6A3) were up-regulated and gathered in the ECM-receptor interaction signal pathway identified by KEGG pathway analysis and GSEA. Evidence derived from the Oncomine and TCGA databases indicated that the 6 collagen genes promote the progression of BCa and are negatively associated with patient prognosis. Moreover, taking COL1A1 as a further research object, the results showed that COL1A1 was up-regulated in MIBC and its knockdown significantly inhibited the proliferation, migration, and invasion of 5637 and T24 cells by inhibiting epithelial-mesenchymal transition (EMT) process and the TGF-β signaling pathway. Conclusion: With integrated bioinformatic analysis and cell experiments, we showed that 6 collagen family members are high progression risk factors and that they can be used as independent effective diagnostic and prognostic biomarkers for BCa.
| S-EPMC6511250 | biostudies-literature
Project description:Non-muscle invasive bladder cancer (NMIBC) generally has a good prognosis; however, recurrence after transurethral resection (TUR), the standard primary treatment, is a major problem. Clinical management after TUR has been based on risk classification using clinicopathological factors, but these classifications are not complete. In this study, we attempted to predict early recurrence of NMIBC based on machine learning of quantitative morphological features. In general, structural, cellular, and nuclear atypia are evaluated to determine cancer atypia. However, since it is difficult to accurately quantify structural atypia from TUR specimens, in this study, we used only nuclear atypia and analyzed it using feature extraction followed by classification using Support Vector Machine and Random Forest machine learning algorithms. For the analysis, 125 patients diagnosed with NMIBC were used; data from 95 patients were randomly selected for the training set, and data from 30 patients were randomly selected for the test set. The results showed that the support vector machine-based model predicted recurrence within 2 years after TUR with a probability of 90% and the random forest-based model with probability of 86.7%. In the future, the system can be used to objectively predict NMIBC recurrence after TUR.
| S-EPMC8964412 | biostudies-literature
Project description:BackgroundBladder cancer (BCa) is a malignant tumor that usually forms cancer cells in the inner lining of the bladder. Hundreds of thousands of people worldwide have BCa diagnosed each year. The purpose of this study was to construct a prognostic model by differential expression of genes between muscular and non-muscular invasive BCa, and to investigate the prognosis of BCa patients.MethodsThe data of BCa patients was sourced from the GEO and TCGA database. Single-cell sequencing data was obtained from three patients in the GSE135337 database, and microarray data for verification was obtained from GSE32894. Univariate, Lasso and multivariate cox regression analyses were performed to construct the prognostic model. The prognostic features, immune features and drug sensitivity of the model were further evaluated. Single-cell data and microarray data were used to validate the differential expression of model genes between muscle-invasive and non-muscle-invasive BCa. The invasion and migration of BCa cells were evaluated using the transwell assay and wound-healing assay. The cell proliferation capacity was simultaneously evaluated using Colony formation experiments. The protein expression of the specific gene was detected by western blot analysis.ResultsWe identified 183 differentially expressed muscle-invasive-related differential genes (MIRDGs), among which four were selected to establish a prognostic model. Based on our signature, patients in different groups displayed varying levels of immune infiltration and immunotherapy profiles. Single-cell sequencing data and microarray data confirmed that four invasion-related genes were expressed at higher levels in muscle-invasive BCa. Given the critical role of S100A9 in the progression of BCa, we performed further analysis. The results showed that protein expression of S100A9 was high in muscle-invasive BCa, and S100A9 knockdown could inhibit the proliferation, migration and invasion of BCa.ConclusionThese findings demonstrated that the prognostic model for BCa patients was reasonably accurate and valid, and it may prove to be of considerable value for the treatment and prognosis of BCa patients in the future. S100A9 may become a better prognostic marker and potential therapeutic target to further guide clinical treatment decisions.
| S-EPMC10483405 | biostudies-literature