Cell-free DNA (cfDNA) has recently emerged as a real-time biomarker for diagnosis, monitoring and prediction of therapy response in tumoral disease. Here, we evaluated cfDNA as a prognostic biomarker for monitoring muscle-invasive bladder cancer (MIBC) patients at different follow-up time points. Blood samples from 37 MIBC patients who underwent radical cystectomy (RC) were collected at cystectomy and 1, 4, 12 and 24 months later. Plasma cfDNA amount and fragmentation patterns were determined. Four mutations were analyzed in cfDNA to detect circulating tumor DNA (ctDNA) during patient follow-up. During a median follow-up of 36 months, 46% of patients progressed; median time to progression was 10 months. cfDNA levels and ctDNA status four months after RC were identified as independent prognostic biomarkers of tumor progression (HR 5.290; p = 0.033) and cancer-specific survival (HR 4.199; p = 0.038), respectively. Furthermore, ctDNA clearance four months after RC was significantly associated with patients’ clinical outcomes. In conclusion, cfDNA levels and ctDNA status four months after RC have prognostic implications in MIBC patients. In addition, cfDNA monitoring is useful to predict patient outcomes after RC. cfDNA analysis in the clinical setting could greatly improve MIBC patient management.

Circulating tumor DNA (ctDNA) has recently emerged as a real-time prognostic and predictive biomarker for monitoring cancer patients. Here, we aimed to ascertain whether tumor-agnostic ctDNA testing would be a feasible strategy to monitor disease progression and therapeutic response in muscle-invasive bladder cancer (MIBC) patients after radical cystectomy (RC). Forty-two MIBC patients who underwent RC were prospectively included. Blood samples from these patients were collected at different follow-up time points. Two specific mutations (TERT c.1-124C>T and ATM c.1236-2A>T) were analyzed in the patients’ plasma samples by droplet digital PCR to determine their ctDNA status. During a median follow-up of 21 months, 24% of patients progressed in a median of six months. ctDNA status was identified as a prognostic biomarker of tumor progression before RC and 4 and 12 months later (HR 6.774, HR 3.673, and HR 30.865, respectively; p < 0.05). Lastly, dynamic changes in ctDNA status between baseline and four months later were significantly associated with patient outcomes (p = 0.045). In conclusion, longitudinal ctDNA analysis using a tumor-agnostic approach is a potential tool for monitoring MIBC patients after RC. The implementation of this testing in a clinical setting could improve disease management and patients’ outcomes.

The inaccuracy of the current prognostic algorithms and the potential changes in the therapeutic management of localized ccRCC demands the development of an improved prognostic model for these patients. To this end, we analyzed whole-transcriptome profiling of 26 tissue samples from progressive and non-progressive ccRCCs using Illumina Hi-seq 4000. Differentially expressed genes (DEG) were intersected with the RNA-sequencing data from the TCGA. The overlapping genes were used for further analysis. A total of 132 genes were found to be prognosis-related genes. LASSO regression enabled the development of the best prognostic six-gene panel. Cox regression analyses were performed to identify independent clinical prognostic parameters to construct a combined nomogram which includes the expression of CERCAM, MIA2, HS6ST2, ONECUT2, SOX12, TMEM132A, pT stage, tumor size and ISUP grade. A risk score generated using this model effectively stratified patients at higher risk of disease progression (HR 10.79; p < 0.001) and cancer-specific death (HR 19.27; p < 0.001). It correlated with the clinicopathological variables, enabling us to discriminate a subset of patients at higher risk of progression within the Stage, Size, Grade and Necrosis score (SSIGN) risk groups, pT and ISUP grade. In summary, a gene expression-based prognostic signature was successfully developed providing a more precise assessment of the individual risk of progression.

Currently, there are no reliable prognostic factors to determine which upper tract urothelial carcinoma (UTUC) patients will progress after radical nephroureterectomy (RNU). We aim to evaluate whether liquid-biopsy-based biomarkers (circulating tumor cells (CTCs), cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA)) were able to predict clinical outcomes in localized UTUC patients undergoing RNU. Twenty patients were prospectively enrolled between 2021 and 2023. Two blood samples were collected before RNU and three months later. CTCs and cfDNA were isolated and evaluated using the IsoFlux system and Quant-iT PicoGreen dsDNA kit, respectively. Droplet digital PCR was performed to determine ctDNA status. Cox regression analysis was performed on CTCs, cfDNA, and ctDNA at two different follow-up time points to examine their influence on tumor progression and cancer-specific survival (CSS). During a median follow-up of 18 months, seven (35%) patients progressed and three (15%) died. Multivariate analysis demonstrated that cfDNA levels three months after RNU are a significant predictor of tumor progression (HR = 1.085; p = 0.006) and CSS (HR = 1.168; p = 0.029). No associations were found between CTC enumeration and ctDNA status with any of the clinical outcomes evaluated. The evaluation of cfDNA levels in clinical practice could improve the disease management of UTUC patients.

The probability of tumor progression in intermediate/high-risk clear cell renal cell carcinoma (ccRCC) is highly variable, underlining the lack of predictive accuracy of the current clinicopathological factors. To develop an accurate prognostic classifier for these patients, we analyzed global gene expression patterns in 13 tissue samples from progressive and non-progressive ccRCC using Illumina Hi-seq 4000. Expression levels of 22 selected differentially expressed genes (DEG) were assessed by nCounter analysis in an independent series of 71 ccRCCs. A clinicopathological-molecular model for predicting tumor progression was developed and in silico validated in a total of 202 ccRCC patients using the TCGA cohort. A total of 1202 DEGs were found between progressive and non-progressive intermediate/high-risk ccRCC in RNAseq analysis, and seven of the 22 DEGs selected were validated by nCounter. Expression of HS6ST2, pT stage, tumor size, and ISUP grade were found to be independent prognostic factors for tumor progression. A risk score generated using these variables was able to distinguish patients at higher risk of tumor progression (HR 7.27; p < 0.001), consistent with the results obtained from the TCGA cohort (HR 2.74; p < 0.002). In summary, a combined prognostic algorithm was successfully developed and validated. This model may aid physicians to select high-risk patients for adjuvant therapy.

Background: Upper tract urothelial carcinoma (UTUC) is a rare and aggressive malignancy with limited prognostic tools to predict disease progression. Due to its low incidence, the molecular pathogenesis of UTUC remains poorly understood, and few studies have explored transcriptomic profiling in this setting. Identifying gene expression biomarkers associated with progression may help improve risk stratification and guide postoperative management. Methods: In this study, we applied a machine learning approach to gene expression data from radical nephroureterectomy (RNU) specimens of 17 consecutive patients with pT2 or pT3 UTUC treated at our institution. RNA was extracted from formalin-fixed paraffin-embedded tissues and sequenced using the Ion AmpliSeq™ Transcriptome Human Gene Expression Kit on an Illumina HiSeq 2500 platform. Differential gene expression was assessed using DESeq2, and results were visualized with volcano plots. Predictive power was evaluated through logistic regression and receiver operating characteristic (ROC) analysis. Gene Ontology enrichment analysis was used to explore biological pathways. Results: A total of 76 genes were differentially expressed between progressive and non-progressive patients. A random forest classifier identified ten key genes with prognostic potential. Validation with logistic regression yielded an area under the ROC curve (AUC) of 0.88, indicating high discriminative ability. These genes were associated with immune regulation, cell cycle control, and tumor progression. Conclusions: This pilot study demonstrates the potential of integrating machine learning with transcriptomic analysis to identify prognostic biomarkers in UTUC. Further validation in larger, independent cohorts is needed to confirm these findings and support their clinical application.