Novel protein signatures suggest progression to muscular invasiveness in bladder cancer

NPEPPS Is a Druggable Driver of Platinum Resistance

There is an unmet need to improve the efficacy of platinum-based cancer chemotherapy, which is used in primary and metastatic settings in many cancer types. In bladder cancer, platinum-based chemotherapy leads to better outcomes in a subset of patients when used in the neoadjuvant setting or in combination with immunotherapy for advanced disease. Despite such promising results, extending the benefits of platinum drugs to a greater number of patients is highly desirable. Using the multiomic assessment of cisplatin-responsive and -resistant human bladder cancer cell lines and whole-genome CRISPR screens, we identified puromycin-sensitive aminopeptidase (NPEPPS) as a driver of cisplatin resistance. NPEPPS depletion sensitized resistant bladder cancer cells to cisplatin in vitro and in vivo. Conversely, overexpression of NPEPPS in sensitive cells increased cisplatin resistance. NPEPPS affected treatment response by regulating intracellular cisplatin concentrations. Patient-derived organoids (PDO) generated from bladder cancer samples before and after cisplatin-based treatment, and from patients who did not receive cisplatin, were evaluated for sensitivity to cisplatin, which was concordant with clinical response. In the PDOs, depletion or pharmacologic inhibition of NPEPPS increased cisplatin sensitivity, while NPEPPS overexpression conferred resistance. Our data present NPEPPS as a druggable driver of cisplatin resistance by regulating intracellular cisplatin concentrations.

SIGNIFICANCE

Targeting NPEPPS, which induces cisplatin resistance by controlling intracellular drug concentrations, is a potential strategy to improve patient responses to platinum-based therapies and lower treatment-associated toxicities.

Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework

Background

Bladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response.

Methods

The expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts.

Results

In the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy.

Conclusion

Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment.

Comparative bioinformatics analysis of prognostic and differentially expressed genes in non-muscle and muscle invasive bladder cancer

Bladder cancer (BC) is classified into non-muscle (NMIBC) and muscle invasive (MIBC) diseases. Several molecular alterations were previously associated with NMIBC and MIBC, but few studies have systematically compared the molecular differences between these subtypes. Here, we analyzed prognostic and differentially expressed genes in NMIBC and MIBC, using an integrative bioinformatics approach. These genes were used in functional enrichment and co-expression protein interaction (COPI) network analyses to reveal common and exclusive biological functions involved in NMIBC and MIBC. In NMIBC, the enriched functions were related to oxidative stress response, cell cycle, glutathione metabolism, ubiquitination and protein translation. Conversely, enriched functions in MIBC were extracellular matrix organization, cell migration and actin cytoskeleton. Several genes in NMIBC did not overlap with those reported to MIBC, suggesting these subtypes may have distinct underlying mechanisms. Particularly, MIBC genes were enriched for functions involved in cell migration and invasion, which could help to molecularly differentiate NMIBC and MIBC. The analysis of COPI networks disclosed high centrality nodes that may be essential for NMIBC and MIBC. Further research will determine to which extent NMIBC and MIBC share common biological functions and identify potential candidates for the differential diagnosis, prognosis and treatment of NMIBC and MIBC. SIGNIFICANCE: This study has systematically compared prognostic and differentially expressed genes between non-muscle (NMIBC) and muscle invasive (MIBC) bladder cancer, using an integrative bioinformatics approach. Many genes and biological functions were exclusively associated with either NMIBC or MIBC, suggesting that these disease subtypes could be driven by distinct molecular mechanisms. Particularly, prognostic and differentially expressed genes in MIBC were involved in cell migration and invasion, which can help to molecularly differentiate the NMIBC and MIBC subtypes. Moreover, the analysis of co-expression protein interaction networks identified high centrality nodes that could be potential candidates for the prognosis and treatment of NMIBC and MIBC.

Identification of five long noncoding RNAs signature and risk score for prognosis of bladder urothelial carcinoma

Nowadays, an increasing number of studies illustrated that bladder urothelial cancer (BLCA) may act as the most common subtype of urological malignancies with a high rate of recurrence and metastasis. In this study, we attempted to establish a prognostic model and identify the possible pathway crosstalk. Long noncoding RNAs (lncRNAs) and mRNA expression and corresponding clinical information of patients with BLCA were downloaded from The Cancer Genome Atlas (TCGA). The differentially expressed genes analysis, univariate Cox analysis, the least absolute shrinkage, and selection operator Cox (LASSO Cox) regression model were then applied to identify five crucial lncRNAs (AC092725.1, AC104071.1, AL023584.1, AL132642.1, and AL137804.1). The multivariate cox analysis was utilized to calculate the regression coefficients (β_i ). The risk-score model was subsequently constructed as follows: (0.13541AC092725.1) + (0.20968AC104071.1) + (0.1525AL023584.1) - (0.14768AL132642.1) + (0.14387AL137804.1). Nomogram and assessment of overall survival (OS) prediction were verificated by the receiver operating characteristic curve in the testing group. As to 3-, 5-year OS prediction, the area under curve (AUC) for the nomogram of training data set was 0.83 and 0.86. Besides, the AUC (0.883 and 0.879) presented excellent predictive power in the testing group. In addition, the calibration plots validated the predictive performance of the nomogram. Weighted correlation network analysis (WGCNA) coupled with functional enrichment analysis contributed to explore the potential pathways, including PI3K-Akt, HIF-1, and Jak-STAT signaling pathways. Construction of the risk-score model and data analysis were both derived from multiple packages on the basis of the R platform chiefly.