Prognostic value and potential biological functions of ferroptosis‑related gene signature in bladder cancer

Squalene Epoxidase: Its Regulations and Links with Cancers

Squalene epoxidase (SQLE) is a key enzyme in the mevalonate-cholesterol pathway that plays a critical role in cellular physiological processes. It converts squalene to 2,3-epoxysqualene and catalyzes the first oxygenation step in the pathway. Recently, intensive efforts have been made to extend the current knowledge of SQLE in cancers through functional and mechanistic studies. However, the underlying mechanisms and the role of SQLE in cancers have not been fully elucidated yet. In this review, we retrospected current knowledge of SQLE as a rate-limiting enzyme in the mevalonate-cholesterol pathway, while shedding light on its potential as a diagnostic and prognostic marker, and revealed its therapeutic values in cancers. We showed that SQLE is regulated at different levels and is involved in the crosstalk with iron-dependent cell death. Particularly, we systemically reviewed the research findings on the role of SQLE in different cancers. Finally, we discussed the therapeutic implications of SQLE inhibitors and summarized their potential clinical values. Overall, this review discussed the multifaceted mechanisms that involve SQLE to present a vivid panorama of SQLE in cancers.

Ferroptosis: An Emerging Target for Bladder Cancer Therapy

Bladder cancer (BC), as one of the main urological cancers in the world, possesses the abilities of multiple-drug resistance and metastasis. However, there remains a significant gap in the understanding and advancement of prognosis and therapeutic strategies for BC. Ferroptosis, a novel type of iron-dependent regulated cell death, depends on lipid peroxidation, which has been proven to have a strong correlation with the development and treatment of BC. Its mechanism mainly includes three pathways, namely, lipid peroxidation, the antioxidant system, and the iron overload pathway. In this review, we reviewed the mechanism of ferroptosis, along with the related therapeutic targets and drugs for BC, as it might become a new anticancer treatment in the future.

Polygenic risk modeling of tumor stage and survival in bladder cancer

Introduction

Bladder cancer assessment with non-invasive gene expression signatures facilitates the detection of patients at risk and surveillance of their status, bypassing the discomforts given by cystoscopy. To achieve accurate cancer estimation, analysis pipelines for gene expression data (GED) may integrate a sequence of several machine learning and bio-statistical techniques to model complex characteristics of pathological patterns.

Methods

Numerical experiments tested the combination of GED preprocessing by discretization with tree ensemble embeddings and nonlinear dimensionality reductions to categorize oncological patients comprehensively. Modeling aimed to identify tumor stage and distinguish survival outcomes in two situations: complete and partial data embedding. This latter experimental condition simulates the addition of new patients to an existing model for rapid monitoring of disease progression. Machine learning procedures were employed to identify the most relevant genes involved in patient prognosis and test the performance of preprocessed GED compared to untransformed data in predicting patient conditions.

Results

Data embedding paired with dimensionality reduction produced prognostic maps with well-defined clusters of patients, suitable for medical decision support. A second experiment simulated the addition of new patients to an existing model (partial data embedding): Uniform Manifold Approximation and Projection (UMAP) methodology with uniform data discretization led to better outcomes than other analyzed pipelines. Further exploration of parameter space for UMAP and t-distributed stochastic neighbor embedding (t-SNE) underlined the importance of tuning a higher number of parameters for UMAP rather than t-SNE. Moreover, two different machine learning experiments identified a group of genes valuable for partitioning patients (gene relevance analysis) and showed the higher precision obtained by preprocessed data in predicting tumor outcomes for cancer stage and survival rate (six classes prediction).

Conclusions

The present investigation proposed new analysis pipelines for disease outcome modeling from bladder cancer-related biomarkers. Complete and partial data embedding experiments suggested that pipelines employing UMAP had a more accurate predictive ability, supporting the recent literature trends on this methodology. However, it was also found that several UMAP parameters influence experimental results, therefore deriving a recommendation for researchers to pay attention to this aspect of the UMAP technique. Machine learning procedures further demonstrated the effectiveness of the proposed preprocessing in predicting patients’ conditions and determined a sub-group of biomarkers significant for forecasting bladder cancer prognosis.