Androgen-Independent Prostate Cancer Is Sensitive to CDC42-PAK7 Kinase Inhibition

Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data

INTRODUCTION

Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs.

AREAS COVERED

Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination.

EXPERT OPINION

Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.

Clinicopathologic Significance of Quaking Expression in Hepatocellular Carcinoma

BACKGROUND/AIM

The RNA binding protein quaking (QKI) is associated with the development and progression of tumor suppressors in various cancers. However, the clinical implications of QKI expression have not yet been fully elucidated. In this study, we aimed to investigate the clinicopathological and prognostic significance of QKI expression in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

We performed QKI, Zinc finger E-box-binding homeobox 1 (ZEB1), E-cadherin, and glutathione peroxidase 4 (GPX4) immunohistochemical staining on 166 HCC patient tissue samples. X-tile bioinformatics software was used to set the cut-off value for high QKI expression. Correlations between QKI expression and various clinicopathological parameters were assessed.

RESULTS

The best cut-off value for high QKI expression was 12.5. High QKI expression was observed in 28 of 166 patients (16.9%) and was an independent prognostic factor for inferior recurrence-free survival (RFS). In addition, high ZEB1 and GPX4 expression correlated with high QKI expression, but not with the loss of E-cadherin expression.

CONCLUSION

High QKI expression was identified in HCCs and associated with poor RFS. QKI might be a prognostic biomarker of HCCs associated with epithelial-to-mesenchymal transition and a potential candidate therapeutic target.

Identification of Molecular Markers Associated with Prostate Cancer Subtypes: An Integrative Bioinformatics Approach

Prostate cancer (PCa) is characterised by androgen dependency. Unfortunately, under anti-androgen treatment pressure, castration-resistant prostate cancer (CRPC) emerges, characterised by heterogeneous cell populations that, over time, lead to the development of different androgen-dependent or -independent phenotypes. Despite important advances in therapeutic strategies, CRPC remains incurable. Context-specific essential genes represent valuable candidates for targeted anti-cancer therapies. Through the investigation of gene and protein annotations and the integration of published transcriptomic data, we identified two consensus lists to stratify PCa patients' risk and discriminate CRPC phenotypes based on androgen receptor activity. ROC and Kaplan-Meier survival analyses were used for gene set validation in independent datasets. We further evaluated these genes for their association with cancer dependency. The deregulated expression of the PCa-related genes was associated with overall and disease-specific survival, metastasis and/or high recurrence risk, while the CRPC-related genes clearly discriminated between adeno and neuroendocrine phenotypes. Some of the genes showed context-specific essentiality. We further identified candidate drugs through a computational repositioning approach for targeting these genes and treating lethal variants of PCa. This work provides a proof-of-concept for the use of an integrative approach to identify candidate biomarkers involved in PCa progression and CRPC pathogenesis within the goal of precision medicine.