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

RNA-binding protein quaking: a multifunctional regulator in tumour progression

BACKGROUND

Quaking (QKI) is a member of the signal transduction and activators of RNA (STAR) family, performing a crucial multifunctional regulatory role in alternative splicing, mRNA precursor processing, mRNA transport and localization, mRNA stabilization, and translation during tumour progression. Abnormal QKI expression or fusion mutations lead to aberrant RNA and protein expression, thereby promoting tumour progression. However, in many types of tumour, QKI played a role as tumour suppressor, the regulatory role of QKI in tumour progression remains ambiguous.

OBJECTIVES

This review aims to analyze the isoform and function of QKI, the impact of QKI-regulated gene expression or signalling pathway alterations on tumour progression, and its potential clinical applications as a predictive marker or target for tumour therapy.

METHODS

We reviewed recent studies and summarized the function of QKI alteration in tumour progression.

RESULTS

QKI mediate post-transcriptional gene regulation including alternative splicing, polyadenylation, mRNA stabilization, mRNA subcellular location, and noncoding RNA by binding to the QRE elements of targeted nucleotide. The dysregulation of QKI is intricately correlated to tumour proliferation, metastasis, angiogenesis, tumor stem cells, the tumour microenvironment, and treatment sensitivity, and represents as a potential biological predictor in tumour diagnosis and prognosis.

CONCLUSIONS

QKI play a critical role as tumour suppressor or an oncogene in tumour progression due to the different splicing sites and transcripts with various tumour subtype or tumor micorenvironment. Ongoing research about QKI's functions and mechanisms persist is required to conduct for better understanding the role of QKI in tumour regulation.

Berberine inhibits prostate cancer progression by inducing ferroptosis: evidence from network pharmacology

The uncertain ferroptosis-related role of berberine in prostate cancer was explored using network pharmacology methodology. Integration of ferroptosis targets in prostate cancer from the Genecard database and berberine targets from the Traditional Chinese Medicine Systems Pharmacology and SwissTargetPrediction databases revealed 17 common targets. Among these, 10 hub genes, including CCNB1 , CDK1 , AURKA , AR , CDC42 , ICAM1 , TYMS , NTRK1 , PTGS 2, and SCD , were identified. Enrichment analyses yielded 799 Gene Ontology terms and 23 Kyoto Encyclopedia of Genes and Genomes pathways associated with berberine-related targets. Molecular docking simulations indicated berberine's binding capacity to all hub genes. In-vitro studies on LNCaP and PC3 cells demonstrated berberine's inhibition of cell proliferation and significant downregulation of TYMS , CCNB1 , AURKA , CDK1 , and SCD in both cell lines. Berberine exhibited cell line-specific effects by reducing AR expression in LNCaP cells and suppressing ICAM1 in PC3 cells. Overall, berberine shows promise in inhibiting prostate cancer progression through modulation of ferroptosis-related genes, including TYMS , AR , CCNB1 , AURKA , CDK1 , ICAM1 , NTRK1 , SCD , and CDC42 .

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.