Recent advances in prostate cancer: WNT signaling, chromatin regulation, and transcriptional coregulators

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

The Prognostic and Therapeutic Potential of Fragile X Mental Retardation 1 (FMR1) Gene Expression in Prostate Adenocarcinoma: Insights into Survival Outcomes and Oncogenic Pathway Modulation

Prostate adenocarcinoma (PRAD) is the second most common tumor associated with death. The role and mechanisms of the fragile X mental retardation 1 (FMR1) gene in PRAD remain unknown. We conducted an analysis of FMR1 expression in PRAD to determine its prognostic importance and connection to carcinogenic pathways such as PI3K_AKT_mTOR. Survival analyses were utilized to establish a correlation between FMR1 expression and patient outcomes. We used the integration of genomic data with bioinformatic predictions to predict the regulatory factors of the FMR1 gene in PRAD. Our data revealed that individuals with higher levels of FMR1 expression experience worse survival outcomes compared to those with lower expression (hazard ratio [HR] = 5.08, 95% confidence interval [CI] = 1.07 - 24, p = 0.0412). FMR1 expression was significantly higher in patients with advanced pathological tumor stages, particularly in the pT3 and pT4 combined stages and the pN1 nodal stage. Furthermore, patients with high Gleason scores (GSs) (combined GSs 8 and 9) exhibited increased levels of FMR1 expression. Our results further identify a possible regulatory link between FMR1 and key oncogenic pathways, including PI3K_AKT_mTOR, and predict the possible mechanism by which FMR1 is regulated in PRAD. Our data suggest that the FMR1 gene could serve as a biomarker for PRAD progression. However, in-depth investigations, including those with large patient samples and in vitro studies, are needed to validate this finding and understand the mechanisms involved.