Functional validation of metabolic genes that distinguish Gleason 3 from Gleason 4 prostate cancer foci

Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients

Prostate cancer (PC) is one of the most common cancers in men and becoming the second leading cause of cancer fatalities. At present, the lack of effective strategies for prognosis of PC patients is still a problem to be solved. Therefore, it is significant to identify potential gene signatures for PC patients' prognosis. Here, we summarized 71 different prognostic gene signatures for PC and concluded 3 strategies for signature construction after extensive investigation. In addition, 14 genes frequently appeared in 71 different gene signatures, which enriched in mitotic and cell cycle. This review provides extensive understanding and integrated analysis of current prognostic signatures of PC, which may help researchers to construct gene signatures of PC and guide future clinical treatment.

PGRMC1: An enigmatic heme-binding protein

Progesterone Receptor Membrane Component 1 (PGRMC1) is a heme-binding protein that has been implicated in a wide range of cell and tissue functions, including cytochromes P450 activity, heme homeostasis, cancer, female reproduction, and protein quality control. Despite an extensive body of literature, a relative lack of mechanistic insight means that how PGRMC1 functions in these different aspects of biology is largely unknown. This review provides an overview of the PGRMC1 literature, highlighting what information is rigorously supported by experimental evidence and where additional investigation is warranted. The central role of PGRMC1 in supporting cytochrome P450 activity is discussed at length. Building on existing models of PGRMC1 function, a speculative model is proposed using the reviewed literature in which PGRMC1 functions as a heme chaperone to shuttle heme from its site of synthesis in the mitochondrion to other subcellular compartments. By spotlighting knowledge gaps, this review will motivate investigators to better understand this enigmatic protein.

Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.

Hyperpolarised 13C-MRI identifies the emergence of a glycolytic cell population within intermediate-risk human prostate cancer

Hyperpolarised magnetic resonance imaging (HP 13C-MRI) is an emerging clinical technique to detect [1-13C]lactate production in prostate cancer (PCa) following intravenous injection of hyperpolarised [1-13C]pyruvate. Here we differentiate clinically significant PCa from indolent disease in a low/intermediate-risk population by correlating [1-13C]lactate labelling on MRI with the percentage of Gleason pattern 4 (%GP4) disease. Using immunohistochemistry and spatial transcriptomics, we show that HP 13C-MRI predominantly measures metabolism in the epithelial compartment of the tumour, rather than the stroma. MRI-derived tumour [1-13C]lactate labelling correlated with epithelial mRNA expression of the enzyme lactate dehydrogenase (LDHA and LDHB combined), and the ratio of lactate transporter expression between the epithelial and stromal compartments (epithelium-to-stroma MCT4). We observe similar changes in MCT4, LDHA, and LDHB between tumours with primary Gleason patterns 3 and 4 in an independent TCGA cohort. Therefore, HP 13C-MRI can metabolically phenotype clinically significant disease based on underlying metabolic differences in the epithelial and stromal tumour compartments.

PAQR6 Upregulation Is Associated with AR Signaling and Unfavorite Prognosis in Prostate Cancers

Progesterone-induced rapid non-genomic signaling events have been confirmed through several membrane progesterone receptors (mPR). Some mPRs were reported to correlate with cancer progression and patient prognosis. In this study, we conducted a comprehensive analysis of all progesterone receptor (PGR)-related genes in prostate cancer tissues and examined the correlations of their expression levels with disease progression and patient survival outcomes. We utilized multiple RNA-seq and cDNA microarray datasets to analyze gene expression profiles and performed logistics aggression and Kaplan-Meier survival analysis after stratifying patients based on tumor stages and Gleason scores. We also used NCBI GEO datasets to examine gene expression patterns in individual cell types of the prostate gland and to determine the androgen-induced alteration of gene expression. Spearman coefficient analysis was conducted to access the correlation of target gene expression with treatment responses and disease progression status. The classic PGR was mainly expressed in stromal cells and progestin and adipoQ receptor (PAQR) genes were the predominant genes in prostate epithelial cells. Progesterone receptor membrane component-1 (PGRMC1) was significantly higher than PGRMC2 in all prostate cell types. In prostate cancer tissues, PAQR6 expression was significantly upregulated, while all other genes were largely downregulated compared to normal prostate tissues. Although both PAQR6 upregulation and PAQR5 downregulation were significantly correlated with tumor pathological stages, only PAQR6 upregulation was associated with Gleason score, free-prostate-specific antigen (fPSA)/total-PSA (tPSA) ratio, and patient overall survival outcomes. In addition, PAQR6 upregulation and PGR/PGRMC1 downregulation were significantly associated with a quick relapse. Conversely, in neuroendocrinal prostate cancer (NEPC) tissues, PAQR6 expression was significantly lower, but PAQR7/8 expression was higher than castration-resistant prostate cancer (CRPC) tissues. PAQR8 expression was positively correlated with androgen receptor (AR) score and AR-V7 expression levels but inversely correlated with NEPC score in metastatic CRPC tumors. This study provides detailed expression profiles of membrane progesterone receptor genes in primary cancer, CRPC, and NEPC tissues. PAQR6 upregulation in primary cancer tissues is a novel prognostic biomarker for disease progression, overall, and progression-free survival in prostate cancers. PAQR8 expression in CRPC tissues is a biomarker for AR activation.

Mifepristone Treatment Promotes Testicular Leydig Cell Tumor Progression in Transgenic Mice

Simple Summary

Recently, the antiprogestin activity of selective progesterone receptor (PR) modulator mifepristone (MF) has proven unsuccessful as a potential anti-cancer agent in various clinical trials. Herein, we analyzed the effects of MF treatment on Leydig cell tumor (LCT) progression in a transgenic mouse model (inhibin-α promoter-driven SV40 T-antigen), as well as on the proliferation of two Leydig tumor cell lines. MF significantly stimulated the proliferation of LCT in vitro. Similarly, a 1-mo MF or P4 treatment stimulated LCT tumor growth in vivo. Only the abundant membrane Pgrmc1 expression was found in LCTs, but no other classical Pgr or nonclassical membrane PRs. Functional analysis showed that PGRMC1 is required for MF and P4 to stimulate the proliferation and invasiveness of LCTs. Our findings provide novel information that the use of MF as an anti-cancer agent should be considered with caution due to its potential PGRMC1 tumor-promoting pathway activation in cancers.

Abstract

The selective progesterone receptor modulator mifepristone (MF) may act as a potent antiproliferative agent in different steroid-dependent cancers due to its strong antagonistic effect on the nuclear progesterone receptor (PGR). Hereby, we analyzed the effects of MF treatment on Leydig cell tumor (LCT) progression in a transgenic mouse model (inhibin-α promoter-driven SV40 T-antigen), as well as on LCT (BLTK-1 and mLTC-1) cell proliferation. MF significantly stimulated the proliferation of LCT in vitro. Similarly, a 1-mo MF or P4 treatment stimulated LCT tumor growth in vivo. Traceable/absent classical Pgr or nonclassical membrane PRs α, β, γ and Pgrmc2, but abundant membrane Pgrmc1 expression, was found in LCTs. MF did not activate glucocorticoid or androgen receptors in LCTs. Functional analysis showed that PGRMC1 is required for MF and P4 to stimulate the proliferation and invasiveness of LCTs. Accordingly, MF and P4 induced PGRMC1 translocation into the nucleus and thereby stimulated the release of TGFβ1 in LCT cells. MF and P4 treatments upregulated Tgfbr1, Tgfbr2, and Alk1 expression and stimulated TGFβ1 release in LCT cells. Our findings provide novel mechanistic insights into the action of MF as a membrane PR agonist that promotes LCT growth through PGRMC1 and the alternative TGFβ1 signaling pathway.

A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer

(1) Background:One of the most common cancers that affect North American men and men worldwide is prostate cancer. The Gleason score is a pathological grading system to examine the potential aggressiveness of the disease in the prostate tissue. Advancements in computing and next-generation sequencing technology now allow us to study the genomic profiles of patients in association with their different Gleason scores more accurately and effectively. (2) Methods: In this study, we used a novel machine learning method to analyse gene expression of prostate tumours with different Gleason scores, and identify potential genetic biomarkers for each Gleason group. We obtained a publicly-available RNA-Seq dataset of a cohort of 104 prostate cancer patients from the National Center for Biotechnology Information's (NCBI) Gene Expression Omnibus (GEO) repository, and categorised patients based on their Gleason scores to create a hierarchy of disease progression. A hierarchical model with standard classifiers in different Gleason groups, also known as nodes, was developed to identify and predict nodes based on their mRNA or gene expression. In each node, patient samples were analysed via class imbalance and hybrid feature selection techniques to build the prediction model. The outcome from analysis of each node was a set of genes that could differentiate each Gleason group from the remaining groups. To validate the proposed method, the set of identified genes were used to classify a second dataset of 499 prostate cancer patients collected from cBioportal. (3) Results: The overall accuracy of applying this novel method to the first dataset was 93.3%; the method was further validated to have 87% accuracy using the second dataset. This method also identified genes that were not previously reported as potential biomarkers for specific Gleason groups. In particular, PIAS3 was identified as a potential biomarker for Gleason score 4 + 3 = 7, and UBE2V2 for Gleason score 6. (4) Insight: Previous reports show that the genes predicted by this newly proposed method strongly correlate with prostate cancer development and progression. Furthermore, pathway analysis shows that both PIAS3 and UBE2V2 share similar protein interaction pathways, the JAK/STAT signaling process.