Cholesterol synthesis pathway genes in prostate cancer are transcriptionally downregulated when tissue confounding is minimized

Molecular basis of neurodegeneration in a mouse model of Polr3-related disease

Pathogenic variants in subunits of RNA polymerase (Pol) III cause a spectrum of Polr3-related neurodegenerative diseases including 4H leukodystrophy. Disease onset occurs from infancy to early adulthood and is associated with a variable range and severity of neurological and non-neurological features. The molecular basis of Polr3-related disease pathogenesis is unknown. We developed a postnatal whole-body mouse model expressing pathogenic Polr3a mutations to examine the molecular mechanisms by which reduced Pol III transcription results primarily in central nervous system phenotypes. Polr3a mutant mice exhibit behavioral deficits, cerebral pathology and exocrine pancreatic atrophy. Transcriptome and immunohistochemistry analyses of cerebra during disease progression show a reduction in most Pol III transcripts, induction of innate immune and integrated stress responses and cell-type-specific gene expression changes reflecting neuron and oligodendrocyte loss and microglial activation. Earlier in the disease when integrated stress and innate immune responses are minimally induced, mature tRNA sequencing revealed a global reduction in tRNA levels and an altered tRNA profile but no changes in other Pol III transcripts. Thus, changes in the size and/or composition of the tRNA pool have a causal role in disease initiation. Our findings reveal different tissue- and brain region-specific sensitivities to a defect in Pol III transcription.

High expression of RUNX1 in colorectal cancer subtype accelerates malignancy by inhibiting HMGCR

Colorectal cancer (CRC) presents a complex landscape, characterized by both inter-tumor and intra-tumor heterogeneity. RUNX1, a gene implicated in modulating tumor cell growth, survival, and differentiation, remains incompletely understood regarding its impact on CRC prognosis. In our investigation, we discerned a positive correlation between elevated RUNX1 expression and aggressive phenotypes across various CRC subtypes. Notably, knockdown of RUNX1 demonstrated efficacy in restraining CRC proliferation both in vitro and in vivo, primarily through inducing apoptosis and impeding cell proliferation. Mechanistically, we unveiled a direct regulatory link between RUNX1 and cholesterol synthesis, mediated by its control over HMGCR expression. Knockdown of RUNX1 in CRC cells triggered HMGCR transcriptional activation, culminating in elevated cholesterol levels that subsequently hindered cancer progression. Clinically, heightened RUNX1 expression emerged as a prognostic marker for adverse outcomes in CRC patients. Our findings underscore the pivotal involvement of RUNX1 in CRC advancement and its potential as a therapeutic target. The unique influence of RUNX1 on cholesterol synthesis and HMGCR transcriptional regulation uncovers a novel pathway contributing to CRC progression.

Combining Molecular Subtypes with Multivariable Clinical Models Has the Potential to Improve Prediction of Treatment Outcomes in Prostate Cancer at Diagnosis

Clinical management of prostate cancer is challenging because of its highly variable natural history and so there is a need for improved predictors of outcome in non-metastatic men at the time of diagnosis. In this study we calculated the model score from the leading clinical multivariable model, PREDICT prostate, and the poor prognosis DESNT molecular subtype, in a combined expression and clinical dataset that were taken from malignant tissue at prostatectomy (n = 359). Both PREDICT score (p < 0.0001, IQR HR = 1.59) and DESNT score (p < 0.0001, IQR HR = 2.08) were significant predictors for time to biochemical recurrence. A joint model combining the continuous PREDICT and DESNT score (p < 0.0001, IQR HR = 1.53 and 1.79, respectively) produced a significantly improved predictor than either model alone (p < 0.001). An increased probability of mortality after diagnosis, as estimated by PREDICT, was characterised by upregulation of cell-cycle related pathways and the downregulation of metabolism and cholesterol biosynthesis. The DESNT molecular subtype has distinct biological characteristics to those associated with the PREDICT model. We conclude that the inclusion of biological information alongside current clinical prognostic tools has the potential to improve the ability to choose the optimal treatment pathway for a patient.

The genes controlling normal function of citrate and spermine secretion are lost in aggressive prostate cancer and prostate model systems

High secretion of the metabolites citrate and spermine is a unique hallmark for normal prostate epithelial cells, and is reduced in aggressive prostate cancer. However, the identity of the genes controlling this biological process is mostly unknown. In this study, we have created a gene signature of 150 genes connected to citrate and spermine secretion in the prostate. We have computationally integrated metabolic measurements with multiple transcriptomics datasets from the public domain, including 3826 tissue samples from prostate and prostate cancer. The accuracy of the signature is validated by its unique enrichment in prostate samples and prostate epithelial tissue compartments. The signature highlights genes AZGP1, ANPEP and metallothioneins with zinc-binding properties not previously studied in the prostate, and the expression of these genes are reduced in more aggressive cancer lesions. However, the absence of signature enrichment in common prostate model systems can make it challenging to study these genes mechanistically.

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Novel 150 gene signature reflecting prostatic citrate and spermine secretion

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Identified several zinc-binding proteins not previously investigated in the prostate

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The signature is absent in prostate model systems

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.

Prostate Cancer-Focus on Cholesterol

Prostate cancer (PC) is the most common malignancy in men. Common characteristic involved in PC pathogenesis are disturbed lipid metabolism and abnormal cholesterol accumulation. Cholesterol can be further utilized for membrane or hormone synthesis while cholesterol biosynthesis intermediates are important for oncogene membrane anchoring, nucleotide synthesis and mitochondrial electron transport. Since cholesterol and its biosynthesis intermediates influence numerous cellular processes, in this review we have described cholesterol homeostasis in a normal cell. Additionally, we have illustrated how commonly deregulated signaling pathways in PC (PI3K/AKT/MTOR, MAPK, AR and p53) are linked with cholesterol homeostasis regulation.

HMGCR inhibition stabilizes the glycolytic enzyme PKM2 to support the growth of renal cell carcinoma

Renal cell carcinoma (RCC) is responsible for most cases of the kidney cancer. Previous research showed that low serum levels of cholesterol level positively correlate with poorer RCC-specific survival outcomes. However, the underlying mechanisms and functional significance of the role of cholesterol in the development of RCC remain obscure. 3-Hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) plays a pivotal role in RCC development as it is the key rate-limiting enzyme of the cholesterol biosynthetic pathway. In this study, we demonstrated that the inhibition of HMGCR could accelerate the development of RCC tumors by lactate accumulation and angiogenesis in animal models. We identified that the inhibition of HMGCR led to an increase in glycolysis via the regulated HSP90 expression levels, thus maintaining the levels of a glycolysis rate-limiting enzyme, pyruvate kinase M2 (PKM2). Based on these findings, we reversed the HMGCR inhibition-induced tumor growth acceleration in RCC xenograft mice by suppressing glycolysis. Furthermore, the coadministration of Shikonin, a potent PKM2 inhibitor, reverted the tumor development induced by the HMGCR signaling pathway.

Using the Method of "Optical Biopsy" of Prostatic Tissue to Diagnose Prostate Cancer

Simple Summary

Analytical discrimination models of Raman spectra of prostate cancer tissue were constructed by using the projections onto latent structures data analysis (PLS-DA) method for different wavelengths of exciting radiation—532 and 785 nm. These models allowed us to divide the Raman spectra of prostate cancer and the spectra of hyperplasia sites for validation datasets with the accuracy of 70–80%, depending on the specificity value. Meanwhile, for the calibration datasets, the accuracy values reached 100% for the excitation of a laser with a wavelength of 785 nm. Due to the registration of Raman “fingerprints”, the main features of cellular metabolism occurring in the tissue of a malignant prostate tumor were confirmed, namely the absence of aerobic glycolysis, over-expression of markers, and a strong increase in the concentration of cholesterol and its esters, as well as fatty acids and glutamic acid.

Abstract

The possibilities of using optical spectroscopy methods in the differential diagnosis of prostate cancer were investigated. Analytical discrimination models of Raman spectra of prostate tissue were constructed by using the projections onto latent structures data analysis(PLS-DA) method for different wavelengths of exciting radiation—532 and 785 nm. These models allowed us to divide the Raman spectra of prostate cancer and the spectra of hyperplasia sites for validation datasets with the accuracy of 70–80%, depending on the specificity value. Meanwhile, for the calibration datasets, the accuracy values reached 100% for the excitation of a laser with a wavelength of 785 nm. Due to the registration of Raman “fingerprints”, the main features of cellular metabolism occurring in the tissue of a malignant prostate tumor were confirmed, namely the absence of aerobic glycolysis, over-expression of markers (FASN, SREBP1, stearoyl-CoA desaturase, etc.), and a strong increase in the concentration of cholesterol and its esters, as well as fatty acids and glutamic acid. The presence of an ensemble of Raman peaks with increased intensity, inherent in fatty acid, beta-glucose, glutamic acid, and cholesterol, is a fundamental factor for the identification of prostate cancer.

Genetic variants in the cholesterol biosynthesis pathway genes and risk of prostate cancer

Previous studies have found the relationship between cholesterol biosynthesis pathway genes and the risk or prognosis of prostate cancer (PCa), while there is no definite evidence that genetic variants in the cholesterol biosynthesis pathway gene is related to PCa risk. Consequently, we performed this study to explore the associations of single-nucleotide polymorphisms (SNPs) in the cholesterol biosynthesis pathway with PCa risk. We systematically evaluated the association of SNPs in 21 cholesterol biosynthesis pathway genes with the risk of PCa using the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial database using a logistic regression model. Gene expression data of PCa from Gene Expression Omnibus (GEO) datasets and the Cancer Genome Atlas (TCGA) database were applied for mRNA expression analysis. The TCGA database was used to perform expression quantitative trait loci (eQTL) analysis. The interaction between demographic factors and SNPs was analyzed using two-by-four tables. We found T allele of rs67415672 in HMGCS1 is a significant protective allele of PCa [adjusted odds ratio (OR) = 0.90, 95% confidence interval (CI) = 0.83-0.97, P = 4.16 × 10^-3]. Moreover, rs67415672 was an eQTL for HMGCS1 (P = 2.23 × 10^-6). The expression of HMGCS1 significantly decreased in PCa primary tumors than that in normal tissues. These findings indicated that the HMGCS1 rs67415672 might be possible functional susceptibility loci for PCa.

Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities

Spatial heterogeneity is a fundamental feature of the tumor microenvironment (TME), and tackling spatial heterogeneity in neoplastic metabolic aberrations is critical for tumor treatment. Genome-scale metabolic network models have been used successfully to simulate cancer metabolic networks. However, most models use bulk gene expression data of entire tumor biopsies, ignoring spatial heterogeneity in the TME. To account for spatial heterogeneity, we performed spatially-resolved metabolic network modeling of the prostate cancer microenvironment. We discovered novel malignant-cell-specific metabolic vulnerabilities targetable by small molecule compounds. We predicted that inhibiting the fatty acid desaturase SCD1 may selectively kill cancer cells based on our discovery of spatial separation of fatty acid synthesis and desaturation. We also uncovered higher prostaglandin metabolic gene expression in the tumor, relative to the surrounding tissue. Therefore, we predicted that inhibiting the prostaglandin transporter SLCO2A1 may selectively kill cancer cells. Importantly, SCD1 and SLCO2A1 have been previously shown to be potently and selectively inhibited by compounds such as CAY10566 and suramin, respectively. We also uncovered cancer-selective metabolic liabilities in central carbon, amino acid, and lipid metabolism. Our novel cancer-specific predictions provide new opportunities to develop selective drug targets for prostate cancer and other cancers where spatial transcriptomics datasets are available.

DNA hypermethylation associated with upregulated gene expression in prostate cancer demonstrates the diversity of epigenetic regulation

Background

Prostate cancer (PCa) has the highest incidence rates of cancers in men in western countries. Unlike several other types of cancer, PCa has few genetic drivers, which has led researchers to look for additional epigenetic and transcriptomic contributors to PCa development and progression. Especially datasets on DNA methylation, the most commonly studied epigenetic marker, have recently been measured and analysed in several PCa patient cohorts. DNA methylation is most commonly associated with downregulation of gene expression. However, positive associations of DNA methylation to gene expression have also been reported, suggesting a more diverse mechanism of epigenetic regulation. Such additional complexity could have important implications for understanding prostate cancer development but has not been studied at a genome-wide scale.

Results

In this study, we have compared three sets of genome-wide single-site DNA methylation data from 870 PCa and normal tissue samples with multi-cohort gene expression data from 1117 samples, including 532 samples where DNA methylation and gene expression have been measured on the exact same samples. Genes were classified according to their corresponding methylation and expression profiles. A large group of hypermethylated genes was robustly associated with increased gene expression (UPUP group) in all three methylation datasets. These genes demonstrated distinct patterns of correlation between DNA methylation and gene expression compared to the genes showing the canonical negative association between methylation and expression (UPDOWN group). This indicates a more diversified role of DNA methylation in regulating gene expression than previously appreciated. Moreover, UPUP and UPDOWN genes were associated with different compartments — UPUP genes were related to the structures in nucleus, while UPDOWN genes were linked to extracellular features.

Conclusion

We identified a robust association between hypermethylation and upregulation of gene expression when comparing samples from prostate cancer and normal tissue. These results challenge the classical view where DNA methylation is always associated with suppression of gene expression, which underlines the importance of considering corresponding expression data when assessing the downstream regulatory effect of DNA methylation.

Consulting prostate cancer cohort data uncovers transcriptional control: Regulation of the MARCH6 gene

Cholesterol accumulation is a hallmark of prostate cancer (PCa) enabled by the upregulation of its synthesis, which presents a potential therapeutic target. This pathway is suppressed by the E3 ubiquitin ligase membrane-associated RING-CH-type finger 6 (MARCH6); however, little is known of MARCH6 regulation, particularly at the transcriptional level. Here, we consulted large transcriptomic PCa datasets to investigate transcription factors and DNA sequence elements that regulate the MARCH6 gene. Amongst 498 primary PCa tissues of The Cancer Genome Atlas, we identified a striking positive correlation between MARCH6 and androgen receptor (AR) gene expression (r = 0.81, p < 1 × 10-117) that held in other primary tumour datasets. Two putative androgen response elements were identified in the MARCH6 gene using motif prediction and mining of publicly accessible chromatin immunoprecipitation-sequencing data. However, MARCH6 expression was not androgen-responsive in luciferase reporter and qRT-PCR assays. Instead, we established that the MARCH6-AR correlation in primary PCa is due to common regulation by the transcription factor Sp1. We located a region 100 bp downstream of the MARCH6 transcriptional start site that contains three Sp1 binding sites and strongly upregulates promoter activity. The functionality of this region, and Sp1-mediated upregulation of MARCH6, was confirmed using pharmacological and genetic inhibition of Sp1. Moreover, modulation of Sp1 activity affected the stability of squalene monooxygenase, a cholesterol biosynthesis enzyme and MARCH6 substrate. We thus establish Sp1 as the first known regulator of the MARCH6 gene and demonstrate that interrogation of transcriptomic datasets can assist in the de novo inference of transcriptional regulation.