Reactive stroma is a tissue feature commonly observed in the tumor microenvironment of prostate cancer and has previously been associated with more aggressive tumors. The aim of this study was to detect differentially expressed genes and metabolites according to reactive stroma content measured on the exact same prostate cancer tissue sample. Reactive stroma was evaluated using histopathology from 108 fresh frozen prostate cancer samples gathered from 43 patients after prostatectomy (Biobank1). A subset of the samples was analyzed both for metabolic (n = 85) and transcriptomic alterations (n = 78) using high resolution magic angle spinning magnetic resonance spectroscopy (HR-MAS MRS) and RNA microarray, respectively. Recurrence-free survival was assessed in patients with clinical follow-up of minimum five years (n = 38) using biochemical recurrence (BCR) as endpoint. Multivariate metabolomics and gene expression analysis compared low (≤15%) against high reactive stroma content (≥16%). High reactive stroma content was associated with BCR in prostate cancer patients even when accounting for the influence of Grade Group (Cox hazard proportional analysis, p = 0.013). In samples with high reactive stroma content, metabolites and genes linked to immune functions and extracellular matrix (ECM) remodeling were significantly upregulated. Future validation of these findings is important to reveal novel biomarkers and drug targets connected to immune mechanisms and ECM in prostate cancer. The fact that high reactive stroma grading is connected to BCR adds further support for the clinical integration of this histopathological evaluation.

BackgroundThe relationship between cholesterol and prostate cancer has been extensively studied for decades, where high levels of cellular cholesterol are generally associated with cancer progression and less favorable outcomes. However, the role of in vivo cellular cholesterol synthesis in this process is unclear, and data on the transcriptional activity of cholesterol synthesis pathway genes in tissue from prostate cancer patients are inconsistent.MethodsA common problem with cancer tissue data from patient cohorts is the presence of heterogeneous tissue which confounds molecular analysis of the samples. In this study we present a general method to minimize systematic confounding from stroma tissue in any prostate cancer cohort comparing prostate cancer and normal samples. In particular we use samples assessed by histopathology to identify genes enriched and depleted in prostate stroma. These genes are then used to assess stroma content in tissue samples from other prostate cancer cohorts where no histopathology is available. Differential expression analysis is performed by comparing cancer and normal samples where the average stroma content has been balanced between the sample groups. In total we analyzed seven patient cohorts with prostate cancer consisting of 1713 prostate cancer and 230 normal tissue samples.ResultsWhen stroma confounding was minimized, differential gene expression analysis over all cohorts showed robust and consistent downregulation of nearly all genes in the cholesterol synthesis pathway. Additional Gene Ontology analysis also identified cholesterol synthesis as the most significantly altered metabolic pathway in prostate cancer at the transcriptional level.ConclusionThe surprising observation that cholesterol synthesis genes are downregulated in prostate cancer is important for our understanding of how prostate cancer cells regulate cholesterol levels in vivo. Moreover, we show that tissue heterogeneity explains the lack of consistency in previous expression analysis of cholesterol synthesis genes in prostate cancer.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4373-y) contains supplementary material, which is available to authorized users.

BackgroundMarine cold-temperature environments are an invaluable source of psychrophilic microbial life for new biodiscoveries. An Arctic marine bacterial strain collection was established consisting of 1448 individual isolates originating from biota, water and sediment samples taken at a various depth in the Barents Sea, North of mainland Norway, with an all year round seawater temperature of 4 °C. The entire collection was subjected to high-throughput screening for detection of extracellular laccase activity with guaiacol as a substrate.ResultsIn total, 13 laccase-positive isolates were identified, all belonging to the Psychrobacter genus. From the most diverse four strains, based on 16S rRNA gene sequence analysis, all originating from the same Botryllus sp. colonial ascidian tunicate sample, genomic DNA was isolated and genome sequenced using a combined approach of whole genome shotgun and 8 kb mate-pair library sequencing on an Illumina MiSeq platform. The genomes were assembled and revealed genome sizes between 3.29 and 3.52 Mbp with an average G + C content of around 42 %, with one to seven plasmids present in the four strains. Bioinformatics based genome mining was performed to describe the metabolic potential of these four strains and to identify gene candidates potentially responsible for the observed laccase-positive phenotype. Up to two different laccase-like multicopper oxidase (LMCO) encoding gene candidates were identified in each of the four strains. Heterologous expression of P11F6-LMCO and P11G5-LMCO2 in Escherichia coli BL21 (DE3) resulted in recombinant proteins exhibiting 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid (ABTS) and guaiacol oxidizing activity.ConclusionsThirteen Psychrobacter species with laccase-positive phenotype were isolated from a collection of Arctic marine bacteria. Four of the isolates were genome sequenced. The overall genome features were similar to other publicly available Psychrobacter genome sequences except for P11G5 harboring seven plasmids. However, there were differences at the pathway level as genes associated with degradation of phenolic compounds, nicotine, phenylalanine, styrene, ethylbenzene, and ethanolamine were detected only in the Psychrobacter strains reported in this study while they were absent among the other publicly available Psychrobacter genomes. In addition, six gene candidates were identified by genome mining and shown to possess T1, T2 and T3 copper binding sites as the main signature of the three-domain laccases. P11F6-LMCO and P11G5-LMCO2 were recombinantly expressed and shown to be active when ABTS and guaiacol were used as substrates.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2445-4) contains supplementary material, which is available to authorized users.

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The relationship between cholesterol and prostate cancer has been extensively studied for decades, where high levels of cellular cholesterol are generally associated with cancer progression and less favorable outcomes. However, the role of in vivo cellular cholesterol synthesis in this process is unclear, and data on the transcriptional activity of cholesterol synthesis pathway genes in tissue from prostate cancer patients are inconsistent. A common problem with cancer tissue data from patient cohorts is the presence of heterogeneous tissue which confounds molecular analysis of the samples. In this study we present a method to minimize systematic confounding from stroma tissue in seven patient cohorts consisting of 1713 prostate cancer and 230 normal tissue samples. When confounding was minimized, differential gene expression analysis over all cohorts showed robust and consistent downregulation of nearly all genes in the cholesterol synthesis pathway. Additional analysis also identified cholesterol synthesis as the most significantly altered metabolic pathway in prostate cancer. This surprising observation is important for our understanding of how prostate cancer cells regulate cholesterol levels in vivo. Moreover, we show that tissue heterogeneity explains the lack of consistency in previous expression analysis of cholesterol synthesis genes in prostate cancer.

Mitochondrial activity in cancer cells has been central to cancer research since Otto Warburg first published his thesis on the topic in 1956. Although Warburg proposed that oxidative phosphorylation in the tricarboxylic acid (TCA) cycle was perturbed in cancer, later research has shown that oxidative phosphorylation is activated in most cancers, including prostate cancer (PCa). However, more detailed knowledge on mitochondrial metabolism and metabolic pathways in cancers is still lacking. In this study we expand our previously developed method for analyzing functional homologous proteins (FunHoP), which can provide a more detailed view of metabolic pathways. FunHoP uses results from differential expression analysis of RNA-Seq data to improve pathway analysis. By adding information on subcellular localization based on experimental data and computational predictions we can use FunHoP to differentiate between mitochondrial and non-mitochondrial processes in cancerous and normal prostate cell lines. Our results show that mitochondrial pathways are upregulated in PCa and that splitting metabolic pathways into mitochondrial and non-mitochondrial counterparts using FunHoP adds to the interpretation of the metabolic properties of PCa cells.

Mitochondrial activity in cancer cells has been central to cancer research since Otto Warburg first published his thesis on the topic in 1956. Although Warburg proposed that oxidative phosphorylation in the tricarboxylic acid (TCA) cycle was perturbed in cancer, later research has shown that oxidative phosphorylation is activated in most cancers, including prostate cancer (PCa). However, more detailed knowledge on mitochondrial metabolism and metabolic pathways in cancers is still lacking. In this study we expand our previously developed method for analyzing functional homologous proteins (FunHoP), which can provide a more detailed view of metabolic pathways. FunHoP uses results from differential expression analysis of RNA-Seq data to improve pathway analysis. By adding information on subcellular localization based on experimental data and computational predictions we can use FunHoP to differentiate between mitochondrial and non-mitochondrial processes in cancerous and normal prostate cell lines. Our results show that mitochondrial pathways are upregulated in PCa and that splitting metabolic pathways into mitochondrial and non-mitochondrial counterparts using FunHoP adds to the interpretation of the metabolic properties of PCa cells.