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

Cholesterol genes were selected from KEGG [26] pathway map for Steroid Biosynthesis and the Mevalonate Pathway in the pathway map for Terpenoid Backbone Biosynthesis. Twenty-five pathway genes were assessed for differential expression, which represent the complete pathways as mapped by KEGG. In addition, four genes from KEGG involved in cholesteryl ester formation and 19 genes from various literature sources involved in cholesterol regulation, uptake efflux and transport, were assessed. The complete list of genes and their main role in cholesterol homeostasis can be found in Table 2.

For expression analysis of genes in the cholesterol pathway we used gene expression measurements from prostate cancer and normal tissue samples from seven publicly available patient cohorts. An overview of data from the seven patient cohorts is given in Table 1. Cancer samples for all cohorts were from radical prostatectomy specimens, except for the Sboner cohort which was from a watchful waiting cohort. Normal samples were adjacent normal prostate tissue from prostatectomy specimens, except for four normal prostate samples in the Chen cohort which were autopsy samples from subjects without prostate cancer. Gene expression measurements from each patient cohort were downloaded and processed in the following way: Gene expression and metadata from Bertilsson were created by our group and processed as previously described [27]. Data are available at Array Express with accession E-MTAB-1041. The best probe for each gene was selected as the one with the highest average rank by p-value in differential expression analysis (average over unstratified, balanced and unbalanced comparison, see below or main text for explanation). Gene expression and metadata from Chen [21, 28] were downloaded from Gene Expression Omnibus (GEO) accession GSE8218. Probes were matched to gene names by the hg133a.db reference using limma in R. The best probe for each gene was selected as the one with the highest average rank by p-value in differential expression analysis. Gene expression and metadata from Taylor [29] were downloaded from GEO accession GSE21034. Probes were matched to genes using the GPL10264 reference available at GEO. Probes with no matching gene were removed from further analysis. The best probe for each gene was selected as the one with the highest rank in a differential expression analysis between prostate cancer and normal samples. In the Taylor dataset, probes from the same gene generally had very similar ranks. Normalized and raw RNA-Seq read counts and gene names from TCGA where downloaded from The Cancer Genome Atlas [https://​cancergenome.​nih.​gov/​], [30]. Normalized read counts were log2-adjusted before further analysis. For the Prensner cohort [31], RNA-Seq raw reads in fastq-format were downloaded with approval from dbGap (project #5870) with accession phs000443.v1.p1. Raw reads were mapped to the hg19 transcriptome using TopHat2 [32], and featureCounts [33] were used to assign the reads mapping to each gene. Normalization of gene counts were performed using the normalization formula from the voom program [34]. Gene expression and metadata from Sboner [35] were downloaded from GEO with accession GSE16560. Probes were matched to gene names using the GPL5474 reference available at GEO. Only four genes in the Sboner cohort had more than one probe. For these genes, the probes with the highest overall expression value were selected as the best probe. Quantile normalized exon expression data and metadata from the Erho cohort [36] were downloaded from GEO with accession GSE46691. Exons identifiers were matched to gene names using the GPL5188 reference available at GEO. The total expression for each gene was calculated as the average expression over all exons for that gene. Differential expression of genes from Bertilsson, Chen and Taylor were identified using the limma package in R as described previously [27], while voom on raw RNA-Seq read counts was used for differential expression of genes from TCGA and Prensner. In total, 1943 samples (1713 prostate cancer and 230 normal) with 25,964 unique gene identifiers were considered over all seven datasets (the seven-study-cohort). Five of the cohorts (Bertilsson, Chen, Taylor, TCGA and Prensner, referred to as the five-study-cohort) contained both prostate cancer and normal samples (in total 1117 samples, 887 cancer and 230 normal). The seven-study-cohort contained 4804 shared genes, and the five-study-cohort contained 9527 shared genes over their respective cohorts. Quality assessment of each cohort was performed by evaluating the Pearson correlations between genes in previously validated gene sets [25, 37] related to ERG-fusion, an established feature of primary prostate cancer [38] (Additional file 1: Figure S1). Samples from the five-study-cohort consistently displayed a higher average ERG-fusion gene correlation in prostate cancer samples compared to normal samples. Cancer samples in the Erho cohort showed a similar average correlation compared to cancer samples in the five-study-cohort, while the Sboner cohort showed weaker average correlation. Altogether six of the cohorts performed well for the quality assessment, while poorer quality was only indicated in the Sboner cohort.

For the stratification of samples into datasets of balanced and unbalanced stroma tissue composition we used a strategy recently developed in our research group [23]. The strategy can be applied to any cohort, as long as assessments of stroma content are available for both cancer and normal samples in the cohort. We will here use the Bertilsson cohort to briefly describe the procedure (Fig. 1). The Bertilsson cohort consists of 116 prostate cancer and 40 normal samples, where each sample has been histopathologically assessed for its tissue composition (%) of stroma, cancer and benign epithelium. We separate both cancer and normal samples according to their stroma content where one group has low stroma content and the other has high stroma content, resulting in 4 groups altogether (58 cancer samples with highest stroma content, 58 cancer samples with lowest stroma content, 20 normal samples with highest stroma content and 20 normal samples with lowest stroma content). The balanced dataset is created by joining the 58 cancer samples with the highest percentages of stroma with the 20 normal samples with the lowest percentages of stroma, to create a dataset with balanced average amounts of stroma in cancer and control samples. In contrast, the unbalanced dataset is created by joining the 58 cancer samples with lowest content of stroma with the 20 normal samples with highest content of stroma, thus maximizing the difference of average stroma content between cancer and normal samples. The balanced dataset represents a comparison between prostate cancer and normal samples where the bias due to increased average stroma content in the normal samples has been minimized. Molecular differences in the balanced dataset are thus directly attributable to differences between cancer and normal tissue. The second dataset represent an unbalanced comparison where molecular differences mostly represent differences between prostate cancer and stroma tissue. Differentially expressed genes are then identified independently for the balanced and unbalanced datasets. The equal number of prostate cancer and normal samples in each dataset ensures a consistent statistical power, meaning that p-values are directly comparable for each gene between the balanced and unbalanced datasets. The number of samples used for balanced and unbalanced analysis in each cohort is provided in Additional file 1: Table S2.

Since the procedure for identification of gene-sets requires histopathology on both prostate cancer and normal samples in the same cohort, only the Bertilsson and Chen could be utilized for this purpose. Both these cohorts include detailed histopathological evaluation on the percentage tissue composition of prostate cancer, benign epithelium and stroma in both prostate cancer and normal samples. Stroma gene-sets were created independently from each of the two cohorts by the exact same procedure. The difference in average tissue composition between the balanced and unbalanced datasets (described in the previous section) facilitates the identification of genes specifically up- or downregulated in stroma compared to benign epithelium and cancer tissue by comparing p-values between the two datasets. Specifically, genes which display up or downregulation characteristic for stroma tissue will have lower p-values in the unbalanced compared to the balanced dataset. We thus used the following formula to rank all genes according to their suitability for creating stroma gene-sets:

The squared term in the denominator was included to reflect that more pronounced differences in p-values are necessary for highly significant genes to be regarded as stroma genes. (Compare a gene with p-value 1e-5 in the unbalanced which is not significant in the balanced dataset, to a gene with p-value 1e-20 in the unbalanced and 1e-15 in the balanced. The former is more likely to be a valid stroma marker than the latter, even though the p-value ratio is the same). The stroma gene-sets included were based on the top 1000 ranked upregulated and top 1000 ranked downregulated genes. To avoid any bias from the cholesterol pathway genes, any genes from Table 2 were removed from all stroma gene-sets during analysis.

The independent stroma gene-sets created from the Bertilsson and Chen cohorts were validated by assessing the percentage of shared genes between the two gene-sets (Fig. 2a). This percentage was compared to the percentage of shared genes expected by chance in 50 randomly generated gene sets of same size. The number of shared genes was also compared in gene sets created using a naïve approach of Pearson correlation to histopathological stroma content, and four previously published gene sets related to the content of stroma in prostate cancer tissue samples from Wang et al. [21] (Fig. 2b).

The assessment of the stroma content in any single sample from any cohort was performed using Gene Set Enrichment Analysis (GSEA) [29]. Two measures will influence the GSEA scores; the number of genes in the applied gene-set, and the total number of genes used for the calculation. We calculated 10 GSEA scores for each sample using varying numbers of the top scoring stroma genes (top 100, 150, 200, 250, 300, 350, 400, 450, 500 and 1000 genes), and normalized the scores in each of the 10 calculations to a 0–100 range over all samples to make them comparable. To enable comparisons between datasets, we only used genes shared by all datasets in each GSEA calculation. Two total gene selections were made, one containing 9527 genes shared by the five-study-cohort, and one with 4804 genes shared for the seven-study-cohort. Averaging over the 10 GSEA scores in each selection produced a total of four GSEA scores for each sample in Bertilsson, Chen, Taylor, TCGA and Prensner (using gene-sets from Bertilsson and Chen for the five-study-cohort and the seven-study-cohort respectively), and two GSEA scores for each sample in Sboner and Erho (Bertilsson and Chen gene-set for the seven-study-cohort). The main reason for the lower number of shared genes in the seven-study-cohort is the relatively few genes measured in Sboner (6100 unique genes). For Bertilsson and Chen, GSEA scores for each sample were converted to predicted stroma percentages using a linear least squares fit, and compared to the stroma percentages obtained from histopathology. Predicted stroma percentages by the fit model based on the Bertilsson and Chen stroma gene-sets respectively in each of the two cohorts were also compared. Finally, GSEA score correlations when using the Bertilsson and the Chen stroma gene-sets were compared for all cohorts.

The GSEA scores representing the content of stroma in each sample were used to separate samples in each new patient cohort into balanced and unbalanced datasets (as described in the section “Stratification of a cohort into balanced and unbalanced datasets” above). This was done independently for each patient cohort. Differentially expressed genes for each cohort were calculated and corrected for multiple testing by Benjamini Hochberg false discovery rate (FDR) separately in each cohort, based on the total number of analyzed genes in each cohort. For the Sboner and Erho cohorts, only the cancer samples were separated into datasets with high and low stroma content, and no differential analysis was performed.

To identify differentially expressed genes in a meta-analysis over the five-study-cohort the following procedure was used: 1) Each gene was sorted according to its expression value over all samples independently in each cohort, and rank-normalized to a score-value between 0 and 100, where 0 is the rank based expression value for the sample with the lowest expression value of the gene, and 100 is rank-based expression value the sample with the highest expression. 2) Rank-normalized values were mean centered independently in each cohort, where the mean centering was weighted by the relative number of prostate cancer and normal samples in the cohort. This was to avoid mean-value biases due to the huge relative difference between cancer and normal samples in each cohort. 3) Samples were separated into one balanced and one unbalanced meta-dataset using their previous assignment to balanced and unbalanced datasets in each individual cohort. Differential analysis based on two classifications were performed, one based on the stroma gene-set from Bertilsson and one on the stroma gene-set from Chen. 4) Differentially expressed genes for the unstratified, balanced and unbalanced datasets were calculated for weighted mean centered rank-normalized values between prostate cancer and normal samples combined for all five cohorts using the Mann-Whitney-Wilcoxon test [39] for rank-based differential expression. P-values of differentially expressed genes were corrected for multiple testing using the Benjamini-Hochberg FDR for the total number of genes analyzed (25,964 unique gene identifiers). If a gene was not present in all datasets, only the datasets that contained that gene were used for differential expression. The seven-study cohort was analyzed in the same way, but with mean centering rather than weighted mean centering used to adjust gene-ranks between cohorts. This was due to the lack of normal samples in the Sboner and Erho cohorts.

The top 500 and top 1000 differentially expressed genes from the rank-based differential expression analysis based on the both the Bertilsson and Chen gene-sets (four lists of genes in total) were subjected independently to DAVID [40] for gene ontology analysis.

We used seven publicly available cohorts of tissue samples from patients with prostate cancer (Bertilsson, Chen, Taylor, TCGA, Prensner, Sboner and Erho, referred to as the seven-study-cohort; N = 1943 samples, 1713 prostate cancer and 230 normal, Table 1). Gene expression measurements in the various cohorts had been generated using different microarray platforms and RNA-sequencing. Of these seven cohorts, two cohorts (Bertilsson and Chen, referred to as the histopathology cohorts, Table 1) contained detailed histopathology on prostate cancer, stroma and benign epithelium in each sample. These two cohorts were used as a basis for stromal assessment in all seven cohorts. A flow-chart of the different steps in this assessment is provided in Fig. 1, and a detailed description of each step is provided in the Methods section. Of the seven cohorts, five cohorts contained measurements of both prostate cancer and samples characterized as normal (Bertilsson, Chen, Taylor, TCGA and Prensner, referred to as the five-study-cohort; 1117 samples, 887 prostate cancer and 230 normal).

A key concept in this study is to utilize GSEA [25] to assess the stroma content in each tissue sample from the seven patient cohorts, and then use this assessment to divide samples into sub-datasets where the confounding effect of stroma tissue is accounted for [23]. To achieve this, we first identified genes which were up- or downregulated with respect to the content of stroma tissue in the two histopathology cohorts (Methods). The top ranked genes from this analysis were collected into gene-sets, which were used for GSEA-based assessment of stroma in samples from all seven cohorts. Robustness of stroma assessments was assured by assessing each sample independently by the two stroma gene-sets identified from the two histopathology cohorts. Although the two stroma gene-sets used were generated from independent cohorts using different microarray platforms, the identified stroma related genes showed 44% overlap for the top 2000 stroma associated genes, compared to 6% for random genes (Fig. 2a). A comparison with four previously published prostate stroma gene lists [21], showed an average overlap of 62%, compared to 8% for random genes (Fig. 2b). Identified stroma genes were robust to two different methods for gene selection, with an average of 73% overlap (Additional file 1: Figure S2).

When varying the size of the stroma gene-sets, as well as the total number of genes used for the GSEA assessment, we observed an average deviation in predicted stroma content of only ~ 1% (Additional file 1: Table S1). This shows that individual genes had minimal influence on the stroma assessments. In the two histopathology cohorts, the predicted stroma percentage from GSEA showed a mean deviation from histopathology between 10% and 11%, (r = 0.77 and r = 0.78), respectively (Fig. 2c). These measurements are in agreement with previously published comparisons between histopathology and gene based stroma predictions in prostate cancer [21, 41]. In both of the histopathology cohorts, the predicted percentages of stroma were highly correlated for the two gene-sets (r = 0.98) (Fig. 2c), and estimated GSEA scores in the additional five cohorts were also highly correlated (r between 0.95 and 0.99) (Additional file 1: Fig S3). GSEA scores for all cohorts were also robust with respect to the size of the stroma gene-sets, with an average standard deviation for 0–100 normalized GSEA scores of ~ 2 (Additional file 1: Table S1). Variations in GSEA scores were not dependent on the platform used for gene expression analysis. As further support for the validity of the stroma gene-sets, samples from several prostate cancer cell-lines included in the Taylor and Prensner cohorts, were consistently at the low end of stroma content when estimated by GSEA assessment. Overall, we conclude that the gene-sets give a stable, robust and reproducible representation of the stroma content in prostate cancer and normal tissue samples in each cohort. However, we observed a prominent baseline difference in the average GSEA scores between the different cohorts (Fig. 2d). This means that an absolute prediction of stroma percentage for each sample which can be compared between cohorts cannot be made, but that relative stroma assessment between samples within the same cohort is feasible and robust.

We used our previously published strategy [23] (Methods) to create stroma balanced and a stroma unbalanced datasets in each of the five cohorts with both cancer and normal samples. The balanced and unbalanced datasets from the same cohort are designed to have the same number of cancer and normal samples, making p-values from differential gene expression analysis directly comparable between the two datasets. (Additional file 1: Table S2). In the balanced dataset samples are selected such that prostate cancer and normal samples have a similar average stroma content, minimizing the tissue confounding introduced by stroma tissue in a conventional unstratified analysis using all samples. Differential analysis in the balanced dataset is thus designed to highlight changes between prostate cancer and normal tissue. In contrast, samples in the unbalanced dataset are selected to maximize the difference in stroma content between cancer and normal samples. In this setting, differentially expressed genes in prostate cancer compared to stroma will be highlighted. For a single gene, comparing results from the balanced and unbalanced datasets can reveal whether a significant differential expression truly results from changes between the normal tissue and cancer, or is due to a difference in the average stroma content between the sample groups.

To create balanced and unbalanced datasets, no absolute estimation of stroma percentage in each sample is necessary. A relative stroma assessment is sufficient, which is here exemplified by GSEA using the two independent stroma gene-sets. This ensures that samples in the same cohort can be sorted according to their stroma content, which enables balanced and unbalanced analysis in cohorts where histopathology is not available.

We used the two stroma gene-sets identified independently from the histopathology cohorts (Bertilsson and Chen) to calculate GSEA scores for all 1943 samples (1713 cancer and 230 normal) from all the seven patient cohorts. In the five-study cohort containing 1117 samples (887 cancer and 230 normal), the calculated GSEA scores showed a systematic bias of increased average stroma content in normal samples (Fig. 2d), which should support the separation of each cohort into balanced and unbalanced datasets. Balanced and unbalanced datasets were therefore created independently using the two available stroma gene-set, resulting in two independent balanced and unbalanced datasets for each cohort. This stratification equalized the average stroma content in the balanced datasets, and enhanced the difference in average stroma content in the unbalanced datasets (Fig. 2d). Differential expression analyses were performed independently for each dataset, and differentially expressed genes were ranked in each dataset according to their p-value. In addition, combined rank-based meta-analysis over the five-study-cohorts and seven-study-cohorts were performed (Methods). The balanced and unbalanced datasets from the two meta-cohorts contained 558/115 and 971/115 prostate cancer/normal samples each, respectively.

Consistent and highly significant downregulation of genes in the cholesterol synthesis pathway between cancer and normal samples was a prominent feature in the balanced analysis of gene expression (Fig. 3a, Table 2, Additional file 1: Figure S4). In a meta-analysis of the five-study-cohort, 21 of the 25 genes assessed were downregulated. These included key genes of cholesterol synthesis such as HMGCR and SQLE (rate limiting enzymes), FDFT1, LSS (catalyzes first step), DHCR7 (catalyzes last step) in addition to NSDHL, MDMO1, EBP, IDI1, CYP51A1, HMGCS1 and SC5D. All these genes had p-values to the power of − 10 or less. The same trend was observed in a meta-analysis over the seven-study-cohort (Additional file 1: Figure S4). In addition, four cohorts in the five-study-cohort individually showed highly significant downregulation of cholesterol genes, though the most highlighted genes varied somewhat between the cohorts (Additional file 1: Figure S4). In the five-study-cohort, 10 central cholesterol genes (HMGCS1, HMGCR, IDI1, FDFT1, SQLE, CYP51A1, MSMO1, NSDHL, EBP and SC5D) ranked among the top 150 most differentially expressed genes in the balanced dataset (average rank of 76) (Additional file 2). This is in contrast to the unstratified and unbalanced datasets, where the average ranks of the same ten genes were 9195 and 14,860, respectively. The unbalanced analysis also shows that upregulation of cholesterol genes is mostly due to differences between cancer tissue and stroma (Fig. 3a). Cholesterol synthesis was a highly important gene ontology term in the balanced dataset, and a clustered set of related terms containing steroid, sterol and cholesterol biosynthesis were among the top three most significant gene ontologies when the 500 most significant genes from the five-study-cohort were analyzed by DAVID (Table 3). In summary, the balanced data prove a characteristic transcriptional downregulation of the cholesterol synthesis pathway in primary prostate cancer. All p-values presented in this and the following sections, as well as Figs. 3 and 4, are conservatively corrected for multiple testing using the total number of 25,964 unique gene identifiers from all cohorts.

The pronounced discrepancies between the balanced and unbalanced datasets serve as an illustration of how cholesterol pathway genes are confounded by stroma tissue during differential analysis. Using HMGCR (the rate-limiting enzyme of cholesterol synthesis) as an example, this gene is strongly significant in both datasets. However, it is downregulated when cancer is compared to normal epithelium in the balanced dataset, and upregulated when cancer is compared to stroma in the unbalanced dataset. This typical pattern occurs when a gene highly expressed in the normal epithelium has an intermediate expression in cancer and is weakly expressed in stroma. Significant expression differences in these situations can only be revealed when the confounding effects of stroma is accounted for. Since this pattern is prevalent throughout the entire cholesterol pathway, we hypothesize that stroma confounding is the main reason that this pathway has not been identified in previous analysis of prostate cancer patient cohorts. The only cohort that did not highlight cholesterol synthesis was the cohort from Chen, which showed a consistent absence of significant cholesterol genes in the balanced dataset (Additional file 1: Figure S4). However, the cholesterol gene expression pattern from the unbalanced dataset in Chen was similar to the other cohorts.

It is important to establish that gene-sets representing stroma content do not impose unwanted biases with respect to the differential expression of cholesterol genes in additional cohorts. Here we present three arguments why this is unlikely for the cholesterol pathway genes in this study. 1) The stroma gene-sets were generated from two independent sources, but produced similar and stable results. 2) Cholesterol genes were either absent or ranked low in the stroma gene-sets. Nevertheless, all genes involved in the cholesterol pathway and regulation were excluded from any stroma gene-set during analysis to ensure unbiased sample stratification. Moreover, re-introduction of these cholesterol genes into the stroma gene-sets did not affect the stratification of samples into balanced and unbalanced datasets in any of the seven cohorts, showing that cholesterol-genes had no impact on the sample stratification. 3) The histopathology cohort from Chen was the only cohort that did not highlight cholesterol pathway genes as significant in the balanced dataset. Yet, all the balanced datasets in the other six cohorts still highlighted cholesterol genes as highly significant when the stroma gene-set derived from the Chen was used to balance the samples. Likewise, cholesterol pathway genes were not highlighted as significant in the balanced dataset from Chen when the stroma gene-set from Bertilsson was used to balance the samples in this cohort. This shows that both the Chen and the Bertilsson stroma gene-sets maintained the divergent balanced expression patterns for cholesterol genes when used in these two cohorts.

We also investigated two additional studies from the literature which could complement the findings in our study. One study emphasized cholesterol biosynthesis as a significant pathway in prostate tissue samples using gene ontology analysis [42]. After analysis of the supplementary data material, genes in the cholesterol pathway showed negative cancer-to-normal fold-changes in that study (Additional file 3). The second study consisted of 50 samples (36 prostate cancer and 14 normal) [43] collected using laser micro dissected tissue to avoid contamination from the stroma. Cancer-to-normal fold changes were negative for all key cholesterol genes in this study as well (Additional file 3). Thus the data in both these studies support the findings in our study.

Given the positive association between cholesterol and prostate cancer incidence, and the positive effect of statins on patient outcome, a consistent transcriptional downregulation of the cholesterol synthesis pathway in prostate cancer is a surprising observation. Although studies in prostate cancer cell-lines have demonstrated a role for cholesterol synthesis in tumor growth and aggressiveness, we have, after an extensive literature search, yet to see solid evidence for in vivo transcriptional upregulation of cholesterol synthesis in prostate cancer compared to normal tissue. Based on our results, we thus speculate how our observations may fit into the established mechanism of cholesterol metabolism for prostate cancer and for cells in general.

The regulation of cellular cholesterol levels is a highly complex and dynamic system, involving multiple feedback mechanisms (Fig. 4b), where downregulation of cellular cholesterol synthesis is not necessarily contradictory to other observations. Cholesterol homeostasis in the cell is controlled by cholesterol synthesis, transport and storage, but the true in vivo balance between these sources has yet to be elucidated. The most established enzymes related to cholesterol homeostasis are HMGCR and LDLR [8]. HMGCR is the rate limiting enzyme for the cholesterol synthesis pathway in the cell, while LDLR controls the uptake of cholesterol from circulating Low Density Lipoprotein (LDL). In addition, the cell can store excess cellular cholesterol in prostasomes [4] or by cholesteryl esterification in lipid droplets [9]. Increased availability of cholesterol from the environment may allow cells to shift their source of cholesterol from synthesis to uptake. Since cholesterol synthesis is energetically expensive [44], this shift can be beneficial for the cancer cell to save energy, and a recent study in prostate cancer cell-lines showed that environmental cholesterol can supplement cellular cholesterol levels as a response to cholesterol synthesis inhibition [45]. Thus molecular precursors for cholesterol in the cell can be used in other pathways important for cancer growth. The shift may also prevent the anti-tumor activity of side products in the cholesterol pathway like oxysterols and isoprenoids, though the in vivo relevance for this mechanism is debated [45‐48]. Additionally, the shift can provide an explanation why statins have a beneficial effect on prostate cancer patients. Statins mostly target cholesterol synthesis in the liver leading to reduced circulating levels of cholesterol [4]. This may limit the cholesterol available for cellular uptake, with activation of the cholesterol synthesis pathway and delayed cancer growth as a result. What contradicts this hypothesis is that not only HMGCR is downregulated in prostate cancer, but also LDLR. However, mechanisms alternative to LDLR for cholesterol and sterol uptake and efflux have been suggested, including changed activity of SLCO transporters [49] (for example SLCO2B1 is strongly upregulated in the five-study-cohort, Fig. 4a) and modulation to cell-membrane structures like lipid rafts [3, 7, 50]. Recently, cholesteryl esters in lipid droplets in prostate cancer PC3 cells were shown to originate from uptake rather than synthesis [51], supporting an increased attention to the role of cholesterol uptake in prostate cancer. Alternatively, statins may upregulate HMGCR in prostate cancer directly through feedback mechanisms [52], again with a possible cancer-preventive effect. Finally, increased HMGCR protein levels have recently been shown to correlate with improved clinical outcome in breast [53], colorectal [54] and ovarian [55] cancer. This may indicate that upregulation of the cholesterol pathway is a benign tumor characteristic, which is in line with the results presented here.

At the transcriptional level, HMGCR and LDLR mRNA are regulated, in particular by SREBF2, and partly by SREBF1 transcription factors, which also regulates most of the enzymes in the cholesterol pathway [4, 44, 56] (Fig. 4b). SREBF is located on the membrane of the endoplasmic reticulum together with its cofactor SCAP. SCAP has a sterol-sensing domain, which activates SREBF-SCAP transport to the Golgi when sterol levels are low (Fig. 4b). In the Golgi, SREBF-SCAP is enzymatically cleaved twice, which creates the nuclear active form of SREBF1/2. In the balanced dataset, SREBF2 is downregulated while SCAP is upregulated (Fig. 4). However, any potential increase in SREBF transport to the Golgi by SCAP is again counterbalanced by downregulation of both cleaving enzymes MBTPS1 and MBTPS2. Thus the effect of SCAP upregulation on transcriptional activity is difficult to assess. HMGCR is also regulated at the translational level and at the level of degradation. We observe a strong downregulation of several key genes involved in HMGCR degradation [56], INSIG1, INSIG2 and AMFR (Fig. 4). Especially INSIG1 is one of the most highly ranked genes differentially expressed in the balanced dataset (average rank 9). This suggests that some HMGCR activity can be maintained through downregulation of INSIG1, and that targeting HMGCR degradation can be an interesting option for modulating cholesterol levels in prostate cancer. Studies in model systems will be necessary to assess the combined effect of decreased transcription on one hand and decreased degradation on the other hand. The mechanisms of translational regulation of HMGCR are not well known, but may involve feedback regulation from side-products of the cholesterol pathway [46].

Another pair of transcription factors implicated in negative regulation of cholesterol is the liver-X-receptors NR1H3 and NR1H2 (also called LXRA and LXRB) (Fig. 4b), which dimerize with RXRA and RXRB to exert their regulatory activity [57]. NR1H3 is upregulated in the balanced analysis (Fig. 4), while its dimerization partner RXRA is strongly downregulated. We observe an upregulation of NR1H3 targets, including the cholesterol efflux genes ABCA1 and ABCG1, the LDLR suppressor MYLIP and a very strong upregulation of APOE (ranked highest among all genes in the balanced dataset). APOE can be an important constituent of High Density Lipoprotein (HDL) particles, where formation partly depends on the export by ABCA1 and ABCG1. However, here our results are in disagreement with other in vivo reports, which associates low levels of ABCA1 and low levels of circulating HDL with prostate cancer [58, 59]. We finally emphasize that the discussion on how our data relate cholesterol metabolism and homeostasis based on transcriptional data are circumstantial, and that more detailed analysis at protein levels in proper model system is necessary to elucidate these mechanisms further.

A recent review reports that statins are ingested regularly by 25% of adults aged 45 years and older in the USA [17]. It is thus a possibility that statin use among the patients may have influenced the molecular makeup of the tumor at the time of surgery. We were not able to obtain sufficient data to conclude on this issue. Nevertheless, we here discuss the limited data and information we were able to find. Information on statin use prior to surgery were available only for the Bertilsson cohort, were a total of 26 samples (18 cancer and 8 normal) were affected. Re-analysis of the Bertilsson cohort did not change the pattern of consistent downregulation of cholesterol pathway genes (Additional file 1: Figure S6). There is one report on the in vivo effect of statin on HMGCR levels in breast cancer [52]. This report demonstrated that statins do not necessarily downregulate HMGCR, and that the effect of statin use was highly heterogeneous among patients. Currently we find it unlikely that statin use has a major impact on the highly significant and consistent results observed in our study, though we acknowledge that the information we have on this issue is too limited to conclude.

In this study we have used a simplistic tissue classification which divide prostate cancer tissue into three tissue types; cancer, stroma and normal epithelium. However, this classification does not completely account for all tissue characteristics observed in prostate cancer, which can be heterogeneous with respect to all three tissue types. Cancer tissue from the prostate can be further classified into histological grades by Gleason score [60]. Gleason grading of samples was provided for six of the seven cohorts, and did not show any bias with respect to balanced and unbalanced dataset (Additional file 1: Table S2). We thus conclude that Gleason grade is not a confounding factor in our analysis. Several studies have shown that normal stroma can transform into reactive stroma when located adjacent to cancer tissue [61]. Thus the balanced analysis may also highlight genes resulting from differences between reactive and normal stroma. The strength of these differences will depend on the fraction of reactive stroma compared to normal stroma in the cancer samples. Histopathological differences between normal and reactive stroma were not assessed in the cohorts used in this study, and thus represents a limitation. Finally, normal epithelium from the prostate can display various precancerous aberrations with distinct molecular profiles [62]. We acknowledge that these are limitations of the current classification, and that further research and data generation in this field should focus on delineating additional molecular tissue profiles as well.

Finally, we should emphasize that the transcriptional changes presented in this study are not directly indicative of changes at the protein level [63]. Additional protein-level experiments using immunohistochemistry or mass spectrometry would be necessary to investigate whether concordant protein changes are also present in prostate cancer. Nevertheless, it has been shown that transcriptional change is the most important mode of HMGCR and LDLR regulation, and that correlations between HMGCR and LDLR mRNA and protein level are comparable to correlatons between mRNA and protein levels in general [64, 65].