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01.12.2018 | Research article | Ausgabe 1/2018 Open Access

BMC Cancer 1/2018

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

Zeitschrift:
BMC Cancer > Ausgabe 1/2018
Autoren:
Morten Beck Rye, Helena Bertilsson, Maria K. Andersen, Kjersti Rise, Tone F. Bathen, Finn Drabløs, May-Britt Tessem
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12885-018-4373-y) contains supplementary material, which is available to authorized users.

Abstract

Background

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.

Methods

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 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.

Results

When 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.

Conclusion

The 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.
Zusatzmaterial
Additional file 1: Figure S1. Quality assessment of the seven patient cohorts used in this study. Figure S2. Percentage of stroma genes shared by comparing genes identified by our described selection procedure to genes identified by a naïve approach using only Pearson correlation to normal stroma content over all samples. Figure S3. GSEA score correlations using the stroma gene sets from Bertilsson and Chen independently for samples in all cohorts. Figure S4. Significantly differentially expressed genes (prostate cancer compared to normal) related to the cholesterol synthesis pathway calculated for each of the five patient cohorts having both prostate cancer and normal samples, as well as the meta-study for the seven-study-cohort. Figure S5. Significantly differentially expressed genes (prostate cancer compared to normal) related to regulation, uptake, efflux and transport of cholesterol, calculated for each of the five patient cohorts containing prostate cancer and normal samples, as well as the meta-study for the seven-study-cohort. Figure S6. Significantly differentially expressed genes (prostate cancer compared to normal) in the Bertilsson cohort after samples from patients reported to have taken statin prior to surgery have been removed (in total 26 samples, 18 cancer and 8 normal). Table S1. GSEA score stabilities using various numbers of the top ranked stroma genes in the gene-sets from Bertilsson and Chen. Table S2. Number of cancer/normal samples and average Gleason score in balanced and unbalanced datasets from all cohorts. (PDF 1575 kb)
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