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LNCaP Atlas: Gene expression associated with in vivoprogression to castration-recurrent prostate cancer

  • Tammy L Romanuik1,
  • Gang Wang1,
  • Olena Morozova1,
  • Allen Delaney1,
  • Marco A Marra1 and
  • Marianne D Sadar1Email author
BMC Medical Genomics20103:43

DOI: 10.1186/1755-8794-3-43

Received: 20 April 2010

Accepted: 24 September 2010

Published: 24 September 2010

Abstract

Background

There is no cure for castration-recurrent prostate cancer (CRPC) and the mechanisms underlying this stage of the disease are unknown.

Methods

We analyzed the transcriptome of human LNCaP prostate cancer cells as they progress to CRPC in vivo using replicate LongSAGE libraries. We refer to these libraries as the LNCaP atlas and compared these gene expression profiles with current suggested models of CRPC.

Results

Three million tags were sequenced using in vivo samples at various stages of hormonal progression to reveal 96 novel genes differentially expressed in CRPC. Thirty-one genes encode proteins that are either secreted or are located at the plasma membrane, 21 genes changed levels of expression in response to androgen, and 8 genes have enriched expression in the prostate. Expression of 26, 6, 12, and 15 genes have previously been linked to prostate cancer, Gleason grade, progression, and metastasis, respectively. Expression profiles of genes in CRPC support a role for the transcriptional activity of the androgen receptor (CCNH, CUEDC2, FLNA, PSMA7), steroid synthesis and metabolism (DHCR24, DHRS7, ELOVL5, HSD17B4, OPRK1), neuroendocrine (ENO2, MAOA, OPRK1, S100A10, TRPM8), and proliferation (GAS5, GNB2L1, MT-ND3, NKX3-1, PCGEM1, PTGFR, STEAP1, TMEM30A), but neither supported nor discounted a role for cell survival genes.

Conclusions

The in vivo gene expression atlas for LNCaP was sequenced and support a role for the androgen receptor in CRPC.

Background

Systemic androgen-deprivation therapy by orchiectomy or agonists of gonadotropic releasing hormone are routinely used to treat men with metastatic prostate cancer to reduce tumor burden and pain. This therapy is based on the dependency of prostate cells for androgens to grow and survive. The inability of androgen-deprivation therapy to completely and effectively eliminate all metastatic prostate cancer cell populations is manifested by a predictable and inevitable relapse, referred to as castration-recurrent prostate cancer (CRPC). CRPC is the end stage of the disease and fatal to the patient within 16-18 months of onset.

The mechanisms underlying progression to CRPC are unknown. However, there are several models to explain its development. One such model indicates the involvement of the androgen signaling pathway[14]. Key to this pathway is the androgen receptor (AR) which is a steroid hormone receptor and transcription factor. Mechanisms of progression to CRPC that involve or utilize the androgen signaling pathway include: hypersensitivity due to AR gene amplification [5, 6]; changes in AR co-regulators such as nuclear receptor coactivators (NCOA1 and NCOA2) [7, 8]; intraprostatic de novo synthesis of androgen[9] or metabolism of AR ligands from residual adrenal androgens[10, 11]; AR promiscuity of ligand specificity due to mutations[12]; and ligand-independent activation of AR by growth factors [protein kinase A (PKA), interleukin 6 (IL6), and epidermal growth factor (EGF)][1315]. Activation of the AR can be determined by assaying for the expression of target genes such as prostate-specific antigen (PSA)[16]. Other models of CRPC include the neuroendocrine differentiation [17], the stem cell model [18] and the imbalance between cell growth and cell death [3]. It is conceivable that these models may not mutual exclusive. For example altered AR activity may impact cell survival and proliferation.

Here, we describe long serial analysis of gene expression (LongSAGE) libraries[19, 20] made from RNA sampled from biological replicates of the in vivo LNCaP Hollow Fiber model of prostate cancer as it progresses to the castration-recurrent stage. Gene expression signatures that were consistent among the replicate libraries were applied to the current models of CRPC.

Methods

In vivoLNCaP Hollow Fiber model

The LNCaP Hollow Fiber model of prostate cancer was performed as described previously[2123]. All animal experiments were performed according to a protocol approved by the Committee on Animal Care of the University of British Columbia. Serum PSA levels were determined by enzymatic immunoassay kit (Abbott Laboratories, Abbott Park, IL, USA). Fibers were removed on three separate occasions representing different stages of hormonal progression that were androgen-sensitive (AS), responsive to androgen-deprivation (RAD), and castration-recurrent (CR). Samples were retrieved immediately prior to castration (AS), as well as 10 (RAD) and 72 days (CR) post-surgical castration.

RNA sample generation, processing, and quality control

Total RNA was isolated immediately from cells harvested from the in vivo Hollow Fiber model using TRIZOL Reagent (Invitrogen) following the manufacturer's instructions. Genomic DNA was removed from RNA samples with DNaseI (Invitrogen). RNA quality and quantity were assessed by the Agilent 2100 Bioanalyzer (Agilent Technologies, Mississauga, ON, Canada) and RNA 6000 Nano LabChip kit (Caliper Technologies, Hopkinton, MA, USA).

Quantitative real-time polymerase chain reaction

Oligo-d(T)-primed total RNAs (0.5 μg per sample) were reverse-transcribed with SuperScript III (Invitrogen Life Technologies, Carlsbad, CA, USA). An appropriate dilution of cDNA and gene-specific primers were combined with SYBR Green Supermix (Invitrogen) and amplified in ABI 7900 real-time PCR machine (Applied Biosystems, Foster City, CA, USA). All qPCR reactions were performed in triplicate. The threshold cycle number (Ct) and expression values with standard deviations were calculated in Excel. Primer sequences for real-time PCRs are: KLK3, F': 5'-CCAAGTTCATGCTGTGTGCT-3' and R:' 5'-CCCATGACGTGATACCTTGA-3'; glyceraldehyde-3-phosphate (GAPDH), F': 5'-CTGACTTCAACAGCGACACC-3' and R:' 5'-TGCTGTAGCCAAATTCGTTG-3'). Real-time amplification was performed with initial denaturation at 95°C for 2 min, followed by 40 cycles of two-step amplification (95°C for 15 sec, 55°C for 30 sec).

LongSAGE library production and sequencing

RNA from the hollow fibers of three mice (biological replicates) representing different stages of prostate cancer progression (AS, RAD, and CR) were used to make a total of nine LongSAGE libraries. LongSAGE libraries were constructed and sequenced at the Genome Sciences Centre, British Columbia Cancer Agency. Five micrograms of starting total RNA was used in conjunction with the Invitrogen I-SAGE Long kit and protocol with alterations [24]. Raw LongSAGE data are available at Gene Expression Omnibus [25] as series accession number GSE18402. Individual sample accession numbers are as follows: S1885, GSM458902; S1886, GSM458903; S1887, GSM458904; S1888, GSM458905; S1889, GSM458906; S1890, GSM458907; S1891, GSM458908; S1892, GSM458909; and S1893, GSM458910.

Gene expression analysis

LongSAGE expression data was analyzed with DiscoverySpace 4.01 software [26]. Sequence data were filtered for bad tags (tags with one N-base call) and linker-derived tags (artifact tags). Only LongSAGE tags with a sequence quality factor (QF) greater than 95% were included in analysis. The phylogenetic tree was constructed with a distance metric of 1-r (where "r" equals the Pearson correlation coefficient). Correlations were computed (including tag counts of zero) using the Regress program of the Stat package written by Ron Perlman, and the tree was optimized using the Fitch program[27] in the Phylip package[28]. Graphics were produced from the tree files using the program TreeView[29]. Tag clustering analysis was performed using the Poisson distribution-based K-means clustering algorithm. The K-means algorithm clusters tags based on count into 'K' partitions, with the minimum intracluster variance. PoissonC was developed specifically for the analysis of SAGE data [30]. The java implementation of the algorithm was kindly provided by Dr. Li Cai (Rutgers University, NJ, USA). An optimal value for K (K = 10) was determined [31].

Principle component analysis

Principle component analysis was performed using GeneSpring™ software version 7.2 (Silicon Genetics, CA). Affymetrix datasets of clinical prostate cancer and normal tissue were downloaded from Gene Expression Omnibus [25] (accession numbers: GDS1439 and GDS1390) and analyzed in GeneSpring™. Of the 96 novel CR-associated genes, 76 genes had corresponding Affymetrix probe sets. These probe sets were applied as the gene signature in this analysis. Principle component (PC) scores were calculated according to the standard correlation between each condition vector and each principle component vector.

Results

LongSAGE library and tag clustering

RNA isolated from the LNCaP Hollow Fiber model was obtained from at least three different mice (13N, 15N, and 13R; biological replicates) at three stages of cancer progression that were androgen-sensitive (AS), responsive to androgen-deprivation (RAD), and castration-recurrent (CR). To confirm that the samples represented unique disease-states, we determined the levels of KLK3 mRNA, a biomarker that correlates with progression, using quantitative real time-polymerase chain reaction (qRT-PCR). As expected, KLK3 mRNA levels dropped in the stage of cancer progression that was RAD versus AS (58%, 49%, and 37%), and rose in the stage of cancer progression that was CR versus RAD (229%, 349%, and 264%) for mice 13R, 15N, and 13N, respectively (Additional file 1). Therefore, we constructed nine LongSAGE libraries, one for each stage and replicate.

LongSAGE libraries were sequenced to 310,072 - 339,864 tags each, with a combined total of 2,931,124 tags, and filtered to leave only useful tags for analysis (Table 1). First, bad tags were removed because they contain at least one N-base call in the LongSAGE tag sequence. The sequencing of the LongSAGE libraries was base called using PHRED software. Tag sequence-quality factor (QF) and probability was calculated to ascertain which tags contain erroneous base-calls. The second line of filtering removed LongSAGE tags with probabilities less than 0.95 (QF < 95%). Linkers were introduced into SAGE libraries as known sequences utilized to amplify ditags prior to concatenation. At a low frequency, linkers ligate to themselves creating linker-derived tags (LDTs). These LDTs do not represent transcripts and were removed from the LongSAGE libraries. A total of 2,305,589 useful tags represented by 263,197 tag types remained after filtering. Data analysis was carried out on this filtered data.
Table 1

Composition of LongSAGE libraries

Library

S1885

S1886

S1887

S1888

S1889

S1890

S1891

S1892

S1893

Mouse-Condition

13N-AS*

13N-RAD†

13N-CR‡

15N-AS

15N-RAD

15N-CR

13R-AS

13R-RAD

13R-CR

Unfiltered Total Tags

310,516

318,102

339,864

338,210

310,072

326,870

337,546

314,440

335,504

No. of Bad Tags

955

1,010

1,083

1,097

983

737

900

744

832

Minus Bad Tags

         

Total Tags

309,561

317,092

338,781

337,113

309,089

326,133

336,646

313,696

334,672

Tag Types

79,201

96,973

99,730

81,850

84,499

88,249

79,859

91,438

90,675

No. of Duplicate Ditags

19,761

12,220

12,678

21,973

17,471

12,836

24,552

12,786

13,127

% of Duplicate Ditags

6.38

3.85

3.74

6.52

5.65

3.94

7.29

4.08

3.92

Average QF§ of Tags

0.85

0.88

0.87

0.86

0.89

0.88

0.88

0.80

0.87

No. of Tags QF < 0.95

63,057

62,872

71,576

68,993

54,627

54,470

68,981

101,215

69,647

Q ≥ 0.95

         

Total Tags

246,504

254,220

267,205

268,120

254,462

271,663

267,665

212,481

265,025

Tag Types

52,033

67,542

66,748

52,606

59,374

64,985

53,715

54,682

64,837

Total Tags Combined

    

2,307,345

    

Tag Types Combined

    

263,199

    

No. of LDTs II Type I

124

72

174

179

84

186

164

118

301

No. of LDTs Type II

19

9

54

56

33

40

60

24

59

Minus LDTs

         

Total Tags

246,361

254,139

266,977

267,885

254,345

271,437

267,441

212,339

264,665

Tag Types

52,031

67,540

66,746

52,604

59,372

64,983

53,713

54,680

64,835

Total Tags Combined

    

2,305,589

    

Tag Types Combined

    

263,197

    

* AS, Androgen-sensitive

† RAD, Responsive to androgen-deprivation

‡ CR, Castration-recurrent

§ QF, Quality Factor

II LDTs, Linker-derived tags

The LongSAGE libraries were hierarchically clustered and displayed as a phylogenetic tree. In most cases, LongSAGE libraries made from the same disease stage (AS, RAD, or CR) clustered together more closely than LongSAGE libraries made from the same biological replicate (mice 13N, 15N, or 13R; Figure 1). This suggests the captured transcriptomes were representative of disease stage with minimal influence from biological variation.
https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-3-43/MediaObjects/12920_2010_Article_178_Fig1_HTML.jpg
Figure 1

Clustering of the nine LongSAGE libraries in a hierarchical tree. The tree was generated using a Pearson correlation-based hierarchical clustering method and visualized with TreeView. LongSAGE libraries constructed from similar stages of prostate cancer progression (AS, androgen-sensitive; RAD, responsive to androgen-deprivation; and CR, castration-recurrent) cluster together. 13N, 15N, and 13R indicate the identity of each animal.

Identification of groups of genes that behave similarly during progression of prostate cancer was conducted through K-means clustering of tags using the PoissonC algorithm [30]. For each biological replicate (mice 13N, 15N, or 13R), all tag types were clustered that had a combined count greater than ten in the three libraries representing disease stages (AS, RAD, and CR) and mapped unambiguously sense to a transcript in reference sequence (RefSeq; February 28th, 2008) [32] using DiscoverySpace4 software [33]. By plotting within cluster dispersion (i.e., intracluster variance) against a range of K (number of clusters; Additional file 1, Figure S2), we determined that ten clusters best embodied the expression patterns present in each biological replicate. This was decided based on the inflection point in the graph (Additional file 1, Figure S2), showing that after reaching K = 10, increasing the number of K did not substantially reduce the within cluster dispersion. K-means clustering was performed over 100 iterations, so that tags would be placed in clusters that best represent their expression trend. The most common clusters for each tag are displayed (Figure 2). In only three instances, there were similar clusters in just two of the three biological replicates. Consequently, consistent changes in gene expression during progression were represented in 11 patterns. Differences among expression patterns for each biological replicate may be explained by biological variation, the probability of sampling a given LongSAGE tag, and/or imperfections in K-means clustering (e.g, variance may not be a good measure of cluster scatter).
https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-3-43/MediaObjects/12920_2010_Article_178_Fig2_HTML.jpg
Figure 2

K-means clustering of tag types with similar expression trends. PoissonC with K = 10 (where K = number of clusters) was conducted over 100 iterations separately for each biological replicate (mice 13N, 15N, and 13R) and the results from the iterations were combined into consensus clusters shown here. Plotted on the x-axes are the long serial analysis of gene expression (LongSAGE) libraries representing different stages of prostate progression: AS, androgen-sensitive; RAD, responsive to androgen-deprivation; and CR, castration-recurrent. Plotted on the y-axes are the relative expression levels of each tag type, represented as a percentage of the total tag count (for a particular tag type) in all three LongSAGE libraries. Different colors represent different tag types. Each of the ten clusters for each biological replicate are labeled as such. 'No equivalent' indicates that a similar expression trend was not observed in the indicated biological replicate. Eleven expression patterns are evident in total and are labeled on the left. K-means clusters were amalgamated into five major expression trends: group 1, up during progression; group 2, down during progression; group 3, peak in the RAD stage; group 4, constant during progression; and group 5, valley in RAD stage.

Gene ontology enrichment analysis

We conducted Gene Ontology (GO) [34] enrichment analysis using Expression Analysis Systematic Explorer (EASE) [35] software to determine whether specific GO annotations were over-represented in the K-means clusters. Enrichment was defined by the EASE score (p-value ≤ 0.05) generated during comparison to all the other clusters in the biological replicate. This analysis was done for each biological replicate (3 mice: 13N, 15N, or 13R).

To enable visual differences between the 11 expression trends, the clusters were amalgamated into five major trends: group 1, up during progression; group 2, down during progression; group 3, peak in the RAD stage; group 4, constant during progression; and group 5, valley in RAD stage (Figure 2). To be consistent, the GO enrichment data was combined into five major trends which resulted in redundancy in GO terms. To simplify the GO enrichment data, similar terms were pooled into representative categories. Categorical gene ontology enrichments of the five major expression trends are shown in Figure 3. These data indicate that steroid binding, heat shock protein activity, de-phosphorylation activity, and glycolysis all decreased in the stage that was RAD, but increased again in the stage that was CR. Interestingly, steroid hormone receptor activity continues to increase throughout progression. Both of these expression trends were observed for genes with GO terms for transcription factor activity or secretion. The GO categories for genes with kinase activity and signal transduction displayed expression trends with peaks and valleys at the stage that was RAD. The levels of expression of genes involved in cell adhesion rose in the stage that was RAD, but dropped again in the stage that was CR.
https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-3-43/MediaObjects/12920_2010_Article_178_Fig3_HTML.jpg
Figure 3

Gene Ontology enrichments of the five major expression trends. Plotted on the x-axis are Gene Ontology (GO) categories enriched in one or more of the five major expression trends. On the z-axis the five major expression trends correspond to Figure 2 and are: group 1, up during progression; group 2, down during progression; group 3, peak in the RAD stage; group 4, constant during progression; and group 5, valley in RAD stage. The y-axis displays the number of biological replicates (number of mice: 1, 2, or 3) exhibiting enrichment. The latter allows one to gauge the magnitude of the GO enrichment and confidence.

Altogether, genes with functional categories that were enriched in expression trends may be consistent with the AR signaling pathway playing a role in progression of prostate cancer to castration-recurrence (Figure 3). For example, GO terms steroid binding, steroid hormone receptor activity, heat shock protein activity, chaperone activity, and kinase activity could represent the cytoplasmic events of AR signaling. GO terms transcription factor activity, regulation of transcription, transcription corepression activity, and transcription co-activator activity could represent the nuclear events of AR signaling. AR-mediated gene transcription may result in splicing and protein translation, to regulate general cellular processes such as proliferation (and related nucleotide synthesis, DNA replication, oxidative phosphorylation, oxioreductase activity, and glycolysis), secretion, and differentiation.

It should be noted, however, that both positive and negative regulators were represented in the GO enriched categories (Figure 3). Therefore, a more detailed analysis was required to determine if the pathways represented by the GO-enriched categories were promoted or inhibited during progression to CRPC. Moreover, many of the GO enrichments that were consistent with changes in the AR signaling pathway were generic, and could be applied to the other models of CRPC.

Consistent differential gene expression associated with progression of prostate cancer

Pair-wise comparisons were made between LongSAGE libraries representing the transcriptomes of different stages (AS, RAD, and CR) of prostate cancer progression from the same biological replicate (3 mice: 13N, 15N, or 13R). Among all three biological replicates, the number of consistent statistically significant differentially expressed tag types were determined using the Audic and Claverie test statistic [36] at p ≤ 0.05, p ≤ 0.01, and p ≤ 0.001 (Table 2). The tags represented in Table 2 were included only if the associated expression trend was common among all three biological replicates. The Audic and Claverie statistical method is well-suited for LongSAGE data, because the method takes into account the sizes of the libraries and tag counts. Tag types were counted multiple times if they were over, or under-represented in more than one comparison. The number of tag types differentially expressed decreased by 57% as the stringency of the p-value increased from p ≤ 0.05 to 0.001.
Table 2

Number of tag types consistently and significantly differentially expressed among all three biological replicates and between conditions*

Comparison

Change

p ≤ 0.001

p ≤ 0.01

p ≤ 0.05

AS† vs. RAD‡

Up in RAD

21

44

83

 

Down in RAD

68

105

149

 

Total

89

149

232

RAD vs. CR§

Up in CR

24

45

89

 

Down in CR

46

59

104

 

Total

70

104

193

AS vs. CR

Up in CR

111

167

294

 

Down in CR

127

168

256

 

Total

238

335

550

* Statistics according to the Audic and Claverie test statistic

† AS, Androgen-sensitive

‡ RAD, Responsive to androgen-deprivation

§ CR, Castration-recurrent

Tag types consistently differentially expressed in pair-wise comparisons were mapped to RefSeq (March 4th, 2008). Tags that mapped anti-sense to genes, or mapped ambiguously to more than one gene were not included in the functional analysis. GO, Kyoto Encyclopedia of Genes and Genomes (KEGG; v45.0) [37] pathway, and SwissProt (v13.0) [38] keyword annotation enrichment analyses were conducted using EASE (v1.21; March 11th, 2008) and FatiGO (v3; March 11th, 2008) [39] (Table 3). This functional analysis revealed that the expression of genes involved in signaling increased during progression, but the expression of genes involved in protein synthesis decreased during progression. Cell communication increased in the stage that was RAD but leveled off in the stage that was CR. Carbohydrate, lipid and amino acid synthesis was steady in the RAD stage but increased in the CR stage. Lastly, glycolysis decreased in the RAD stage, but was re-expressed in the CR stage (Table 3).
Table 3

Top five enrichments of functional categories of tags consistently and significantly differentially expressed among all three biological replicates and between stages of prostate cancer*

Top 5 GO † categories

P-value ‡

Top 5 KEGG § annotations

P-value II

Top 5 SwissProt annotations

P-value II

AS vs. RAD: Up in RAD¶

Cell communication

2.E-02

Stilbene, coumarine and lignin biosynthesis

1.E-02

Antioxidant

7.E-04

Extracellular

2.E-02

Butanoate metabolism

2.E-02

Cell adhesion

5.E-03

Extracellular matrix

2.E-02

2,4-Dichlorobenzoate degradation

2.E-02

Signal

6.E-03

Synaptic vesicle transport

3.E-02

Cell adhesion molecules (CAMs)

2.E-02

Fertilization

7.E-03

Synapse

4.E-02

Alkaloid biosynthesis II

5.E-02

Amyotrophic lateral sclerosis

7.E-03

AS vs. RAD: Down in RAD

Glycolysis

3.E-05

Glycolysis/Gluconeogenesis

3.E-05

Glycolysis

3.E-07

Glucose catabolism

1.E-04

Ribosome

2.E-03

Pyrrolidone carboxylic acid

8.E-05

Hexose catabolism

1.E-04

Carbon fixation

3.E-03

Pyridoxal phosphate

2.E-04

Hexose metabolism

2.E-04

Fructose and mannose metabolism

2.E-02

Gluconeogenesis

3.E-04

Monosaccharide catabolism

2.E-04

Urea cycle and metabolism of amino groups

3.E-02

Coiled coil

5.E-03

RAD vs. CR: Up in CR

Acid phosphatase activity

4.E-02

gamma-Hexachlorocyclohexane degradation

5.E-03

Lyase

2.E-03

Lyase activity**

7.E-02

Glycolysis/Gluconeogenesis

3.E-02

Immune response

5.E-03

Carbohydrate metabolism**

9.E-02

O-Glycan biosynthesis

5.E-02

Signal

6.E-03

Extracellular**

1.E-01

Ether lipid metabolism**

6.E-02

Glycolysis

7.E-03

Catabolism**

1.E-01

Phenylalanine, tyrosine and tryptophan biosynthesis**

6.E-02

Progressive external ophthalmoplegia

1.E-02

RAD vs. CR: Down in CR

Cytosolic ribosome

2.E-09

Ribosome

2.E-11

Ribosomal protein

6.E-10

Large ribosomal subunit

1.E-07

Urea cycle and metabolism of amino groups

1.E-02

Ribonucleoprotein

3.E-08

Cytosol

2.E-07

Arginine and proline metabolism

4.E-02

Acetylation

1.E-05

Cytosolic large ribosomal subunit

2.E-07

Type II diabetes mellitus**

1.E-01

Elongation factor

1.E-03

Protein biosynthesis

2.E-07

Phenylalanine metabolism**

1.E-01

rRNA-binding

2.E-03

AS vs. CR: Up in CR

Synapse

4.E-03

Butanoate metabolism

2.E-03

Glycoprotein

2.E-03

Extracellular

5.E-03

Ascorbate and aldarate metabolism

2.E-02

Vitamin C

7.E-03

Transition metal ion binding

7.E-03

Phenylalanine metabolism

2.E-02

Lipoprotein

1.E-02

Metal ion binding

2.E-02

Linoleic acid metabolism

2.E-02

Signal

1.E-02

Extracellular matrix

2.E-02

gamma-Hexachlorocyclohexane degradation

2.E-02

Heparin-binding

1.E-02

AS vs. CR: Down in CR

Cytosolic ribosome

4.E-12

Ribosome

2.E-09

Acetylation

2.E-07

Biosynthesis

7.E-11

Carbon fixation

9.E-04

Ribosomal protein

1.E-06

Macromolecule biosynthesis

2.E-10

Glycolysis/Gluconeogenesis

3.E-03

Glycolysis

7.E-05

Protein biosynthesis

1.E-08

Glycosphingolipid biosynthesis - lactoseries

4.E-02

Ribonucleoprotein

8.E-05

Eukaryotic 43 S preinitiation complex

2.E-08

Glutamate metabolism**

8.E-02

Protein biosynthesis

1.E-04

* Statistics according to the Audic and Claverie test statistic (p ≤ 0.05)

† GO, Gene Ontology

‡ P-value represents the raw EASE (Expression Analysis Systematic Explorer) score

§ KEGG, Kyoto Encyclopedia of Genes and Genomes

II Unadjusted p-value was computed using FatiGO

¶ AS, androgen-sensitive; RAD, responsive to androgen-deprivation; CR, castration-recurrent

** Not statistically significant (p > 0.05)

Tag types differentially expressed between the RAD and CR stages of prostate cancer were of particular interest (Table 4). This is because these tags potentially represent markers for CRPC and/or are involved in the mechanisms of progression to CRPC. These 193 tag types (Table 2) were mapped to databases RefSeq (July 9th, 2007), Mammalian Gene Collection (MGC; July 9th, 2007) [40], or Ensembl Transcript or genome (v45.36d) [41]. Only 135 of the 193 tag types were relevant (Table 4) with 48 tag types that mapped ambiguously to more than one location in the Homo Sapiens transcriptome/genome, and another 10 tag types that mapped to Mus musculus transcriptome/genome. Mus musculus mappings may be an indication of minor contamination of the in vivo LNCaP Hollow Fiber model samples with host (mouse) RNA. These 135 tag types represented 114 candidate genes with 7 tag types that did not map to the genome, 5 tag types that mapped to unannotated genomic locations, and 9 genes that were associated with more than one tag type. Table 4 shows the LongSAGE tag sequences and tag counts per million tags in all nine libraries. Tags were sorted into groups based on expression trends. These trends are visually represented in Additional file 1, Figure S3. Mapping information was provided where available.
Table 4

Gene expression trends of LongSAGE tags that consistently and significantly altered expression in CR prostate cancer*

 

13N

15N

13R

   
 

AS§

RADII

CR¶

AS

RAD

CR

AS

RAD

CR

   

Tag Sequence

S1885

S1886

S1887

S1888

S1889

S1890

S1891

S1892

S1893

Trend‡

Gene**

Accession§§

TCTAGAGAACACTGTGC

12†

79

382

7

67

136

7

52

200

A

ACPP‡‡

NM_001099

TAATTTTTCTAAGGTGT

101

311

648

119

397

895

120

546

918

A

C1ORF80

ENSG00000186063

TGAGAGAGGCCAGAACA

8

39

150

4

39

144

7

33

95

A

N/A

Genomic

CTCATAAGGAAAGGTTA

637

952

1680

653

1170

1540

688

1620

1930

A

RNF208

BC090061

GATTTCTATTTGTTTTT

89

169

446

116

208

339

86

311

555

A

SERINC5

ENSG00000164300

GTTGGGAAGACGTCACC

426

571

742

273

417

741

262

363

495

A

STEAP1

NM_012449

GAGGATCACTTGAGGCC

191

299

449

134

189

589

187

203

314

B

AMACR‡‡

BC009471

TTGTTGATTGAAAATTT

219

197

528

273

197

479

232

391

586

B

AMD1‡‡

NM_001634

TTTGCTTTTGTTTTGTT

53

16

169

34

51

129

7

28

72

B

AQP3

NM_004925

GTTCGACTGCCCACCAG

45

28

101

52

47

122

34

42

106

B

ASAH1††

NM_177924

TAATAAACAGGTTTTTA

426

232

648

332

315

700

138

250

491

B

ASAH1‡‡

NM_177924

TCACAGCTGTGAAGATC

85

110

277

161

71

258

310

438

945

B

BTG1

NM_001731

AAAAGAGAAAGCACTTT

24

75

199

19

35

85

15

90

552

B

CAMK2N1

NM_018584

CAAAACAGGCAGCTGGT

4

71

169

15

83

162

37

75

268

B

CAMK2N1††

NM_018584

AGGAGGAAGAATGGACT

33

59

187

49

67

247

26

42

223

B

CCNH

NM_001239

TTTTAAAAATATAAAAT

89

83

243

97

130

269

64

170

382

B

COMT

NM_000754

GAATGAAATAAAAAATA

134

252

626

209

240

357

116

160

272

B

DHRS7

NM_016029

AAAGTGCATCCTTTCCC

118

146

318

153

220

394

288

231

646

B

FGFRL1

NM_001004356

AAACTGAATAAGGAGAA

24

51

236

19

51

438

19

146

283

B

GALNT3

NM_004482

TTTAAGGAAACATTTGA

4

4

75

4

4

81

0

0

57

B

GALNT3††

NM_004482

CCAACCGTGCTTGTACT

191

327

521

202

279

534

172

363

510

B

GLO1

NM_006708

GAGGGCCGGTGACATCT

300

378

1170

321

476

1230

254

447

1030

B

H2AFJ

NM_177925

TATCATTATTTTTACAA

57

63

161

67

63

181

75

94

181

B

HSD17B4

NM_000414

AATGCACTTATGTTTGC

16

8

64

22

16

77

19

28

98

B

N/A

No map

ACCTTCGCAGGGGAGAG

0

0

19

0

4

41

0

5

34

B

N/A

Genomic

ATAACCTGAAAGGAAAG

0

16

56

7

4

74

0

28

87

B

N/A

No map

GTGATGTGCACCTGTTG

0

0

38

4

0

30

0

5

45

B

N/A

No map

GTTTGGAGGTACTAAAG

20

43

94

34

87

169

34

90

234

B

N/A

Genomic

TTTTCAAAAATTGGAAA

0

35

180

7

4

59

0

19

61

B

N/A

No map

GAAAAATTTAAAGCTAA

394

397

569

433

598

788

853

862

1060

B

NGFRAP1

NM_206917

CAAATTCAGGGAGCACA

0

4

139

4

16

228

0

14

136

B

OPRK1

NM_000912

CTATTGTCTGAACTTGA

0

8

109

0

12

70

0

9

227

B

OR51E2

BC020768

ATGCTAATTATGGCAAT

4

12

75

4

8

74

0

5

57

B

PCGEM1

NR_002769

CAGAAAGCATCCCTCAC

4

43

195

0

16

111

7

33

264

B

PLA2G2A‡‡

NM_000300

TAATTTTAGTGCTTTGA

16

75

154

37

59

162

4

57

132

B

PTGFR

NM_000959

TTGTTTGTAAATAGAAT

0

12

94

0

4

162

0

14

72

B

QKI

NM_206853

TAAACACTGTAAAATCC

0

4

75

0

4

66

0

0

42

B

QKI††

NM_206853

AGCAGATCAGGACACTT

20

35

112

15

16

140

15

42

98

B

S100A10

NM_002966

CTGCCATAACTTAGATT

37

55

161

93

63

192

56

99

264

B

SBDS

NM_016038

TGGCTGAGTTTATTTTT

20

24

79

41

8

96

4

42

147

B

SFRS2B

NM_032102

GAAGATTAATGAGGGAA

126

142

277

108

130

402

101

188

325

B

SNX3

NM_003795

ATGGTACTAAATGTTTT

16

47

124

37

28

88

11

19

76

B

SPIRE1

NM_020148

TATATATTAAGTAGCCG

45

39

101

45

75

133

41

75

178

B

STEAP2‡‡

NM_152999

CAACAATATATGCTTTA

24

32

82

75

32

136

26

99

212

B

STEAP2††

NM_152999

TTTCATTGCCTGAATAA

24

43

150

34

59

114

22

61

178

B

TACC1‡‡

NM_006283

TTGGCCAGTCTGCTTTC

8

16

67

4

4

77

0

5

38

B

TMEM30A

ENSG00000112697

ATATCACTTCTTCTAGA

12

4

26

7

4

26

0

52

140

C

ADAM2‡‡

NM_001464

ATGTGTGTTGTATTTTA

812

338

768

1010

315

1020

269

702

865

C

BNIP3

NM_004052

CCACGTTCCACAGTTGC

601

291

599

530

346

700

381

339

559

C

ENO2

NM_001975

CTGATCTGTGTTTCCTC

16

0

26

0

4

41

19

0

34

C

HLA-B

BC013187

AGCCCTACAAACAACTA

382

441

596

508

456

619

400

631

1010

C

MT-ND3

ENSG00000198840

ATATTTTCTTTGTGGAA

20

12

90

7

0

48

4

0

23

C

N/A

No map

CAAGCATCCCCGTTCCA

2400

2130

2440

2730

1720

2250

1020

2010

2340

C

N/A

ENSG00000211459

GTTGTAAAATAAACTTT

118

83

172

228

87

247

112

203

378

C

N/A

Genoic

TTGGATTTCCAAAGCAG

12

0

19

0

0

33

0

0

26

C

N/A

Genomic

TCTTTTAGCCAATTCAG

138

181

420

381

326

468

389

334

457

C

NKX3-1††

NM_006167

TGATTGCCCTTTCATAT

73

39

86

86

39

107

108

99

181

C

P4HA1

NM_000917

GTAACAAGCTCTGGTAT

28

16

56

49

24

66

11

19

72

C

PJA2

NM_014819

ACAGTGCTTGCATCCTA

85

75

139

108

98

203

101

118

196

C

PPP2CB

NM_004156

AGGCGAGATCAATCCCT

57

39

101

37

24

122

131

66

268

C

PSMA7

NM_002792

TATTTTGTATTTATTTT

73

59

180

93

51

111

22

94

253

C

SLC25A4

NM_001151

TTATGGATCTCTCTGCG

1050

1260

1820

1140

1300

2260

1990

1010

1530

C

SPON2

NM_012445

CAGTTCTCTGTGAAATC

767

515

1060

855

503

914

467

608

1200

C

TMEM66

NM_016127

AAATAAATAATGGAGGA

138

59

255

82

118

284

165

90

159

C

TRPM8

NM_024080

ATGTTTAATTTTGCACA

61

87

154

157

59

195

217

85

344

C

WDR45L

NM_019613

GGGCCCCAAAGCACTGC

861

543

1180

1020

657

1590

1240

739

937

E

C19orf48

NM_199249

TCCCCGTGGCTGTGGGG

1670

1390

2290

1740

1410

1720

3370

970

1180

E

DHCR24‡‡

BC004375

GCATCTGTTTACATTTA

487

201

345

444

208

468

684

226

423

E

ELOVL5

NM_021814

GAAATTAGGGAAGCCTT

317

153

311

310

181

542

359

193

298

E

ENDOD1

XM_290546

GGATGGGGATGAAGTAA

2780

1160

4780

2950

1350

3620

2930

1230

1890

E

KLK3‡‡

NM_001648

TGAAAAGCTTAATAAAT

313

142

322

474

181

332

273

179

314

E

TPD52

NM_001025252

GTTGTGGTTAATCTGGT

1770

634

1270

1800

806

1190

2480

659

960

F

B2M

NM_004048

GAAACAAGATGAAATTC

4380

1170

2260

5300

1110

2720

3750

2220

2830

F

PGK1

NM_000291

AGCACCTCCAGCTGTAC

2150

1130

648

2060

1560

939

1560

1200

722

G

EEF2

NM_001961

GCACAAGAAGATTAAAA

536

228

124

762

425

195

838

278

174

G

GAS5

NR_002578

CCGCTGCGTGAGGGCAG

451

169

56

429

197

44

516

94

0

G

HES6

NM_018645

GCCCAGGTCACCCACCC

585

55

4

519

79

7

456

66

0

G

LOC644844

XM_927939

ATGCAGCCATATGGAAG

2650

386

82

2470

216

129

1210

259

98

G

ODC1

NM_002539

CGCTGGTTCCAGCAGAA

1420

811

479

1250

959

553

800

589

374

G

RPL11

NM_000975

AAGACAGTGGCTGGCGG

2650

1730

1220

2460

1860

1350

2120

1630

1270

G

RPL37A‡‡

NM_000998

TTCTTGTGGCGCTTCTC

925

543

217

1030

708

273

1130

419

306

G

RPS11††

NM_001015

GGTGAGACACTCCAGTA

463

252

165

485

346

192

363

245

159

G

SLC25A6

NM_001636

AGGTTTTGCCTCATTCC

982

515

281

1200

491

243

688

782

166

H

ABHD2

NM_007011

TGAAGGAGCCGTCTCCA

317

272

187

392

295

199

366

259

140

H

ATP5G2

NM_001002031

CTCAGCAGATCCAAGAG

191

185

67

254

232

66

142

231

79

H

C17orf45

NM_152350

CTGTGACACAGCTTGCC

308

397

172

209

307

125

295

226

110

H

CCT2

NM_006431

TCTGCACCTCCGCTTGC

495

606

277

426

570

276

366

471

204

H

EEF1A2

NM_001958

GCCCAAGGACCCCCTGC

114

114

38

138

98

41

101

42

4

H

FLNA‡‡

NM_001456

TTATGGGATCTCAACGA

564

425

180

642

452

317

430

490

253

H

GNB2L1

NM_006098

TCTGCAAAGGAGAAGTC

81

102

38

105

87

26

165

80

30

H

HMGB2

NM_002129

CTTGTGAACTGCACAAC

268

228

124

231

177

103

273

160

57

H

HN1

NM_016185

TCTGAAGTTTGCCCCAG

313

291

150

254

299

155

187

226

72

H

MAOA

NM_000240

TTAATTGATAGAATAAA

483

350

199

422

287

103

273

235

83

H

MAOA

NM_000240

GGCAGCCAGAGCTCCAA

1200

1260

420

1050

672

350

681

819

23

H

MARCKSL1

NM_023009

CCCTGCCTTGTCCCTCT

353

240

112

310

263

107

176

193

102

H

MDK

NM_001012334

CTGTGGATGTGTCCCCC

649

476

169

459

389

214

430

297

117

H

N/A

No map

CTCCTCACCTGTATTTT

1120

771

262

1220

979

313

666

730

261

H

RPL13A‡‡

NM_012423

GCAGCCATCCGCAGGGC

1980

1770

809

2300

1730

928

2150

1570

1020

H

RPL28

NM_000991

GGATTTGGCCTTTTTGA

3470

2070

1370

4170

2910

1540

2800

2870

2500

H

RPLP2‡‡

NM_001004

TCTGTACACCTGTCCCC

2320

1670

850

1930

1880

825

2130

1490

1120

H

RPS11

NM_001015

GCTTTTAAGGATACCGG

1510

1050

626

1860

1120

593

1550

1550

960

H

RPS20‡‡

NM_001023

CCCCAGCCAGTCCCCAC

921

519

281

788

664

357

1100

438

291

H

RPS3

NM_001005

CCCCCAATGCTGAGGCC

89

138

26

90

94

30

90

80

30

H

SF3A2

NM_007165

GCCGCCATCTCCGAGAG

195

102

30

168

118

55

172

108

30

H

TKT

NM_001064

GGCCATCTCTTCCTCAG

349

307

202

317

346

173

277

254

121

H

YWHAQ

NM_006826

AGGCTGTGTTCCTCCGT

16

39

11

34

67

22

26

38

8

I

ACY1

NM_000666

TGCCTCTGCGGGGCAGG

446

649

427

399

664

424

501

462

317

I

CD151

NM_004357

GGCACAGTAAAGGTGGC

175

216

142

332

350

173

456

316

204

I

CUEDC2

NM_024040

TCACACAGTGCCTGTCG

49

71

7

30

47

15

34

66

4

I

CXCR7

NM_001047841

TGTGAGGGAAGCTGCTT

53

87

15

67

102

52

52

90

42

I

FKBP10

BC016467

TGCTTTGCTTCATTCTG

28

63

26

22

79

26

49

118

61

I

GRB10

NM_005311

GTACTGTATGCTTGCCA

170

212

82

134

153

88

123

188

113

I

KPNB1‡‡

NM_002265

GTGGCAGTGGCCAGTTG

106

193

97

123

173

96

94

137

76

I

N/A

ENSG00000138744

GGGGAGCCCCGGGCCCG

61

63

26

30

51

18

34

57

0

I

NAT14

NM_020378

TGTTCAGGACCCTCCCT

28

67

26

60

63

26

60

28

0

I

NELF

NM_015537

TTTTCCTGGGGATCCTC

41

130

15

37

87

33

56

104

45

I

PCOTH

NM_001014442

GAAACCCGGTAGTCTAG

41

75

4

37

75

26

52

151

30

I

PLCB4

NM_000933

GTCTGACCCCAGGCCCC

126

205

82

119

193

103

157

179

38

I

PPP2R1A

NM_014225

GGCCCGAGTTACTTTTC

231

150

75

161

232

136

142

160

45

I

RPL35A††

NM_000996

GTTCGTGCCAAATTCCG

881

696

390

1100

712

523

497

782

461

I

RPL35A‡‡

NM_000996

TTACCATATCAAGCTGA

877

535

311

1130

598

405

636

791

578

I

RPL39‡‡

NM_001000

GCTGCAGCACAAGCGGC

268

244

127

45

216

125

157

71

11

I

RPS18††

NM_022551

AGCTCTTGGAGGCACCA

203

319

206

142

421

243

269

259

162

I

SELENBP1

NM_003944

TGCTGGTGTGTAAGGGG

69

102

45

82

87

37

105

75

30

I

SH3BP5L

NM_030645

GAGAGTAACAGGCCTGC

191

150

71

112

181

111

108

165

64

I

SYNC1

NM_030786

CTGAAAACCACTCAAAC

394

508

225

306

547

236

310

381

200

I

TFPI

NM_006287

TAAAAAAGGTTTCATCC

183

248

127

86

130

66

142

268

87

I

TFPI

NM_006287

CTCCCTCCTCTCCTACC

28

32

4

30

39

7

71

24

0

I

TK1

NM_003258

CATTTTCTAATTTTGTG

544

744

236

407

771

181

288

664

185

J

N/A

No map

TGATTTCACTTCCACTC

3480

5260

3910

3700

6110

3590

3040

5960

2600

K

MT-CO3

ENSG00000198938

TTTCTGTCTGGGGAAGG

130

236

82

123

201

111

101

188

113

K

PIK3CD

NM_005026

GCCGCTACTTCAGGAGC

256

370

199

224

330

169

142

316

38

K

RAMP1

NM_005855

ATGGTTACACTTTTGGT

93

161

94

75

208

118

60

226

95

K

UTX

NM_021140

CACTACTCACCAGACGC

2820

3900

3020

2740

4290

2440

2620

3120

1260

K

VPS13B††

ENSG00000132549

CTAAGACTTCACCAGTC

7120

11000

9730

6390

10900

8330

3610

8870

7850

L

N/A

ENSG00000210082

* Statistics according to the Audic and Claverie test statistic (p ≤ 0.05)

† Tag count per 1 million = (observed tag count/total tags in the library) × 1,000,000

‡ Trends are visually represented from A to P in Additional file 1, Figure S3. In addition to p-value considerations, significantly different trends were also required to display uniform directions of change in each biological replicate.

§ AS, Androgen-sensitive

II RAD, Responsive to androgen-deprivation

¶ CR, Castration-recurrent

** Human Genome Nomenclature Committee (HGNC)-approved gene names were used when possible. Non-HGNC-approved gene names were not italicized.

†† Tag maps antisense to gene

‡‡ Gene is known to display this expression trend in castration-recurrence

§§ Accession numbers were displayed following the priority (where available): RefSeq > Mammalian Gene Collection > Ensembl Gene. If the tag mapped to more than one transcript variant of the same gene, the accession number of the lowest numerical transcript variant was displayed.

We cross-referenced these 114 candidate genes with 28 papers that report global gene expression analyses on tissue samples from men with 'castration-recurrent', 'androgen independent,' 'hormone refractory,' 'androgen-ablation resistant,' 'relapsed,' or 'recurrent' prostate cancer, or animal models of castration-recurrence [4269]. The candidate genes were identified with HUGO Gene Nomenclature Committee (HGNC) approved gene names, aliases, descriptions, and accession numbers. The gene expression trends of 18 genes of 114 genes were previously associated with CRPC. These genes were: ACPP, ADAM2, AMACR, AMD1, ASAH1, DHCR24, FLNA, KLK3, KPNB1, PLA2G2A, RPL13A, RPL35A, RPL37A, RPL39, RPLP2, RPS20, STEAP2, and TACC (Table 4). To our knowledge, the gene expression trends of the remaining 96 genes have never before been associated with CRPC (Tables 4 & 5).

A literature search helped to gauge the potential of these 96 genes to be novel biomarkers or therapeutic targets of CRPC. The results of this literature search are presented in Table 5. We found 31 genes that encode for protein products that are known, or predicted, to be plasma membrane bound or secreted extracellularly (Bioinformatic Harvester). These genes were: ABHD2, AQP3, B2 M, C19orf48, CD151, CXCR7, DHRS7, ELOVL5, ENDOD1, ENO2, FGFRL1, GNB2L1, GRB10, HLA-B, MARCKSL1, MDK, NAT14, NELF, OPRK1, OR51E2, PLCB4, PTGFR, RAMP1, S100A10, SPON2, STEAP1, TFPI, TMEM30A, TMEM66, TRPM8, and VPS13B. Secretion of a protein could facilitate detection of the putative biomarkers in blood, urine, or biopsy sample. Twenty-one of the candidate genes are known to alter their levels of expression in response to androgen. These genes were: ABHD2, B2 M, BTG1, C19orf48, CAMK2N1, CXCR7, EEF1A2, ELOVL5, ENDOD1, HSD17B4, MAOA, MDK, NKX3-1, ODC1, P4HA1, PCGEM1, PGK1, SELENBP1, TMEM66, TPD52, and TRPM8 [9, 22, 7081]. Genes regulated by androgen may be helpful in determining the activation status of AR in CRPC. Enriched expression of a protein in prostate tissue could be indicative of whether a tumor is of prostatic origin. Eight of these 96 genes are known to be over-represented in prostate tissue [75, 8285]. These genes were: ELOVL5, NKX3-1, PCGEM1, PCOTH, RAMP1, SPON2, STEAP1, and TPD52. Twenty-six genes (ABHD2, BNIP3, EEF1A2, ELOVL5, GALNT3, GLO1, HSD17B4, MARCKSL1, MDK, NGFRAP1, ODC1, OR51E2, PCGEM1, PCOTH, PGK1, PP2CB, PSMA7, RAMP1, RPS18, SELENBP1, SLC25A4, SLC25A6, SPON2, STEAP1, TPD52, and TRPM8) have known associations to prostate cancer [57, 82, 86102]. Six genes (C1orf80, CAMK2N1, GLO1, MAOA, PGK1, and SNX3) have been linked to high Gleason grade [58, 103, 104], and twelve genes (B2 M, CAMK2N1, CD151, COMT, GALNT3, GLO1, ODC1, PCGEM1, PCOTH, SBDS, TMEM30A, and TPD52) have been implicated in the 'progression' of prostate cancer [58, 82], and 15 more genes (CD151, CXCR7, DHRS7, GNB2L1, HES6, HN1, NKX3-1, PGK1, PIK3CD, RPL11, RPS11, SF3A2, TK1, TPD52, and VPS13B) in the metastasis of prostate cancer [105, 106].
Table 5

Characteristics of genes with novel association to castration-recurrence in vivo

    

Associated with

    

Associated with

Gene*

S or PM†

Reg. by A‡

Spec. to P§

CaPII

GG¶

Prog.**

Mets††

CR‡‡

Gene

S or PM

Reg. by A

Spec. to P

CaP

GG

Prog.

Mets

CR

ABHD2

PM

Y↑

-

Y↑

-

-

-

-

NKX3-1

-

Y↑

Y

-

-

-

Y

-

ACY1

-

-

-

-

-

-

-

-

ODC1

-

Y↑

-

Y↑

-

Y↓

-

Y↑

AQP3

PM

-

-

-

-

-

-

-

OPRK1

PM

-

-

-

-

-

-

-

ATP5G2

-

-

-

-

-

-

-

-

OR51E2

PM

-

-

Y↑

-

-

-

-

B2M

S&PM

Y↑

-

-

-

Y↑

-

Y↓

P4HA1

-

Y

-

-

-

-

-

-

BNIP3

-

-

-

Y↓

-

-

-

-

PCGEM1

-

Y↑

Y

Y↑

-

Y↑

-

-

BTG1

-

Y↓

-

-

-

-

-

-

PCOTH

-

-

Y

Y↑

-

Y↑

-

-

C17orf45

-

-

-

-

-

-

-

-

PGK1

-

Y↑

-

Y↓

Y↑

-

Y ↑↓§§

-

C19orf48

S

Y↑

-

-

-

-

-

-

PIK3CD

-

-

-

-

-

-

Y↑

Y↑

C1orf80

-

-

-

-

Y↑

-

-

-

PJA2

-

-

-

-

-

-

-

-

CAMK2N1

-

Y↓

-

-

Y↑

Y↑

-

-

PLCB4

PM

-

-

-

-

-

-

-

CCNH

-

-

-

-

-

-

-

-

PPP2CB

-

-

-

Y↓

-

-

-

-

CCT2

-

-

-

-

-

-

-

-

PPP2R1A

-

-

-

-

-

-

-

-

CD151

PM

-

-

-

-

Y↑

Y↑

-

PSMA7

-

-

-

Y↓

-

-

-

-

COMT

-

-

-

-

-

Y↓

-

-

PTGFR

PM

-

-

-

-

-

-

-

CUEDC2

-

-

-

-

-

-

-

-

QKI

-

-

-

-

-

-

-

-

CXCR7

PM

Y↓

-

-

-

-

Y↑

Y↑

RAMP1

PM

-

Y

Y↑

-

-

-

-

DHRS7

PM

-

-

-

-

-

Y↓

-

RNF208

-

-

-

-

-

-

-

-

EEF1A2

-

Y↑

-

Y↑

-

-

-

-

RPL11

-

-

-

-

-

-

Y↓

-

EEF2

-

-

-

-

-

-

-

-

RPL28

-

-

-

-

-

-

-

-

ELOVL5

PM

Y

Y

Y↓

-

-

-

-

RPS11

-

-

-

-

-

-

Y↓

-

ENDOD1

S

Y↑

-

-

-

-

-

-

RPS18

-

-

-

Y↑

-

-

-

-

ENO2

PM

-

-

-

-

-

-

-

RPS3

-

-

-

-

-

-

-

-

ENSG00000210082

-

-

-

-

-

-

-

-

S100A10

PM

-

-

-

-

-

-

-

ENSG00000211459

-

-

-

-

-

-

-

-

SBDS

-

-

-

-

-

Y↑

-

-

FGFRL1

PM

-

-

-

-

-

-

-

SELENBP1

-

Y↓

-

Y↓

-

-

-

-

FKBP10

-

-

-

-

-

-

-

-

SERINC5

-

-

-

-

-

-

-

-

GALNT3

-

-

-

Y↑

-

Y↓

-

-

SF3A2

-

-

-

-

-

-

Y↑

-

GAS5

-

-

-

-

-

-

-

-

SFRS2B

-

-

-

-

-

-

-

-

GLO1

-

-

-

Y↑

Y↑

Y↑

-

-

SH3BP5L

-

-

-

-

-

-

-

-

GNB2L1

PM

-

-

-

-

-

Y↑

-

SLC25A4

-

-

-

Y↑

-

-

-

-

GRB10

PM

-

-

-

-

-

-

-

SLC25A6

-

-

-

Y↑

-

-

-

-

H2AFJ

-

-

-

-

-

-

-

-

SNX3

-

-

-

-

Y↑

-

-

-

HES6

-

-

-

-

-

-

Y↑

Y↑

SPIRE1

-

-

-

-

-

-

-

-

HLA-B

PM

-

-

-

-

-

-

-

SPON2

S

-

Y

Y↑

-

-

-

-

HMGB2

-

-

-

-

-

-

-

Y↑

STEAP1

PM

-

Y

Y↑

-

-

-

-

HN1

-

-

-

-

-

-

Y↑

-

SYNC1

-

-

-

-

-

-

-

-

HSD17B4

-

Y↑

-

Y↑

-

-

-

-

TFPI

S

-

-

-

-

-

-

-

LOC644844

-

-

-

-

-

-

-

-

TK1

-

-

-

-

-

-

Y↑

-

MAOA

-

Y

-

-

Y↑

-

-

-

TKT

-

-

-

-

-

-

-

-

MARCKSL1

PM

-

-

Y↑

-

-

-

-

TMEM30A

S&PM

-

-

-

-

Y↑

-

-

MDK

S&PM

Y↓

-

Y↑

-

-

-

Y↑

TMEM66

S&PM

Y↑

-

-

-

-

-

-

MT-CO3

-

-

-

-

-

-

-

-

TPD52

-

Y↑

Y

Y↑

-

Y↑

Y↓

-

MT-ND3

-

-

-

-

-

-

-

-

TRPM8

PM

Y↑

-

Y↑

-

-

-

Y↓

NAAA

-

-

-

-

-

-

-

Y↑

UTX

-

-

-

-

-

-

-

-

NAT14

PM

-

-

-

-

-

-

-

VPS13B

PM

-

-

-

-

-

Y↑

-

NELF

PM

-

-

-

-

-

-

-

WDR45L

-

-

-

-

-

-

-

-

NGFRAP1

-

-

-

Y↓

-

-

-

-

YWHAQ

-

-

-

-

-

-

-

-

* Human Genome Nomenclature Committee (HGNC)-approved gene names were used when possible. Non-HGNC-approved gene names were not italicized.

† S or PM, gene product is thought to be secreted (S) or localize to the plasma membrane (PM)

‡ Reg. by A, gene expression changes in response to androgen in prostate cells

§ Spec. to P, gene expression is specific to- or enriched in- prostate tissue compared to other tissues

II CaP, gene is differentially expressed in prostate cancer compared to normal, benign prostatic hyperplasia, or prostatic intraepithelial neoplasia

¶ GG, gene is differentially expressed in higher Gleason grade tissue versus lower Gleason grade tissue

** Prog., gene expression correlates with late-stage prostate cancer or is a risk factor that predicts progression

†† Mets, gene expression is associated with prostate cancer metastasis in human samples or in vivo models

‡‡ CR, gene is associated with castration-recurrent prostate cancer in human tissue or in vivo models, but exhibits an opposite trend of this report

§§ Y, yes; ↑, high gene expression; ↓, low gene expression

Novel CR-associated genes identify both clinical samples of CRPC and clinical metastasis of prostate cancer

The expression of novel CR-associated genes were validated in publically available, independent sample sets representing different stages of prostate cancer progression (Gene Expression Omnibus accession numbers: GDS1390 and GDS1439). Dataset GDS1390 includes expression data of ten AS prostate tissues, and ten CRPC tissues from Affymetrix U133A arrays [47]. Dataset GDS1439 includes expression data of six benign prostate tissues, seven localized prostate cancer tissues, and seven metastatic prostate cancer tissues from Affymetrix U133 2.0 arrays [97].

Unsupervised principal component analysis based on the largest three principal components revealed separate clustering of tumor samples representing AS and CR stages of cancer progression, with the exception of two CR samples and one AS sample (Figure 4a).
https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-3-43/MediaObjects/12920_2010_Article_178_Fig4_HTML.jpg
Figure 4

Principle component analyses of clinical samples. A, Principle component analysis based on the expression of novel CR-associated genes in the downloaded dataset GDS1390 clustered the AS and CR clinical samples into two groups. B, Principle component analysis based on the expression of novel CR-associated genes in the downloaded dataset GDS1439 clustered the clinical samples (benign prostate tissue, benign; localized prostate cancer, Loc CaP; and metastatic prostate cancer, Met CaP) into three groups.

Metastatic prostate cancer is expected to have a more progressive phenotype and is associated with hormonal progression. Therefore, the gene expression signature obtained from the study of hormonal progression may be common to that observed in clinical metastases. Unsupervised principal component analysis based on the largest three principal components revealed separate clustering of not only benign and malignant, but also localized and metastatic tissue samples (Figure 4b).

Discussion

Genes that change levels of expression during hormonal progression may be indicative of the mechanisms involved in CRPC. Here we provide the most comprehensive gene expression analysis to date of prostate cancer with approximately 3 million long tags sequenced using in vivo samples of biological replicates at various stages of hormonal progression to improve over the previous libraries that are approximately 70,000 short tags or less. Previous large-scale gene expression analyses have been performed with tissue samples from men with advanced prostate cancer [4258], and animal or xenograft models of CRPC [5969]. Most of these previous studies compared differential expression between CRPC samples with the primary samples obtained before androgen ablation. This experimental design cannot distinguish changes in gene expression that are a direct response to androgen ablation, or from changes in proliferation/survival that have been obtained as the prostate cancer cells progress to more a more advanced phenotype. Here we are the first to apply an in vivo model of hormonal progression to compare gene expression between serial samples of prostate cancer before (AS), and after androgen ablation therapy (RAD) as well as when the cells become CR. This model is the LNCaP Hollow Fiber model [21] which has genomic similarity with clinical prostate cancer [23] and mimics the hormonal progression observed clinically in response to host castration as measured by levels of expression of PSA and cell proliferation. Immediately prior to castration, when the cells are AS, PSA levels are elevated and the LNCaP cells proliferate. A few days following castration, when the cells are RAD, PSA levels drop and the LNCaP cells cease to proliferate, but do not apoptose in this model. Approximately 10 weeks following castration, when the cells are CR, PSA levels rise and the LNCaP cells proliferate in the absence of androgen. This model overcomes some limitations in other studies using xenografts that include host contamination of prostate cancer cells. The hollow fibers prevent infiltration of host cells into the fiber thereby allowing retrieval of pure populations of prostate cells from within the fiber. The other important benefit of the fiber model is the ability to examine progression of cells to CRPC at various stages within the same host mouse over time, because the retrieval of a subset of fibers entails only minor surgery. The power to evaluate progression using serial samples from the same mouse minimizes biological variation to enhance the gene expression analyses. However, limitations of this model include the lack of cell-cell contact with stroma cells, and lack of heterogeneity in tumors. Typically, these features would allow paracrine interactions as expected in clinical situations. Consistent with the reported clinical relevance of this model [23], here principal component analysis based on the expression of these novel genes identified by LongSAGE, clustered the clinical samples of CRPC separately from the androgen-dependent samples. Principal component analysis based on the expression of these genes also revealed separate clustering of the different stages of tumor samples and also showed separate clustering of the benign samples from the prostate cancer samples. Therefore, some common changes in gene expression profile may lead to the survival and proliferation of prostate cancer and contribute to both distant metastasis and hormonal progression. We used this LNCaP atlas to identify changes in gene expression that may provide clues of underlying mechanisms resulting in CRPC. Suggested models of CRPC involve: the AR; steroid synthesis and metabolism; neuroendocrine prostate cancer cells; and/or an imbalance of cell growth and cell death.

Androgen receptor (AR)

Transcriptional activity of AR

The AR is suspected to continue to play an important role in the hormonal progression of prostate cancer. The AR is a ligand-activated transcription factor with its activity altered by changes in its level of expression or by interactions with other proteins. Here, we identified changes in expression of some known or suspected modifier of transcriptional activity of the ARin CRPC versus RAD such as Cyclin H (CCNH) [107], proteasome macropain subunit alpha type 7 (PSMA7) [108], CUE-domain-containing-2 (CUEDC2) [109], filamin A (FLNA) [110], and high mobility group box 2 (HMGB2) [111]. CCNH and PSMA7 displayed increased levels of expression, while CUEDC2, FLNA, and HMGB2 displayed decreased levels of expression in CR. The expression trends of CCNH, CUEDC2, FLNA, and PSMA7 in CRPC may result in increased AR signaling through mechanisms involving protein-protein interactions or altering levels of expression of AR. CCNH protein is a component of the cyclin-dependent activating kinase (CAK). CAK interacts with the AR and increases its transcriptional activity [107]. Over-expression of the proteosome subunit PSMA7 promotes AR transactivation of a PSA-luciferase reporter [108]. A fragment of the protein product of FLNA negatively regulates transcription by AR through a physical interaction with the hinge region [110]. CUEDC2 protein promotes the degradation of progesterone and estrogen receptors [109]. These steroid receptors are highly related to the AR, indicating a possible role for CUEDC2 in AR degradation. Thus decreased expression of FLNA or CUEDC2 could result in increased activity of the AR. Decreased expression of HMGB2 in CRPC is predicted to decrease expression of at least a subset of androgen-regulated genes that contain palindromic AREs [111]. Here, genes known to be regulated by androgen were enriched in expression trend categories with a peak or valley at the RAD stage of prostate cancer progression. Specifically, 8 of the 13 tags (62%) exhibiting these expression trends 'E', 'F', 'J', 'K', or 'L' represented known androgen-regulated genes, in contrast to only 22 of the remaining 122 tags (18%; Tables 4 & 5). Overall, this data supports increased AR activity in CRPC, which is consistent with re-expression of androgen-regulated genes as previously reported [68] and similarity of expression of androgen regulated genes between CRPC and prostate cancer before androgen ablation [23].

Steroid synthesis and metabolism

In addition to changes in expression of AR or interacting proteins altering the transcriptional activity of the AR, recent suggestion of sufficient levels of residual androgen in CRPC provides support for an active ligand-bound receptor [112]. The AR may become re-activated in CRPC due to the presence of androgen that may be synthesized by the prostate de novo [4] or through the conversion of adrenal androgens. Here, the expression of 5 genes known to function in steroid synthesis or metabolism were significantly differentially expressed in CRPC versus RAD. They are 24-dehydrocholesterol reductase (DHCR24) [113], dehydrogenase/reductase SDR-family member 7 (DHRS7) [114], elongation of long chain fatty acids family member 5 (ELOVL5) [115, 116], hydroxysteroid (17-beta) dehydrogenase 4 (HSD17B4) [117], and opioid receptor kappa 1 (OPRK1) [118]. Increased levels of expression of these genes may be indicative of the influence of adrenal androgens, or the local synthesis of androgen, to reactivate the AR to promote the progression of prostate cancer in the absence of testicular androgens.

Neuroendocrine

Androgen-deprivation induces neuroendocrine differentiation of prostate cancer. Here, the expression of 8 genes that are associated with neuroendocrine cells were significantly differentially expressed in CRPC versus RAD. They either responded to androgen ablation such as hairy and enhancer of split 6 (HES6) [119], karyopherin/importin beta 1 (KPNB1) [120], monoamine oxidase A (MAOA)[121], and receptor (calcitonin) activity modifying protein 1 (RAMP1) [122]], or were increased expressed in CRPC such as ENO2 [122], OPRK1 [118], S100 calcium binding protein A10 (S100A10) [123], and transient receptor potential cation channel subfamily M member 8 (TRPM8) [124].

Proliferation and Cell survival

The gene expression trends of GAS5 [125], GNB2L1 [126], MT-ND3, NKX3-1 [127], PCGEM1 [128], PTGFR [129], STEAP1 [130], and TMEM30A [131] were in agreement with the presence of proliferating cells in CRPC. Of particular interest is that we observed a transcript anti-sense to NKX3-1, a tumor suppressor, highly expressed in the stages of cancer progression that were AS and CR, but not RAD. Anti-sense transcription may hinder gene expression from the opposing strand, and therefore, represents a novel mechanism by which NKX3-1 expression may be silenced. There were also some inconsistencies including the expression trends of BTG1 [132], FGFRL1 [133], and PCOTH [134] and that may be associated with non-cycling cells. Overall, there was more support at the transcriptome level for proliferation than not, which was consistent with increased proliferation observed in the LNCaP Hollow Fiber model [21].

Gene expression trends of GLO1 [135], S100A10 [136], TRPM8 [137], and PI3KCD [138] suggest cell survival pathways are active following androgen-deprivation and/or in CRPC, while gene expression trends of CAMK2N1 [139], CCT2 [140], MDK [141, 142], TMEM66 [143], and YWHAQ [136] may oppose such suggestion. Taken together, these data neither agree nor disagree with the activation of survival pathways in CRPC. In contrast to earlier reports in which MDK gene and protein expression was determined to be higher in late stage cancer [63, 142], we observed a drop in the levels of MDK mRNA in CRPC versus RAD. MDK expression is negatively regulated by androgen [65]. Therefore, the decreased levels of MDK mRNA in CRPC may suggest that the AR is reactivated in CRPC.

Other

The significance of the gene expression trends of AMD1, BNIP3, GRB10, MARCKSL1, NGRAP1, ODC1, PPP2CB, PPP2R1A, SLC25A4, SLC25A6, and WDR45L that function in cell growth or cell death/survival were not straightforward. For example, BNIP3 and WDR45L, both relatively highly expressed in CRPC versus RAD, may be associated with autophagy. BNIP3 promotes autophagy in response to hypoxia [144], and the WDR45L-related protein, WIPI-49, co-localizes with the autophagic marker LC3 following amino acid depletion in autophagosomes [145]. It is not known if BNIP3 or putative WDR45L-associated autophagy results in cell survival or death. Levels of expression of NGFRAP1 were increased in CRPC versus RAD. The protein product of NGFRAP1 interacts with p75 (NTR). Together they process caspase 2 and caspase 3 to active forms, and promote apoptosis in 293T cells [146]. NGFRAP1 requires p75 (NTR) to induce apoptosis. However, LNCaP cells do not express p75 (NTR), and so it is not clear if apoptosis would occur in this cell line [147].

Overall, genes involved in cell growth and cell death pathways were altered in CRPC. Increased tumor burden may develop from a small tip in the balance when cell growth outweighs cell death. Unfortunately, the contributing weight of each gene is not known, making predictions difficult based on gene expression alone of whether proliferation and survival were represented more than cell death in this model of CRPC. It should be noted that LNCaP cells are androgen-sensitive and do not undergo apoptosis in the absence of androgens. The proliferation of these cells tends to decrease in androgen-deprived conditions, but eventually with progression begins to grow again mimicking clinical CRPC.

Conclusion

Here, we describe the LNCaP atlas, a compilation of LongSAGE libraries that catalogue the transcriptome of human prostate cancer cells as they progress to CRPC in vivo. Using the LNCaP atlas, we identified differential expression of 96 genes that were associated with castration-recurrence in vivo. These changes in gene expression were consistent with the suggested model for a role of the AR, steroid synthesis and metabolism, neuroendocrine cells, and increased proliferation in CRPC.

Author's information

M.D.S. and M.A.M. are Terry Fox Young Investigators. M.A.M. is a Senior Scholar of the Michael Smith Foundation for Health Research.

Abbreviations

ACPP

prostate acid phosphatise

ACTH: 

adrenocorticotropic hormone

AR: 

androgen receptor

AREs: 

androgen response elements

AS: 

androgen-sensitive

BAX

BCL2-associated X protein

BCL-2

B-cell CLL/lymphoma 2

BCL2L1

BCL2-like 1

CAK: 

cyclin-dependent activating kinase

CCND1

cyclin D1

CCNH: 

Cyclin H

CDKN1A

cyclin-dependent kinase inhibitor 1A

CDKN1B

cyclin-dependent kinase inhibitor 1B

CHG

chromogranin

CR: 

castration-recurrent

CRPC: 

castration-recurrent prostate cancer

CUEDC2: 

CUE-domain-containing-2

DHCR24: 

24-dehydrocholesterol reductase

DHRS7: 

dehydrogenase/reductase SDR-family member 7

EASE: 

Expression Analysis Systematic Explorer

ELOVL5: 

elongation of long chain fatty acids family member 5

ENO2: 

neuronal enolase 2

FLNA: 

filamin A

GO: 

Gene Ontology

HES6: 

hairy and enhancer of split 6

HGNC: 

HUGO Gene Nomenclature Committee

HMGB2: 

high mobility group box 2

HMGCS1

3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1

HPA: 

hypothalamus-pituitary-adrenal

HSD17B3

hydroxysteroid (17-beta) dehydrogenase 3

HSD17B4: 

hydroxysteroid (17-beta) dehydrogenase 4

HSD17B5

hydroxysteroid (17-beta) dehydrogenase 5

IL6: 

interleukin 6

KEGG: 

Kyoto Encyclopedia of Genes and Genomes

KLK3

kallikrein 3

KPNB1: 

karyopherin/importin beta 1

LHRH: 

Leutinizing hormone releasing hormone

LongSAGE: 

long serial analysis of gene expression

MAOA: 

monoamine oxidase A

NCOA

nuclear receptor coactivator

NKX3-1

NK3 homeobox 1

NTS

neurotensin

OPRK1: 

opioid receptor kappa 1

PKA: 

protein kinase A

PSA: 

prostate-specific antigen also known as KLK3

PSMA7: 

proteasome macropain subunit alpha type 7

PTHrP: 

parathyroid hormone-related protein

qRT-PCR: 

quantitative real time-polymerase chain reaction

RAD: 

responsive to androgen-deprivation

RAMP1: 

receptor (calicitonin) activity modifying protein 1

RB1

retinoblastoma 1

S100A10: 

S100 calcium binding protein A10

SQLE

squalene epoxidase

TRPM8: 

transient receptor potential cation channel subfamily M member 8.

Declarations

Acknowledgements

The authors would like to thank Jean Wang for her excellent technical assistance and Dr. Simon Haile for helpful discussions. This work was supported by funding from NIH, Grant CA105304 (M.D.S.).

Authors’ Affiliations

(1)
Genome Sciences Centre, British Columbia Cancer Agency

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