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Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process

  • Uma R Chandran1Email author,
  • Changqing Ma2,
  • Rajiv Dhir2,
  • Michelle Bisceglia2,
  • Maureen Lyons-Weiler2,
  • Wenjing Liang2,
  • George Michalopoulos2,
  • Michael Becich1, 2 and
  • Federico A Monzon2
BMC Cancer20077:64

DOI: 10.1186/1471-2407-7-64

Received: 20 September 2006

Accepted: 12 April 2007

Published: 12 April 2007

Abstract

Background

Prostate cancer is characterized by heterogeneity in the clinical course that often does not correlate with morphologic features of the tumor. Metastasis reflects the most adverse outcome of prostate cancer, and to date there are no reliable morphologic features or serum biomarkers that can reliably predict which patients are at higher risk of developing metastatic disease. Understanding the differences in the biology of metastatic and organ confined primary tumors is essential for developing new prognostic markers and therapeutic targets.

Methods

Using Affymetrix oligonucleotide arrays, we analyzed gene expression profiles of 24 androgen-ablation resistant metastatic samples obtained from 4 patients and a previously published dataset of 64 primary prostate tumor samples. Differential gene expression was analyzed after removing potentially uninformative stromal genes, addressing the differences in cellular content between primary and metastatic tumors.

Results

The metastatic samples are highly heterogenous in expression; however, differential expression analysis shows that 415 genes are upregulated and 364 genes are downregulated at least 2 fold in every patient with metastasis. The expression profile of metastatic samples reveals changes in expression of a unique set of genes representing both the androgen ablation related pathways and other metastasis related gene networks such as cell adhesion, bone remodelling and cell cycle. The differentially expressed genes include metabolic enzymes, transcription factors such as Forkhead Box M1 (FoxM1) and cell adhesion molecules such as Osteopontin (SPP1).

Conclusion

We hypothesize that these genes have a role in the biology of metastatic disease and that they represent potential therapeutic targets for prostate cancer.

Background

Prostate cancer is the most common cancer in men resulting in over 232,090 new cases and 30,350 deaths annually [1]. For prostate cancer patients, metastatic disease reflects the most adverse clinical outcome. Osseous involvement with severe bone pain and spinal cord complications occur commonly in patients with metastatic disease [2]. However there is considerable heterogeneity in outcome after primary diagnosis and currently there are no morphologic or circulating biomarkers that can accurately predict the development of metastatic disease.

Metastatic prostate cancer represents the tumor's ability to escape from the primary organ and eventually colonize a distant site. Disruption of a complex set of biological processes must occur in order for tumor cells to leave the prostate and establish themselves in a different environment. Their altered interaction with the prostate microenvironment, including the stroma and extracellular matrix, their ability to migrate into the vasculature and establish themselves in secondary organs with recruitment of vascular supply represent disruption of normal cellular processes [3]. Understanding the molecular events involved in the development of metastatic prostate cancer has the potential to identify biological determinants that can aid in prognosis and development of more effective therapies.

Using gene expression microarrays, a number of studies have characterized expression profiles of prostate cancer, normal tissue and metastatic cancers. In some cases, correlations between tumor expression signatures, clinical parameters and outcome have been identified [411]. Unique profiles have been reported for untreated and short-term androgen ablation treated organ-confined disease and for metastatic disease, with a subset of genes differentiating metastatic androgen ablation resistant prostate cancer (AARPC) from androgen dependent metastatic cancers [10, 1214]. In general, metastatic prostate cancer is characterized by changes in expression of genes involved in signal transduction, cell cycle, cell adhesion, migration and mitosis. In addition to these genes, AARPCs exhibit changes in expression of the androgen receptor and enzymes involved in the sterol biosynthesis pathway [12].

Some of the genes previously reported as highly downregulated in prostate tumors may reflect the differences in cellular content of metastatic and organ-confined tissues rather than intrinsic differences in biology. In contrast with organ-confined prostate tumors which are composed of a mixture of glandular epithelial, smooth muscle and other stromal cells, metastatic tissue samples are almost exclusively epithelial, with minimal supporting stroma and absence of smooth muscle. In this study, we characterize gene expression in androgen ablation resistant metastatic tumors after removing potentially uninformative stromal genes. The deleted stromal genes consist of those reported in a recent report characterizing the gene expression patterns in the prostate stroma, tumor and normal epithelium [15]. Our results provide novel insights into the biology of metastasis.

Methods

Tumor sample procurement

All tissue samples were acquired from the Health Sciences Tissue Bank of the University of Pittsburgh Medical Center under stringent Institutional Review Board guidelines with appropriate informed consent. The 18 donor and 64 primary prostate tumor samples have been described previously [7]. Specimens were received directly from the operating room. Samples (>500 mg) were excised and snap frozen in liquid nitrogen within 30 min of excision and stored at -80°C until extraction of RNA. Metastatic tumor samples were obtained from a warm autopsy program and processed similarly to primary tumors. An H&E stained frozen section of each sample was evaluated by a pathologist, to determine epithelial and stromal content and verify the presence of tumor in the sample. Dissection of the frozen tissue block was performed with the guidance of a marked H & E slide to minimize the presence of host tissue in the metastatic samples. All samples used in the study contained >80% tumor. Metastatic tumor samples were minced and divided into two equal portions to be extracted with the sample protocol used for each set of primary tumors.

Clinical profile of cases

The clinical characteristics of the 64 primary tumor samples used in the Affymetrix portion of our study have been previously described [6, 7]. These cases have a mean follow-up time of 3 years. The metastatic samples consisted of 24 tissues derived from 4 patients (Table 1). All patients with metastatic disease had received androgen ablation therapy and had shown progression of disease while on androgen ablation. The clinical characteristics for the additional 10 primary prostate tumor cases used in the CodeLink study are shown in Table 1.
Table 1

Clinical variables for primary and metastatic prostate cancer samples used in this study

Prostate Cancer Tissue Samples

No. of Samples

Number of Patients

Microarray Platform

Clinical Information

 

64

64

Affymetrix

Please see reference [7]

    

Gleason Score

No. of Cases

    

7

10

Primary Tumors

10

10

CodeLink

Pathological Stage

No. of Cases

    

2B

6

    

3A

3

    

3B

1

    

Patient ID

No. of Samples

    

FB6561

11

    

FB666

1

    

FB667

8

    

FB669

5

    

Metastatic Sites

No. of Samples

Metastatic

24

4

Affymetrix

Liver

5

    

Para Aortic Lymph Node

3

    

Para Tracheal Lymph Node

8

    

Retroperitoneal Lymph Node

3

    

Lung

1

    

Adrenal

2

RNA extraction

RNA purification for the 64 primary samples has been previously described [6]. The set of metastatic samples analyzed with the Affymetrix platform was extracted with the same methodology. The set of metastatic samples and primary tumors analyzed with the CodeLink platform were extracted using the RNeasy kit (Qiagen, San Diego, CA). For the metastatic samples, one sample did not have enough for extraction with the Qiagen method, only 23 metastatic samples are included in the CodeLink assays. The concentration of each total RNA sample was measured with a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies,Wilmington, DE). RNA integrity was determined by capillary electrophoresis using an Agilent 2100 Bioanalyzer (Agilent, Willmington, DE).

cRNA preparation and gene expression assays

cRNA was prepared and hybridized to Affymetrix GeneChip HGU95av2, HGU95b and HGU95c arrays (Affymetrix, Santa Clara, CA) as previously described [6]. For gene expression profiling with the CodeLink Gene Expression System (GE Healthcare, Piscataway, NJ), biotin-labeled cRNA was prepared as previously described [16]. Ten micrograms of biotin-labeled cRNA product from each sample were then fragmented with RNA fragmentation buffer at 94°C for 20 minutes. Hybridization mix was prepared according to the manufacturer's instructions and the final volume was adjusted to 260ul using nuclease-free water. The hybridization mix was heat denatured at 90°C for 5 minutes, cooled on ice and then applied to Human Uniset 20 K arrays (GE Healthcare, Piscataway, NJ). Arrays were incubated at 37°C for 18 h with shaking at 60 rpm in an Innova hybridization oven (New Brunswick, Edison, PA).

After hybridization, arrays were placed in a pre-heated (46°C) chamber filled with 0.75 × TNT (0.75 M Tris-HCL, pH 7.6, 3.75 M NaCl, Tween-20, and milli-Q water) and incubated at 46°C for 1 hour. Arrays were then stained with Streptavidin-Alexa Fluor 647 (Molecular Probes, Grand Island, NY) for 30 minutes at room temperature. Upon the completion of staining, the arrays were washed three sequential times in fresh 1 × TNT (1 M Tris-HCL, pH 7.6, 5 M NaCl, Tween-20, and milli-Q water) and then washed two final times in fresh solutions of 0.05% Tween-20 and 0.1 × SSC with gentle agitation. All arrays were dried by centrifugation at 2000 rpm for 3 minutes.

Affymetrix arrays were scanned in an Affymetrix GCS3000 Scanner (Affymetrix, Santa Clara, CA). CodeLink arrays were scanned with the GenePix 4000B scanner using GenePix Pro 4.1 software (Molecular Devices, Sunnyvale, CA).

Gene expression data analysis

The raw scanned array images from the Affymetrix GeneChip U95 arrays were processed using GCOS 1.1 software using the MAS5 algorithm (Affymetrix Corporation, Santa Clara, Ca) to generate probe cel intensity (*.cel) files. Data normalization to remove variation in overall chip intensities was performed by global scaling to a chip mean target intensity of 200 (MAS 5.0). Data for U95Av2, B and C arrays were combined for further analyses.

To identify differentially regulated genes in both datasets, these were analyzed with the Significance Analysis of Microarrays software (SAM v 1.2) [17]. Prior to analysis, genes that showed low variation across all samples were removed by using the filtering option in the Avadis 3.3 Pride Software (Strand Life Sciences, Bangalore, India) data analysis tool. To avoid false results due to difference in the tissue composition of metastatic and primary tumors, genes identified as being highly expressed in the prostatic stroma as per Stuart et al [15] were also removed. In all 1506 stromal genes and 7678 invariant genes were removed from the Affymetrix dataset. SAM generated gene lists with the lowest false discovery rates (FDR) were further analyzed for gene ontology (GO) and pathway annotations using NIH's DAVID annotation tool [18].

For CodeLink arrays, image files were analyzed with the CodeLink Expression Analysis Software version 4.1 (GE HealthCare) with use of the normalized intensity values in downstream analysis. For cross-platform comparison, Affymetrix probe sets and Codelink identifiers were mapped to Unigene ids using the DAVID annotation tool (see above). Expression data from both platforms was compared using z-transformation. Hierarchical clustering was performed using Eisen's Cluster and Treeview [19]. Data from Affymetrix experiments has been submitted to NCBI's Gene Expression Omnibus (GEO) as series GSE6919, with the following accession numbers GSE6604 (normal donor prostate), GSE6605 (metastatic prostate tumors), GSE6606 (primary prostate tumors) and GSE6608 (normal prostate tissue adjacent to tumor). Data from the CodeLink platform have been submitted to GEO with the accession number GSE6752 (primary and metastatic prostate tumors).

Quantitative real-time PCR

Differential expression of ten genes in primary and metastatic prostate cancer samples was verified with quantitative real-time PCR (QPCR) with the ABI PRISM® 7000 sequence detection system (Applied Biosystems, Foster City, CA). Three selected RNA samples from each patient were pooled together (except for patient FB666 n = 1) and therefore four RNA samples, each representing one patient, were tested. RNA samples were first heat-denatured at 70°C for 10 minutes in an Eppendorf master cycler (Eppendorf, Westbury, NY) and then chilled immediately on ice. cDNAs were reversely transcribed from one microgram of RNA using the M-MLV Reverse Transcriptase kit (Invitrogen, Carlsbad, CA), as recommended by the manufacturer. QPCR was performed based on the manufacturer's instructions with TaqMan Gene Expression Assays (Applied Biosystems) for the following genes: EGR3, SYNPO2, ANGPT2, SPP1, FOXM1, ADM, RDX, TGFBRAP1, MAK and EGR1(assay IDs: Hs00231780_m1, Hs00326493_m1, Hs01048047_mH, Hs00959010_m1, Hs00153543_m1, Hs00969450_g1, Hs00988414_g1; Hs01093285_m1; Hs01048300_m1, Hs00152928_m1). When multiple TaqMan assays for one gene were available, the assay that interrogated the sequence closest to the target sequence in the Affymetrix arrays was chosen. PCR cycles were performed according to the assay instructions in an ABI PRISM® 7000 Sequence Detection System (Applied Biosystems). Relative quantification of the expression level of each transcript in each sample was calculated using the Delta-Delta CT method in the ABI PRISM 7000 Sequence Detection System Software (Applied Biosystems) [20]. Human reference RNA from Stratagene (Stratagene Corp., La Jolla, CA) was used as the calibrator (untreated control) and human glucuronidase beta (GUSB) gene was used as the endogenous reference gene (Forward primer: GGA ATT TTG CCG ATT TCA TGA; Reverse primer: CCG AGT GAA GAT CCC CTT TTT; Probe: 6FAM-AAC AGT CAC CGA CGA GAG TGC TGG G-TAMRA).

Results

Differential gene expression in metastatic prostate cancer and the role of stromal content in defining true downregulated genes

Differential expression analysis of the metastatic and primary tumor samples shows that a large number of the most highly downregulated genes such as TAGLN, ACTG2, TPM1, MYH111 and DES have been previously identified as expressed mostly in the prostatic stromal cells [15]. Since only the epithelial component of prostate cancer is present in metastatic tumors, this result most likely reflects the lack of stroma in metastases, and not a true down-regulation of these genes in the metastatic epithelial cells. Therefore, based on a recent report characterizing cell type specific gene expression in the prostate [15], we removed the set of genes expressed mainly by the stromal cells of the primary tumors. In all 1506 transcripts associated with a stromal signature were deleted prior to further analysis. Since the stromal genes were characterized using the U95Av2 chip and our analysis includes u95Av2, B and C chips, only stromal genes represented by probe sets on U95Av2 were removed in this modified analysis. SAM analysis shows that 1277 genes are up and 977 genes are downregulated at least 2 fold at the lowest FDR (0.01), in metastatic prostate samples (see Additional file 1). A list of the top 50 up and top 50 down regulated genes at the lowest fdr, after removing ESTs and uncharacterized clones is shown in Table 2. This list includes signal transducers, cell cycle regulators, metabolic enzymes and cell adhesion molecules. Some of the most upregulated genes in our list are EIF1AX, AR, HSPD1 and HSPCA, K-ALPHA1, MLL5, UGT2B15, and some of the most downregulated genes include WNT5B5, ANXA11, FOS and SFRP1.
Table 2

Top 100 genes differentially expressed in metastatic samples compared to primary tumor samples

Gene Symbol

Probe_ID

d_Value

Fold Change

EIF1A

34278_at

19.46

3.56

AR

1577_at

16.04

10.09

AK3

48822_s_at

15.18

3.03

EIF1A

663_at

14.17

2.69

PABPC1

44806_at

14.17

3.57

---

45092_at

13.16

3.66

HSPD1

37720_at

12.80

2.80

---

54219_at

12.78

3.23

LCHN

58324_at

12.39

3.49

MLL5

58271_at

12.15

2.45

---

59350_at

11.98

3.22

---

49558_at

11.93

2.73

LARP

41829_at

11.60

3.14

IBTK

52482_at

11.41

3.30

GIT1

43805_f_at

11.41

2.49

---

56056_at

11.31

2.64

FLJ20736

64662_at

11.21

4.32

AR

1578_g_at

11.20

6.19

RALA

39253_s_at

11.19

3.61

HN1

56429_g_at

11.17

2.89

---

54236_at

11.04

3.82

HSPCA

32316_s_at

10.94

2.68

NUCKS

59778_f_at

10.93

2.44

SOD1

36620_at

10.87

2.16

K-ALPHA-1

32272_at

10.72

2.23

---

46558_at

10.70

4.70

---

33207_at

10.62

2.25

VPS28

43061_i_at

10.61

2.19

BASP1

32607_at

10.57

4.15

CBX4

51842_at

10.49

3.06

---

43680_at

10.45

4.85

GRB2

33855_at

10.44

2.55

---

63147_at

10.43

3.86

UGT2B15

63915_f_at

10.35

3.07

METTL2

48730_s_at

10.26

2.24

---

59101_at

10.11

7.12

MLL5

58690_at

10.02

2.24

G3BP

41133_at

10.00

2.25

AK3

32331_at

9.96

2.31

H2AV

39092_at

9.92

3.25

SDCCAG3

43014_at

9.91

6.04

FLJ10613

59989_s_at

9.82

2.20

YY1

891_at

9.70

2.08

---

49326_at

9.59

2.20

---

42646_at

9.50

5.45

LOC90462

54342_at

9.44

4.55

---

55393_at

9.42

3.51

DDX17

41260_at

9.35

4.97

---

63115_at

9.34

3.07

---

57160_at

9.34

2.11

ETR101

36097_at

-6.98

-3.85

MAGED2

34859_at

-7.00

-2.22

---

50411_at

-7.00

-3.57

MGC4342

48094_at

-7.01

-2.22

HT023

43461_g_at

-7.01

-2.78

SFRP1

32521_at

-7.02

-6.25

CRYL1

56407_at

-7.04

-2.04

REA

37364_at

-7.05

-2.17

ARL5

59499_at

-7.08

-2.50

NY-REN-45

57833_s_at

-7.09

-2.08

POMT1

46723_at

-7.12

-3.23

MRC2

63996_at

-7.13

-2.38

B3GALT3

53879_at

-7.16

-3.13

NR4A1

280_g_at

-7.21

-7.14

MUM2

65822_at

-7.23

-2.38

LOC113246

46712_at

-7.31

-2.17

GLTSCR2

61109_at

-7.33

-2.44

FLJ20542

50164_at

-7.36

-3.13

APCDD1

56272_at

-7.42

-2.70

AIM1

32112_s_at

-7.45

-3.70

FOS

1916_s_at

-7.51

-7.69

FOS

1915_s_at

-7.56

-9.09

PILB

43370_at

-7.56

-2.08

C20orf178

45298_at

-7.59

-2.13

B3GAT1

65859_at

-7.60

-2.13

FLJ10283

46261_at

-7.68

-2.27

RAB34

45269_at

-7.71

-10.00

FLJ20069

61701_at

-7.71

-3.23

PYGB

59669_at

-7.77

-2.70

RPS27L

56410_at

-7.80

-2.27

TOMM20

36198_at

-7.91

-2.13

---

43819_g_at

-7.91

-3.13

STAT6

41222_at

-7.92

-2.78

SELM

64449_at

-7.95

-9.09

BOC

52999_at

-8.15

-4.17

---

44746_at

-8.20

-2.13

SMBP

46307_at

-8.28

-3.23

WNT5B

61330_at

-8.34

-3.33

FLJ22386

50198_at

-8.43

-4.55

WNT5B

58787_at

-8.61

-2.27

ZDHHC4

45807_at

-8.81

-2.13

YF13H12

36170_at

-8.84

-2.08

HPIP

38063_at

-9.04

-2.33

ANXA11

55664_at

-9.16

-2.94

WNT5B

66142_s_at

-9.33

-4.17

WAS

38963_i_at

-9.43

-2.50

JFC1

44820_f_at

-9.45

-3.33

JFC1

48805_f_at

-9.48

-2.44

WNT5B

61292_s_at

-9.86

-7.14

CIRBP

39864_at

-10.94

-3.70

Gene expression data from the Affymetrix platform for 25 metastatic and 64 primary tumor samples was analyzed for differential gene expression by SAM. The differentially expressed genes with the lowest FDR were sorted by fold change. The top 100 genes, organized by fold change are shown.

Metastatic samples are heterogenous in gene expression

Using immunohistochemistry (IHC) Shah et al. have shown that metastatic samples are highly heterogenous in expression of prostate specific markers leading to the hypothesis that at the molecular level, metastatic prostate cancer may represent multiple diseases even within the same patient [21]. We examined the expression of several transcripts markers including some studied by Shah et al. and confirmed the heterogeneity of expression levels in metastatic prostate cancer tissues. Expression values in donor samples, primary and metastatic samples were compared. Prostate specific antigen (PSA/KLK3) remains high in some metastatic samples and is low or absent in others, even within the same patient (Figure 1). Interestingly, AMACR, another biomarker for prostate cancer [22] expresses a heterogenous expression pattern similar to PSA. HPN, which is overexpressed in primary cancer maintains high expression in the metastatic samples in our study. AR, while overexpressed in 23 out of the 24 metastatic samples, shows highly variable expression values in individual samples. The proto-oncogenes FOS and JUNB, which are both overexpressed in primary tumors, are consistently downregulated in all metastatic samples.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-7-64/MediaObjects/12885_2006_Article_714_Fig1_HTML.jpg
Figure 1

Box plots of gene expression values for selected genes in donor prostate samples, primary prostate cancer and metastatic prostate samples.

Genes regulated in all metastatic cases

Hierarchical clustering analysis reveals that gene expression in metastatic samples is more variable between patients than between different metastatic sites from each patient (Figure 2). Although the 24 metastatic samples represent tissues from 6 metastatic sites (Table 1), no organ specific clusters were detected (Figure 2) whereas samples from the same patient tend to cluster together. Statistical comparison of organ-specific expression profiles was not attempted due to unequal distribution of samples from different metastatic sites.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-7-64/MediaObjects/12885_2006_Article_714_Fig2_HTML.jpg
Figure 2

Hierarchical clustering of primary and metastatic prostate cancer samples. The 24 metastatic (Mets P1, Mets P2, Mets P3 and Mets P4) and 64 primary tumor samples were clustered. The top row of color coded boxes represents metastatic or primary samples; the bottom row represents the organ from which the sample was obtained.

In order to identify probe sets that are similarly regulated in every patient, and therefore likely to represent a specific metastatic profile, the SAM differentially expressed gene list at a FDR of 2% was further filtered. For each gene on this list, a patient specific median expression value was calculated from the multiple samples from each patient. Patient P4 had only sample and this sample's signal value was considered the median value. The median values were then compared to the median value of the primary samples and those probe sets whose median value showed equal or more than a 2 fold change in every patient were considered part of the metastatic prostate cancer signature. Under this criteria 415 transcripts are upregulated and 364 are downregulated in all patients with metastasis (see Additional file 2). A truncated gene list consisting of genes regulated at least 3 fold in all patients is shown in Table 3. Upregulation of AR in all samples from metastatic cancer patients represents a known "androgen resistant" or AARPC (androgen ablation resistant prostate cancer) phenotype [12]. The transcripts identified as differentially expressed in our study exhibit similarities with a previous study of AARPC tumors [10, 12]. Cytokeratins 5 and 15 (KRT5/KRT15), markers of basal cells in prostate glands, show uniform downregulation in all metastatic tumors, confirming the absence of basal epithelial cells.
Table 3

Transcripts with median values with at least 3 fold difference between metastatic and primary tumor samples

Gene Symbol

Probe_ID

P1

P2

P3

P4

Upregulated Transcripts

    

HBB

32052_at

22.37

5.78

13.25

56.28

SPP1

34342_s_at

24.16

26.78

4.75

5.39

HBA1///HBA2

31525_s_at

15.14

4.47

13.65

108.11

LGR4

43585_at

7.39

7.43

20.89

24.82

AR

1577_at

14.35

12.97

12.24

14.78

PRO1073

49666_s_at

4.56

13.25

10.01

13.5

UTRN

42646_at

10.11

6.02

12.11

16.31

HNT

59070_at

5.37

9.69

12.08

13.67

SDCCAG3

43014_at

7.99

8.57

11.24

17.32

LOC64744

42739_at

7.3

9.64

9.57

14.51

---

1089_i_at

5.06

4.14

12.12

22.01

SPP1

2092_s_at

14.05

12.94

3.35

4.07

UBE2H

58777_at

9.5

7.45

6.55

15.13

SRPK1

63687_at

6.06

4.36

10.61

12.82

NCK2

33003_at

5

9.14

8.5

7.34

HIST1H3H

36757_at

7.26

17.07

8.47

5.61

PPP4R2

48663_at

5.09

6.97

8.59

16.16

C8orf16

47339_at

6.54

8.15

9.53

7.39

---

55943_at

3.41

7.47

15.31

8.01

---

64642_s_at

8.25

6.42

7.04

10.6

EP400

47518_at

5.94

9.27

4.51

9.32

GOLT1A

45144_at

3.9

6.17

8.37

12.32

---

52853_g_at

9.83

7.1

6.25

7.03

LOC284058

44791_at

8.25

10.17

4.55

5.86

DAPK1

51580_at

3.42

6.04

8.03

11.32

NFATC2IP

38864_at

3.26

4.83

9.58

9.19

SEL1L

40689_at

4.71

7.84

6.13

10.94

TM4SF9

47746_at

3.43

6.26

8.92

7.52

MLLT2

65205_at

3.43

7.13

6.57

13.01

SC4MOL

46802_at

22.91

7.35

5.62

6.17

---

62671_at

6.38

7.13

5.74

11.03

BIRC6

46558_at

5.67

8.59

7.5

5.92

MAP4K4

51474_at

4.86

4.32

8.7

8.52

MLLT2

53300_at

4.65

3.99

9.39

8.1

---

52851_at

8.71

5.94

6.35

6.25

MRRF

51635_at

4.23

4.87

7.39

8.23

ACAS2

62783_at

4.29

6.14

7.02

5.9

---

60658_at

3.4

6.84

5.19

9.1

SUMO1

49551_at

4.05

7.2

4.77

7.75

AR

1578_g_at

7.56

4.86

5.32

6.37

GALNT7

59101_at

8.41

4.12

5.11

6.54

GPR75

44203_at

5.14

8.32

3.9

6.31

TBL1XR1

65001_r_at

3.53

12.3

4.06

7.19

HSD17B12

43292_at

4.74

8.88

3.64

6.28

MRPS28

43095_at

5.79

5.39

5.58

5.14

FN1

64719_at

27.07

6.02

4.05

4.93

GPR158

44214_at

7.21

3.33

4.32

6.62

---

48069_at

6.27

9.88

3.38

4.55

FLJ21657

58778_at

4.34

5.6

6.17

5.18

MLL5

43301_at

4.76

3.61

5.87

10.34

---

55761_at

3.78

4.88

5.65

6.93

DLG1

47231_at

3.4

4.77

6.22

5.7

MYO5B

63281_r_at

3.29

6.17

4.29

6.84

---

49268_at

3.55

19.86

3.61

6.75

FUS

43501_at

3.93

3.78

6.42

8.97

CCDC35

54684_at

4.9

8.14

3.55

5.43

---

43435_at

6.85

4.83

4.82

5.49

SMA4

32921_at

4.68

5.53

5.74

4.26

NCOA1

45953_at

6.53

4.13

3.58

6.06

S100A8

41096_at

4.22

5.89

3.8

22.58

PRKCBP1

53493_at

4.65

7.37

4.5

5.35

RNPC2

65083_at

3.18

3.96

6.01

9.19

CAMSAP1

62630_at

4.45

5.8

3.36

5.36

EEF1G

41903_at

5.19

4.58

4.31

5.34

EIF5

51379_at

3.44

4.08

5.62

11.07

MAML3

49879_at

3.39

3.22

10.27

5.87

C21orf106

59651_at

3.19

4.02

5.23

6.44

VCIP135

42715_at

3.37

3.61

5.52

8.55

FOXO3A

55502_at

3.48

4.37

6.97

4.74

C7orf20

49143_s_at

4.23

4.62

4.41

5.78

GNMT

46482_at

3.59

4.84

4.24

4.64

DONSON

48549_at

4.1

3.58

4.66

5.28

---

43436_g_at

4.98

3.75

3.58

5.09

PKP4

66327_at

3.31

3.88

4.56

6.2

PCBP2

55393_at

3.73

3.19

4.36

6.29

CPEB4

57169_at

3.7

3.92

4.14

4.48

CUGBP1

34683_at

4.26

3.76

3.13

4.78

FALZ

47458_at

4.21

3.65

3.82

4.09

---

51586_at

3.51

4

4.99

3.89

RALA

39253_s_at

3.92

4.3

3.29

3.85

MLL5

45092_at

4.36

3.21

4.48

3.39

PABPC1

44806_at

3.74

3.98

4.2

3.07

EIF1AX

34278_at

3.99

3.47

3.84

3.19

C7orf2

42173_at

3.15

3.27

5.07

4.04

---

63147_at

3.25

5.4

3.12

4.04

RAD23B

41157_at

3.2

3.46

3.64

4.45

---

61037_at

3.44

3.56

3.47

3.73

NFATC1

39143_at

3.13

3.21

9.06

3.78

JARID1A

50532_at

3.22

3.32

3.54

4.12

PDLIM5

37366_at

3.02

3.58

3.42

3.16

Downregulated Transcripts

    

NEFH

33767_at

-117.15

-147.36

-9.9

-17.18

C10orf116

32527_at

-35.49

-29.63

-46.85

-66.5

KLK11

40035_at

-23.65

-19.24

-39.73

-62.15

FAM3B

59657_at

-15.81

-27.92

-26.09

-25.97

PGM5

52140_at

-23.87

-26.5

-44.27

-17.72

MRGPRF

52946_at

-15.61

-18.57

-30.59

-70.95

KRT15

37582_at

-21.85

-20.74

-19.22

-33.68

PTN

34820_at

-11.62

-31.95

-10.24

-27.11

SELM

64449_at

-6.36

-8.4

-29.23

-39.36

MYLK

46276_at

-5.87

-15.22

-22.57

-20.86

SYNPO2

50361_at

-15.14

-15.77

-20.15

-84.14

KRT5

613_at

-13.21

-11.12

-22.66

-32.96

FOS

2094_s_at

-10.72

-25.75

-13.72

-16.45

PKP1

51214_at

-11.57

-16.34

-11.83

-17.85

---

42921_at

-9.96

-11.67

-15.61

-16.5

RAB34

45269_at

-14.36

-11.54

-17.49

-10.35

---

48927_at

-10.61

-14.93

-8.77

-21.91

ALOX15B

37430_at

-12.47

-12.41

-14.17

-9.1

FOS

1915_s_at

-7.59

-26.38

-11.03

-12.11

TMEM16G

62387_at

-9.63

-13.32

-12.59

-9.93

---

64676_at

-17.3

-9.39

-6.32

-13.05

SFRP1

32521_at

-13.1

-5.73

-8.29

-16.73

NDFIP2

60510_at

-7.2

-9.23

-11.72

-15.15

FHOD3

50298_at

-9.96

-12.84

-5.59

-10.96

WNT5B

61292_s_at

-8.72

-11.85

-5.42

-13.92

SYNPO2

48039_at

-11.04

-8.8

-12.64

-9.34

BOC

64423_s_at

-3.63

-8.16

-11.8

-54.66

SLC20A2

1137_at

-9.27

-5.08

-10.51

-12.61

COL8A2

52652_g_at

-7.95

-9.99

-11.56

-9.75

---

52678_at

-9.69

-9.99

-3.76

-17.93

FOS

1916_s_at

-7.58

-21.81

-6.93

-11.58

ARGBP2

51939_at

-7.77

-13.86

-10.4

-8.71

CTGF

64342_at

-4.21

-4.15

-20.44

-14.87

EPHB6

39930_at

-8.61

-9.66

-8.32

-19.41

SYNPO2

60532_at

-9.77

-5.54

-8.77

-9.03

NR4A1

280_g_at

-8.68

-13.49

-5.82

-8.58

DKFZP564O0823

54033_at

-4.67

-3.72

-11.83

-20

GSTO2

45609_at

-4.73

-6.81

-9.6

-16.18

---

49321_at

-7.91

-8.41

-9.24

-3.88

EGR3

40375_at

-9.89

-7.71

-8.49

-6.44

SYNPO2

61681_at

-7.85

-8.33

-4.56

-18.57

PI15

58361_at

-3.59

-4.26

-12.77

-11.74

FOSB

36669_at

-8.81

-6.27

-7.6

-8.39

OGN

43507_g_at

-3.56

-8.26

-7.19

-25.54

MOXD1

36834_at

-5.4

-11.7

-10

-3.85

LSAMP

43930_at

-3.05

-7.62

-9.76

-7.67

EGR2

37863_at

-7.7

-5.52

-7.23

-15.41

DKFZp686D0853

49770_at

-10.18

-7.66

-7.16

-4.39

LGP1

52826_at

-13.75

-5.94

-3.83

-8.11

ME3

35216_at

-7.45

-9.26

-6.54

-5.32

PPP1R14A

58774_at

-6.68

-6.14

-7.31

-7.87

FLJ22386

50198_at

-6.8

-3.64

-6.98

-6.65

NR4A1

279_at

-5.31

-8.04

-5.11

-8.48

WFDC1

64111_at

-3.79

-11.21

-6.64

-6.66

ZFP36

40448_at

-6.39

-6.86

-7.25

-3.61

CACHD1

43554_at

-6.68

-3.34

-17.46

-6.57

RLN1

35070_at

-6.78

-11.78

-5.14

-6.39

---

49975_at

-6.43

-6.16

-6.74

-10.11

CYBRD1

65852_at

-6.43

-4.79

-6.7

-7.23

PER3

53766_at

-15.43

-6.79

-5.56

-6.29

MN1

37283_at

-4.47

-7.36

-5.55

-7.48

DNCI2

35788_at

-4.2

-8.68

-3.02

-10.64

MRVI1

43966_at

-6.76

-5.28

-12.19

-6.09

AZGP1

35834_at

-6.32

-3.86

-38.18

-6.18

MGC14839

48949_at

-8.96

-4.19

-8.25

-3.61

SMTN

64499_s_at

-5.2

-15.22

-7.18

-4.42

HSPC157

50179_at

-5.66

-3.18

-6.63

-8.09

WFDC2

33933_at

-5.3

-6.5

-5.73

-6.81

BTG2

36634_at

-6.99

-3.13

-9.25

-5.22

AXIN2

64129_at

-4.97

-6.97

-7.18

-4.2

PDGFC

45217_at

-4.32

-7.53

-8.81

-3.97

MLLT10

63345_at

-7.2

-5.85

-5.9

-3.84

BMP7

49273_g_at

-4.58

-4.89

-6.82

-13.13

MCC

49504_r_at

-5.9

-5.71

-5.08

-5.84

HEXA

39340_at

-8.15

-5.65

-4.18

-5.88

GSTT2

1099_s_at

-6.47

-5.05

-6.8

-4.66

SSPN

65647_at

-5.4

-5.88

-3.12

-17.61

UPK3A

36379_at

-5.37

-4.71

-5.81

-6.91

PDE5A

54668_at

-4.44

-5.17

-5.87

-9.56

PSD3

63832_at

-3.19

-6.04

-4.98

-6.58

ALDH7A1

61965_at

-5.85

-5.14

-5.88

-3.13

FMOD

33431_at

-7.62

-4.3

-4.9

-6.04

TSPAN2

53693_at

-6.38

-4.49

-4.54

-6.77

DKFZP586H2123

40017_at

-6.52

-6.49

-4.32

-3.91

EFS

33883_at

-5.43

-3.58

-6.35

-5.18

PODN

63953_at

-4.16

-5.3

-4.84

-4.98

DUSP1

1005_at

-6.53

-16.66

-3.02

-3.19

SLC22A17

58898_s_at

-4.93

-5.81

-4.66

-4.44

CDH10

47535_at

-4.87

-3.19

-8.27

-4.65

---

64163_at

-3.66

-5.03

-4.79

-4.7

---

42587_at

-4.68

-4.62

-4.9

-3.45

TSPAN2

57331_at

-4.44

-8.06

-3.42

-4.71

SORBS1

56409_at

-5.45

-5.7

-3.17

-3.53

C21orf63

50658_s_at

-4.54

-3.36

-4.15

-5.31

NBL1

37005_at

-3.34

-4.27

-4.31

-6.36

CIRBP

39864_at

-4.38

-3.53

-4.19

-6.8

KLF4

48587_at

-3.77

-3.62

-4.57

-12.5

ZCSL2

45320_at

-3.1

-3.19

-5.88

-5.13

C12orf10

53911_at

-3.62

-4.44

-3.86

-6.46

CERKL

60314_at

-4.68

-3.03

-7.37

-3.62

NOV

39250_at

-3.2

-3.9

-4.38

-7.37

EPB41L5

60293_at

-4.33

-4.97

-3.06

-3.92

WNT5B

66142_s_at

-3.94

-3.87

-4.49

-4.16

ACYP2

64090_s_at

-3.36

-4.33

-3.68

-5.82

C9orf103

56186_at

-3.14

-4.62

-4.03

-3.73

FBXO2

57811_at

-3.51

-3.37

-4.16

-5.33

CD38

40323_at

-3.25

-3.37

-4.27

-4.27

BCAS1

37821_at

-4.96

-3.19

-4.26

-3.34

TMSL8

36491_at

-3.03

-4.11

-3.45

-7.67

ISL1

39990_at

-3.12

-3.78

-3.61

-3.91

HSPB8

56474_at

-3.45

-3.87

-3.04

-7.5

B3GALT3

53879_at

-3.04

-4.02

-3.77

-3.48

CYBRD1

50955_at

-3.7

-3.51

-3.21

-5.6

EFEMP2

63644_at

-3.25

-3.91

-3.28

-3.97

TU3A

45260_at

-3.14

-3.94

-3.22

-4.82

LOC57228

34176_at

-3.68

-5.3

-3.41

-3.16

IER2

36097_at

-4.79

-3.2

-3.11

-3.88

DKFZP564K1964

65860_at

-3.53

-3.11

-3.52

-4.62

A patient-specific median expression value was calculated from the multiple samples for each patient. These median values were then compared to the primary tumor expression value and those genes with 3-fold difference between metastatic and primary prostate cancer is shown.

Biological annotation of differentially expressed genes in metastatic prostate cancer

The list of differentially expressed transcripts at least 2 fold in all patients was further analyzed for biological themes and gene ontology (GO) using the NIH's DAVID annotation tool. This analysis revealed that metastatic prostate cancer exhibits altered regulation of amino acid, carbohydrate and nucleotide metabolism consistent with the proliferative capacity and altered energy needs of metastatic tumors (data not shown). In the context of prostate cancer biology, genes involved in cell-adhesion, bone remodeling, cell-cycle and transcription are of particular interest (Table 4). Disruption of cell adhesion and altered interaction with the extracellular matrix is a hallmark of metastatic tumors [3]. In agreement with this, the secreted phosphoprotein and cell adhesion molecule osteopontin (SPP1) is one of the most highly upregulated transcripts in our metastatic samples. Elevated expression of SPP1 has been correlated with poor prognosis in prostate tumors and other cancers and it has often been implicated in metastasis to bone and other organs [2, 2328]. In all 29 probe sets representing cell adhesion genes are altered in all metastatic samples. This gene list includes FN1, ITGB8, THBS2, HNT and CDH10. Genes involved in bone remodeling such as BMP4 and ANKH are also altered in expression, although none of the samples in our study are bone metastatic samples suggesting that these proteins may also be involved in cancer metastasis to other organs.
Table 4

GO and pathway annotation of genes and pathways altered in metastatic prostate cancer

Probe ID

Gene Symbol

Overall FC

FC_P1

FC_P2

FC_P3

FC_P4

Bone remodeling

     

34342_s_at

SPP1

24.7

24.16

26.78

4.75

5.39

2092_s_at

SPP1

11.8

14.05

12.94

3.35

4.07

47958_r_at

ANKH

-2

-2.23

-2.31

-2.03

-3.66

65035_at

TF1P11

-2.31

-2.28

-2.63

-2.97

-3.77

44596_at

TWIST2

-2.45

-2.7

-2.12

-2.94

-2.35

40333_at

BMP4

-2.5

-2.86

-2.89

-3

-5.23

49273_g_at

BMP7

-2.81

-4.58

-4.89

-6.82

-13.13

Cell Adhesion

     

34342_s_at

SPP1

24.7

24.16

26.78

4.75

5.39

2092_s_at

SPP1

11.8

14.05

12.94

3.35

4.07

64719_at

FN1

7.5

27.07

6.02

4.05

4.93

59070_at

HNT

5

5.37

9.69

12.08

13.67

62628_at

PCDHGC3

3.4

2.24

5.44

2.97

2.42

44892_at

MLLT4

3.3

2.63

2.07

6.21

7.48

47064_at

HNT

3.27

2.98

4.37

8.07

8.4

66327_at

PKP4

2.79

3.31

3.88

4.56

6.2

35246_at

TYRO3

2.4

-5.43

-3.58

-6.35

-5.18

659_g_at

THBS2

1.9

2.39

2.37

2.26

2.7

59623_at

PCDH18

-2.22

-2.09

-2.89

-2.17

-2.39

53497_at

ITGB8

-2.38

-2.88

-2.69

-3.51

-13.81

60876_at

COL8A2

-2.38

-2.26

-3.26

-3.61

-2.25

47007_s_at

NINJ2

-2.63

-2.55

-3.4

-3.3

-5.93

103_at

THBS4

-2.7

-2.02

-3.37

-2.16

-3.26

46520_at

ROBO2

-2.86

-2.58

-4.96

-4.14

-4.46

45939_at

CNTN3

-3.33

-3.38

-2.14

-7.41

-11.94

47535_at

CDH10

-3.7

-4.87

-3.19

-8.27

-4.65

56192_at

PCDH7

-3.7

-2.5

-5.33

-5.74

-3

52999_at

BOC

-4.17

-2.42

-8.8

-12.25

-6.16

43930_at

LSAMP

-4.35

-3.05

-7.62

-9.76

-7.67

33883_at

EFS

-4.76

-5.43

-3.58

-6.35

-5.18

64342_at

CTGF

-5.26

-4.21

-4.15

-20.44

-14.87

64423_s_at

BOC

-6.67

-3.63

-8.16

-11.8

-54.66

52652_g_at

COL8A2

-10

-7.95

-9.99

-11.56

-9.75

51214_at

PKP1

-14.29

-11.57

-16.34

-11.83

-17.85

52140_at

PGM5

-25

-23.87

-26.5

-44.27

-17.72

Cell_cycle

     

47231_at

DLG1

3.57

3.4

4.77

6.22

5.7

45574_g_at

TPX2

3.49

11.09

2.57

3.24

8.6

54219_at

7-Sep

3.23

3.3

2.79

2.17

3.27

53998_at

CLASP2

3.09

2.67

2.84

4.18

4.13

37933_at

RBBP6

3.08

2.27

5.42

7.52

8.79

53568_at

7-Sep

2.97

3.94

2.4

3.53

2.49

51815_at

TERF1

2.54

2.11

4.78

5.1

3.51

1797_at

CDKN2D

2.41

3.65

2.76

2.46

2.47

50084_at

DNCH1

2.29

2.42

2.15

2.11

2.29

66955_at

EML4

2.13

2.22

3.3

6.8

5.09

1833_at

CDK2

2.12

3.14

2.56

2.05

2.45

52744_at

HRPT2

2.07

2.73

2.62

3.4

3.36

60568_at

BCL2

2.07

2.18

3.27

3.35

3.75

41632_at

E2F3

2.05

2.34

2.04

2.2

2.15

59821_at

BCL2

1.87

2.89

3.13

3.58

3.86

36101_s_at

VEGF

1.53

2.5

2.38

2.53

3.06

63158_at

GRLF1

1.27

3.55

2.63

2.3

3.45

65908_at

CHES1

-2.1

-3.3

-2.11

-2.34

-5.51

46664_at

PYCARD

-2.14

-2.36

-2.06

-2.94

-2.08

48980_at

ZAK

-2.25

-2.02

-4.8

-2.06

-2.07

36838_at

KLK10

-2.38

-3.38

-2.94

-2.07

-6.13

50199_s_at

RGC32

-2.73

-2.49

-3.64

-5.45

-4.17

39780_at

PPP3CB

-2.74

-3.82

-2.36

-3.06

-2.85

33864_at

ZMYND11

-2.78

-4.77

-2.85

-3.79

-3.79

49504_r_at

MCC

-3.45

-5.9

-5.71

-5.08

-5.84

37005_at

NBL1

-3.83

-3.34

-4.27

-4.31

-6.36

234_s_at

PTN

-3.86

-2.92

-5.02

-2.15

-3.33

37283_at

MN1

-4.63

-4.47

-7.36

-5.55

-7.48

45217_at

PDGFC

-4.82

-4.32

-7.53

-8.81

-3.97

1005_at

DUSP1

-6.16

-6.53

-16.66

-3.02

-3.19

36669_at

FOSB

-7.85

-8.81

-6.27

-7.6

-8.39

34820_at

PTN

-12.55

-11.62

-31.95

-10.24

-27.11

37430_at

ALOX15B

-21

-12.47

-12.41

-14.17

-9.1

Transcription

     

1577_at

AR

10

14.35

12.97

12.24

14.78

65001_r_at

TBL1XR1

7

3.53

12.3

4.06

7.19

1578_g_at

AR

6.18

7.56

4.86

5.32

6.37

42733_i_at

FOXP1

5.6

2.61

5.92

14.54

14.09

52769_at

POLR2A

5.1

2.93

4.04

12.61

11.14

54342_at

ZNF605

4.55

2.75

6.89

4.85

4.04

65083_at

RNPC2

4.1

3.18

3.96

6.01

9.19

42734_r_at

FOXP1

3.5

2.01

4.49

7.53

8.6

48423_at

ZNF621

3.4

2.11

8.51

3.74

7.33

51543_at

ZNF395

3.32

2.99

5.52

6.73

6.51

44546_at

ZNF148

3

3.32

2.59

4.72

4.18

49633_at

HES6

3

5.76

2.26

2.34

6.72

51842_at

CBX4

3

3.32

2.61

5.21

3.08

54981_r_at

SFPQ

3

2.27

4.87

4.9

5.33

60076_at

SOX4

3

3.32

3.77

2.09

2.72

43580_at

MORF4L2

2.93

2.42

3.49

3.38

6.52

34715_at

FOXM1

2.84

6.82

2.17

2.9

2.99

40674_s_at

HOXC6

2.81

3.09

2.48

6.67

4.56

56981_at

ZKSCAN1

2.77

2.65

2.4

2.6

4.04

55293_at

ADNP

2.73

2.44

3.6

3.98

3.9

43545_at

ZNF281

2.7

2.17

3.21

3.05

4.19

32653_at

BRD8

2.56

2.11

3.73

3.22

4.45

53846_at

FLJ21616

2.55

3.1

2.96

5.41

5.92

45953_at

NCOA1

2.42

6.53

4.13

3.58

6.06

46006_at

ERCC8

2.35

2.35

2.7

3.36

5.84

50911_at

RLF

2.35

2.18

2.38

4.07

4.92

58641_at

MAML3

2.33

2.86

2.31

5.61

5.44

42571_at

MORF4L2

2.32

2.21

3.7

3.99

3.48

43120_at

MLL3

2.32

2.55

2.48

2.14

2.74

31437_r_at

ESR2

2.3

2.06

3.06

3.13

4.09

50532_at

JARID1A

2.3

3.22

3.32

3.54

4.12

44939_at

MLL3

2.22

2.55

2.48

2.14

2.74

55502_at

FOXO3A

2.21

2.44

3.6

3.98

3.9

52328_at

SP3

2.2

2.13

2.62

3.38

3.11

54220_r_at

NLK

2.16

2.39

4.23

3.94

4.41

41632_at

E2F3

2.04

2.34

2.04

2.2

2.15

66313_at

HIPK1

2

2.09

2.85

2.95

3.98

42193_r_at

---

1.9

2.16

2.04

2.66

3.29

39540_at

ZBTB7A

1.89

2.42

3.27

3.03

2.61

63158_at

GRLF1

1.79

3.55

2.63

2.3

3.45

65908_at

CHES1

-2.13

-3.3

-2.11

-2.34

-5.51

36770_at

STAT2

-2.17

-2.3

-3.47

-2.82

-3.75

64963_at

EYA4

-2.17

-3.06

-3.31

-2.55

-5.01

65945_at

PNRC2

-2.17

-2.08

-2.26

-2.61

-2.24

1454_at

SMAD3

-2.27

-2.17

-2.51

-2.02

-2.07

54658_at

PSPC1

-2.33

-3.18

-2.22

-3.63

-13.16

44596_at

TWIST2

-2.5

-2.7

-2.12

-2.94

-2.35

46704_at

KLF3

-2.63

-2.38

-2.29

-2.19

-3.33

51253_at

ZBTB4

-2.63

-2.92

-3.18

-2.63

-3.93

287_at

ATF3

-2.78

-2.11

-2.33

-2.63

-3.3

33864_at

ZMYND11

-2.78

-4.77

-2.85

-3.79

-3.79

63526_f_at

KLF6

-2.78

-3.21

-2.48

-2.71

-3.33

64932_at

VPS36

-2.86

-4.56

-2.88

-2.48

-2.27

45680_at

ZNF537

-2.94

-2.77

-3.37

-2.78

-3.74

43431_at

SOX2

-3.03

-2.09

-3.8

-7.43

-7.34

47620_at

XAB2

-3.33

-2.7

-8.25

-7.6

-8.49

465_at

HTATIP

-3.45

-2.52

-2.37

-4.49

-2.47

37863_at

EGR2

-4.55

-7.7

-5.52

-7.23

-15.41

48587_at

KLF4

-4.76

-3.77

-3.62

-4.57

-12.5

36634_at

BTG2

-5.26

-6.99

-3.13

-9.25

-5.22

279_at

NR4A1

-6.25

-5.31

-8.04

-5.11

-8.48

280_g_at

NR4A1

-7.14

-8.68

-13.49

-5.82

-8.58

40375_at

EGR3

-8.33

-9.89

-7.71

-8.49

-6.44

53766_at

PER3

-9.09

-15.43

-6.79

-5.56

-6.29

MAP Kinase Pathway

     

1562_g_at

DUSP8

5.8

2.42

5.01

16.6

5.86

1104_s_at

HSPA1A/HSPA1B

4.1

7.81

2.77

2.81

5.02

51474_at

MAP4K4

3.57

4.86

4.32

8.7

8.52

60658_at

---

3.5

-3

-2.96

-3.6

-3.73

50375_at

SOS1

3

2.97

4.08

4.57

3.79

33855_at

GRB2

2.55

2.29

2.61

3.85

2.1

42838_f_at

MAP3K8

2.3

2.25

3.4

2.78

2.08

54220_r_at

NLK

2.16

2.39

4.23

3.94

4.41

790_at

NGFB

1.86

2.08

2.35

3.37

2.9

48980_at

ZAK

-2.27

-2.02

-4.8

-2.06

-2.07

43053_g_at

PAK1

-2.7

-6.31

-2.46

-2.83

-2.11

39780_at

PPP3CB

-2.78

-3.82

-2.36

-3.06

-2.85

468_at

FGF13

-2.78

-4.35

-4.35

-2.11

-2.56

65904_at

---

-2.94

-2.05

-3.48

-4.18

-3.43

1292_at

DUSP2

-3.13

-3.72

-3.66

-3.17

-2.19

57299_s_at

RRAS

-3.23

-3

-2.96

-3.6

-3.73

1970_s_at

FGFR2

-5.56

-2.55

-13.13

-8.88

-4.92

1005_at

DUSP1

-6.25

-6.53

-16.66

-3.02

-3.19

279_at

NR4A1

-6.25

-5.31

-8.04

-5.11

-8.48

280_g_at

NR4A1

-7.14

-8.68

-13.49

-5.82

-8.58

1916_s_at

FOS

-7.69

-7.58

-21.81

-6.93

-11.58

1915_s_at

FOS

-9.09

-7.59

-26.38

-11.03

-12.11

2094_s_at

FOS

-16.67

-10.72

-25.75

-13.72

-16.45

Genes whose median value shows at least 2 fold change in every metastatic patient were annotated by NIH's DAVID tool. Genes belonging to selected GO categories with a significant number of differentially expressed genes are shown. Overall_FC, the fold change in the mean expression value of all metastatic samples compared to the primary samples;FC_P1, FC_P2, FC_P3, FC_P4; a patient-specific median expression value was calculated using all the samples from each patient. This was compared to the median value for the primary tumors. Patient P4 had only sample and this was considered the median.

Disruption of the cell cycle is highlighted by the presence of a large number of cell-cycle related transcripts in the list of differentially expresed genes in all metastatic samples. The list contains 37 cell cycle genes, and includes SEP4, SEP7, PTN and VEGF. Similarly, a large number of transcription factors (67) including AR, SRY, FOS and EGR3, are differentially expressed. Two members of the winged-helix family of transcription factors, FoxP1 and FoxM1, show upregulation in the metastatic samples. Interestingly, FoxM1b has been shown to promote progression of prostate carcinomas in an experimental model [29].

The MAP kinase signaling pathway was also identified as being important in the metastatic process, with 26 probe sets involved in this pathway being differentially expressed in all metastatic samples. The regulated genes include DUSP1, DUSP2, DUSP8, MAP3K8, MAP4K4, FGF13, FGFR2 and FOS. Involvement of MAP kinase in androgen receptor signaling has been previously described [30].

Validation of differentially expressed transcripts with an independent set of primary tumors and different gene expression platforms confirms gene expression profiles of metastatic prostate cancer

Gene expression analysis with the CodeLink Uniset 20 K microarray was carried out for 23 of the metastatic samples and compared to an independent set of 10 primary tumors. Similar to the Affymetrix analysis (see above), hierarchical clustering of the CodeLink data set reveals heterogeneity in expression and no organ-specific clustering (data not shown). Comparison of results with the Affymetrix based dataset, based on genes with common Unigene ids on both platforms, show a similar pattern of differentially expressed genes. Of the top 1000 up and down regulated transcripts from each platform, approximately 70% share common unigene ids and of these 22% of the genes are identified as regulated by both platforms (see Additional file 3). This level of correlation is significant, given the well-documented difficulties in cross-platform comparisons of expression data [31, 32]. Examples of z-transformed expression values for selected genes in both platforms are shown in Figure 3.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-7-64/MediaObjects/12885_2006_Article_714_Fig3_HTML.jpg
Figure 3

Comparison of Z-transformed expression values between the Affymetrix and Codelink platforms. Gene expression data from Affymetrix and CodeLink experiments was Z-transformed to allow comparison. Data for selected differentially expression genes is shown.

Additionally, real-time quantitative PCR (QPCR) assays were performed for a selected set of genes in pooled samples for each patient with metastatic disease and 5 of the primary tumors from the CodeLink set. The transcripts for this analysis were chosen to represent diverse biological processes and were chosen from the differentially expressed genes identified as up/down-regulated in the Affymetrix/Codelink data comparison. As shown in Figure 4, qPCR assays confirmed the results from the microarray platforms. SYNPO2 and EGR3, which are downregulated and RDX and FOXM1, which are upregulated in the microarray analysis exhibit a very similar expression pattern in the qPCR analysis. Interestingly, FoxM1 is consistently upregulated in metastases, while RDX was upregulated in only two of the four patients with metastatic disease, confirming the heterogeneity of metastatic prostate cancer.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-7-64/MediaObjects/12885_2006_Article_714_Fig4_HTML.jpg
Figure 4

Validation of differentially expressed genes with quantitative real time PCR. QPCR was performed on RNA from samples used in the CodeLink analysis. Three selected metastatic RNA samples from each patient were pooled together (except for patient FB666 n = 1) and therefore four RNA samples, each representing one metastatic patient and 5 primary tumors were tested. The insert shows average expression values for metastatic and primary tumors.

Discussion

Despite extensive research, the molecular mechanisms of metastatic prostate cancer and androgen resistance development are still poorly understood. Our study shows that a number of biological processes including cell adhesion, cell cycle and transcription regulation are altered in metastatic disease when compared to primary tumors, and point to specific transcripts that participate in the metastatic process.

Previous investigators have reported differences in gene expression profiles of metastatic and primary prostate cancer [10, 12, 14, 21]. Our results show partial overlap with these previous characterizations of metastatic disease. Some genes that are in concordance with these studies include transcription factors such as FOXM1, and c-FOS.1. Differences in patient demographics, pathology and treatment, non-standard tissue handling, experimental and statistical methods may all contribute to differences in gene lists. Differences with other published gene lists might also reflect the fact that in our study, only samples from patients with androgen-insensitive prostate cancer were used. Additionally, in our experimental design we have incorporated features that increase the significance of our findings and increase the likelihood that the genes identified truly reflect the biology of metastatic prostate cancer. First, by subtracting transcripts previously identified as being expressed by the prostatic stroma, we have incorporated previous knowledge about the expression profiles of different components of prostate tumors in order to focus on those transcripts intrinsic to metastatic cells. This takes into account the fact that metastatic tumors do not contain all the tissue elements present in organ-confined tumors. The major benefit of this strategy is to better define the genes that are down-regulated in metastatic tumor cells. Second, by analyzing multiple tumor samples from each patient, we have addressed the fact that metastatic prostate cancer shows significant heterogeneity, even within the same patient [21]. This is corroborated by our results. and we address this issue by focusing our analysis on the transcripts that show significant differential expression in all metastatic sites within and between patients.

Multiple biological processes appear to be altered in metastatic prostate cancer. One common theme that has emerged from studies of metastatic disease is the central role of the androgen receptor in the development of androgen resistant disease. Several mechanisms including amplification of the AR gene, upregulation of mRNA expression to allow binding by low levels of androgens, mutations in the ligand binding domain (LBD) that allow the receptor to be activated by antagonists, and alteration in the normal AR signaling pathway, have been proposed to explain the ability of prostate cancer to recur in the presence of androgen ablation therapy [33, 34]. Consistent with previous observations, AR is up-regulated in all metastatic samples in our study. Similarly, gene expression changes of the MAP kinase pathway in metastasis may be related to the development of the AARPC phenotype. The gene list from our analysis shares some similarities with mouse xenograft prostate cancers models (CWR22) of androgen independence. MSMB, CCND1, EFNA3, FKBP and ADM, HGF are similarly regulated in mouse models and in our study [3537].

Changes in the expression level of several additional transcripts may reveal clues about the mechanism of metastasis and androgen resistance. We find upregulation of the enzyme UGT2B15 in all metastatic patients. Upregulation of UGT2B15 in androgen independent prostate cancer has been reported previously [14]. This increase appears paradoxical, since UGT2B15 is involved in hormone inactivation. However, as suggested by Stanbrough et al [14], upregulation of multiple genes related with androgen metabolism might reflect that metastatic tumor cells have an increased capacity to convert weak androgens into testosterone or DHT. However, in contrast to their findings, transcripts for AKR1C3, SRD5A1, HSD3B2, AKR1C2, AKR1C1 are not consistently upregulated in the metastatic samples in our study. Interestingly, when reviewing individual values for each sample, some metastases indeed show higher levels for some of these transcripts, which might reflect the heterogeneity of metastatic prostate cancer phenotypes. Another possible explanation for this discrepancy is that the metastatic samples used by Stanbrough et al. are all from bone metastases and this type of sample is not represented in our study. Clearly, further investigation into the role of these pathway genes in the development of androgen resistance by metastatic samples is needed.

Several genes involved in cell-cell interaction and cell adhesion appear to be up-regulated in these tumors. SPP1 (osteopontin), a secreted, integrin-binding glycoprotein with adhesive properties, has been shown to be correlated with metastasis to the bone and with poor prognosis in various cancers and is highly upregulated in all the metastatic samples in our study. Elevated plasma osteopontin levels have also been correlated with lower survival and bone metastasis in hormone resistant prostate cancer [25]. Interestingly, Stanborough and collaborators also identified SPP1 as upregulated in their metastatic samples, however, their interpretation was that this increase was part of the bone response to the metastases. Our study confirms that upregulation of SPP1 is a feature intrinsic to androgen-resistant metastatic prostate cancer, independent of the site of metastasis. It has been postulated that metastasis to specific target organs may require not only expression of SPP1 but an additional set of signaling molecules that promote metastasis to the specific organ. SPP1 when expressed with IL11 has been shown to promote metastasis of breast cancer cells to the bone [38] but not to the adrenal medulla. Further detailed studies are required to address the specific role of SPP1 and other co-expressed genes in prostate cancer metastasis and whether SPP1 represents a potential therapeutic target for androgen-resistant disease. Interestingly, the gene expression profile termed as "bone module" and postulated as a hallmark of tumor metastasis to bone [39] is not dysregulated in our study, most likely reflecting the fact that we did not assay bone metastatic samples. It is also possible, that the role that SPP1 plays in metastasis to bone and/or other organs may involve distinct mechanisms [38].

Metastatic tumors have been described as undergoing an epithelial to mesenchymal transition with loss of the differentiated phenotype. Downregulation of transcription factors such as JUN has been observed in advanced stages of other cancers and its loss of activity has been postulated to be involved in this transition [40]. In our study, both FOS and JUNB, which are upregulated in primary tumors compared to normal prostate tissue are highly downregulated in the metastatic samples. FBN, also representative of the EMT transition [38] is overexpressed in our metastatic samples. Our analysis has also identified a number of additional genes, such as KLK11,STC1 and S100A8 that are uniformly regulated in all metastatic patients. The role of S100A8 in prostate cancer has been studied with evidence suggesting that it is elevated in prostate cancer and may be involved in MAP kinase and NFK-B signalling [41, 42]. STC1, involved in calcium homeostasis, has been reported to have osteoblastic and angiogenic modulator properties with altered expression in some cancers [4345]. The serine protease KLK11 appears to be regulated in prostate cancer with negative correlation between aggressiveness and expression [46].

A recent study observed overexpression of 62 genes due to surgical manipulation related ischemia of the prostate [47]. In our study, 12 out of the 62-gene ischemia profile are downregulated in all metastatic samples. This gene list includes DUSP1, BTG2, IER2, PTGS2, NR4A1, AMD1, C20orf35, KLF4, RAB4A, KLF6, CTGF and GOLPH2. In our data set, these genes represent only 0.01% of the total number of genes differentially regulated in all metastatic samples. Since our metastatic samples all originate from autopsy studies, it is likely that they had been exposed to longer ischemia than the organ confined samples obtained from surgical specimens. Thus, if the differences we observed were related to the ischemia, we would have expected an increase in the expression of these genes, and not the observed downregulation. Therefore, it is unlikely that surgical manipulation can explain the differential gene expression between metastatic and primary tumors.

Conclusion

In summary, our results support the roles for specific cell adhesion, androgen metabolism and transcription factor genes in the development of androgen-independent metastatic prostate cancer. Furthermore, the differentially expressed transcripts in metastatic tumors that we report have been validated with two independent sets of primary tumors, two gene expression microarray platforms, and selected genes were further validated by qRT-PCR. Our results corroborate the notion that metastatic prostate cancer is quite heterogeneous within a single patient. Despite this heterogeneity our experimental design allowed us to identify common expression profiles for androgen-independent metastatic prostate cancer.

Abbreviations

(SAM): 

Significance Analysis of Microarray

(FDR): 

false discovery rate

(AARPC): 

androgen ablation resistant prostate cancer

Declarations

Acknowledgements

This work was funded by NCI/NIH grant 5 U01 CA88110-02 (GM) "Molecular Reclassification of Prostate Cancer" Director's Challenge for the Molecular Classification of Cancer Consortium; by the Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC) Grant from the PA Department of Health ME-01740 (MJB) and a College of American Pathologists Foundation Scholar's Award to FAM.

Authors’ Affiliations

(1)
Departmental of Biomedical Informatics, University of Pittsburgh
(2)
Department of Pathology, University of Pittsburgh

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  48. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/7/64/prepub

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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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