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Expression analysis of secreted and cell surface genes of five transformed human cell lines and derivative xenograft tumors



Since the early stages of tumorigenesis involve adhesion, escape from immune surveillance, vascularization and angiogenesis, we devised a strategy to study the expression profiles of all publicly known and putative secreted and cell surface genes. We designed a custom oligonucleotide microarray containing probes for 3531 secreted and cell surface genes to study 5 diverse human transformed cell lines and their derivative xenograft tumors. The origins of these human cell lines were lung (A549), breast (MDA MB-231), colon (HCT-116), ovarian (SK-OV-3) and prostate (PC3) carcinomas.


Three different analyses were performed: (1) A PCA-based linear discriminant analysis identified a 54 gene profile characteristic of all tumors, (2) Application of MANOVA (Pcorr < .05) to tumor data revealed a larger set of 149 differentially expressed genes. (3) After MANOVA was performed on data from individual tumors, a comparison of differential genes amongst all tumor types revealed 12 common differential genes. Seven of the 12 genes were identified by all three analytical methods. These included late angiogenic, morphogenic and extracellular matrix genes such as ANGPTL4, COL1A1, GP2, GPR57, LAMB3, PCDHB9 and PTGER3. The differential expression of ANGPTL4 and COL1A1 and other genes was confirmed by quantitative PCR.


Overall, a comparison of the three analyses revealed an expression pattern indicative of late angiogenic processes. These results show that a xenograft model using multiple cell lines of diverse tissue origin can identify common tumorigenic cell surface or secreted molecules that may be important biomarker and therapeutic discoveries.


The process of tumorigenesis has long been recognized to depend upon complex interactions of a tumor with its non-transformed tissue environment [1]. Beyond transformation and increased proliferation, many pathways are activated both in the growing tumor and its environment to culminate in an established solid tumor. For example, adhesive pathways are activated to enable transformed cells to aggregate and form a microtumor. Subsequently, microtumors must avoid destruction by the immune system and elicit vasculature formation for continued growth [2, 3]. In support of these events, cell-matrix adhesion proteins, cell surface antigens, angiogenic factors and modulatory agents have been found differentially expressed in several experimental models of tumorigenesis [46] and in tumor biopsy samples relative to control tissues [7, 8]. Experimental models with established tumorigenic human cell lines have compared the gene expression profiles between the cultured parental cells and after implantation into immune-deficient murine hosts [6]. In this study, we examined this problem with a more focused approach with respect to the transcripts as well as a broader survey by examining multiple tumor sources in order to identify differential genes common to multiple solid tumors in a xenograft model of tumorigenesis.

To recapitulate the attachment and growth of a micro- or metastatic tumor, our experimental tumorigenesis model examined human xenograft tumors in nude mice. It is believed that primary or metastatic microtumors about 1 mm3 in size are metastable; they are either (i) resolved by the immune system, (ii) remain in a steady-state with balanced proliferation and apoptosis or (iii) undergo aggressive growth as long as a vasculature is developed to provide nutrients to the growing mass [9]. Since the end-point of the xenograft assay is the formation of a solid tumor, genes supporting vasculogenesis and angiogenesis are likely differentially expressed relative to the parental cell lines that were adapted to culture in vitro. However, the extent of vascularization to support an established tumor will vary according to the tumor type and tissue environment as a result of variable levels of proteases, receptors or regulators of pericyte and/or endothelial migration, proliferation, and differentiation [3, 10]. Additionally, some tumors such as early grade astrocytomas can leverage existing normal brain blood vessels without substantial vasculogenesis for subsequent angiogenic sprouting of new vessels from preexisting vessels [11]. Further, vascularization depends upon a tuned interaction in the tissue microenvironment between endothelial cells and pericytes [12, 13]. Vascularization of solid tumors may also be heterogeneous with a rapidly growing margin surrounding a hypoxic core following regression of co-opted vessels that supported early tumor growth [10]. Complicating this picture is the potential for Vascular mimicry' where breast tumor derived cells express endothelial markers and may serve as rudimentary channels [14].

Many angiogenesis studies have used cultured primary vascular endothelial cells and shown the significant roles of VEGF, FGF, PDGF, chemokines and cell-matrix adhesion proteins [3, 15, 16]. These assays for endothelial cell migration include the chorioallantoic membrane [17], matrigel migration assays [18] or 3D-collagen assays [19]. However, the limits of studying the angiogenic process with established endothelial cells in vitro have been recognized. Tumorigenesis involves both heterophilic and homophilic cellular communication and adhesion between not only endothelial cells but also pericytes and smooth muscle cells; hence other cell surface proteins and secreted factors are absent from such assays [3].

A search for tumorigenic genes common to tumors of diverse origin should be as broad as possible and hence should not be limited to a single tumor type or tissue source. In order to find common tumorigenic genes regardless of tissue origin, we chose to study a panel of 5 adenocarcinoma cell lines from breast, colon, and lung, ovarian and prostate tumors. These cell lines reproducibly yield solid tumors in a standard xenograft assay in immuno-compromised mice [2022]. While there may be individual differences in capillary branching or density between tumor types, the xenograft assay requires vascular development to support solid tumor formation in a relatively avascular subcutaneous site.

Since the early tumorigenic events largely rely upon secreted factors, cell surface receptors or integral membrane proteins, we devised a strategy to employ a custom microarray to focus on the expression of genes chosen on the basis of their cellular localization. Hence, we implemented an experimental microarray strategy with high replication and coverage of all possible secreted and cell surface proteins. Also, focusing on all known and predicted cell surface and secreted genes allowed us to design more intra-chip replicates for improved data reliability. While prioritizing on the 'Function' category of the Gene Ontology [23], the range of 'Biological Processes' covered by the gene selection remained broad. In contrast to early concerns that a sub-selection of genes might result in a systemic bias, relatively small numbers of genes were found to be common to all xenograft tumors due to the robust experimental design and statistical analysis.


We developed a custom 60-mer oligonucleotide microarray to focus on an ontologically restricted set of secreted and cell surface genes for higher data reliability using a matrix design with intra-chip replicates in addition to replicate chips. Due to the limits of the Gene Ontology classification, multiple strategies had to be used to derive a relatively complete collection of secreted and cell surface genes. For example, some proteins have multiple localization sites on the basis of newer experimental evidence absent from curated databases; e.g. SORCS3, HDGF. For proteins with multiple cellular localizations, the literature (PubMed, NCBI) was the annotation source for finding other secreted and cell surface proteins. Finally, other putative secreted and transmembrane-encoding genes and exons were analyzed from hypothetical predictions from the UCSC Human Genome. Redundant genes were removed by a combination of blastn/blastp comparisons and manual curation, but many putative membrane-encoding exons of potential proteins were included. A final tally of 3531 genes was composed of 1057 secreted genes, 1338 G-protein coupled receptor (GPCR) genes with the remainder classified as various integral membrane proteins and cell surface proteins. An ontological view of the custom chip's content is shown in Fig. 1. Finally, in consideration of potential global changes of a selected set of genes, numerous positive and negative controls were included in the array design; including genes characteristic of some tumors (e.g. the estrogen receptor for a subset of breast tumors) and many 'housekeeping' transcripts (e.g. β-actin) commonly used to normalize quantitative PCR studies. However, co-hybridizing all samples with a reference cDNA derived from a mixture of 10 human cell lines enabled 'normalization' with respect to feature, chip, and dye for the MANOVA analysis. This strategy minimizes the potential concern for a skewed normalization by a sub-selected gene population or possible differential behavior of the included 'housekeeping' genes in the xenograft tumors.

Figure 1

Gene ontology of custom chip probes. The ontological classification of 3531 cell surface or secreted genes was extracted from the Gene Ontology at the third level. Genes lacking GO annotations at this level were derived from level 2.

Identification of characteristic tumor-specific genes by all tumor data or individual tumor types by multivariate analysis of variation

We performed several multivariate analyses of the microarray data to find characteristic tumorigenic genes. The MAANOVA tools [24] were chosen for their sensitivity and robustness in measuring differential expression versus previous T-test and log-ratio methods using thresholds for induction or suppression. This was particularly important in these studies that used a relatively complex design with on-chip and inter-chip probe replication, multiple tumor samples and tumor types, dye-swap and a common reference RNA sample for all hybridizations. Thus, this strategy helps avoid any systematic bias from using a chip containing probes for only secreted and cell surface genes. We developed a custom database [25] to allow dynamic re-grouping of data to facilitate multiple analytical models such as all tumor data or individual tumor types and their parental cell lines.

Initially, we identified the differentially expressed genes in all tumors relative to all parental cells regardless of tissue origin. Hence compared all the xenograft tumor data to all the parental cell line data without regard to tumor type. Similarly, both the tumor and parental cell line data were compared to the all reference cDNA hybridization data. These data were analyzed by both principal components analysis (PCA) and multivariate analysis of variance (MANOVA).

Principal components analysis

To visualize all tumor and parental cell data and assess overall quality, we subjected the entire dataset to principal components analysis. As shown in Fig. 2, a discrete segmentation of the data into 3 major aggregates corresponding to xenografts (circles), parental cell lines ("X's") and the universal reference cDNA (solid dots) can be identified. The third principal component shown by the vertical Y-axis provided the best separation between parental cell data and the xenograft tumor data, Fig. 2.

Figure 2

Principal components analysis of array data. The mean expression values of all samples from all arrays were analyzed by principal components analysis. The first 3 principal components of the analysis are shown from the best vantage point to show separation of the three classes. Open circles represent the parental cell lines, "X" denotes the various xenograft tumors, and the small solid dots are the reference cDNA sample (derived from the Universal RNA) co-hybridized with all experimental samples. The cell lines corresponding to the various tissue sources of the parental cell lines were: Ovary, SKOV3; Prostate, PC3; Breast, MDA MB-231; Colon, HCT116; and Lung, A549.

Linear discriminant analysis

In order to identify a profile characteristic of xenograft tumors where the combination of multiple genes might be more predictive than any single gene, we performed a linear discriminant analysis. Hence, we iteratively 'trimmed' versions of the third principal component since it had the highest correlation to sample type. The 'trimmed' list of coefficients were tested to determine their accuracy in assigning samples to either the tumor or cell line categories. This analysis retained 70 of the largest coefficients of the third principal component and represents a simple linear discriminant (LD) of 70 probes that corresponds to 54 genes. The profile of 70 probes fairly accurately distinguishes between the two sample types of parental cell lines and xenograft tumors, Fig. 3A. In 'leave-one-out' testing where each of the 99 samples was removed in separate analyses, this method generated a profile that was 79.8% accurate in predicting a xenograft tumor. The same method applied to 1000 label-permuted datasets never exceeded 65% accuracy with a median and minimum accuracy of 49% and 39.3% respectively. This suggests that the gene profile generated by our analysis can distinguish between the xenograft data and the cell line data in a verifiable manner.

Figure 3

Genes identified by linear discriminant analysis. The top 70 PCA coefficients along the third principal component were selected. Panel A: Plot of linear discriminant profile of 70 probes that distinguish xenograft tumors from parental cell lines. Positive values in orange indicate "Xenograft tumor" while negative values in blue indicate "Parental Cell line". The y-axis shows either numbered tumor (left) or parental cell (right) samples and the x-axis is an arbitrarily scaled output reflecting the accuracy in assigning a sample as a xenograft tumor or parental cell line. The numbered tumors were grouped according to tissue type as indicated by C for colon (HCT116), B for breast (MDA MB-231), L for lung (A549), P for prostate (PC3) and O for ovary (SKOV-3). Panel B: Graphical representation of the LD-p54 genes expression profiles. For genes with multiple probes, the highest value is shown. Classified by a non-redundant filtering of the Gene Ontology biological process terms, the genes are shown with a color scale representing relative fold induction to all parental cell line data. The left-most color column designated by 'X' is the average ratio, while the remaining five columns correspond to Colon (HCT116), Breast (MDA MB-231), Lung (A549), Prostate (PC3) and Ovarian (SKOV-3) carcinoma xenografts respectively.

Ontological classification of genes identified by a linear discriminant

The 54-gene profile derived from the linear discriminant (LD-p54) was distributed amongst numerous biological processes using the Gene Ontology classification terms, Table 1. Many genes were classified in multiple biological process categories as a result of their biological complexity; e.g. fibronectin (FN1) is classified into 8 biological processes including cell motility, response to stress, cell communication, response to external stimuli, extracellular matrix structural constituent, protein binding and glycosaminoglycan binding. Other genes are involved with cell adhesion or extracellular matrix, cellular growth or the regulation of cellular proliferation, various membrane proteins with known or inferred functions, transporters or channels, and proteases or protease inhibitors. A non-redundant ontological classification of the genes identified by the linear discriminant is shown with a graphical representation of their behavior across all tumor types, Fig 3B. Since the linear discriminant analysis uses a weighted sum, not all of the identified genes behaved consistently across all xenograft tumors; e.g. CD164 or COL4A1, Fig 3B.

Table 1 Gene ontology classification of 54 genes identified by a linear discriminant.

Analysis of variation of all xenograft data

The expression data was also subjected to ANOVA using all xenograft and parental cell line data. In this analysis, the type of tumor or parental line was ignored. This analysis identified 156 probes representing 149 differentially regulated genes at the 99.9% confidence level, Table 2. The range of induction or suppression of this set of genes (ANOVA-p149) was 6-fold induction and 5-fold suppression. Twenty-nine of the 54 genes found by the above linear discriminant analysis were found in the list of 149 ANOVA-qualified probes. An ontological clustering of the ANOVA-p149 genes revealed patterns of proteases and protease inhibitors, cell-matrix adhesion genes, receptors, ion channels, various ligands including chemokines and interleukins, additional angiogenic genes and several genes of unknown function, Tables 3 and 4 show the major ontological groups.

Table 2 Differentially expressed genes from three analyses. ANOVA of xenograft data vs parental cell lines found 149 differential genes (designated ' Ap'), Linear discriminant analysis found 54 genes (designated 'LD') and ANOVA of individual xenograft tumors yielded a consensus of 12 genes (designated 'Ai'). For each gene, its presence is denoted by '1' and its absence noted by '0'. The maximum MANOVA Pvalue is reported along with the aggregate ratio (designated by 'R'). For genes with multiple independent probes, the probe reporting the maximum Pvalue is shown. Seven genes common to all three lists are in bold text.
Table 3 Biological process classification of 175 genes derived from three analyses. The 149 genes derived from the ANOVA analysis of xenograft versus parental cell line data, the 54 genes identified by the linear discriminant analysis and the 12 genes derived from the intersect of ANOVA of individual tumors are shown. Gene Ontology terms were extracted at level 3 for the Unigene gene names. Not shown are genes multiply annotated into additional singular categories or genes absent from the Gene Ontology. Percentages were calculated from a total of 317 classifications into 31 Biological Process terms.
Table 4 Molecular function classification of 175 genes derived from three analyses. As in Table 3, the gene names from three analyses were annotated according to the Gene Ontology Molecular Function categories. Not shown are genes multiply annotated into additional singular categories or genes absent from the Gene Ontology. Percentages were calculated from a total of 251 gene classifications into 52 Molecular Function terms.

Verification of selected genes by quantitative PCR analysis

The differential expression of selected genes was confirmed by quantitative real-time PCR using the same RNA samples subjected to microarray hybridization. The vast majority of the genes tested by PCR validated the array analysis, Fig. 5. In some instances, discrepancies in fold-induction can be explained by methodological differences since the array data were all normalized to the co-hybridized universal-RNA sample, while the PCR data were normalized to a β-actin probe (data not shown). Differential expression of ANGPTL4, GP2, GNAO1, CCR4, FGF23, SPP1 and COL1A1 were qualitatively consistent in both the PCR and array analyses. However, two of the down-regulated genes identified by the array analysis, both G-protein coupled receptors, were found by PCR to be elevated, albeit with large variability; GPR10 was induced 281-fold SD = 469 and GPR110 induced 50-fold SD = 105. Of the two down-regulated genes examined by quantitative PCR, CD81 was consistent in both assays, while CD44 was measured by PCR as unchanged or minimally induced yet array analysis indicated CD44 was suppressed. However, the aggregate 2-fold CD44 induction as measured by quantitative PCR is the threshold of what is considered significantly distinguishable from unchanged. Finally, while we did not perform PCR with species-specific probes for every gene present in the ANOVA-p149 list, we were able to confirm differential expression of several human genes from mouse genes such as the osteopontin genes, Fig. 5. While this analysis does not rule out the possibility of partial contamination of the array results by some weak cross-hybridization, to guard against this possibility we carefully designed probes to be species-specific under the stringent hybridization conditions used in this study.

Figure 5

Quantitative PCR analysis of selected genes. Two tumors of each tumor type were analyzed by quantitative PCR. The measured fold change relative to cell line was determined. RNA amounts per well being normalized by betaactin signal. In general <2-fold changes are not significant. Hence a call of 1.5 fold down may not actually differ from 1.5 up. Specific tumor types are indicated by the first initial followed by the tumor number: i.e. C1 = colon tumor #1, O1 = ovary tumor #1, L1 = lung tumor #1, B1 = breast tumor #1, P1 = prostate tumor #1.

ANOVA analysis of individual tumor types

To accommodate the possibility that tumor type was an important contributor to differential gene behavior, we performed a third analysis by examining the intersection between the differential genes of each individual tumor type. For this restrictive analysis, we simply examined each tumor type relative to its parental cell line by ANOVA. Approximately 91–312 genes were differentially expressed at 99.9% confidence for each cell line: SKOV-3, 125 differential genes; MDA, 312 differential genes; HCT116, 124 differential genes; A549, 159 differential genes; and PC3, 91 differential genes (data not shown). Twelve genes were found in common amongst these separately analyzed tumor types, ANGPLT4, COL1A1, epithelial membrane protein 3 (EMP3), GNAO1, glycoprotein 2 (GP2), GPR57, HAS1, HLAA, laminin beta 3 (LAMB3), PCDHB9, protease inhibitor 3 (PI3), and PTGER3, Table 2.

Comparison of multiple analyses

In a typical analysis of multivariate data, a particular method is often chosen as a filter for subsequent analyses. In this study, due to the high statistical reliability imparted by the high replicate probe count (n = 18 to 30) enabled by the custom array design, we chose to compare the results of 3 different approaches to the intact dataset but modeled as either all data or individual tumor types. An estimate of the statistical significance of the overlap in differentially expressed genes common to the three analytical methods gave a Pvalue of < 1 × 10-6 as described in the legend to Fig. 6. As shown in Fig. 6, seven of the twelve differential genes found amongst individual tumor ANOVA analyses were common to the linear discriminant gene profile (LD-p54): ANGPLT4, COL1A1, GP2, GPR57, LAMB3, PCDHB9, and PTGER3.

Figure 6

Overlap of differentially expressed genes identified by three analyses: ANOVA-p149 = 149 genes derived from the ANOVA analysis of all data, LD-p54 = linear discriminant list of 54 genes from all data, and ANOVA-i12 = twelve genes resulting from a comparison of differentially expressed genes from the ANOVA analysis of individual tumors compared to parental cell lines. An estimate for the statistical significance for the overlap of differentially expressed genes by the 3 analytical methods was estimated by calculating the product of individual probabilities for the results of each analytical method applied to 3531 genes. The null hypothesis in this case is that each method's "call" as to a given gene's differential expression is independent of the call made by the other two methods. Thus if p1, p2, and p3 represent the chance that each method calls a given gene as differentially expressed (easily estimated as number of genes called/ number of total genes), the chance that all three methods do so is simply pAll = p1*p2*p3 = (54/3531)*(149/3531)*(12/3531) = 2.193e-5. Under our null hypothesis, the total number of genes called by all three methods k will follow a binomial distribution with parameters p = pAll, n = 3531 where P(k = L) ~ Bin(pAll, N). Standard calculation techniques allow us to calculate a p-value for this; i.e. p = P(k > = K) – the chance under the null hypothesis we see as much or more overlap than was actually observed. For our data, we thus have p = P(k> = 7) < 1E-6. Thus, if the methods identified random noise as differential expression, they would be very unlikely to produce the overlap observed, thus supporting the statistical significance of the results. The heat maps indicate relative fold-induction or suppression in a linear color-encoded scale shown at the bottom. Mean ratios are indicated by X, C = colon, B = breast, L = lung, P = prostate, O = ovary.

Real-time PCR analysis generally confirmed either induction or suppression in multiple tumor samples but with higher induction ratios; e.g. from Fig. 5, the level of ANGPTL4 was measured by PCR as induced 19 to 453 fold with a average fold induction of 185 SD = 170 for 10 tumors (2 of each type). The aggregate induction of ANGPTL4 in the array analysis was 2.09 fold (Pcorr < 2e-9). Similarly, COL1A1 was measured by PCR as induced in most tumors with an average 9.8-fold (SD = 9.1) versus a 3.64-fold induction found by microarray analysis. Finally, in ovarian and prostate tumors, angiopoietin 2 (ANGPT2) measured by PCR was elevated 6-fold (data not shown) versus the 2.2-fold induction found by microarray analysis.


Overall, the pathways represented by the differential genes in xenograft tumors support a model for late angiogenic expression patterns. In light of the collection of xenografts after 28–29 days post-implantation, is not surprising to find patterns of differential gene expression that reflect a portion of the tumorigenic process rather than a preponderance of early transforming events. This premise is largely supported by the abundance of extracellular matrix, cell adhesion and angiopoetic genes common to the three analyses.

Ten of the 12 induced genes identified by the ANOVA of xenografts were either well-characterized functions or biological roles, particularly angiogenesis (ANGPTL4), morphogenesis (LAMB3, COL1A1, PCDHB9, or cellular mobility or communication (HAS1, PTGER3, PCDHB9, and LAMB3). The role of extracellular matrix genes in tumor growth has been previously noted [7, 8]. Interestingly, five of the extracellular matrix genes from the linear discriminant analysis were collagens (COL1A1, COL4A1, COL5A1, COL5A2 and COL12A1) and four of these collagens (COL1A1, COL4A1, COL5A1, and COL5A2) have been previously found induced in primary renal cell carcinomas (4.8, 5.0, 3.25 and 3.6 fold respectively [26]. COL1A1 has also been found induced in most breast carcinomas [27, 28], and a subset of ovarian and colon carcinomas [28].

Consistent with an overall pattern of late-stage angiogenesis in xenograft tumors, ANGPTL4 was found consistently induced relative to the parental cell lines by all analyses. ANGPTL4 originally was described as an induced target of peroxisome proliferator-activated receptor gamma that is involved in glucose homeostasis and differentiation of adipose tissue [29]. Subsequently ANGPTL4 was shown to possess angiogenic activity in the chick allochorionic migration assay [30]. More recently, ANGPTL4 was shown to bind and inhibit lipoprotein lipase [31], a function consistent with the cachexia induced by tumors, where a reduction of fatty acid incorporation into fat cells serves the energy needs of the tumor rather than the host. ANGPTL4's angiogenic action has been reported to be independent of VEGF in a renal carcinoma model [30]. Similarly to previous observations of induced angiopoietins in primary renal cell carcinomas (ANGPT2 8.18-fold induced and ANGPTL4 18–32-fold induced [26], we found both ANGPTL4 induction (2.09 fold, Pcorr < 2e-9), and ANGPT2 induction (2.23-fold Pcorr < .005).

Other post-VEGF angiogenic pathways

The role of other elevated angiogenic genes downstream of VEGF bears discussion. The induction of the prostaglandin E receptor 3 (PTGER3- 6.4-fold, Pcorr < .001) is of interest since prostaglandins can induce VEGFA production [32, 33] via a hypoxia-induced pathway [34]. Coincident with these observations, IGFBP7 which was differential by ANOVA and linear discriminant analysis, modulates IGF mitogenic activity [35]. IGFBP7 also stimulates prostacyclin synthesis [36] perhaps to take advantage of our observed 6-fold increased PTGER3 expression. Similarly, a human-specific probe for TEM5, a marker of tumor endothelial angiogenesis [37], was also found mildly increased (1.37-fold Pcorr < .001) possibly as a result of vasculogenic mimicry [14, 38].

Other factors such as FGF can play an angiogenic role. One FGF isoform was found significantly differential in some tumor combinations; FGF7 was elevated in colon and prostate xenograft tumors (1.5-fold, Pcorr < 8.7e-6 and 3.7-fold, Pcorr < 7.5e-7) respectively but 2-fold suppressed in ovarian tumors (Pcorr <.006), Fig. 4. FGF7 was previously shown to stimulate the growth of endothelial cells of small but not large vessels in the rat cornea [39] and hence supports the notion of vascular remodeling versus vasculogenesis. That differential expression of this gene was found only in some tumor combinations is consistent with the concept that each type of tumor will display individual differences in the balance angiogenic activators and inhibitors, yet the end physiological result, increased tumor vascularization, is the same [3]. Finally, as noted above, genes that help destabilize or remodel vessels such as ANGPT2 and ANGPTL4 were induced, consistent with an overall pattern of late-stage angiogenesis.

Figure 4

Comparison of differential expression of genes in parental cells versus reference cDNA synthesized from universal RNA (left) and all tumors versus parental cell lines (right). Genes differentially expressed in the parental cells relative to the reference cDNA were analyzed by a 2-way ANOVA (Pcorr < .001). A subset of the differentially expressed genes is shown. The corresponding cognate tumors with differential expression at a 99.9% confidence level by ANOVA analysis of tumors vs parental cell line data are shown. The heat maps indicate relative fold-induction or suppression in a linear color-encoded scale shown at the bottom. Mean ratios are indicated by X, C = colon, B = breast, L = lung, P = prostate, O = ovary.

Linking angiogenic pathways to neuropeptide signaling pathways

Additional support for the late, post-VEGF angiogenic pattern of gene expression in xenografts froms from the observed 5-fold induction of NPY1R by both ANOVA and linear discriminant analyses. NPY1R has been reported to play a role downstream of VEGF in vasoconstriction [40] and capillary sprouting and differentiation [41]. Consistent with the observation of NPY1R induction, the potent effect of ligand neuropeptide (NPY) upon angiogenesis was shown to yield branching vasodilated structures distinct from those generated by VEGF [17]. Similarly, neuropeptide Y has been reported to trigger angiogenesis via the NPY2 receptor in ischemic muscle of mice [41]. Interestingly, neuropilin 1 (NRP1) which can act as a co-receptor with VEGFR2 [42] was found suppressed (1.31-fold, Pcorr < .006) while other VEGF receptor levels were not significantly altered. Finally, previous expression profile studies have found NPY1R to be substantially induced in many breast, prostate and pancreatic carcinomas [28].

Additionally, two other differential genes involved in neuropeptide signaling were observed: melanocortin-2 receptor (MC2R)and SORCS3/neurotensin receptor gene. Both MC2R and the SORCS3 were found differentially expressed by ANOVA. MC2R is a GPCR that binds the ACTH peptide while SORCS3 is a homolog of the rat sortilin gene with VPS10 domains characteristic to neuropeptide-binding proteins [4345]. ACTH has been found to increase angiogenesis of cultured endothelial cells in a 3D-collagen assay [19] and other neuropeptides have been implicated in stimulating VEGF in prostate cancer cells [46].


In this study we compared the expression profiles of secreted and cell surface genes from five different tissue sources. Multiple tumors were derived from each parental cell line to examine the potential for tumor heterogeneity arising from the primary isolate, but we found relatively consistent behavior within any tumor group. However, we also found tumor-specific genes for each tumor type while identifying a profile of genes shared amongst all tumor types by multiple analytical approaches. Overall, our results comprise a foundation of commonly regulated tumorigenic genes across tissues such as fundamental angiogenic inducers and regulators. Given the diverse and complex expression behavior of primary human tumors from any single tissue source [27, 28], in the future it will be necessary to examine several established lines from many histologically similar primary tumors as well as different tumor types from the same tissue. Similarly, it will be important to compare the effect of orthotopic implantation sites to the subcutaneous injection site in these preliminary studies. To resolve xenograft micro-heterogeneity, microarray analysis of micro-dissected xenograft or primary tumors can be used. Micro-dissection will also allow the assessment of potential vasculogenic mimicry by aggressive tumor cells that can express endothelial genes [38]. Additionally, the xenograft model can be more readily extended to monitor time-dependent expression profile changes in the development of tumors. Such results can be used in combination or as a filter with other biomarker technologies such as tissue arrays [47] or mass spectroscopy [48] to fully characterize clinical specimens for diagnostic or prognostic purposes. By identifying genes known to participate in angiogenesis and tumorigenesis, our work establishes a baseline to evaluate and compare the full spectrum of gene profile changes in xenografts and clinical specimens. Hence, time and tissue-specific gene and protein profiles may be useful for the discovery of both biomarkers and new therapeutic strategies.


Custom array design

A two-stage strategy was employed to design the custom oligonucleotide microarray chip. First, for the known secreted and cell surface proteins, we performed keyword filtering of the gene descriptions and annotations of curated public databases such as SwissProt/Trembl [49], the Gene Ontology tables [23], the UCSC Human Genome assembly (hg13, NCBI Build 31 [50]), the GPCR database [51] and public gene tables from technical supply vendors (Affymetrix, Agilent and Illumina). Some of the keywords used were "secreted", "trans-membrane", "glycosylated" and "olfactory". Redundancies and false positives were removed by manual curation.

In order to accommodate continued optimization of a custom chip design, we chose a chip platform that met several criteria: it must allow rapid changes to the master template even for small production batches, possess relative high density, exhibit strong signal-to-noise properties and have high reproducibility (CV < 10%). Hence, a custom oligonucleotide microarray chip (Agilent, Palo Alto, CA) was designed using the curated collection of secreted and cell surface proteins with human-specific 60-mer probes derived from the 3' 1500 nt region of each mRNA sequence. The custom chip was designed with a matrix of technical probe replicates and multiple probes for some genes; e.g. 2 or 3 probes with 1, 3 or 5 copies each per array represented some genes. All probes were curated by elimination of sequences with unfavorable Tm properties, predicted secondary structure or homo-polymer regions. Finally, Blastn [52] analysis was used to confirm human specificity by comparison to mouse sequences.

Cell lines and mice

All cell lines (A549, MDA MB-231, HCT-116, SK-OV3, and PC3) were obtained from the ATCC (Manassas, VA). Xenograft tumors were generated from each parental cell line by either implantation of cells or passage of a fragment from a primary tumor (Piedmont Research Center, Morrisville, NC). For the A549, MDA MB-231 and SKOV-3 lines, le-7 cells grown with 10% fetal calf serum in Dulbecco's modified Eagle's medium at 37°C in 5% C02 were implanted subcutaneously into the flank of between 8 and 10 BalbC (Harlan Labs, Indianapolis, IN) mice. Between 50 and 75% of the mice yielded a palpable primary xenograft tumor. For the HCT116 and PC3 xenograft tumors, 1 mm3 tumor fragments between 103–110 mg were excised from a primary xenograft tumor and passaged into secondary mice for the HCT-116 and PC3 xenograft tumors. For PC3 tumors, 8 male mice were implanted with fragments; otherwise recipient mice were female. The number of tumors processed for hybridization were 5 for SK-OV-3, 5 for PC3, 4 for MDA MB-231, 3 for HCT-116 and 5 for A549.

RNA preparation

For the parental cell lines, total RNA was harvested from 4 x 106 cells using a High Pure RNA isolation kit (Roche Applied Science, Indianapolis, IN) according to manufacturer's instructions. Tumors were excised 22–29 days post-implantation under accredited procedures (Piedmont Research Center, Morrisville, NC), snap-frozen in liquid nitrogen and stored at -80°C until use. Total RNA was prepared from frozen specimens by 24 hr immersion at -80°C in RNAlater-ICE (Ambion, Austin, TX) to 'transition' solid tumors for subsequent homogenization by grinding with a liquid nitrogen-chilled mortar/pestle, followed by resuspension in Trizol (Sigma-Aldrich, E. St. Louis, Mo) and sonication to complete the tissue disruption. Total RNA was extracted using Phase-lock gels (Brinkmann, Westbury, NY), ethanol precipitated, resuspended in RNase-free water, and aliquoted prior to use. Quality control of the total RNA was facilitated by the use of a microcapillary electrophoresis system (Agilent 2100 Bioanlyzer; Agilent Technologies, Palo Alto, CA).

Experimental design and array hybridization

To identify cell surface genes that are consistently differentially regulated amongst the derivative tumors, multiple tumor specimens and their parental source cell lines were hybridized to the custom chips. All biological specimens were co-hybridized with a reference cDNA synthesized from mRNA that is mixture of 10 human established cell lines (Universal RNA; Stratagene, Carlsbad, CA). For each array, amino-allyl labeled single-stranded cDNA was synthesized from 10 (g of sample total RNA and from 10 ug universal RNA using the Agilent Fluorescent Direct Label Kit according to manufacturer's instructions, except that a dNTP mix containing 5-[3-Aminoallyl]-2'-deoxyuridine 5'-triphosphate (AA-dUTP; Sigma-Aldrich) was used (final concentration: 100 (M dATP, dCTP, dGTP; 50 (M dTTP, AA-dUTP). Amino-allyl labeled cDNA was purified using QIAquick PCR columns (Qiagen, Valencia CA) and coupled to either N-hydroxysuccinimidyl-esterified Cy3 or Cy5 dyes (Cy-Dye mono-functional NHS ester; Amersham, Piscataway NJ). Dye-conjugated cDNAs were purified from free dye using the CyScribe GFX purification kit (Amersham). Targets were hybridized to the microarray for 16 hrs at 60°C using an Agilent In Situ Hybridization Kit per manufacture's instructions, washed 10 min in 6× SSC, 0.005% Triton X-102 at 22°C, 0.1× SSC, 0.005% Triton X-102 for 10 min at 4°C, dried under a stream of nitrogen, and scanned with an Agilent Microarray Scanner. Hybridization signals were extracted with Agilent Feature Extraction Software version 7.1, which yielded the median of all pixel intensities for each feature. Since two identical arrays of 8500 features were printed on each chip, a complete dye-swap comparison could be performed per chip. For example, on the left array, a Cy3-labeled biological specimen was co-hybridized with Cy5-labeled cDNA made from universal RNA. For the cognate dye-swap experiment on the right array, a Cy-5 labeled biological specimen was co-hybridized with Cy3-labeled cDNA made from universal RNA. No tumor samples were mixed with any other tumors.

To enable identification of differentially expressed genes with higher statistical reliability, we performed a matrix of hybridizations. The hybridization matrix follows: for the 5 SK-OV-3, A549 and PC3 tumor specimens, 3 of the tumor samples were hybridized to 2 chips each (hence 4 arrays per tumor sample) while 2 tumor samples were hybridized to a 1 chip each (hence 2 arrays for each of these tumors). For the 4 MDA MB-231 tumor specimens, 3 of the tumor samples were hybridized to 2 chips each and 1 tumor was hybridized to a single chip of 2 arrays. For the 3 HCT-116 tumor specimens, all 3 tumors were hybridized to 2 chips each (4 arrays each). For the parental cell lines, HCT-116 cells were hybridized to 6 chips (12 arrays) while the other cell lines were hybridized to 2 chips each (4 arrays). Since most probes were present minimally in triplicate on each array, whenever a tumor sample was hybridized to 2 chips n = (3*4) = 12 per probe. However, since dye-swap hybridizations were routinely performed, n = 6 for the Cy3 and Cy5 signals respectively.

Quantitative PCR

Real-time (RT-) PCR analysis of selected RNA transcripts was performed using either a GeneAmp 5700 Sequence Detection System or an ABI PRISM 7900HT Sequence Detection System with SyBr green chemistry (Applied Biosystems, Foster City, CA). The cDNA produced by reverse transcribing the equivalent of 10 ng of total RNA was loaded per RT-PCR reaction. The following primers pairs were used: beta actin (ACTB) CCTGGCACCCAGCACAAT, GCCGATCCACACGGAGTACT; Human osteopontin (HSPP) AGCAAAATGAAAGAGAACATGAAATG, TTCAACCAATAAACTGAGAAAGAAGC; murine osteopontin (mSpp) ATTTTGGGCTCTTAGCTTAGTCTGTT, GGTTACAACGGTGTTTGCATGA; angiopoietin-like 4 (ANGPTL4)ATGTGGCCGTTCCCTGC, TCTTCTCTGTCCACAAGTTTCCAG; chemokine (C-C motif) receptor 4 (CCR4)ATTCCTGAGCCAGTGTCAGGAG, CTGTCTTTCCACTGTGGGTGTAAG; fibroblast growth factor 23 (FGF23)GGCAAAGCCAAAATAGCTCC, CTGCCACATGACGAGGGATAT; G protein, alpha activating activity polypeptide O (GNAO1) CTAGTCTTTGGGAAACGGGTTGT, AAATCCAACACGGCAAAGGA; glycoprotein 2 (GP2) GCTTTCCACTCCAATTCACACA, CCTGGCCTTGATTCTGTTAATACC; collagen, type I, alpha 1 (COL1A1)TCCCCAGCTGTCTTATGGCT, CAGCACGGAAATTCCTCC; G protein-coupled receptor 10 (GPR10)CATGCTCGAGTCATCAGCCA, TTTCACTGCCCCCTTTGTGT; G protein-coupled receptor 110 (GPR110)AAGCTCTGGAGGCCGACTG, GGCCTTGTCATCCCGACTC; (CD44)TACAGCATCTCTCGGACGGAG, GGTGCTATTGAAAGCCTTGCA; (CD81)CCCTAAGTGACCCGGACACTT, CGTTATATACACAGGCGGTGATG. The identity of each amplicon was confirmed by melting curve analysis at the end of the RT-PCR run.

Array analysis

While the array vendor's feature extraction software 'processed' the hybridization signal to correct for image intensity, background and minor spatial artifacts, chip-to-chip comparisons such as 'reference' versus 'experimental' sample were handled by a custom database [25] built in MySQL [53] with a web interface served by Apache [54]. The database allows the control of experimental design and specification of comparisons and analyses to be performed. Some calculations, like T-Tests and ratios, can be performed in the database or its interface layer, but MATLAB (Mathworks, Natick, MA) was used for ANOVA and principal components analysis (PCA).

For identification of differentially expressed genes, we used the MAANOVA package [55] an implementation of ANOVA for microarray analysis [24]. Array data were loaded into the database and minimally pre-processed for use with this package: where replicate features of the same probe existed in the array design, an arithmetic mean was calculated to yield a single expression level for each probe for each chip. Each tumor or cell line sample was hybridized to 3 separate chips. All signals were Log2 transformed prior to subsequent analyses. These data were used to fit a linear model with factors Gene, Array, Array × Gene, Dye, Dye × Gene, and Sample × Gene. This last attribute is the quantity used for analysis, representing the differential expression of a given gene under a given experimental condition, with the other factors serving to normalize the data. In order to identify differential expression these residuals were analyzed with three statistical tests: a standard ANOVA F-test and two minor variations. A probe had to pass these three tests, generally at 99.9% significance, in order to be called as differentially expressed. A permutation analysis and one-step multiple comparisons correction were applied in conjunction with these tests. It should be noted that since three tests are applied, three P-values result, and when single P-values are listed; the maximum of the three P-values is reported. Finally, since all samples were co-hybridized with cDNAs made from a universal RNA sample, for comparisons of differential gene behavior, approximate 'ratios' were calculated by dividing the paired individual tumor/universal RNA ratio by the paired parental cell/universal RNA ratio.

Ontology annotation

Unigene Gene names were classified by the consistent terms of the Gene Ontology(tm) consortium [23] and the fatiGO interface to the Gene Ontology [56].


  1. 1.

    Paget S: The distribution of secondary growths in cancer of the breast. Lancet. 1889, 1: 571-3. 10.1016/S0140-6736(00)49915-0.

    Article  Google Scholar 

  2. 2.

    Khong HT, Restifo NP: Natural selection of tumor variants in the generation of "tumor escape" phenotypes. Nat Immunol. 2002, 3: 999-1005. 10.1038/ni1102-999.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  3. 3.

    Bergers G, Benjamin LE: Tumorigenesis and the angiogenic switch. Nat Rev Cancer. 2003, 3: 401-10. 10.1038/nrc1093.

    PubMed  CAS  Article  Google Scholar 

  4. 4.

    Glinsky GV, Krones-Herzig A, Glinskii AB, Gebauer G: Microarray analysis of xenograft-derived cancer cell lines representing multiple experimental models of human prostate cancer. Mol Carcinog. 2003, 37: 209-21. 10.1002/mc.10139.

    PubMed  CAS  Article  Google Scholar 

  5. 5.

    Pedersen N, Mortensen S, Sorensen SB, Pedersen MW, Rieneck K, Bovin LF, Poulsen HS: Transcriptional gene expression profiling of small cell lung cancer cells. Cancer Res. 2003, 63: 1943-53.

    PubMed  CAS  Google Scholar 

  6. 6.

    Creighton C, Kuick R, Misek DE, Rickman DS, Brichory FM, Rouillard JM, Omenn GS, Hanash S: Profiling of pathway-specific changes in gene expression following growth of human cancer cell lines transplanted into mice. Genome Biol. 2003, 4: R46-10.1186/gb-2003-4-7-r46.

    PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Perou CM: Molecular portraits of human breast tumours. Nature. 2000, 406: 747-52. 10.1038/35021093.

    PubMed  CAS  Article  Google Scholar 

  8. 8.

    Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM: Delineation of prognostic biomarkers in prostate cancer. Nature. 2001, 412: 822-6. 10.1038/35090585.

    PubMed  CAS  Article  Google Scholar 

  9. 9.

    Fidler IJ: The pathogenesis of cancer metastasis: the "seed and soil" hypothesis revisted. Nature Reviews. 2003, 3: 453-8. 10.1038/nrc1098.

    PubMed  CAS  Google Scholar 

  10. 10.

    Holash J, Wiegand SJ, Yancopoulos GD: New model of tumor angiogenesis: dynamic balance between vessel regression and growth mediated by angiopoietins and VEGF. Oncogene. 1999, 18: 5356-62. 10.1038/sj.onc.1203035.

    PubMed  CAS  Article  Google Scholar 

  11. 11.

    Vajkoczy P, Farhadi M, Gaumann A, Heidenreich R, Erber R, Wunder A, Tonn JC, Menger MD, Breier G: Microtumor growth initiates angiogenic sprouting with simultaneous expression of VEGF, VEGF receptor-2, and angiopoietin-2. J Clin Invest. 2002, 109: 777-85. 10.1172/JCI200214105.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  12. 12.

    Benjamin LE, Hemo I, Keshet E: A plasticity window for blood vessel remodelling is defined by pericyte coverage of the preformed endothelial network and is regulated by PDGF-B and VEGF. Development. 1998, 125: 1591-8.

    PubMed  CAS  Google Scholar 

  13. 13.

    Gerhardt H, Betsholtz C: Endothelial-pericyte interactions in angiogenesis. Cell Tissue Res. 2003, 314: 15-23. 10.1007/s00441-003-0745-x.

    PubMed  Article  Google Scholar 

  14. 14.

    Shirakawa K, Wakasugi H, Heike Y, Watanabe I, Yamada S, Saito K, Konishi F: Vasculogenic mimicry and pseudo-comedo formation in breast cancer. Int J Cancer. 2002, 99: 821-8. 10.1002/ijc.10423.

    PubMed  CAS  Article  Google Scholar 

  15. 15.

    Aonuma M, Iwahana M, Nakayama Y, Hirotani K, Hattori C, Murakami K, Shibuya M, Tanaka NG: Tumorigenicity depends on angiogenic potential of tumor cells: dominant role of vascular endothelial growth factor and/or fibroblast growth factors produced by tumor cells. Angiogenesis. 1998, 2: 57-66. 10.1023/A:1009054410624.

    PubMed  CAS  Article  Google Scholar 

  16. 16.

    Hattori K: Vascular endothelial growth factor and angiopoietin-1 stimulate postnatal hematopoiesis by recruitment of vasculogenic and hematopoietic cells. J Exp Med. 2001, 193: 1005-14. 10.1084/jem.193.9.1005.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  17. 17.

    Ekstrand AJ, Cao R, Bjorndahl M, Nystrom S, Jonsson-Rylander AC, Hassani H, Hallberg B, Nordlander M, Cao Y: Deletion of neuropeptide Y (NPY) 2 receptor in mice results in blockage of NPY-induced angiogenesis and delayed wound healing. Proc Natl Acad Sci USA. 2003, 100: 6033-8. 10.1073/pnas.1135965100.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  18. 18.

    Maeshima Y, Colorado PC, Torre A, Holthaus KA, Grunkemeyer JA, Ericksen MB, Hopfer H, Xiao Y, Stillman IE, Kalluri R: Distinct antitumor properties of a type IV collagen domain derived from basement membrane. J Biol Chem. 2000, 275: 21340-8. 10.1074/jbc.M001956200.

    PubMed  CAS  Article  Google Scholar 

  19. 19.

    Mallet C, Feraud O, Ouengue-Mbele G, Gaillard I, Sappay N, Vittet D, Vilgrain I: Differential expression of VEGF receptors in adrenal atrophy induced by dexamethasone: a protective role of ACTH. Am J Physiol Endocrinol Metab. 2003, 284: E156-67.

    PubMed  CAS  Article  Google Scholar 

  20. 20.

    Giard DJ, Aaronson SA, Todaro GJ, Arnstein P, Kersey JH, Dosik H, Parks WP: In vitro cultivation of human tumors: establishment of cell lines derived from a series of solid tumors. J Natl Cancer Inst. 1973, 51: 1417-23.

    PubMed  CAS  Google Scholar 

  21. 21.

    Cailleau R, Young R, Olive M, Reeves WJ: Breast tumor cell lines from pleural effusions. J Natl Cancer Inst. 1974, 53: 661-74.

    PubMed  CAS  Google Scholar 

  22. 22.

    Kaighn ME, Narayan KS, Ohnuki Y, Lechner JF, Jones LW: Establishment and characterization of a human prostatic carcinoma cell line (PC-3). Invest Urol. 1979, 17: 16-23.

    PubMed  CAS  Google Scholar 

  23. 23.

    Gene Ontology. []

  24. 24.

    Kerr MK, Churchill GA: Experimental design for gene expression microarrays. Biostatistics. 2001, 2: 183-201. 10.1093/biostatistics/2.2.183.

    PubMed  Article  Google Scholar 

  25. 25.

    Osborn S, Kennedy S, Chin D: A flexible pipeline for experimental design processing and analysis of microarray data. IEEE Comp Systems Bioinformatics. 2003, IEEE Computer Society, Los Alamitos, CA, 411-412.

    Google Scholar 

  26. 26.

    Lenburg ME, Liou LS, Gerry NP, Frampton GM, Cohen HT, Christman MF: Previously unidentified changes in renal cell carcinoma gene expression identified by parametric analysis of microarray data. BMC Cancer. 2003, 3: 31-10.1186/1471-2407-3-31.

    PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Perou CM: Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci USA. 1999, 96: 9212-7. 10.1073/pnas.96.16.9212.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  28. 28.

    Su AI: Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res. 2001, 61: 7388-93.

    PubMed  CAS  Google Scholar 

  29. 29.

    Yoon JC, Chickering TW, Rosen ED, Dussault B, Qin Y, Soukas A, Friedman JM, Holmes WE, Spiegelman BM: Peroxisome proliferator-activated receptor gamma target gene encoding a novel angiopoietin-related protein associated with adipose differentiation. Molec Cell Biol. 2000, 20: 5343-5349. 10.1128/MCB.20.14.5343-5349.2000.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  30. 30.

    Le Jan S: Angiopoietin-like 4 is a proangiogenic factor produced during ischemia and in conventional renal cell carcinoma. Am J Pathol. 2003, 162: 1521-8.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  31. 31.

    Yoshida K, Shimizugawa T, Ono M, Furukawa H: Angiopoietin-like protein 4 is a potent hyperlipidemia-inducing factor in mice and inhibitor of lipoprotein lipase. J Lipid Res. 2002, 43: 1770-2. 10.1194/jlr.C200010-JLR200.

    PubMed  CAS  Article  Google Scholar 

  32. 32.

    Harada S, Nagy JA, Sullivan KA, Thomas KA, Endo N, Rodan GA, Rodan SB: Induction of vascular endothelial growth factor expression by prostaglandin E2 and El in osteoblasts. J Clin Invest. 1994, 93: 2490-6.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  33. 33.

    Gallo O, Franchi A, Magnelli L, Sardi I, Vannacci A, Boddi V, Chiarugi V, Masini E: Cyclooxygenase-2 pathway correlates with VEGF expression in head and neck cancer Implications for tumor angiogenesis and metastasis. Neoplasia. 2001, 3: 53-61. 10.1038/sj.neo.7900127.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  34. 34.

    Fukuda R, Kelly B, Semenza GL: Vascular endothelial growth factor gene expression in colon cancer cells exposed to prostaglandin E2 is mediated by hypoxia-inducible factor 1. Cancer Res. 2003, 63: 2330-4.

    PubMed  CAS  Google Scholar 

  35. 35.

    Oh Y, Nagalla SR, Yamanaka Y, Kim HS, Wilson E, Rosenfeld RG: Synthesis and characterization of insulin-like growth factor-binding protein (IGFBP)-7 Recombinant human mac25 protein specifically binds IGF-I and -II. J Biol Chem. 1996, 271: 30322-5. 10.1074/jbc.271.48.30322.

    PubMed  CAS  Article  Google Scholar 

  36. 36.

    Yamauchi T, Umeda F, Masakado M, Isaji M, Mizushima S, Nawata H: Purification and molecular cloning of prostacyclin-stimulating factor from serum-free conditioned medium of human diploid fibroblast cells. Biochem J. 1994, 303: 591-8.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  37. 37.

    Carson-Walter EB, Watkins DN, Nanda A, Vogelstein B, Kinzler KW, St Croix G: Cell surface tumor endothelial markers are conserved in mice and humans. Cancer Res. 2001, 61: 6649-55.

    PubMed  CAS  Google Scholar 

  38. 38.

    Hendrix MJ, Seftor EA, Hess AR, Seftor RE: Molecular plasticity of human melanoma cells. Oncogene. 2003, 22: 3070-5. 10.1038/sj.onc.1206447.

    PubMed  CAS  Article  Google Scholar 

  39. 39.

    Gillis P, Savla U, Volpert OV, Jimenez B, Waters CM, Panos RJ, Bouck NP: Keratinocyte growth factor induces angiogenesis and protects endothelial barrier function. J Cell Sci. 1999, 112: 2049-57.

    PubMed  CAS  Google Scholar 

  40. 40.

    Zukowska-Grojec Z, Dayao EK, Karwatowska-Prokopczuk E, Hauser GJ, Doods HN: Stress-induced mesenteric vasoconstriction in rats is mediated by neuropeptide Y Y1 receptors. Am J Physiol. 1996, 270: H796-800.

    PubMed  CAS  Google Scholar 

  41. 41.

    Lee EW: Neuropeptide Y induces ischemic angiogenesis and restores function of ischemic skeletal muscles. J Clin Invest. 2003, 111: 1853-62. 10.1172/JCI200316929.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  42. 42.

    Soker S, Takashima S, Miao HQ, Neufeld G, Klagsbrun M: Neuropilin-1 is expressed by endothelial and tumor cells as an isoform-specific receptor for vascular endothelial growth factor. Cell. 1998, 92: 735-45. 10.1016/S0092-8674(00)81402-6.

    PubMed  CAS  Article  Google Scholar 

  43. 43.

    Hampe W, Rezgaoui M, Hermans-Borgmeyer I, Schaller HC: The genes for the human VPS10 domain-containing receptors are large and contain many small exons. Hum Genet. 2001, 08: 529-36. 10.1007/s004390100504.

    Article  Google Scholar 

  44. 44.

    Lintzel J, Franke I, Riedel IB, Schaller HC, Hampe W: Characterization of the VPS10 domain of SorLA/LR11 as binding site for the neuropeptide HA. Biol Chem. 2002, 383: 1727-33. 10.1515/BC.2002.193.

    PubMed  CAS  Article  Google Scholar 

  45. 45.

    Vincent JP, Mazella J, Kitabgi P: Neurotensin and neurotensin receptors. Trends Pharmacol Sci. 1999, 20: 302-9. 10.1016/S0165-6147(99)01357-7.

    PubMed  CAS  Article  Google Scholar 

  46. 46.

    Levine L, Lucci JA, Pazdrak B, Cheng JZ, Guo YS, Townsend CM, Hellmich MR: Bombesin stimulates nuclear factor kappa B activation and expression of proangiogenic factors in prostate cancer cells. Cancer Res. 2003, 63: 3495-502.

    PubMed  CAS  Google Scholar 

  47. 47.

    Hoos A, Urist MJ, Stojadinovic A, Mastorides S, Dudas ME, Leung DH, Kuo D, Brennan MF, Lewis JJ, Cordon-Cardo C: Validation of tissue microarrays for immunohistochemical profiling of cancer specimens using the example of human fibroblastic tumors. Am J Pathol. 2001, 158: 1245-51.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  48. 48.

    Petricoin EF: Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002, 359: 572-7. 10.1016/S0140-6736(02)07746-2.

    PubMed  CAS  Article  Google Scholar 

  49. 49.

    Swiss-Prot. []

  50. 50.

    UCSC Genome. []

  51. 51.

    GPCR Database. []

  52. 52.

    NIH Blast. []

  53. 53.

    MySQL. []

  54. 54.

    Apache. []

  55. 55.

    Maanova Tools. []

  56. 56.

    FatiGO. []

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This work was completed in collaboration with Surromed, Mountain View, CA. We also wish to acknowledge the helpful comments of Beth Hollister of the Piedmont Research Center, Morrisville, NC.

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Correspondence to Arie Abo or Daniel J Chin.

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Authors' contributions

RAS helped design and implement the experimental strategy by developing many protocols, carried out many of the hybridization experiments and analyzed the PCR data. RT carried out the sample preparation, labelings, microarray and PCR experiments. SK performed the statistical analysis and assisted database design. SO designed and built the microarray database and LIMs. RH and YL helped curate, annotate and design the custom microarray chip design. CN carried out the xenograft studies. AA conceived of the experimental design. DJC designed & managed the experimental strategy, helped curate the gene lists and wrote the manuscript with input from co-authors.

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Stull, R.A., Tavassoli, R., Kennedy, S. et al. Expression analysis of secreted and cell surface genes of five transformed human cell lines and derivative xenograft tumors. BMC Genomics 6, 55 (2005).

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  • Linear Discriminant Analysis
  • Xenograft Tumor
  • Parental Cell Line
  • Vasculogenic Mimicry
  • Primary Renal Cell Carcinoma