Coexpression and expression quantitative trait loci analyses of the angiogenesis gene-gene interaction network in prostate cancer

Introduction

Prostate cancer (PCa) accounted for 26% of cancer incidence and 9% of cancer deaths in American men in 2015 (1). PCa is a clinically heterogeneous disease but its risk classification is insufficient. About 30% of men are classified as a low risk group, but these men developed to high-grade cancer (2). Due to this inadequate classification, it is difficult for physicians to select a suitable treatment plan for PCa patients. Thus, understanding PCa etiology mechanisms and identifying biomarkers for improving prediction accuracy of PCa aggressiveness is essential to treating patients.

Angiogenesis plays an important role in prostate tumor growth and development. Angiogenesis involves the division and migration of endothelial cells and leads to microvasculature formation (3,4). Based on the genome-wide association studies (GWAS), angiogenesis genetic variants or single nucleotide polymorphisms (SNPs) in or near IL-16 (rs4072111) and FGFR2 (rs11199874, rs10749408 and rs10788165) are significantly associated with PCa aggressiveness (5,6). In candidate gene studies, several angiogenesis SNPs are shown to be associated with PCa prognosis. The pro-angiogenic genes include VEGFs, FGF, EGF, HIF, TGF-β and TNF-α; the anti-angiogenic genes include endostatin, IFN, MMPs, and ILs; and pro-/anti-angiogenic genes include MMPs and ILs (7). The functional mechanism of the majority of these genetic variants remain unclear.

It has often been shown that single gene effects are not sufficient to explain the complicated relationships among genes (8-11). Our previous study suggested an angiogenesis gene-gene interaction network associated with PCa aggressiveness (12). This network was built upon the SNP-SNP interactions of five gene pairs (MMP16-ROBO1, MMP16-CSF1, FBLN5-CSF1, CSF1-HSPG2, and MMP16-EGFR) and published protein-protein interactions. There are 12 genes identified (FBLN5, ROBO1, E2F1, STAT1, HSPG2, MMP16, ITGB3, ESR1, EGFR, CSF1, SP1, and AP1) in this network.

Among these angiogenesis genes, a majority of them were shown to be associated with PCa risk or prognosis. A key biological role of FBLN5 in human cells is communication among cells and between cells to matrix. FBLN5 is consistently downregulated in prostate tumors in data from expression microarray and RT-PCR (13). A recent study suggested ROBO1 as a tumor suppressor in PCa (14). Several groups consistently reported an association between an overexpression of E2F1, a cell cycle-specific transcription factor, with progression of PCa, especially PCa metastasis (15-18). Patterson et al. (19) suggested that STAT1 expression affects the chemoresistant phenotype especially to docetaxel treatment. Therefore, STAT1 plays a key role in docetaxel resistance in PCa treatment. The HSPG2 is a five-domain proteoglycan that interacts with extracellular matrix components and cell-surface molecules. Expression of HSPG2 is associated with high Gleason scores (20), prostate tumor growth, and enhanced angiogenesis (21). Overexpression of HSPG2 is required for invasion of prostate tumors (22). MMP16 has been shown to be down-regulated in malignant prostate tissues (23). Xu et al. [2103] identified ITGB3 as one of the genes related to tumor metastasis in various PCa cell lines (24). Estrogen receptor 1 (ESR1) is a ligand-activated transcription factor with domains for binding to hormones and DNA. ESR1 is associated with PCa risk because it stimulates proliferation of prostate cells and deregulates apoptosis (25). The EGFR binds the epidermal growth factor (EGF) and has been shown to play an important role in regulating prostate cellular growth and function (26-28). Overexpression of SP1 was observed in several cancers, including PCa, and increased angiogenesis and decreased cancer cell death (29). AP1 activation is shown to be essential for inducing proliferation and anchorage independence in PCa cells (30).

This angiogenesis gene-gene interaction network is novel, but its functional mechanism is unclear. SNPs may impact the process of angiogenesis through influencing gene expressions. Gene expressions are involved in many important biological processes. It has been shown that 83% of genes are differentially expressed among individuals; evaluating gene expression variations may provide useful information for disease or other phenotype development (31). For identifying functional roles of genetic variants, using expression quantitative trait loci (eQTL) analyses to evaluate gene expressions as intermediate phenotypes has been applied to identify downstream genes (32,33). In addition to evaluating each gene individually, it has been shown that coexpressions can be used to demonstrate that these genes have a functional relationship, such as physical interaction between the encoded proteins. Evaluating coexpressions can also be used to identify a group of interactive genes (34). Therefore, the goal of this study is to evaluate functional mechanisms of this reported angiogenesis gene-gene interaction network through performing coexpression and eQTL analyses.

Methods

Gene and SNP selection

The 11 candidate genes (FBLN5, ROBO1, E2F1, STAT1, HSPG2, MMP16, ITGB3, ESR1, EGFR, CSF1, and SP1) were selected based on the reported angiogenesis gene-gene interaction network associated with PCa aggressiveness (12). The AP1 was not included in the analysis due to lack of availability in the TCGA data. A total of 434 SNPs in these 11 angiogenesis genes was evaluated.

The Cancer Genome Atlas (TCGA) study population and genetic data

The TCGA SNP and gene expression data were used in this study. TCGA is a large-scale study led by the National Institutes of Health (NIH) to map the genomic changes that occur in more than 30 human cancer types. Its goal is to support new discoveries and accelerate research aimed at improving the prevention, diagnosis, and treatment of cancer (35). The eQTL and gene coexpression analyses were performed for Caucasians in this study due to small sample sizes of other race groups. Among 438 PCa patients recruited between 2009 and 2013 in the TCGA study, we applied the program LAMP-LD (36) to estimate ancestry based on the HapMap (37) data for the two major populations (Caucasian and African American). The ancestry test was performed using 347,481 SNP data from the blood samples. Caucasians were defined as European and those with greater than 80 percent European ancestry (38,39). The genotype call rate for our candidate SNPs was >95%. Among 411 PCa patients with both valid genotype and gene expression data, 350 patients were defined as Caucasian. RNA profiling was performed using Illumina HiSeq 2000 RNA Sequencing. The normalized RNAseq level 3 expression data were used in our analyses. The details are listed on the TCGA website (http://cancergenome.nih.gov).

Statistical analyses

All possible pairwise coexpressions among the candidate genes were evaluated using the Spearman coefficient (r). To our knowledge, there is no well-defined cutoff to define a strong coexpression. To identify a meaningful coexpression cutoff, we generated a null distribution of coexpressions from a set of 10 randomly selected genes with all 20,503 genes in the testing array. In this null distribution for coexpression (Figure 1), there were only 0.12% gene pairs with r≥0.6. Thus, we used a conservative cutoff of r=0.6 to define strong coexpressions as those with a r≥0.6.

Figure 1 Distribution of coexpression strength of noise gene pairs. Gene coexpressions measured by the Spearman correlations for 10 random genes with all genes in the array (20,503 genes) from the 350 prostate tumor samples in the TCGA study. Only 0.12% coexpressions with a r≥0.6. TCGA, the Cancer Genome Atlas.

The eQTL analyses were applied to evaluate associations between one SNP and one gene expression. There are two types of eQTLs. Cis-eQTLs are eQTLs that map the approximate location of their gene-of-origin gene. In contrast, trans-eQTLs are those that map far from the location of their gene-of-origin gene. We performed a total of 4,774 (=11 gene expression × 434 SNPs) pairwise eQTL analyses, including both cis- (locally) and trans- (at a distance) eQTL analyses. The difference of gene expressions among the three genotypes (homozygous wild, heterozygous and homozygous variant types) was evaluated using the Kruskal-Wallis test. Adjusting for multiple comparisons, the false discovery rate (FDR) q value (40) was applied. We declared significance by using the FDR q value less than 20% as the cutoff. Linkage disequilibrium (LD), correlations between SNPs, was evaluated using r². The strong LD was defined as r²>0.8.

Results

The participants’ demographic and clinical characteristics are summarized in Table 1. All 55 possible pairwise coexpressions among the 11 genes were evaluated using the Spearman correlations (Table 2). The ITGB3 and CSF1 are co-expressed with several genes in this angiogenesis network. Among them, 19 pairs (34.5%) had a correlation ≥0.5 and nine gene pairs (16.4%) had a strong coexpression (r≥0.6). These top co-expressed gene pairs are EGFR-SP1 (r=0.73), ITGB3- HSPG2 (r=0.71), ITGB3- CSF1 (r=0.70), MMP16-FBLN5 (r=0.68), ITGB3-MMP16 (r=0.65), ITGB3-ROBO1 (r=0.62), CSF1-HSPG2 (r=0.61), CSF1-FBLN5 (r=0.6), and CSF1-ROBO1 (r=0.60). All these strong coexpressions have a P value <1×10⁻¹³.

Table 1 Participant’s demographic and clinical characteristics
Full table

Table 2 Coexpressions among the 11 angiogenesis genes
Full table

Among the 4,774 eQTL tests, there were 21 significant tests with a FDR q value<0.2 (Table 3). These associations were found in a total of five gene pairs (MMP16-ESR1, ESR1-ROBO1, CSF1-ROBO1, HSPG2-ROBO1, and FBLN5-CSF1) and one SNP-expression pair within the same gene (MMP16, cis-eQTL). The most significant test is the association between rs2982705 in ESR1 and MMP16 expression (raw P=7.5×10⁻⁵, FDR q=0.12). rs162268 in ROBO1 is significantly associated with ESR1 expression (raw P=1.1×10⁻⁴, FDR q=0.12). rs6788511 in ROBO1 is associated with CSF1 gene expression (raw P=1.3×10⁻⁴, FDR q=0.12). The box plots of the top six eQTL tests are shown in Figure S1. In the top eQTL list, several SNPs in the same gene were shown. We further evaluated LD among these SNPs in the same gene. The LD plots are listed in Figure S2. The majority of SNPs in the same gene have strong LD (r²>0.8).

Table 3 The significant expression quantitative trait loci (eQTL) results^†
Full table

The results of coexpression and eQTL analyses are summarized in Figure 2 and Table 4. Our findings demonstrate 11 gene-gene interactions in the angiogenesis network. Among the previous SNP-SNP interactions in the five gene pairs (MMP16-ROBO1, MMP16-CSF1, FBLN5-CSF1, CSF1-HSPG2, and MMP16-EGFR), two of them (FBLN5-CSF1 and CSF1-HSPG2) are supported by direct coexpression and/or eQTL results. The interaction between FBLN5 and CSF1 was observed in both coexpression (r=0.6) and eQTL analyses (rs3093045 in CSF1 associated with the FBLN5 expressions, P=6.0×10⁻⁴). The interaction between CSF1 and HSPG2 was supported by the strong coexpression (r=0.61). For three other gene pairs (MMP16-ROBO1, MMP16-CSF1, and MMP16-EGFR), no direct gene interactions were observed but they may have interacted through another gene. As shown in Table 4, our findings suggest that the interaction of MMP16 and ROBO1 may be through ESR1 or ITGB3. For MMP16 and CSF1, they may be interacted through FBLN5 or ITGB3, while the crosstalk of MMP16 and EGFR may be through ITGB3.

Figure 2 Coexpressions and eQTL results in the angiogenesis network of prostate cancer aggressiveness. r, the Spearman coefficient; eQTL P, P value of expression quantitative trait loci analysis using the Kruskal-Wallis test. The genes and solid lines are from on the published network. The dashed lines are based on the coexpression and eQTL results. The circled genes are based on the SNP-SNP interactions in (12).

Table 4 Summary table of results of co-expressions, eQTL analyses, and SNP-SNP interactions in the angiogenesis network
Full table

Conclusions

Our findings reveal potential functional mechanisms for the angiogenesis gene-gene interaction network, which has a reported association with PCa aggressiveness (12). This study successfully provides direct and indirect functional evidence of these gene-gene interactions. Among the five gene pairs (MMP16-ROBO1, MMP16-CSF1, FBLN5-CSF1, CSF1-HSPG2, and MMP16-EGFR) with SNP-SNP interactions (12), two of them (FBLN5-CSF1, CSF1-HSPG2) are supported by direct coexpression and/or eQTL results; the other three gene pairs may interact indirectly through another gene in this network.

Among 55 possible gene pairs in the angiogenesis network, 12 gene pairs (21.8%) have strong coexpressions or significant eQTL (or SNP-expression) results. Among them, the interactions of two gene pairs (CSF1-ROBO1 and FBLN5-CSF1) are supported by both coexpressions and eQTL results in this study. The relationships between these genes are understudied, although these genes individually are shown to associate with PCa risk and progression. Among the coexpression gene pairs identified, CSF1 and ITGB3 were involved in multiple coexpression pairs, thus a network correlation was implied. To assess a potential biological relevance of these genes and explore the underlying functional mechanism, we updated our genetic regulatory network, which were reported previously (41) (Figure 2). The interconnectedness of biochemical process, coexpression and eQTL networks of the identified genes showed that the six proteins were involved directly or indirectly in the EGFR signaling pathway. This network suggested that these genes are regulated by multiple proteins like receptors and transcription factors. The most prominent protein in the network is CSF1 which was directly co-expressed or interacted with the four angiogenesis proteins (Table 4, FBLN5, ROBO1, HSPG2, and ITGB3). Indeed the CSF1-ITGB3 pair showed a strong coexpression (r=0.7). Although studies of crosstalk between ITGB3 and CSF1 in PCa are limited, both CSF1 and ITGB3 were included in a gene signature of vitamin D exposure in breast cancer (42).

Potential functional role, relation to angiogenesis, and relation to prostate etiology of the 11 candidate genes are briefly summarized in Table 5. The exact role of colony stimulating factor-1 (CSF1) in PCa is not fully established yet. However, previous studies on other cancers, such as that of breast, ovary and endometrial tissues, reported that an overexpression of CSF1 was observed in cancer patients (69,70), increased tumor angiogenesis (69), promoted metastatic potential in breast cancer (64) and was associated with poor outcome in ovarian cancer (65). These biological roles of CSF1 were also confirmed in a murine pancreatic cancer study (41). Recently in their preclinical study, Garcia et al. [2014] demonstrated that CSF1 can be a potential target for PCa treatment (66).

Table 5 Summary of functional role, relation to angiogenesis and prostate cancer (PCa) etiology of the 11 candidate genes
Full table

Integrins play important roles in signal transduction and are known to be involved in carcinogenesis, including colorectal (54), lung (71), and prostate cancers (59). The ITGB3 is a key player in tumor growth and metastasis and a key regulator in reactive oxygen species-induced migration and invasion of cancer cells (54). Ni et al. reported that downregulated expression of ITGB3 reduced cell proliferation, migration, and invasion in cancer cells (55). For PCa, ITGB3 is a part of a gene expression signature for detecting the presence of prostate tumors in stroma with 97% accuracy (59). ITGB3 expression was significantly lower in tumor-adjacent stroma compared to normal stroma (60). These results were consistent in the immunohistochemistry assays: ITGB3 protein showed lower protein expression in tumor-adjacent stroma compared to the normal stroma.

Among 12 interacted gene pairs, only EGFR-SP1 interaction was previously reported. The epidermal growth factor receptor (EGFR) is involved in the proliferation of epithelial cells and tumorigenesis. Our previous study and a meta-analysis study identified EGFR in both analyses of gene and pathway levels associated with PCa risk (12,63). Specificity protein 1 (Sp1) is a transcription factor that affects the expression of genes involved in various cellular processes and oncogenesis. Further, expression of Sp1 is associated with the prognosis of patients (67) and can contribute to predicted PCa recurrence (68). Among two SNPs in the promoter region of EGFR, -216G/T was located in a Sp1 recognition site. Transient transfection assay showed significantly increased promoter activity in the -216G allele as compared with the -216T allele. These findings were confirmed in an additional transient transfection assay in the Sp1-deficient cell line and electrophoretic mobility shift assay, which confirmed a significantly higher binding efficiency of Sp1 protein to the T allele compared with the G allele (72). Liu et al. demonstrated that -216 SNP in the EGFR promoter was associated with altered promoter activity by Sp1 binding and gene expression both in vitro and in vivo (72). SP1 has been shown to directly or indirectly regulate some PCa related genes, of which EGFR is one (73).

ROBO1, a roundabout (ROBO) immunoglobulin, is involved in the cell motility and migration (47). Khusial et al. [2010] reported that expression of ROBO1 affect process of motility in cancer cells (74). Parray et al. demonstrated that ROBO1 acts as a tumor suppressor gene and downregulation of ROBO1 was significantly associated with invasive PCa. Further, the study suggested that ROBO1 is a promising biomarker to differentiate metastatic cases from early stage cases. ROBO1 acts like a natural inhibitor of metastasis; therefore, this protein provides an opportunity to develop novel therapies targeting ROBO1 for treating metastatic PCa (14). Regarding Fibulin 5 (FBLN5), it is one of the extracellular matrix (ECM) glycoproteins and interacts with many ECM components, such as laminin, elastin, endostatin, and fibronectin (43). These FBLN proteins are involved in the formation and stabilization of basement membranes, fibers, and connective tissues (44). Furthermore, fibulins are involved in fibrogenesis, vasculogenesis, and tumorigenesis (45). The expression of Fibulin-5 is impaired in various human cancer tissues including PCa (13). These expression data were confirmed by immunohistochemistry results. The study also reported that Fibulin-5 was predominantly located in the stroma, (with a strong gradient from the periurethral to the peripheral zone) and lost in prostate tumors.

This study evaluated both cis- and trans-eQTLs in the angiogenesis network. Among the six primary gene pairs of eQTL results, only one set of MMP16 contains cis-eQTLs. Five other sets are gene pairs that interacted at a distance. Our study findings demonstrate that the gene-gene interaction network, conducted based on the SNP-SNP interaction and bioinformatics approach, can be an effective tool for identifying potential trans-genes, which is beneficial for understanding the etiology of PCa prognosis. Another large-scale eQTL study also has demonstrated that some disease-associated SNPs affect multiple genes in trans-eQTLs, which are known to be changed for individuals with diseases, and these results have been successfully replicated (75).

The strength of this study is that it defines strong coexpressions (r≥0.6) based on the empirically null distribution of coexpressions in the same testing dataset. To our knowledge, there is no standard cutoff to define strong coexpressions. Using these conservative criteria, we still can identify nine strong coexpressions. The confirmed coexpressions, which are validated in several datasets, can be used to identify gene clusters with functional interactions. A study examined mRNA coexpressions in 60 human datasets and identified 8,805 gene coexpressions in at least three datasets (34). The clinical use of coexpressions is also promising. It has been shown that coexpression provides a better prediction and classification than single-gene expression in PCa progression even after controlling for clinical variables (76).

These findings provide functional evidence to support the association among genes in the angiogenesis network. The conventional prediction model only considers an additive effect of individual biomarkers. Several studies show that interactions of multiple biomarkers are more powerful than individual biomarkers (12,76-78). The integrated biomarkers (such as SNP-SNP interaction, coexpression, or eQTL results) of these genes may be useful for building a risk prediction model for PCa aggressiveness. Future large scale gene expression, eQTL, protein expression studies, or functional experiments are warranted to further validate the interactions of angiogenesis genes.

Figure S1 Top six eQTL resutls. eQTL, expression quantitative trait loci.

Figure S2 Linkage disequilibrium plots for the SNPs in the top eQTL list. (A) Four ROBO1 SNPs with significant ESR1-ROBO1 eQTL results; (B) three MMP16 SNPs with the significant MMP16-MMP16 eQTL results; (C) ten ROBO1 SNPs with the significant CSR1-ROBO1 eQTL results. The values in the triangles are r². The strong LD is defined based on r²>0.8. SNP, single nucleotide polymorphism; eQTL, expression quantitative trait loci.

Acknowledgments

We thank our anonymous reviewers for their valuable comments on this manuscript, which have led to much many improvements to the article.

Funding: This study was supported by the National Cancer Institute (R01CA128813, PI: JY Park and R21CA202417, PI: HY Lin).

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Translational Cancer Research for the series “Social Behavioral and Genetic Risk factors for Cancer”. The article has undergone external peer review.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tcr.2016.10.55). The series “Social Behavioral and Genetic Risk factors for Cancer” was commissioned by the editorial office without any funding or sponsorship. DTC served as the unpaid Guest Editor of the series and serves as an unpaid editorial board member of Translational Cancer Research. HYL served as the unpaid Guest Editor of the series. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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