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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2014 May 1.
Published in final edited form as:
PMCID: PMC3708315

Gene variants in angiogenesis and lymphangiogenesis and cutaneous melanoma progression



Angiogenesis and lymphangiogenesis are important in the progression of melanoma. We investigated associations between genetic variants in these pathways with sentinel lymph node (SLN) metastasis and mortality in two independent series of melanoma patients.


Participants at Moffitt Cancer Center were 552 patients, all Caucasian, with primary cutaneous melanoma referred for SLN biopsy. A total of 177 patients had SLN metastasis, among whom 60 died from melanoma. Associations between 238 SNPs in 26 genes and SLN metastasis were estimated as odds ratios and 95%CI using logistic regression. Competing risk regression was used to estimate hazard ratios and 95%CI for each SNP and melanoma-specific mortality. We attempted to replicate significant findings using data from a genome-wide association study comprising 1,115 melanoma patients, who were referred for SLN biopsy from MD Anderson Cancer Center (MDACC), among whom 189 patients had SLN metastasis and 92 patients died from melanoma.


In the Moffitt dataset, we observed significant associations in 18 SNPs with SLN metastasis and 17 SNPs with mortality. Multiple SNPs in COL18A1, EGFR, FLT1, IL10, PDGFD, PIK3CA and TLR3 were associated with risk of SLN metastasis and/or patient mortality. The MDACC data set replicated an association between mortality and rs2220377 in PDGFD. Further, in a meta-analysis, three additional SNPs were significantly associated with SLN metastasis (EGFR rs723526 and TLR3 rs3775292) and melanoma specific death (TLR3 rs7668666).


These findings suggest that genetic variation in angiogenesis and lymphangiogenesis contributes to regional nodal metastasis and progression of melanoma.


Additional research attempting to replicate these results is warranted.

Keywords: SNP, lymph/angiogenesis, melanoma, sentinel lymph node


Melanoma incidence is rising in the US and throughout the world. In the US, an estimated 76,250 people will be diagnosed with cutaneous melanoma in 2012 (1) and by 2015, the estimated lifetime risk of developing this disease in the US will be one in fifty (2, 3). These estimates may underestimate the true incidence of melanoma due to the large number of patients treated in the outpatient setting. Physicians diagnosing new cases of melanoma in the outpatient setting are required to report to the State’s cancer registry. However, a half of dermatologists are not aware of this obligation and therefore a large number of melanomas are not reported. Thus, US melanoma statistics markedly underestimate the true incidence of the disease (4).

Melanoma is most commonly diagnosed at a clinically localized stage when it is highly curable by surgical removal. However, not all patients present with localized disease. Moreover, determining the risk of metastasis in patients diagnosed with localized melanoma continues to pose a challenge to both clinicians and pathologists (5). A combination of clinical and pathological features is used for predicting outcomes in patients with localized melanoma. The most important prognostic features include tumor thickness, ulceration, Clark level of invasion and mitotic rate (69). However, these prognostic factors are limited in their ability to reliably distinguish patients that will manifest regional lymph node metastasis or develop widely disseminated disease and hence are at high risk of dying from melanoma. Currently, the most important predictor of distant metastasis and survival in patients with melanoma is sentinel lymph node (SLN) metastasis (1012). However, SLN biopsy requires examination of multiple microscopic SLN sections (5) and carries risk including wound complications, mild to moderate degrees of lymphedema, thrombophlebitis and loss of range of motion (9, 13). There is therefore an urgent need to identify new predictive and prognostic biomarkers (5, 14) in order to improve prediction models and to target therapies to individual melanoma patients.

Angiogenesis, the development of new blood vessels, and lymphangiogenesis, the development of new lymphatic vessels or the activation of pre-existing vessels promote tumor growth and metastasis (15, 16). These two related processes are governed by many of the same genes (1719) and the expression of these genes or their protein products has been correlated with tumor stage, progression, metastasis and survival in melanoma (5, 14, 2027). It is therefore plausible that genetic variations in components of these two pathways may contribute to inter-individual differences in melanoma progression and survival.

Although strong biological rationale exists for the involvement of angiogenesis and lymphangiogenesis in melanoma, few studies have examined the association between genetic variants in these pathways and melanoma progression and survival. Suggestive findings have been reported in studies examining the association of MMP-9 SNPs with intransit metastasis (28) and tumorigenesis (29), MMP-1 SNPs with progression and prognosis (30), VEGF SNP with tumor growth (31), IL1β SNP with thinner invasive melanoma (32), EGF functional SNP with tumorigenesis (23), and IL10 SNP with advanced disease (33). However, no study to date, has examined associations of SNPs in these pathways with metastasis to SLNs.

In this study, we examined the association between 238 SNPs in 26 angiogenesis and lymphangiogenesis genes and the risk of SLN metastasis and mortality using data from 552 patients referred to the Moffitt Cancer Center (Tampa, FL) for SLN biopsy for purposes of melanoma staging. We sought to replicate the statistically significant results in an independent population of 1,115 melanoma patients who had been genotyped as part of a genome-wide association study at MD Anderson Cancer Center (Houston, TX).


Study patients and data collection

A total of 572 Caucasian patients who underwent wide excision surgery for pathologically-confirmed cutaneous melanoma followed by SLN biopsy between 1994 and 2010 at the Moffitt Cancer Center (Tampa, FL) and 552 (96.5%) had sufficient tumor material available for analysis. A total of 177 patients had pathologically documented metastasis to the SLN (SLN positive), 375 patients were negative for SLN metastasis (SLN negative). Demographic and clinical information including age at diagnosis, gender, tumor site, histology, Breslow thickness, ulceration and Clark level of invasion were obtained from medical records. Date of death, cause of death and vital status information were obtained from the Moffitt Tumor Registry and a search of the National Death Index through December 2009. The study was approved by the Institutional Review Board of the University of South Florida (Tampa, FL). We attempted to replicate our statistically significant SNP results using data from a genome-wide association study (GWAS) conducted at MD Anderson Cancer Center (MDACC) (34). Among 1,952 Caucasian patients with pathologically-confirmed melanoma included in the MDACC GWAS, SLN biopsy was performed on 1,115 cases and SLN metastasis identified in 189.° Demographic and clinical information were obtained from a prospectively maintained database. Date of death and vital status information were obtained from the medical record, the MDACC Tumor Registry and a search of the National Death Index through October 2011.

Genotyping for Moffitt samples

For the initial study, genomic DNA was extracted from formalin-fixed paraffin embedded tissue blocks obtained from the Tissue Core Facility at the Moffitt Cancer Center. The study pathologist (JM) reviewed 5-µm sections of each tissue block and selected blocks containing only non-tumor tissue. DNA extractions were carried out using the DNeasy tissue kit (Qiagen, Valencia, CA) according to the manufacturer’s protocol.

Twenty-six genes encoding proteins involved in angiogenesis and lymphangiogenesis were identified from the published literature (35, 36) and public pathway database (Cancer Genome Anatomy Project, Kyoto Encyclopedia of Gene and Genomes and Gene Ontology) searches. Genes differentially expressed between melanoma tumor and normal tissue or involved in the progression of melanoma or other tumor types were selected. TagSNPs (r2 > 0.8) and putative functional SNPs with minor allele frequencies (MAFs) ≥ 0.05 were then selected from the unrelated Caucasian sample within HapMap Consortium release 27 (37). A total of 261 SNPs (supplementary table) in both pathways were genotyped using the Illumina GoldenGate™ assay (Illumina, San Diego, CA) at the Center for Genome Technology at the Hussman Institute for Human Genomics, University of Miami. Out of the 261 SNPs, 23 failed, resulting in 238 SNPs that were genotyped successfully. Genotyping was attempted on 572 patient DNA samples. Among these samples, we excluded 20 with call rates <80% (3.5%; one SLN positive cases, 18 SLN negative cases and one case with no pathological data), resulting in 552 samples that were successfully genotyped (177 SLN positive cases and 375 SLN negative cases). The concordance for 3 duplicate samples was >99%.

Statistical Analysis for Moffitt samples

Participants’ demographic and clinical characteristics were compared between SLN positive and negative cases. Age at diagnosis was summarized using mean and standard deviation, and the Students t-test was used to compare mean age at diagnosis between SLN positive and negative cases. Categorical variables were summarized using numbers and percentages and χ2 test/Fisher exact tests were used to test for differences between groups.

The likelihood ratio test in logistic regression was used to estimate associations between SNPs in angiogenesis genes and SLN metastasis. For evaluating SNPs associated with melanoma death, the competing risk regression (CRR) approach was applied. Survival time was determined as the number of months between date of SLN biopsy and date of death due to melanoma (primary event), death due to other causes, or last contact for censored observations. All models were adjusted for age at diagnosis and Breslow depth. Statistical tests were two-sided with an alpha level < 0.05 considered statistically significant. For each SNP, the minimum P-value over three genetic models (additive, dominant, and recessive models) was used to represent the best-fitting model. For multiple comparison adjustment, the false discovery rate (FDR) q-value (38) for each SNP was evaluated. CRR was performed using the cmprsk R package, and other statistical analyses were implemented with SAS 9.2 (SAS Institute, NC).

Genotyping and data quality control for MDACC samples

Samples were collected from the University of Texas MD Anderson Cancer Center. DNA samples for the first-stage genome-wide association study were genotyped using the Illumina HumanOmni1-Quad_v1-0_B array and were called using the BeadStudio algorithm, at the John Hopkins University Center for Inherited Disease Research (CIDR). We were able to satisfactorily analyze 1,012,904 of 1,016,423 SNPs attempted (99.6%) with a mean sample call rate of 99.86%. SNPs with minor allele frequency (MAF) ≤0.01, call rate<95%, were excluded. After applying the above criteria, 818,237 genotyped autosomal or X chromosome SNPs and 740 pseudoautosomal SNPs were available for the final association analysis. Samples with >10% missing rate across all SNPs (n=41), with identity problem (n=11), expectedly (n=67) or unexpectedly (n=5) duplicated or related (n=15) identified using IBD co-efficient in PLINK, or identified as outliers by PCA (n=39) were removed. Imputation of ungenotyped SNPs in the whole genome was performed through MACH using HapMap reference data with a denser set of markers. We were able to fill in the untyped markers in the study subjects by means of maximum likelihood estimation. After imputation, we had 2.65 million SNPs available for this analysis. The average posterior probability for the most likely genotype was 0.99.

Statistical analysis for MDACC set

All statistical analyses were conducted by ProbABEL software (39). All genetic effects (additive, dominant and recessive) for the variant allele of each SNP on SLN metastasis were examined via likelihood ratio test under the null hypothesis of χ2 distribution with one degree of freedom. Logistic regression models were built to estimate all three genetic models of reference allele on risk of SLN metastasis. Cox regression model was used to estimate genetic effects of genetic polymorphisms on overall survival. All models in Moffitt and MD Anderson data were adjusted for age at diagnosis and Breslow depth.

Combined Meta-analysis

A meta-analysis was performed in Stata (version 8.2) to combine results across the two studies when odds ratios were in the same direction and the P value for each study was less than 0.1. The Q test of heterogeneity was estimated to quantify the proportion of total variation due to heterogeneity across studies. No significant heterogeneity was observed between the studies (all P > 0.33). Combined ORs, 95% CIs, and P values were generated using inverse-variance weighting to calculate a fixed-effect model.


The mean ages at diagnosis of SLN positive subjects was non-significantly lower than those of SLN negative subjects in both data set (P=0.08, Table 1). The distributions of males and females and tumor site did not differ between SLN positive and negative subjects (Table 1). As expected, ulceration, higher level of invasion (Clark level V) and thicker (Breslow, ≥3 mm) tumors and melanoma-related deaths were more common in SLN positive than SLN negative subjects.

Table 1
Clinico-pathological characteristics of the study populations

The MAFs of successfully genotyped SNPs ranged from 0.14−;0.48. In the Moffitt data, significant associations (raw P-value<0.05, maximum FDR q-value=0.085) were observed for 18 SNPs in 11 genes for SLN metastasis: four SNPs in PIK3CA, three SNPs in COL18A1, two SNPs in PDGFD and FLT1, and one SNP in CXCL12, EGFR, FGFR4, TNFRSF1b, FLT4, TLR3 and VEGFA (Table 2). Significant associations (raw P-value<0.05, maximum FDR q-value=0.051) were observed for 17 SNPs in 8 genes for melanoma-related mortality: four SNPs in PDGFD, three SNPs in EGFR and IL10, two SNPs in PIK3CA and TLR3 and one SNP in FGFR4, FLT1 and LZTS1 (Table 3). Carriers of a minor allele at these SNPs, except for PDGFD rs17423306 and LZTS1 rs2645385, were associated with an increased risk of melanoma specific mortality.

Table 2
ORs for genetic variants in angiogenesis genes associated with sentinel node metastasis
Table 3
Hazard ratios for genetic variants in angiogenesis genes significantly associated with melanoma death

GWAS data from MDACC set replicated the result only for rs2220377 in PDGFD (Table 3) with a FDR q-value=0.37. Furthermore, we performed a meta-analysis that combined results across the two studies for four SNPs [including rs2220377] with odds ratios in the same direction and P-value < 0.1 in each study. We identified three additional SNPs in the meta-analysis that were significantly associated with SLN metastasis (EGFR rs723526 and TLR3 rs3775292) and melanoma specific death (TLR3 rs7668666) (Figure 1).

Figure 1
A meta-analysis was performed in Stata (version 8.2) to combine results across the two studies when the estimates were in the same direction and the P value for each study was less than 0.1. The Q test of heterogeneity was estimated to quantify the proportion ...


This study evaluated the association between 238 SNPs in 26 angiogenesis and lymphangiogenesis pathway genes with SLN metastasis as well as mortality in patients with melanoma. To our knowledge, this is the first study to comprehensively examine genetic variation in the angiogenesis and lymphangiogenesis pathways in relation to melanoma progression. We found suggestive evidence that genetic variants of PDGFD, EGFR, and TLR3 may influence the risk of SLN metastasis and/or death with replication provided for several of the implicated SNPs in a genome-wide scan and meta-analysis. The mechanism of progression of malignant melanoma cells is still poorly understood. However, it is known that angiogenesis and lymphangiogenesis have been investigated primarily on progression of cancers (15, 16) and play an important role in the progression of melanoma. Previous studies reported that new blood and lymphatic vessels promote the metastatic spread of tumors and correlated with tumor stage, progression, metastasis and survival in melanoma (5, 14, 2027). Therefore, angiogenesis and lymphangiogenesis were suggested as a prognostic indicator for risk for progression of melanoma. A few of the best characterized genes for these pathways are PDGF, EGFR and TLR3.

Platelet-derived growth factors (PDGFs) are important in proliferation, apoptosis, transformation, migration, invasion, angiogenesis, and metastasis (40). Among four PDGF family members, PDGF-D is involved in inflammation and angiogenesis (41, 42). Studies have shown that PDGFD is an inducer of transformation in vitro and promoter of tumorigenesis in vivo (43) and that PDGFD stimulates tumor vessel pericyte abundance and enhances tumor growth rate in melanoma (44). No published studies have examined the significance of genetic variation of PDGFD in melanoma. In the current data, a common SNP (rs2220377) in the intron region of the PDGFD gene was associated with an 80% increase in the risk of melanoma-specific death. These results were replicated in the independent MDACC validation set.While the intronic rs2220377 variant itself is unlikely to have functional impact, the variant may be in linkage disequilibrium with another SNP that enhances function of PDGFD, creating a milieu favorable to growth and metastasis of tumor cells.

TLRs are involved in acquired immunity and the detection of pathogens including cancer debris (45).Genetic variation in the TLRs has been investigated extensively for association with infectious and non-infectious diseases, including cancer. Recently, Gast et al. evaluated 47 SNPs in 8 TRLs for relationships with melanoma susceptibility and survival using 763 melanoma cases and 736 matched controls (46). SNPs in TLR2, TLR3 and TLR4 showed statistically significant differences in the distribution of inferred haplotypes between cases and controls. However, no individual SNP was significantly associated with disease susceptibility.TLR3 is abnormally upregulated on cells isolated from melanoma biopsies and its activation induces melanoma cell migration both in vivo and in vitro (47). In the present study, one SNP in the intron region of TLR3 (rs3775292) was significantly associated with SLN metastasis and another SNP (rs7668666) with melanoma specific death, both statistically significant in a meta-analysis combining Moffitt and the MDACC series.

The epidermal growth factor receptor (EGFR) is a critical protein in proliferation of epithelial cells and is involved in oncogenesis. De Wit et al. identified significant differential expression of EGFR in various stages of melanocytic tumor progression including 19% of nevocellular nevi, 61% of dysplastic nevi, 89% of primary cutaneous melanomas and 91% of melanoma metastases (48). Furthermore, they observed that staining intensity was stronger in malignant lesions compared to benign lesions (48). These data were supported by recent studies demonstrating over-expression of cytoplasmic EGFR in melanoma as compared to benign nevi (49) and higher expression levels in melanoma patients with SLN positive than patients with SLN negative tumors (50). In the present study, a common variant in the promoter region of the EGFR gene (rs723526) was found to be associated with a significant 2.3-fold increase in risk of SLN metastasis in the meta-analysis.

Associations in several other angiogenesis and lymphangiogeneis genes, including FGFR4, FLT1, and PIK3CA were identified in Moffitt data consistent with findings of several previous studies (25, 51, 52). However, these associations were not validated in the MDACC series indicating the need for further studies to determine the impact of these genes on melanoma outcome.

The present study benefits from several strengths including a relatively large number of melanoma cases, the selection of genes based on strong biological rationale, pathologically confirmed cases, and the availability of SLN biopsy results, as well as availability of clinical and survival data for all cases. Furthermore, we attempted to replicate our results in a recent GWAS of melanoma. Our meta-analytic approach, using a liberal p-value less than 0.1 for selecting and then pooling data on SNPs in the discovery and replication datasets, may be exploratory. Therefore, the identified two SNPs (TLR3 rs3775292 and TLR3 rs7668666; Figure 1) are good candidates that need to be confirmed in future studies. Although we evaluated a comprehensive list of genes in angiogenesis and lymphangiogenesis pathways, our list was not complete and other genes and SNPs not evaluated in the present study could potentially contribute to SLN metastasis or survival in melanoma.

In conclusion, findings from this exploratory analysis support several previous studies indicating a role for angiogenesis and lymphangiogenesis pathways in regional nodal metastasis and progression of melanoma, and specifically implicate 3 genes (EGFR, PDGFD and TLR3) in these processes. Additional studies are warranted to further investigate these findings and to localize potential causal variants.

Supplementary Material



Financial Support: This work was supported in part by the Melanoma Pre-SPORE grant (PI: V. Sondak) funded by the Bankhead-Coley Florida Biomedical Program and Grant Number 5R25CA14732 (PI: K. Egan) from The National Cancer Institute (NCI) of The National Institutes of Health (NIH). Genotyping for the MD Anderson cohort was performed in part through the Center for Inherited Disease Research (CIDR; contract HHSN268200782096C), and was supported by NIH grants R01CA100264, P30CA016672, and R01CA133996; the UTMDACC NIH SPORE in Melanoma 2P50CA093459; and the Marit Peterson Fund for Melanoma Research.


Disclose of Potential conflicts of interest: No potential conflicts of interest were disclosed.


1. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012;62:10–29. [PubMed]
2. Rigel DS. Trends in dermatology: melanoma incidence. Arch Dermatol. 2010;146:318. [PubMed]
3. Ries LAG M, Krapcho M. SEER cancer statistics review, 1975–2005. Bethesda, MD National Cancer Institute. 2008
4. Cartee TV, Kini SP, Chen SC. Melanoma reporting to central cancer registries by US dermatologists: an analysis of the persistent knowledge and practice gap. J Am Acad Dermatol. 2011;65:S124–S132. [PubMed]
5. Dadras SS, Lange-Asschenfeldt B, Velasco P, Nguyen L, Vora A, Muzikansky A, et al. Tumor lymphangiogenesis predicts melanoma metastasis to sentinel lymph nodes. Mod Pathol. 2005;18:1232–1242. [PubMed]
6. Balch CM, Gershenwald JE, Soong SJ, Thompson JF, Atkins MB, Byrd DR, et al. Final version of 2009 AJCC melanoma staging and classification. J Clin Oncol. 2009;27:6199–6206. [PMC free article] [PubMed]
7. Balch CM, Wilkerson JA, Murad TM, Soong SJ, Ingalls AL, Maddox WA. The prognostic significance of ulceration of cutaneous melanoma. Cancer. 1980;45:3012–3017. [PubMed]
8. Breslow A. Thickness, cross-sectional areas and depth of invasion in the prognosis of cutaneous melanoma. Ann Surg. 1970;172:902–908. [PubMed]
9. Gershenwald JE, Ross MI. Sentinel-lymph-node biopsy for cutaneous melanoma. N Engl J Med. 2011;364:1738–1745. [PubMed]
10. Balch CM, Gershenwald JE, Soong SJ, Thompson JF, Ding S, Byrd DR, et al. Multivariate analysis of prognostic factors among 2,313 patients with stage III melanoma: comparison of nodal micrometastases versus macrometastases. J Clin Oncol. 2010;28:2452–2459. [PMC free article] [PubMed]
11. Gershenwald JE, Thompson W, Mansfield PF, Lee JE, Colome MI, Tseng CH, et al. Multi-institutional melanoma lymphatic mapping experience: the prognostic value of sentinel lymph node status in 612 stage I or II melanoma patients. J Clin Oncol. 1999;17:976–983. [PubMed]
12. Morton DL, Thompson JF, Cochran AJ, Mozzillo N, Elashoff R, Essner R, et al. Sentinel-node biopsy or nodal observation in melanoma. N Engl J Med. 2006;355:1307–1317. [PubMed]
13. Morton DL, Cochran AJ, Thompson JF, Elashoff R, Essner R, Glass EC, et al. Sentinel node biopsy for early-stage melanoma: accuracy and morbidity in MSLT-I, an international multicenter trial. Ann Surg. 2005;242:302–311. discussion 11-3. [PubMed]
14. Dadras SS, Paul T, Bertoncini J, Brown LF, Muzikansky A, Jackson DG, et al. Tumor lymphangiogenesis: a novel prognostic indicator for cutaneous melanoma metastasis and survival. Am J Pathol. 2003;162:1951–1960. [PubMed]
15. Carmeliet P. Angiogenesis in life, disease and medicine. Nature. 2005;438:932–936. [PubMed]
16. Sleeman JP, Thiele W. Tumor metastasis and the lymphatic vasculature. Int J Cancer. 2009;125:2747–2756. [PubMed]
17. Cao Y. Why and how do tumors stimulate lymphangiogenesis? Lymphat Res Biol. 2008;6:145–148. [PubMed]
18. Halin C, Detmar M. Chapter 1 Inflammation, angiogenesis, and lymphangiogenesis. Methods Enzymol. 2008;445:1–25. [PubMed]
19. Gao J, Knutsen A, Arbman G, Carstensen J, Franlund B, Sun XF. Clinical and biological significance of angiogenesis and lymphangiogenesis in colorectal cancer. Dig Liver Dis. 2009;41:116–122. [PubMed]
20. Alonso R, Suarez A, Castro P, Lacave AJ, Gutierrez C. Influence of interleukin-10 genetic polymorphism on survival rates in melanoma patients with advanced disease. Melanoma Res. 2005;15:53–60. [PubMed]
21. Massi D, Puig S, Franchi A, Malvehy J, Vidal-Sicart S, Gonzalez-Cao M, et al. Tumour lymphangiogenesis is a possible predictor of sentinel lymph node status in cutaneous melanoma: a case-control study. J Clin Pathol. 2006;59:166–173. [PMC free article] [PubMed]
22. Okamoto I, Roka F, Krogler J, Endler G, Kaufmann S, Tockner S, et al. The EGF A61G polymorphism is associated with disease-free period and survival in malignant melanoma. J Invest Dermatol. 2006;126:2242–2246. [PubMed]
23. Shahbazi M, Pravica V, Nasreen N, Fakhoury H, Fryer AA, Strange RC, et al. Association between functional polymorphism in EGF gene and malignant melanoma. Lancet. 2002;359:397–401. [PubMed]
24. Shields JD, Borsetti M, Rigby H, Harper SJ, Mortimer PS, Levick JR, et al. Lymphatic density and metastatic spread in human malignant melanoma. Br J Cancer. 2004;90:693–700. [PMC free article] [PubMed]
25. Streit S, Mestel DS, Schmidt M, Ullrich A, Berking C. FGFR4 Arg388 allele correlates with tumour thickness and FGFR4 protein expression with survival of melanoma patients. Br J Cancer. 2006;94:1879–1886. [PMC free article] [PubMed]
26. Tobler NE, Detmar M. Tumor and lymph node lymphangiogenesis--impact on cancer metastasis. J Leukoc Biol. 2006;80:691–696. [PubMed]
27. Vuoristo MS. The polymorphisms of interleukin-10 gene influence the prognosis of patients with advanced melanoma. Cancer Genet Cytogenet. 2007;176:54–57. [PubMed]
28. Cotignola J, Reva B, Mitra N, Ishill N, Chuai S, Patel A, et al. Matrix Metalloproteinase-9 (MMP-9) polymorphisms in patients with cutaneous malignant melanoma. BMC Med Genet. 2007;8:10. [PMC free article] [PubMed]
29. Nan H, Niu T, Hunter DJ, Han J. Missense polymorphisms in matrix metalloproteinase genes and skin cancer risk. Cancer Epidemiol Biomarkers Prev. 2008;17:3551–3557. [PMC free article] [PubMed]
30. Liu H, Wei Q, Gershenwald JE, Prieto VG, Lee JE, Duvic M, et al. Influence of single nucleotide polymorphisms in the MMP1 promoter region on cutaneous melanoma progression. Melanoma Res. 2011;22:169–175. [PMC free article] [PubMed]
31. Howell WM, Bateman AC, Turner SJ, Collins A, Theaker JM. Influence of vascular endothelial growth factor single nucleotide polymorphisms on tumour development in cutaneous malignant melanoma. Genes Immun. 2002;3:229–232. [PubMed]
32. Howell WM, Turner SJ, Theaker JM, Bateman AC. Cytokine gene single nucleotide polymorphisms and susceptibility to and prognosis in cutaneous malignant melanoma. Eur J Immunogenet. 2003;30:409–414. [PubMed]
33. Howell WM, Turner SJ, Bateman AC, Theaker JM. IL-10 promoter polymorphisms influence tumour development in cutaneous malignant melanoma. Genes Immun. 2001;2:25–31. [PubMed]
34. Amos CI, Wang LE, Lee JE, Gershenwald JE, Chen WV, Fang S, et al. Genome-wide association study identifies novel loci predisposing to cutaneous melanoma. Hum Mol Genet. 2011;20:5012–5023. [PMC free article] [PubMed]
35. Amankwah EK, Sellers TA, Park JY. Gene variants in the angiogenesis pathway and prostate cancer. Carcinogenesis. 2012 [PMC free article] [PubMed]
36. Holopainen T, Bry M, Alitalo K, Saaristo A. Perspectives on lymphangiogenesis and angiogenesis in cancer. J Surg Oncol. 2011;103:484–488. [PubMed]
37. Project IH. International Hapmap Project. [cited 2008 January 30th];2008 Available from:
38. Storey J. A direct approach to false discovery rates. Journal of the Royal Statistical Society. 2002;Series B64:479–498.
39. Aulchenko YS, Struchalin MV, van Duijn CM. ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics. 11:134. [PMC free article] [PubMed]
40. Wang Z, Kong D, Banerjee S, Li Y, Adsay NV, Abbruzzese J, et al. Down-regulation of platelet-derived growth factor-D inhibits cell growth and angiogenesis through inactivation of Notch-1 and nuclear factor-kappaB signaling. Cancer Res. 2007;67:11377–11385. [PubMed]
41. Uutela M, Wirzenius M, Paavonen K, Rajantie I, He Y, Karpanen T, et al. PDGF-D induces macrophage recruitment, increased interstitial pressure, and blood vessel maturation during angiogenesis. Blood. 2004;104:3198–3204. [PubMed]
42. Li R, Maminishkis A, Wang FE, Miller SS. PDGF-C and -D induced proliferation/migration of human RPE is abolished by inflammatory cytokines. Invest Ophthalmol Vis Sci. 2007;48:5722–5732. [PubMed]
43. LaRochelle WJ, Jeffers M, Corvalan JR, Jia XC, Feng X, Vanegas S, et al. Platelet-derived growth factor D: tumorigenicity in mice and dysregulated expression in human cancer. Cancer Res. 2002;62:2468–2473. [PubMed]
44. Furuhashi M, Sjoblom T, Abramsson A, Ellingsen J, Micke P, Li H, et al. Platelet-derived growth factor production by B16 melanoma cells leads to increased pericyte abundance in tumors and an associated increase in tumor growth rate. Cancer Res. 2004;64:2725–2733. [PubMed]
45. Wang RF, Miyahara Y, Wang HY. Toll-like receptors and immune regulation: implications for cancer therapy. Oncogene. 2008;27:181–189. [PubMed]
46. Gast A, Bermejo JL, Claus R, Brandt A, Weires M, Weber A, et al. Association of inherited variation in Toll-like receptor genes with malignant melanoma susceptibility and survival. PLoS One. 2011;6:e24370. [PMC free article] [PubMed]
47. Goto Y, Arigami T, Kitago M, Nguyen SL, Narita N, Ferrone S, et al. Activation of Toll-like receptors 2, 3, and 4 on human melanoma cells induces inflammatory factors. Mol Cancer Ther. 2008;7:3642–3653. [PMC free article] [PubMed]
48. de Wit PE, Moretti S, Koenders PG, Weterman MA, van Muijen GN, Gianotti B, et al. Increasing epidermal growth factor receptor expression in human melanocytic tumor progression. J Invest Dermatol. 1992;99:168–173. [PubMed]
49. Akslen LA, Puntervoll H, Bachmann IM, Straume O, Vuhahula E, Kumar R, et al. Mutation analysis of the EGFR-NRAS-BRAF pathway in melanomas from black Africans and other subgroups of cutaneous melanoma. Melanoma Res. 2008;18:29–35. [PubMed]
50. Boone B, Jacobs K, Ferdinande L, Taildeman J, Lambert J, Peeters M, et al. EGFR in melanoma: clinical significance and potential therapeutic target. J Cutan Pathol. 2011;38:492–502. [PubMed]
51. Curtin JA, Stark MS, Pinkel D, Hayward NK, Bastian BC. PI3-kinase subunits are infrequent somatic targets in melanoma. J Invest Dermatol. 2006;126:1660–1663. [PubMed]
52. Omholt K, Krockel D, Ringborg U, Hansson J. Mutations of PIK3CA are rare in cutaneous melanoma. Melanoma Res. 2006;16:197–200. [PubMed]