Pathway analysis for genome-wide association study of basal cell carcinoma of the skin

TGF-β/VEGF-A Genetic Variants Interplay in Genetic Susceptibility to Non-Melanocytic Skin Cancer

Differential genetically determined expression of transforming growth factor-β (TGF-β pathway and of vascular endothelial growth factor-A (VEGF-A) might modulate the molecular “milieu” involved in the etio-pathogenesis of non-melanoma skin cancer (NMSC). We have evaluated the frequency of some functionally relevant SNPs of TGF-β and VEGF-A genes in 70 NMSC patients and 161 healthy controls, typed for TGF-β1 rs1800471, TGF-β2 rs900, TGF-βR1 rs334348 and rs334349, TGF-βR2 rs4522809 and VEGF-A rs3025039 SNPs. TGF-βR2 rs1800629G allele and related genotypes were found to be associated with a possible protective role against NMSC, whereas VEGF-A rs3025039T was associated with an increased risk. To evaluate the effect of genotype combinations on NMSC susceptibility, we determined the frequencies of 31 pseudo-haplotypes due to non-random linkage among alleles of loci not lying on the same chromosome. Two pseudo-haplotypes that imply a minor allele of TGF-βR2 or minor allele of VEGF-A SNPs combined with major alleles of the other SNPs were, respectively, associated with a protective effect, and susceptibility to NMSC. In addition, a pseudo-haplotype involving minor alleles of TGF-β2 rs900, TGF-βR1 rs334348 and rs4522809 SNPs might be a susceptibility marker for NMSC. In conclusion, our data suggest that a complex interplay among the genetic polymorphisms of TGF-β, TGF-β receptors and VEGF-A genes might influence the net effect of genetic background of the patients on NMSC development. This might be relevant in the risk evaluation, diagnosis and treatment of NMSC.

Association between VEGFA gene polymorphisms and bleeding complications in patients maintaining therapeutic international normalized ratio

Aim: This study was designed to identify the possible effects of VEGFA polymorphisms on the occurrence of bleeding complications in patients with mechanical heart valves who have achieved therapeutic international normalized ratio (INR). Materials & methods: 13 SNPs of VEGFA were analyzed. Uni- and multi-variate analyses were conducted to identify associations between polymorphisms and bleeding complications. Results & conclusion: Patients with the CC genotype of rs35410204 had an approximately tenfold higher bleeding complication than those with the T allele. For rs866236, patients who had wild-type homozygotes showed an approximately 2.9-fold higher bleeding complication than C allele carriers. This study demonstrated that bleeding complications during warfarin therapy are associated with VEGFA polymorphisms in patients with mechanical heart valves.

How far from the SNP may the causative genes be?

While GWAS identify many disease-associated SNPs, using them to decipher disease mechanisms is hindered by the difficulty in mapping SNPs to genes. Most SNPs are in non-coding regions and it is often hard to identify the genes they implicate. To explore how far the SNP may be from the affected genes we used a pathway-based approach. We found that affected genes are often up to 2 Mbps away from the associated SNP, and are not necessarily the closest genes to the SNP. Existing approaches for mapping SNPs to genes leave many SNPs unmapped to genes and reveal only 86 significant phenotype-pathway associations for all known GWAS hits combined. Using the pathway-based approach we propose here allows mapping of virtually all SNPs to genes and reveals 435 statistically significant phenotype-pathway associations. In search for mechanisms that may explain the relationships between SNPs and distant genes, we found that SNPs that are mapped to distant genes have significantly more large insertions/deletions around them than other SNPs, suggesting that these SNPs may sometimes be markers for large insertions/deletions that may affect large genomic regions.

Integrated network analysis and logistic regression modeling identify stage-specific genes in Oral Squamous Cell Carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is associated with substantial mortality and morbidity but, OSCC can be difficult to detect at its earliest stage due to its molecular complexity and clinical behavior. Therefore, identification of key gene signatures at an early stage will be highly helpful.

METHODS

The aim of this study was to identify key genes associated with progression of OSCC stages. Gene expression profiles were classified into cancer stage-related modules, i.e., groups of genes that are significantly related to a clinical stage. For prioritizing the candidate genes, analysis was further restricted to genes with high connectivity and a significant association with a stage. To assess predictive power of these genes, a classification model was also developed and tested by 5-fold cross validation and on an independent dataset.

RESULTS

The identified genes were enriched for significant processes and functional pathways, and various genes were found to be directly implicated in OSCC. Forward and stepwise, multivariate logistic regression analyses identified 13 key genes whose expression discriminated early- and late-stage OSCC with predictive accuracy (area under curve; AUC) of ~0.81 in a 5-fold cross-validation strategy.

CONCLUSIONS

The proposed network-driven integrative analytical approach can identify multiple genes significantly related to an OSCC stage; the classification model that is developed with these genes may help to distinguish cancer stages. The proposed genes and model hold promise for monitoring of OSCC stage progression, and our findings may facilitate cancer detection at an earlier stage, resulting in improved treatment outcomes.

Integrated pathway and epistasis analysis reveals interactive effect of genetic variants at TERF1 and AFAP1L2 loci on melanoma risk

Genome-wide association studies (GWASs) have characterized 13 loci associated with melanoma, which only account for a small part of melanoma risk. To identify new genes with too small an effect to be detected individually but which collectively influence melanoma risk and/or show interactive effects, we used a two-step analysis strategy including pathway analysis of genome-wide SNP data, in a first step, and epistasis analysis within significant pathways, in a second step. Pathway analysis, using the gene-set enrichment analysis (GSEA) approach and the gene ontology (GO) database, was applied to the outcomes of MELARISK (3,976 subjects) and MDACC (2,827 subjects) GWASs. Cross-gene SNP-SNP interaction analysis within melanoma-associated GOs was performed using the INTERSNP software. Five GO categories were significantly enriched in genes associated with melanoma (false discovery rate ≤ 5% in both studies): response to light stimulus, regulation of mitotic cell cycle, induction of programmed cell death, cytokine activity and oxidative phosphorylation. Epistasis analysis, within each of the five significant GOs, showed significant evidence for interaction for one SNP pair at TERF1 and AFAP1L2 loci (pmeta-int  = 2.0 × 10(-7) , which met both the pathway and overall multiple-testing corrected thresholds that are equal to 9.8 × 10(-7) and 2.0 × 10(-7) , respectively) and suggestive evidence for another pair involving correlated SNPs at the same loci (pmeta-int  = 3.6 × 10(-6) ). This interaction has important biological relevance given the key role of TERF1 in telomere biology and the reported physical interaction between TERF1 and AFAP1L2 proteins. This finding brings a novel piece of evidence for the emerging role of telomere dysfunction into melanoma development.

Moving into a new era of periodontal genetic studies: relevance of large case-control samples using severe phenotypes for genome-wide association studies

Studies to elucidate the role of genetics as a risk factor for periodontal disease have gone through various phases. In the majority of cases, the initial 'hypothesis-dependent' candidate-gene polymorphism studies did not report valid genetic risk loci. Following a large-scale replication study, these initially positive results are believed to be caused by type 1 errors. However, susceptibility genes, such as CDKN2BAS (Cyclin Dependend KiNase 2B AntiSense RNA; alias ANRIL [ANtisense Rna In the Ink locus]), glycosyltransferase 6 domain containing 1 (GLT6D1) and cyclooxygenase 2 (COX2), have been reported as conclusive risk loci of periodontitis. The search for genetic risk factors accelerated with the advent of 'hypothesis-free' genome-wide association studies (GWAS). However, despite many different GWAS being performed for almost all human diseases, only three GWAS on periodontitis have been published - one reported genome-wide association of GLT6D1 with aggressive periodontitis (a severe phenotype of periodontitis), whereas the remaining two, which were performed on patients with chronic periodontitis, were not able to find significant associations. This review discusses the problems faced and the lessons learned from the search for genetic risk variants of periodontitis. Current and future strategies for identifying genetic variance in periodontitis, and the importance of planning a well-designed genetic study with large and sufficiently powered case-control samples of severe phenotypes, are also discussed.

A review of next-generation genetic testing for the dermatologist

Dermatologists have been placed in a prime position to make new genetic discoveries. Tissue is easily obtained from the skin or mucosa for the study of germline and somatic mosaic disorders. This, along with the recent development of next-generation sequencing, makes dermatology an exciting field with essentially endless possibilities for discovering genes responsible for disease, better understanding complex molecular pathways, and eventually developing targeted therapies. To take advantage of this great opportunity, a basic understanding of the advances in genetic testing is vital. Herein we give an overview of next-generation sequencing, including some of the applications it may be used for. We also review various study designs for genetic discovery, each of their benefits and downfalls, and how they may be applied to the study of dermatologic disease.

Association between a multi-locus genetic risk score and inflammatory bowel disease

To date, the utility of single genetic markers to improve disease risk assessment still explains only a small proportion of genetic variance for many complex diseases. This missing heritability may be explained by additional variants with weak effects. To discover and incorporate these additional genetic factors, statistical and computational methods must be evaluated and developed. We develop a multi-locus genetic risk score (GRS) based approach to analyze genes in NADPH oxidase complex which may result in susceptibility to development of inflammatory bowel disease (IBD). We find the complex is highly associated with IBD (P = 7.86 × 10⁻¹⁴) using the GRS-based association method. Similar results are also shown in permutation analysis (P = 6.65 × 10⁻¹¹). Likelihood ratio test shows that the single nucleotide polymorphisms (SNPs) in the complex without nominal signals have significant contribution to the overall genetic effect within the complex (P = 0.015). Our results show that the multi-locus GRS association model can improve the genetic risk assessment on IBD by taking into account both confirmed and as yet unconfirmed disease susceptibility variants.

Teaching the bioinformatics of signaling networks: an integrated approach to facilitate multi-disciplinary learning

The number of bioinformatics tools and resources that support molecular and cell biology approaches is continuously expanding. Moreover, systems and network biology analyses are accompanied more and more by integrated bioinformatics methods. Traditional information-centered university teaching methods often fail, as (1) it is impossible to cover all existing approaches in the frame of a single course, and (2) a large segment of the current bioinformation can become obsolete in a few years. Signaling network offers an excellent example for teaching bioinformatics resources and tools, as it is both focused and complex at the same time. Here, we present an outline of a university bioinformatics course with four sample practices to demonstrate how signaling network studies can integrate biochemistry, genetics, cell biology and network sciences. We show that several bioinformatics resources and tools, as well as important concepts and current trends, can also be integrated to signaling network studies. The research-type hands-on experiences we show enable the students to improve key competences such as teamworking, creative and critical thinking and problem solving. Our classroom course curriculum can be re-formulated as an e-learning material or applied as a part of a specific training course. The multi-disciplinary approach and the mosaic setup of the course have the additional benefit to support the advanced teaching of talented students.

Pathway analysis for genome-wide association study of lung cancer in Han Chinese population

Genome-wide association studies (GWAS) have identified a number of genetic variants associated with lung cancer risk. However, these loci explain only a small fraction of lung cancer hereditability and other variants with weak effect may be lost in the GWAS approach due to the stringent significance level after multiple comparison correction. In this study, in order to identify important pathways involving the lung carcinogenesis, we performed a two-stage pathway analysis in GWAS of lung cancer in Han Chinese using gene set enrichment analysis (GSEA) method. Predefined pathways by BioCarta and KEGG databases were systematically evaluated on Nanjing study (Discovery stage: 1,473 cases and 1,962 controls) and the suggestive pathways were further to be validated in Beijing study (Replication stage: 858 cases and 1,115 controls). We found that four pathways (achPathway, metPathway, At1rPathway and rac1Pathway) were consistently significant in both studies and the P values for combined dataset were 0.012, 0.010, 0.022 and 0.005 respectively. These results were stable after sensitivity analysis based on gene definition and gene overlaps between pathways. These findings may provide new insights into the etiology of lung cancer.

Pathway analysis of genomic data: concepts, methods, and prospects for future development

Genome-wide data sets are increasingly being used to identify biological pathways and networks underlying complex diseases. In particular, analyzing genomic data through sets defined by functional pathways offers the potential of greater power for discovery and natural connections to biological mechanisms. With the burgeoning availability of next-generation sequencing, this is an opportune moment to revisit strategies for pathway-based analysis of genomic data. Here, we synthesize relevant concepts and extant methodologies to guide investigators in study design and execution. We also highlight ongoing challenges and proposed solutions. As relevant analytical strategies mature, pathways and networks will be ideally placed to integrate data from diverse -omics sources to harness the extensive, rich information related to disease and treatment mechanisms.

A community-based study of nucleotide excision repair polymorphisms in relation to the risk of non-melanoma skin cancer

Nucleotide excision repair (NER) is responsible for protecting DNA in skin cells against ultraviolet radiation-induced damage. Using a candidate pathway approach, a matched case-control study nested within a prospective, community-based cohort was carried out to test the hypothesis that single nucleotide polymorphisms (SNPs) in NER genes are associated with susceptibility to non-melanoma skin cancer (NMSC). Histologically-confirmed cases of NMSC (n=900) were matched to controls (n=900) on age, gender, and skin type. Associations were measured between NMSC and 221 SNPs in 26 NER genes. Using the additive model, two tightly linked functional SNPs in ERCC6 were significantly associated with increased risk of NMSC: rs2228527 (odds ratio (OR) 1.57, 95% confidence interval (CI) 1.20 – 2.05), and rs2228529 (OR 1.57, 95% CI 1.20 – 2.05). These associations were confined to basal cell carcinoma of the skin (BCC) (rs2228529, OR 1.78, 95% CI 1.30 – 2.44; rs2228527 OR 1.78, 95% CI 1.31 – 2.43). These hypothesis-generating findings suggest functional variants in ERCC6 may be associated with an increased risk of NMSC that may be specific to BCC.