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Variants of SCARB1 and VDR Involved in Complex Genetic Interactions May Be Implicated in the Genetic Susceptibility to Clear Cell Renal Cell Carcinoma.

ABSTRACT: The current data are still inconclusive in terms of a genetic component involved in the susceptibility to renal cell carcinoma. Our aim was to evaluate 40 selected candidate polymorphisms for potential association with clear cell renal cell carcinoma (ccRCC) based on independent group of 167 patients and 200 healthy controls. The obtained data were searched for independent effects of particular polymorphisms as well as haplotypes and genetic interactions. Association testing implied position rs4765623 in the SCARB1 gene (OR = 1.688, 95% CI: 1.104-2.582, P = 0.016) and a haplotype in VDR comprising positions rs739837, rs731236, rs7975232, and rs1544410 (P = 0.012) to be the risk factors in the studied population. The study detected several epistatic effects contributing to the genetic susceptibility to ccRCC. Variation in GNAS1 was implicated in a strong synergistic interaction with BIRC5. This effect was part of a model suggested by multifactor dimensionality reduction method including also a synergy between GNAS1 and SCARB1 (P = 0.036). Significance of GNAS1-SCARB1 interaction was further confirmed by logistic regression (P = 0.041), which also indicated involvement of SCARB1 in additional interaction with EPAS1 (P = 0.008) as well as revealing interactions between GNAS1 and EPAS1 (P = 0.016), GNAS1 and MC1R (P = 0.031), GNAS1 and VDR (P = 0.032), and MC1R and VDR (P = 0.035).

SUBMITTER: Pospiech E

PROVIDER: S-EPMC4402472 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Variants of SCARB1 and VDR Involved in Complex Genetic Interactions May Be Implicated in the Genetic Susceptibility to Clear Cell Renal Cell Carcinoma.

Pośpiech Ewelina E Ligęza Janusz J Wilk Wacław W Gołas Aniela A Jaszczyński Janusz J Stelmach Andrzej A Ryś Janusz J Blecharczyk Aleksandra A Wojas-Pelc Anna A Jura Jolanta J Branicki Wojciech W

BioMed research international 20150406

The current data are still inconclusive in terms of a genetic component involved in the susceptibility to renal cell carcinoma. Our aim was to evaluate 40 selected candidate polymorphisms for potential association with clear cell renal cell carcinoma (ccRCC) based on independent group of 167 patients and 200 healthy controls. The obtained data were searched for independent effects of particular polymorphisms as well as haplotypes and genetic interactions. Association testing implied position rs4 ...[more]

PMID: 25945350

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Project description:Background and objectivePrevious germline studies on renal cell carcinoma (RCC) have usually pooled clear and non-clear cell RCCs and have not adequately accounted for population stratification, which might have led to an inaccurate estimation of genetic risk. Here, we aim to analyze the major germline drivers of RCC risk and clinically relevant but underexplored germline variant types.MethodsWe first characterized germline pathogenic variants (PVs), cryptic splice variants, and copy number variants (CNVs) in 1436 unselected RCC patients. To evaluate the enrichment of PVs in RCC, we conducted a case-control study of 1356 RCC patients ancestry matched with 16 512 cancer-free controls using approaches accounting for population stratification and histological subtypes, followed by characterization of secondary somatic events.Key findings and limitationsClear cell RCC patients (n = 976) exhibited a significant burden of PVs in VHL compared with controls (odds ratio [OR]: 39.1, p = 4.95e-05). Non-clear cell RCC patients (n = 380) carried enrichment of PVs in FH (OR: 77.9, p = 1.55e-08) and MET (OR: 1.98e11, p = 2.07e-05). In a CHEK2-focused analysis with European participants, clear cell RCC (n = 906) harbored nominal enrichment of low-penetrance CHEK2 variants-p.Ile157Thr (OR: 1.84, p = 0.049) and p.Ser428Phe (OR: 5.20, p = 0.045), while non-clear cell RCC (n = 295) exhibited nominal enrichment of CHEK2 loss of function PVs (OR: 3.51, p = 0.033). Patients with germline PVs in FH, MET, and VHL exhibited significantly earlier age of cancer onset than patients without germline PVs (mean: 46.0 vs 60.2 yr, p < 0.0001), and more than half had secondary somatic events affecting the same gene (n = 10/15, 66.7%). Conversely, CHEK2 PV carriers exhibited a similar age of onset to patients without germline PVs (mean: 60.1 vs 60.2 yr, p = 0.99), and only 30.4% carried somatic events in CHEK2 (n = 7/23). Finally, pathogenic germline cryptic splice variants were identified in SDHA and TSC1, and pathogenic germline CNVs were found in 18 patients, including CNVs in FH, SDHA, and VHL.Conclusions and clinical implicationsThis analysis supports the existing link between several RCC risk genes and RCC risk manifesting in earlier age of onset. It calls for caution when assessing the role of CHEK2 due to the burden of founder variants with varying population frequency. It also broadens the definition of the RCC germline landscape of pathogenicity to incorporate previously understudied types of germline variants.Patient summaryIn this study, we carefully compared the frequency of rare inherited mutations with a focus on patients' genetic ancestry. We discovered that subtle variations in genetic background may confound a case-control analysis, especially in evaluating the cancer risk associated with specific genes, such as CHEK2. We also identified previously less explored forms of rare inherited mutations, which could potentially increase the risk of kidney cancer.

Project description:Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development.Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, |log(FC)| > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets.Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.

Dataset Information

Variants of SCARB1 and VDR Involved in Complex Genetic Interactions May Be Implicated in the Genetic Susceptibility to Clear Cell Renal Cell Carcinoma.

Publications

Variants of SCARB1 and VDR Involved in Complex Genetic Interactions May Be Implicated in the Genetic Susceptibility to Clear Cell Renal Cell Carcinoma.

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