Identification of S100A8-correlated genes for prediction of disease progression in non-muscle invasive bladder cancer

<p>Abstract</p> <p>Background</p> <p><it>S100 calcium binding protein A8 </it>(<it>S100A8</it>) has been implicated as a prognostic indicator in several types of cancer. However, previous studies are limited in their ability to predict the clinical behavior of the cancer. Here, we sought to identify a molecular signature based on <it>S100A8 </it>expression and to assess its usefulness as a prognostic indicator of disease progression in non-muscle invasive bladder cancer (NMIBC).</p> <p>Methods</p> <p>We used 103 primary NMIBC specimens for microarray gene expression profiling. The median follow-up period for all patients was 57.6 months (range: 3.2 to 137.0 months). Various statistical methods, including the leave-one-out cross validation method, were applied to identify a gene expression signature able to predict the likelihood of progression. The prognostic value of the gene expression signature was validated in an independent cohort (n = 302).</p> <p>Results</p> <p>Kaplan-Meier estimates revealed significant differences in disease progression associated with the expression signature of <it>S100A8</it>-correlated genes (log-rank test, <it>P </it>< 0.001). Multivariate Cox regression analysis revealed that the expression signature of <it>S100A8</it>-correlated genes was a strong predictor of disease progression (hazard ratio = 15.225, 95% confidence interval = 1.746 to 133.52, <it>P </it>= 0.014). We validated our results in an independent cohort and confirmed that this signature produced consistent prediction patterns. Finally, gene network analyses of the signature revealed that <it>S100A8</it>, <it>IL1B</it>, and <it>S100A9 </it>could be important mediators of the progression of NMIBC.</p> <p>Conclusions</p> <p>The prognostic molecular signature defined by <it>S100A8</it>-correlated genes represents a promising diagnostic tool for the identification of NMIBC patients that have a high risk of progression to muscle invasive bladder cancer.</p

Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation

Although genome-wide association studies have identified over 100 risk loci that explain ~33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown. Here we use genotype data from 59,089 men of European and African American ancestries combined with cell-type-specific epigenetic data to build a genomic atlas of single-nucleotide polymorphism (SNP) heritability in PrCa. We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines. The majority of SNP heritability lies in regions marked by H3k27 acetylation in prostate adenoc7arcinoma cell line (LNCaP) or by DNaseI hypersensitive sites in cancer cell lines. We find a high degree of similarity between European and African American ancestries suggesting a similar genetic architecture from common variation underlying PrCa risk. Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa

Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation

Although genome-wide association studies have identified over 100 risk loci that explain similar to 33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown. Here we use genotype data from 59,089 men of European and African American ancestries combined with cell-type-specific epigenetic data to build a genomic atlas of single-nucleotide polymorphism (SNP) heritability in PrCa. We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines. The majority of SNP heritability lies in regions marked by H3k27 acetylation in prostate adenoc7arcinoma cell line (LNCaP) or by DNaseI hypersensitive sites in cancer cell lines. We find a high degree of similarity between European and African American ancestries suggesting a similar genetic architecture from common variation underlying PrCa risk. Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa

AA9int: SNP interaction pattern search using non-hierarchical additive model set.

Motivation The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions.Results We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies.Availability and implementation The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/.Supplementary information Supplementary data are available at Bioinformatics online