HSD3B and gene-gene interactions in a pathway-based analysis of genetic susceptibility to bladder cancer

The role of CDK8 gene polymorphisms in bladder cancer susceptibility and prognosis: a study in the Chinese Han population

BACKGROUND

Cyclin-dependent kinase 8 (CDK8) has been implicated in various tumors, with its role differing across tumor types. However, the association between CDK8 polymorphisms and bladder cancer (BC) remains unclear. This study investigated the association between CDK8 polymorphisms and BC susceptibility and prognosis.

METHODS

This case-control study included 271 patients with BC and 381 healthy controls. Two-tag single-nucleotide polymorphisms in the CDK8 gene (rs17083838 and rs7992670) were genotyped using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Statistical analyses were performed using SNPstats and SPSS software to assess genetic associations.

RESULTS

The AG/AA genotypes of rs17083838 were associated with a significantly reduced risk of BC under the dominant model (P < 0.001, odds ratio [95% confidence interval] = 0.50 [0.33-0.76]). Stratified analysis revealed that the AG genotype of rs17083838 increased the risk of postoperative recurrence in patients with stage IV BC (P = 0.007). For rs7992670, females with the AG/AA genotype exhibited a 2.07-fold higher risk of BC than males, whereas smokers with the same genotype showed a 2.13-fold higher risk than non-smokers. The GG genotype of rs7992670 was associated with better overall survival in patients with stage III BC (P = 0.023). Among patients with recurrent muscle-invasive BC, those with the GG/AA genotype showed significantly improved survival compared with those carrying the AG genotype (P = 0.023).

CONCLUSIONS

CDK8 polymorphisms influence BC susceptibility and prognosis, with rs17083838 showing a protective effect and rs7992670 being associated with increased risk and survival outcomes in specific subgroups.

IMPACT

This study highlights the potential of CDK8 polymorphisms as biomarkers for BC susceptibility and prognosis, emphasizing the need for further research.

Genetic interactions effects for cancer disease identification using computational models: a review

Genome-wide association studies (GWAS) provide clear insight into understanding genetic variations and environmental influences responsible for various human diseases. Cancer identification through genetic interactions (epistasis) is one of the significant ongoing researches in GWAS. The growth of the cancer cell emerges from multi-locus as well as complex genetic interaction. It is impractical for the physician to detect cancer via manual examination of SNPs interaction. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2001 and 2020. In this contemporary review, various computational methods are as follows: multifactor dimensionality reduction-based approaches, statistical strategies, machine learning, and optimization-based techniques are carefully reviewed and presented with their evaluation results. Moreover, these computational approaches' strengths and limitations are described. The issues behind the computational methods for identifying the cancer disease through genetic interactions and the various evaluation parameters used by researchers have been analyzed. This review is highly beneficial for researchers and medical professionals to learn techniques adapted to discover the epistasis and aids to design novel automatic epistasis detection systems with strong robustness and maximum efficiency to address the different research problems in finding practical solutions effectively.

A network approach to prioritizing susceptibility genes for genome-wide association studies

The heritability of complex diseases including cancer is often attributed to multiple interacting genetic alterations. Such a non-linear, non-additive gene-gene interaction effect, that is, epistasis, renders univariable analysis methods ineffective for genome-wide association studies. In recent years, network science has seen increasing applications in modeling epistasis to characterize the complex relationships between a large number of genetic variations and the phenotypic outcome. In this study, by constructing a statistical epistasis network of colorectal cancer (CRC), we proposed to use multiple network measures to prioritize genes that influence the disease risk of CRC through synergistic interaction effects. We computed and analyzed several global and local properties of the large CRC epistasis network. We utilized topological properties of network vertices such as the edge strength, vertex centrality, and occurrence at different graphlets to identify genes that may be of potential biological relevance to CRC. We found 512 top-ranked single-nucleotide polymorphisms, among which COL22A1, RGS7, WWOX, and CELF2 were the four susceptibility genes prioritized by all described metrics as the most influential on CRC.

Intracrine Regulation of Estrogen and Other Sex Steroid Levels in Endometrium and Non-gynecological Tissues; Pathology, Physiology, and Drug Discovery

Our understanding of the intracrine (or local) regulation of estrogen and other steroid synthesis and degradation expanded in the last decades, also thanks to recent technological advances in chromatography mass-spectrometry. Estrogen responsive tissues and organs are not passive receivers of the pool of steroids present in the blood but they can actively modify the intra-tissue steroid concentrations. This allows fine-tuning the exposure of responsive tissues and organs to estrogens and other steroids in order to best respond to the physiological needs of each specific organ. Deviations in such intracrine control can lead to unbalanced steroid hormone exposure and disturbances. Through a systematic bibliographic search on the expression of the intracrine enzymes in various tissues, this review gives an up-to-date view of the intracrine estrogen metabolisms, and to a lesser extent that of progestogens and androgens, in the lower female genital tract, including the physiological control of endometrial functions, receptivity, menopausal status and related pathological conditions. An overview of the intracrine regulation in extra gynecological tissues such as the lungs, gastrointestinal tract, brain, colon and bone is given. Current therapeutic approaches aimed at interfering with these metabolisms and future perspectives are discussed.

CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions

BACKGROUND

Detecting and visualizing nonlinear interaction effects of single nucleotide polymorphisms (SNPs) or epistatic interactions are important topics in bioinformatics since they play an important role in unraveling the mystery of "missing heritability". However, related studies are almost limited to pairwise epistatic interactions due to their methodological and computational challenges.

RESULTS

We develop CINOEDV (Co-Information based N-Order Epistasis Detector and Visualizer) for the detection and visualization of epistatic interactions of their orders from 1 to n (n ≥ 2). CINOEDV is composed of two stages, namely, detecting stage and visualizing stage. In detecting stage, co-information based measures are employed to quantify association effects of n-order SNP combinations to the phenotype, and two types of search strategies are introduced to identify n-order epistatic interactions: an exhaustive search and a particle swarm optimization based search. In visualizing stage, all detected n-order epistatic interactions are used to construct a hypergraph, where a real vertex represents the main effect of a SNP and a virtual vertex denotes the interaction effect of an n-order epistatic interaction. By deeply analyzing the constructed hypergraph, some hidden clues for better understanding the underlying genetic architecture of complex diseases could be revealed.

CONCLUSIONS

Experiments of CINOEDV and its comparison with existing state-of-the-art methods are performed on both simulation data sets and a real data set of age-related macular degeneration. Results demonstrate that CINOEDV is promising in detecting and visualizing n-order epistatic interactions. CINOEDV is implemented in R and is freely available from R CRAN: http://cran.r-project.org and https://sourceforge.net/projects/cinoedv/files/ .

The GATA3 gene is involved in leprosy susceptibility in Brazilian patients

Leprosy outcome is a complex trait and the host-pathogen-environment interaction defines the emergence of the disease. Host genetic risk factors have been successfully associated to leprosy. The 10p13 chromosomal region was linked to leprosy in familial studies and GATA3 gene is a strong candidate to be part of this association. Here, we tested tag single nucleotide polymorphisms at GATA3 in two case-control samples from Brazil comprising a total of 1633 individuals using stepwise strategy. The A allele of rs10905284 marker was associated with leprosy resistance. Then, a functional analysis was conducted and showed that individuals carrying AA genotype express higher levels of GATA-3 protein in lymphocytes. So, we confirmed that the rs10905284 is a locus associated to leprosy and influences the levels of this transcription factor in the Brazilian population.

A screening-testing approach for detecting gene-environment interactions using sequential penalized and unpenalized multiple logistic regression

Gene-environment (G × E) interactions are biologically important for a wide range of environmental exposures and clinical outcomes. Because of the large number of potential interactions in genomewide association data, the standard approach fits one model per G × E interaction with multiple hypothesis correction (MHC) used to control the type I error rate. Although sometimes effective, using one model per candidate G × E interaction test has two important limitations: low power due to MHC and omitted variable bias. To avoid the coefficient estimation bias associated with independent models, researchers have used penalized regression methods to jointly test all main effects and interactions in a single regression model. Although penalized regression supports joint analysis of all interactions, can be used with hierarchical constraints, and offers excellent predictive performance, it cannot assess the statistical significance of G × E interactions or compute meaningful estimates of effect size. To address the challenge of low power, researchers have separately explored screening-testing, or two-stage, methods in which the set of potential G × E interactions is first filtered and then tested for interactions with MHC only applied to the tests actually performed in the second stage. Although two-stage methods are statistically valid and effective at improving power, they still test multiple separate models and so are impacted by MHC and biased coefficient estimation. To remedy the challenges of both poor power and omitted variable bias encountered with traditional G × E interaction detection methods, we propose a novel approach that combines elements of screening-testing and hierarchical penalized regression. Specifically, our proposed method uses, in the first stage, an elastic net-penalized multiple logistic regression model to jointly estimate either the marginal association filter statistic or the gene-environment correlation filter statistic for all candidate genetic markers. In the second stage, a single multiple logistic regression model is used to jointly assess marginal terms and G × E interactions for all genetic markers that pass the first stage filter. A single likelihood-ratio test is used to determine whether any of the interactions are statistically significant. We demonstrate the efficacy of our method relative to alternative G × E detection methods on a bladder cancer data set.

MicroRNA binding site polymorphisms as biomarkers in cancer management and research

MicroRNAs (miRNAs) are important regulators of eukaryotic gene expression. They have been implicated in a broad range of biological processes, and miRNA-related genetic alterations probably underlie several human diseases. Single nucleotide polymorphisms of transcripts may modulate the posttranscriptional regulation of gene expression by miRNAs and explain interindividual variability in cancer risk and in chemotherapy response. On the basis of recent association studies published in the literature, the present review mainly summarizes the potential role of miRNAs as molecular biomarkers for disease susceptibility, diagnosis, prognosis, and drug-response prediction in tumors. Many clues suggest a role for polymorphisms within the 3' untranslated regions of KRAS rs61764370, SET8 rs16917496, and MDM4 rs4245739 as SNPs in miRNA binding sites highly promising in the biology of human cancer. However, more studies are needed to better characterize the composite spectrum of genetic determinants for future use of markers in risk prediction and clinical management of diseases, heading toward personalized medicine.

Impacts of CA9 gene polymorphisms on urothelial cell carcinoma susceptibility and clinicopathologic characteristics in Taiwan

BACKGROUND

Carbonic anhydrase 9 (CA9) is reportedly overexpressed in several types of carcinomas and is generally considered a marker of malignancy. The current study explored the effect of CA9 gene polymorphisms on the susceptibility of developing urothelial cell carcinoma (UCC) and the clinicopathological status.

METHODOLOGY AND PRINCIPAL FINDINGS

A total of 442 participants, including 221 healthy people and 221 patients with UCC, were recruited for this study. Four single-nucleotide polymorphisms (SNPs) of the CA9 gene were assessed by a real-time PCR with the TaqMan assay. After adjusting for other co-variants, the individuals carrying at least one A allele at CA9 rs1048638 had a 2.303-fold risk of developing UCC than did wild-type (CC) carriers. Furthermore, UCC patients who carried at least one A allele at rs1048638 had a higher invasive stage risk (p< 0.05) than did patients carrying the wild-type allele. Moreover, among the UCC patients with smoker, people with at least one A allele of CA9 polymorphisms (rs1048638) had a 4.75-fold (95% CI = 1.204-18.746) increased risk of invasive cancer.

CONCLUSION

The rs1048638 polymorphic genotypes of CA9 might contribute to the prediction of susceptibility to and pathological development of UCC. This is the first study to provide insight into risk factors associated with CA9 variants in carcinogenesis of UCC in Taiwan.

Layered signaling regulatory networks analysis of gene expression involved in malignant tumorigenesis of non-resolving ulcerative colitis via integration of cross-study microarray profiles

Background

Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups.

Methodology/Principal Findings

In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks.

Results

Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one.

Conclusions

The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis, which was of significant importance to monitor disease progression and improve therapeutic interventions.