SNP-SNP interaction network in angiogenesis genes associated with prostate cancer aggressiveness. PLoS One. 2013;8:e59688. [PMID: 23593148 PMCID: PMC3618555 DOI: 10.1371/journal.pone.0059688]

Matrix Metalloproteinases and Glaucoma

Matrix metalloproteinases (MMPs) are enzymes that decompose extracellular matrix (ECM) proteins. MMPs are thought to play important roles in cellular processes, such as cell proliferation, differentiation, angiogenesis, migration, apoptosis, and host defense. MMPs are distributed in almost all intraocular tissues and are involved in physiological and pathological mechanisms of the eye. MMPs are also associated with glaucoma, a progressive neurodegenerative disease of the eyes. MMP activity affects intraocular pressure control and apoptosis of retinal ganglion cells, which are the pathological mechanisms of glaucoma. It also affects the risk of glaucoma development based on genetic pleomorphism. In addition, MMPs may affect the treatment outcomes of glaucoma, including the success rate of surgical treatment and side effects on the ocular surface due to glaucoma medications. This review discusses the various relationships between MMP and glaucoma.

Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network

Objectives

Genome-wide association studies (GWAS) are performed to study the associations between genetic variants with respect to certain phenotypic traits such as cancer. However, the method that is commonly used in GWAS assumes that certain traits are solely affected by a single mutation. We propose a network analysis method, in which we generate association networks of single-nucleotide polymorphisms (SNPs) that can differentiate case and control groups. We hypothesize that certain phenotypic traits are attributable to mutations in groups of associated SNPs.

Methods

We propose a method based on a network analysis framework to study SNP-SNP interactions related to cancer incidence. We employed logistic regression to measure the significance of all SNP pairs from GWAS for the incidence of colorectal cancer and computed a cancer risk score based on the generated SNP networks.

Results

We demonstrated our method in a dataset from a case-control study of colorectal cancer in the South Sulawesi population. From the GWAS results, 20,094 pairs of 200 SNPs were created. We obtained one cluster containing four pairs of five SNPs that passed the filtering threshold based on their p-values. A locus on chromosome 12 (12:54410007) was found to be strongly connected to the four variants on chromosome 1. A polygenic risk score was computed from the five SNPs, and a significant difference in colorectal cancer risk was obtained between the case and control groups.

Conclusions

Our results demonstrate the applicability of our method to understand SNP-SNP interactions and compute risk scores for various types of cancer.

Role of mitochondrial genetic interactions in determining adaptation to high altitude human population

Physiological and haplogroup studies performed to understand high-altitude adaptation in humans are limited to individual genes and polymorphic sites. Due to stochastic evolutionary forces, the frequency of a polymorphism is affected by changes in the frequency of a near-by polymorphism on the same DNA sample making them connected in terms of evolution. Here, first, we provide a method to model these mitochondrial polymorphisms as "co-mutation networks" for three high-altitude populations, Tibetan, Ethiopian and Andean. Then, by transforming these co-mutation networks into weighted and undirected gene-gene interaction (GGI) networks, we were able to identify functionally enriched genetic interactions of CYB and CO3 genes in Tibetan and Andean populations, while NADH dehydrogenase genes in the Ethiopian population playing a significant role in high altitude adaptation. These co-mutation based genetic networks provide insights into the role of different set of genes in high-altitude adaptation in human sub-populations.

Genetic Polymorphisms of IFNG, IFNGR1, and Androgen Receptor and Chronic Prostatitis/Chronic Pelvic Pain Syndrome in a Chinese Han Population

BACKGROUND

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) refers to a common disorder with unclear etiology and unsatisfactory treatment, which reduces the male's quality of life.

OBJECTIVE

To examine the effects of genetic polymorphisms of IFNG, IFNGR1, and androgen receptor (AR) on CP/CPPS.

METHODS

The single nucleotide polymorphisms (SNPs) of IFNG, IFNGR1, and AR were genotyped with the improved multiplex ligation detection reaction. The GTEx, RegulomeDB, HaploReg, and 3DSNP databases were adopted to predict the regulatory functions of the genotyped SNPs. The correlation between SNPs and CP/CPPS was analyzed with the χ 2 test, logistic regression, and two genetic models (codominant and log-additive models). The nomogram was built to predict the risk of CP/CPPS occurrence.

RESULTS

On the whole, 130 CP/CPPS patients and 125 healthy controls were recruited in the study, and 18 SNPs of IFNG, IFNGR1, and AR were genotyped. The results of functional annotation indicated that the 18 genotyped SNPs might have regulatory effects in the whole blood. The rs144488434 was correlated with the elevated CP/CPPS risk (odds ratio (OR): 2.40, 95% confidence interval (CI): 1.12-5.13, χ 2 = 5.37, and P = 0.021) by the χ 2 test. In the built genetic models, rs10457655 was correlated with the elevated National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) scores (codominant model: GA/GG: crude mean difference (MD) = 0.98, 95% CI: -1.71-3.67 and AA/GG: crude MD = 9.10, 95% CI: 0.58-17.62, P = 0.10). In subgroup analysis, rs2069718 was correlated with the elevated CP/CPPS risk (log-additive model: crude OR = 2.18, 95% CI: 1.03-4.64, and P = 0.034) in patients ≥ 35 years. The nomogram integrating age, rs2069718, rs10457655, and rs144488434 showed good performance to predict the risk of CP/CPPS.

CONCLUSIONS

Genetic polymorphisms of IFNG, IFNGR1, and AR might act as the genetic factors for CP/CPPS susceptibility, which deserved further explorations.

Interaction of HULC polymorphisms with Helicobacter pylori infection plays a strong role for the prediction of gastric cancer risk

Aim: Gene-environment interactions have better efficacy in predicting cancer susceptibility than a single gene. Materials & methods: Eight tag single nucleotide polymorphisms encompassing the whole HULC gene were detected by KASP platform (LGC Genomics, Hoddesdon, UK) in 631 gastric cancer (GC) cases and 953 controls. Results: The HULC gene rs7770772 polymorphism could increase GC risk (recessive model: odds ratio = 1.95). The multifactor dimensionality reduction (MDR) analysis suggested that the 2D model HULC rs7770772-Helicobacter pylori had better effect on GC risk prediction (maximum testing accuracy = 0.7005). No significant result was observed in our experimental expression quantitative trait loci analysis. Conclusion: 2D model HULC rs7770772-H. pylori might have superior efficacy for GC risk than a single factor.

Exploring Prostate Cancer Patients' Interest and Preferences for Receiving Genetic Risk Information About Cancer Aggressiveness

The number of cases of aggressive prostate cancer is increasing. Differentiating between aggressive and indolent cases has resulted in increased difficulty for the physician and patient to decide on the best treatment option. Due to this challenge, efforts are underway to profile genetic risk for prostate cancer aggressiveness, which may help physicians and patients at risk for developing aggressive prostate cancer to select an appropriate treatment option. This study explores patients’ interest in receiving genetic results, preference for how genetic risk information should be communicated, and willingness to share results with adult male first-degree relatives (FDRs). A nine-item survey was adapted to assess their beliefs and attitudes about genetic testing for prostate cancer aggressiveness. In addition, participants (n = 50) responded to hypothetical scenarios and questions associated with perceived importance of risk disclosure, preferences for receiving genetic risk information, and sharing of results with FDRs. As the hypothetical risk estimate for aggressive prostate cancer increased, patients’ willingness to receive genetic risk information increased. This study found that most patients preferred receiving genetic risk education in the form of a DVD (76%), one-page informational sheet (75%), or educational booklet (70%). Almost all patients (98%) reported that they would be willing to share their test results with FDRs. The results of this study highlight prostate cancer patients’ desire to receive and share genetic risk information. Future research should focus on assessing the long-term benefits of receiving genetic information for prostate cancer patients and implications of sharing this information with FDRs.

KLK3 SNP-SNP interactions for prediction of prostate cancer aggressiveness

Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP-SNP interactions in the four pathways (the angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways) associated with PCa aggressiveness, we tested 8587 SNPs for 20,729 cases from the PCa consortium. We identified 3 KLK3 SNPs, and 1083 (P < 3.5 × 10-9) and 3145 (P < 1 × 10-5) SNP-SNP interaction pairs significantly associated with PCa aggressiveness. These SNP pairs associated with PCa aggressiveness were more significant than each of their constituent SNP individual effects. The majority (98.6%) of the 3145 pairs involved KLK3. The 3 most common gene-gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH13, and KLK3-TGFBR3. Predictions from the SNP interaction-based polygenic risk score based on 24 SNP pairs are promising. The prevalence of PCa aggressiveness was 49.8%, 21.9%, and 7.0% for the PCa cases from our cohort with the top 1%, middle 50%, and bottom 1% risk profiles. Potential biological functions of the identified KLK3 SNP-SNP interactions were supported by gene expression and protein-protein interaction results. Our findings suggest KLK3 SNP interactions may play an important role in PCa aggressiveness.

The Role of the Metzincin Superfamily in Prostate Cancer Progression: A Systematic-Like Review

Prostate cancer remains a leading cause of cancer-related morbidity in men. Potentially important regulators of prostate cancer progression are members of the metzincin superfamily of proteases, principally through their regulation of the extracellular matrix. It is therefore timely to review the role of the metzincin superfamily in prostate cancer and its progression to better understand their involvement in this disease. A systematic-like search strategy was conducted. Articles that investigated the roles of members of the metzincin superfamily and their key regulators in prostate cancer were included. The extracted articles were synthesized and data presented in tabular and narrative forms. Two hundred and five studies met the inclusion criteria. Of these, 138 investigated the role of the Matrix Metalloproteinase (MMP) subgroup, 34 the Membrane-Tethered Matrix Metalloproteinase (MT-MMP) subgroup, 22 the A Disintegrin and Metalloproteinase (ADAM) subgroup, 8 the A Disintegrin and Metalloproteinase with Thrombospondin Motifs (ADAMTS) subgroup and 53 the Tissue Inhibitor of Metalloproteinases (TIMP) family of regulators, noting that several studies investigated multiple family members. There was clear evidence that specific members of the metzincin superfamily are involved in prostate cancer progression, which can be either in a positive or negative manner. However, further understanding of their mechanisms of action and how they may be used as prognostic indicators or molecular targets is required.

IL-34 and CSF-1, deciphering similarities and differences at steady state and in diseases

Although IL-34 and CSF-1 share actions as key mediators of monocytes/macrophages survival and differentiation, they also display differences that should be identified to better define their respective roles in health and diseases. IL-34 displays low sequence homology with CSF-1 but has a similar general structure and they both bind to a common receptor CSF-1R, although binding and subsequent intracellular signaling shows differences. CSF-1R expression has been until now mainly described at a steady state in monocytes/macrophages and myeloid dendritic cells, as well as in some cancers. IL-34 has also 2 other receptors, protein-tyrosine phosphatase zeta (PTPζ) and CD138 (Syndecan-1), expressed in some epithelium, cells of the central nervous system (CNS), as well as in numerous cancers. While most, if not all, of CSF-1 actions are mediated through monocyte/macrophages, IL-34 has also other potential actions through PTPζ and CD138. Additionally, IL-34 and CSF-1 are produced by different cells in different tissues. This review describes and discusses similarities and differences between IL-34 and CSF-1 at steady state and in pathological situations and identifies possible ways to target IL-34, CSF-1, and its receptors.

Alcohol Intake and Alcohol-SNP Interactions Associated with Prostate Cancer Aggressiveness

Excessive alcohol intake is a well-known modifiable risk factor for many cancers. It is still unclear whether genetic variants or single nucleotide polymorphisms (SNPs) can modify alcohol intake's impact on prostate cancer (PCa) aggressiveness. The objective is to test the alcohol-SNP interactions of the 7501 SNPs in the four pathways (angiogenesis, mitochondria, miRNA, and androgen metabolism-related pathways) associated with PCa aggressiveness. We evaluated the impacts of three excessive alcohol intake behaviors in 3306 PCa patients with European ancestry from the PCa Consortium. We tested the alcohol-SNP interactions using logistic models with the discovery-validation study design. All three excessive alcohol intake behaviors were not significantly associated with PCa aggressiveness. However, the interactions of excessive alcohol intake and three SNPs (rs13107662 [CAMK2D, p = 6.2 × 10-6], rs9907521 [PRKCA, p = 7.1 × 10-5], and rs11925452 [ROBO1, p = 8.2 × 10-4]) were significantly associated with PCa aggressiveness. These alcohol-SNP interactions revealed contrasting effects of excessive alcohol intake on PCa aggressiveness according to the genotypes in the identified SNPs. We identified PCa patients with the rs13107662 (CAMK2D) AA genotype, the rs11925452 (ROBO1) AA genotype, and the rs9907521 (PRKCA) AG genotype were more vulnerable to excessive alcohol intake for developing aggressive PCa. Our findings support that the impact of excessive alcohol intake on PCa aggressiveness was varied by the selected genetic profiles.

Genome-Wide Search for SNP Interactions in GWAS Data: Algorithm, Feasibility, Replication Using Schizophrenia Datasets

In this study, we looked for potential gene-gene interaction in susceptibility to schizophrenia by an exhaustive searching for SNP-SNP interactions in 3 GWAS datasets (phs000021:phg000013, phs000021:phg000014, phs000167) using our recently published algorithm. The search space for SNP-SNP interaction was confined to 8 biologically plausible ways of interaction under dominant-dominant or recessive-recessive modes. First, we performed our search of all pair-wise combination of 729,454 SNPs after filtering by SNP genotype quality. All possible pairwise interactions of any 2 SNPs (5 × 10¹¹) were exhausted to search for significant interaction which was defined by p-value of chi-square tests. Nine out the top 10 interactions, protein coding genes were partnered with non-coding RNA (ncRNA) which suggested a new alternative insight into interaction biology other than the frequently sought-after protein-protein interaction. Therefore, we extended to look for replication among the top 10,000 interaction SNP pairs and high proportion of concurrent genes forming the interaction pairs were found. The results indicated that an enrichment of signals over noise was present in the top 10,000 interactions. Then, replications of SNP-SNP interaction were confirmed for 14 SNPs-pairs in both replication datasets. Biological insight was highlighted by a potential binding between FHIT (protein coding gene) and LINC00969 (lncRNA) which showed a replicable interaction between their SNPs. Both of them were reported to have expression in brain. Our study represented an early attempt of exhaustive interaction analysis of GWAS data which also yield replicated interaction and new insight into understanding of genetic interaction in schizophrenia.

Measuring gene-gene interaction using Kullback-Leibler divergence

Genome-wide association studies (GWAS) are used to investigate genetic variants contributing to complex traits. Despite discovering many loci, a large proportion of "missing" heritability remains unexplained. Gene-gene interactions may help explain some of this gap. Traditionally, gene-gene interactions have been evaluated using parametric statistical methods such as linear and logistic regression, with multifactor dimensionality reduction (MDR) used to address sparseness of data in high dimensions. We propose a method for the analysis of gene-gene interactions across independent single-nucleotide polymorphisms (SNPs) in two genes. Typical methods for this problem use statistics based on an asymptotic chi-squared mixture distribution, which is not easy to use. Here, we propose a Kullback-Leibler-type statistic, which follows an asymptotic, positive, normal distribution under the null hypothesis of no relationship between SNPs in the two genes, and normally distributed under the alternative hypothesis. The performance of the proposed method is evaluated by simulation studies, which show promising results. The method is also used to analyze real data and identifies gene-gene interactions among RAB3A, MADD, and PTPRN on type 2 diabetes (T2D) status.

The interaction effects between TLR4 and MMP9 gene polymorphisms contribute to aortic aneurysm risk in a Chinese Han population

Background

A cross-talk between Toll-like receptor 4 (TLR4) and matrix metalloproteinase 9 (MMP9) plays a vital role in aortic pathophysiology. The objective of this study was to evaluate the interactions between TLR4 and MMP9 polymorphisms in the risk of aortic aneurysm (AA) and its subtypes.

Methods

KASP method was used to detect polymorphisms of TLR4 (rs11536889 and rs1927914) and MMP9 (rs17576) in 472 AA patients and 498 controls. According to location and size, AA patients were further classified into abdominal AA (AAA), thoracic AA (TAA), and large AA (>5.0 cm), small AA(≤5.0 cm), respectively.

Results

The significant interaction effect of TLR4rs1927914 with MMP9rs17576 polymorphisms was observed for the risk of TAA (P_interaction = 0.038, OR = 6.186) and large AA (P_interaction = 0.044, OR = 5.892). There were epistatic effects between TLR4rs1927914 and MMP9rs17576 polymorphisms on the risk of overall AA, AAA, TAA and large AA when they were present together. Moreover, the cumulative effects of the pairwise interaction TLR4rs1927914-MMP9rs17576 were associated with an increased risk of overall AA (P_trend = 0.032) and AAA (P_trend = 0.031).

Conclusions

The novel interaction between TLR4rs1927914 and MMP9rs17576 polymorphisms could increase the risk of AA disease or its subtypes by exerting epistatic and cumulative effects.

Electronic supplementary material

The online version of this article (10.1186/s12872-019-1049-8) contains supplementary material, which is available to authorized users.

Interactions of PVT1 and CASC11 on Prostate Cancer Risk in African Americans

BACKGROUND

African American (AA) men have a higher risk of developing prostate cancer than white men. SNPs are known to play an important role in developing prostate cancer. The impact of PVT1 and its neighborhood genes (CASC11 and MYC) on prostate cancer risk are getting more attention recently. The interactions among these three genes associated with prostate cancer risk are understudied, especially for AA men. The objective of this study is to investigate SNP-SNP interactions in the CASC11-MYC-PVT1 region associated with prostate cancer risk in AA men.

METHODS

We evaluated 205 SNPs using the 2,253 prostate cancer patients and 2,423 controls and applied multiphase (discovery-validation) design. In addition to SNP individual effects, SNP-SNP interactions were evaluated using the SNP Interaction Pattern Identifier, which assesses 45 patterns.

RESULTS

Three SNPs (rs9642880, rs16902359, and rs12680047) and 79 SNP-SNP pairs were significantly associated with prostate cancer risk. These two SNPs (rs16902359 and rs9642880) in CASC11 interacted frequently with other SNPs with 56 and 9 pairs, respectively. We identified the novel interaction of CASC11-PVT1, which is the most common gene interaction (70%) in the top 79 pairs. Several top SNP interactions have a moderate to large effect size (OR, 0.27-0.68) and have a higher prediction power to prostate cancer risk than SNP individual effects.

CONCLUSIONS

Novel SNP-SNP interactions in the CASC11-MYC-PVT1 region have a larger impact than SNP individual effects on prostate cancer risk in AA men.

IMPACT

This gene-gene interaction between CASC11 and PVT1 can provide valuable information to reveal potential biological mechanisms of prostate cancer development.

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.

Quantum dots tethered membrane type 3 matrix metalloproteinase-targeting peptide for tumor optical imaging

Membrane type matrix metalloproteinases (MT-MMPs) play important roles in malignant tumor progression through the degradation of the extracellular matrix and signal transduction. However, a member of the family, MT3-MMP, has attracted the least concern compared with other MT-MMPs. Here, a novel MT3-MMP-targeting peptide with high affinity and specificity has been developed by a phage-display peptide screening technology and multiple biophysics measurements, including single-molecule recognition force spectroscopy and isothermal titration calorimetry. The binding peptides are conjugated on the surface of CdSe/ZnS quantum dots (QDs) and consequently acted as a ligand that specifically targets MT3-MMP overexpressed tumor cells. The imaging nanoprobes used QDs as the photographic developer for optical imaging in vivo. The nanoprobes exhibited a desirable targeting effect and generated good biodistribution profiles for visualization and imaging of MT3-MMP overexpressed tumor. The peptide could be useful to evaluate the distribution and expression of MT3-MMP. Furthermore, the peptide-functionalized QDs show potential application for cancer diagnosis.

SNP interactions of PGC with its neighbor lncRNAs enhance the susceptibility to gastric cancer/atrophic gastritis and influence the expression of involved molecules

Multidimensional interactions of multiple factors are more important in promoting cancer initiation. Gene-gene interactions between protein-coding genes have been paid great attention, while rare studies refer to the interactions between encoding and noncoding genes. Our research group previously found encoding gene PGC polymorphisms could affect the susceptibility to atrophic gastritis (AG) and gastric cancer (GC). Interestingly, several SNPs in long noncoding RNA (lncRNA) genes, just adjacent to PGC, were found to be associated with AG risk and GC prognosis afterward. This study aims to explore the SNP interactions between PGC and its neighbor lncRNAs on the risk of AG and GC. Genotyping for seven PGC SNPs and seven lncRNA SNPs was conducted using Sequenom MassARRAY platform in a total of 2228 northern Chinese subjects, including 536 GC cases, 810 AG cases, and 882 controls. We found 15 pairwise PGC-lncRNAs SNPs had interactions: Five pairs were associated with AG risk, and ten pairs were associated with GC risk. Moreover, two GC-related interactions PGC rs6939861 with lnc-C6orf-132-1 rs7749023 and rs7747696 survived the Bonferroni correction (P_correction  = 0.049 and 0.007, respectively). Several combinations showed obvious epistasis and cumulative effects on disease risk. Some three-way interactions of SNPs with smoking and drinking could also be observed. Besides, a few interacting SNPs showed correlations with the expression levels of PGC protein and related lncRNAs in serum. Our study would provide research clues for further screening combination biomarkers uniting both protein-coding and noncoding genes with the potential in prediction of the susceptibility to GC and its precursor.

Single-nucleotide polymorphisms of stemness genes predicted to regulate RNA splicing, microRNA and oncogenic signaling are associated with prostate cancer survival

Prostate cancer (PCa) is a clinically and molecularly heterogeneous disease, with variation in outcomes only partially predicted by grade and stage. Additional tools to distinguish indolent from aggressive disease are needed. Phenotypic characteristics of stemness correlate with poor cancer prognosis. Given this correlation, we identified single-nucleotide polymorphisms (SNPs) of stemness-related genes and examined their associations with PCa survival. SNPs within stemness-related genes were analyzed for association with overall survival of PCa in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Significant SNPs predicted to be functional were selected for linkage disequilibrium analysis and combined and stratified analyses. Identified SNPs were evaluated for association with gene expression. SNPs of CD44 (rs9666607), ABCC1 (rs35605 and rs212091) and GDF15 (rs1058587) were associated with PCa survival and predicted to be functional. A role for rs9666607 of CD44 and rs35605 of ABCC1 in RNA splicing regulation, rs212091 of ABCC1 in miRNA binding site activity and rs1058587 of GDF15 in causing an amino acid change was predicted. These SNPs represent potential novel prognostic markers for overall survival of PCa and support a contribution of the stemness pathway to PCa patient outcome.

Impact of SNP-SNP interactions of DNA repair gene ERCC5 and metabolic gene GSTP1 on gastric cancer/atrophic gastritis risk in a Chinese population

AIM

To investigate the interactions of the DNA repair gene excision repair cross complementing group 5 (ERCC5) and the metabolic gene glutathione S-transferase pi 1 (GSTP1) and their effects on atrophic gastritis (AG) and gastric cancer (GC) risk.

METHODS

Seven ERCC5 single nucleotide polymorphisms (SNPs) (rs1047768, rs2094258, rs2228959, rs4150291, rs4150383, rs751402, and rs873601) and GSTP1 SNP rs1695 were detected using the Sequenom MassARRAY platform in 450 GC patients, 634 AG cases, and 621 healthy control subjects in a Chinese population.

RESULTS

Two pairwise combinations (ERCC5 rs2094258 and rs873601 with GSTP1 rs1695) influenced AG risk (P_interaction = 0.008 and 0.043, respectively), and the ERCC5 rs2094258-GSTP1 rs1695 SNP pair demonstrated an antagonistic effect, while ERCC5 rs873601-GSTP1 rs1695 showed a synergistic effect on AG risk OR = 0.51 and 1.79, respectively). No pairwise combinations were observed in relation to GC risk. There were no cumulative effects among the pairwise interactions (ERCC5 rs2094258 and rs873601 with GSTP1 rs1695) on AG susceptibility (P_trend > 0.05). When the modification effect of Helicobacter pylori (H. pylori) infection was evaluated, the cumulative effect of one of the aforementioned pairwise interactions (ERCC5 rs873601-GSTP1 rs1695) was associated with an increased AG risk in the case of negative H. pylori status (P_trend = 0.043).

CONCLUSION

There is a multifarious interaction between the DNA repair gene ERCC5 SNPs (rs2094258 and rs873601) and the metabolic gene GSTP1 rs1695, which may form the basis for various inter-individual susceptibilities to AG.

SNP-SNP interactions of three new pri-miRNAs with the target gene PGC and multidimensional analysis of H. pylori in the gastric cancer/atrophic gastritis risk in a Chinese population

Gastric cancer (GC) is a multistep complex disease involving multiple genes, and gene–gene interactions have a greater effect than a single gene in determining cancer susceptibility. This study aimed to explore the interaction of the let-7e rs8111742, miR-365b rs121224, and miR-4795 rs1002765 single nucleotide polymorphisms (SNPs) with SNPs of the predicted target gene PGC and Helicobacter pylori status in GC and atrophic gastritis (AG) risk. Three miRNA SNPs and seven PGC SNPs were detected in 2448 cases using the Sequenom MassArray platform. Two pairwise combinations of miRNA and PGC SNPs were associated with increased AG risk (let-7e rs8111742 – PGC rs6458238 and miR-4795 rs1002765 – PGC rs9471643). Singly, miR-365b rs121224 and PGC rs6912200 had no effect individually but in combination they demonstrated an epistatic interaction associated with AG risk. Similarly, let-7e rs8111742 and miR-4795 rs1002765 SNPs interacted with H. pylori infection to increase GC risk (rs8111742: P_interaction = 0.024; rs1002765: P_interaction = 0.031, respectively). A three-dimensional interaction analysis found miR-4795 rs1002765, PGC rs9471643, and H. pylori infection positively interacted to increase AG risk (P_{interaction =} 0.027). Also, let-7e rs8111742, PGC rs6458238, and H. pylori infection positively interacted to increase GC risk (P_{interaction =} 0.036). Furthermore, both of these three-dimensional interactions had a dosage–effect correspondence (P_trend < 0.001) and were verified by MDR. In conclusion, the miRNAs SNPs (let-7e rs8111742 and miR-4795 rs1002765) might have more superior efficiency when combined with PGC SNPs and/or H. pylori for GC or AG risk than a single SNP on its own.

Immunogenetics of prostate cancer: a still unexplored field of study

The immune system is a double-edged sword with regard to the prostate cancer (PCa) battle. Immunogenetics, the study of the potential role of immune-related polymorphisms, is taking its first steps in the treatment of this malignancy. This review summarizes the most recent papers addressing the potential of immunogenetics in PCa, reporting immune-related polymorphisms associated with tumor aggressiveness, treatment toxicity and patients' prognosis. With some peculiarities, RNASEL, IL-6, IL-10, IL-1β and MMP7 have arisen as the most significant biomarkers in PCa treatment and management, having a potential clinical role. Validation prospective clinical studies are required to translate immunogenetics into precision treatment of PCa.

SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns

Motivation

Testing SNP-SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP-SNP interactions are underdeveloped.

Results

We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR , EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP-SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns.

Availability and Implementation

The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/ .

Contact

hlin1@lsuhsc.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Association Study of Polymorphisms of Epidermal Growth Factor and Epidermal Growth Factor Receptor With Benign Prostatic Hyperplasia in a Korean Population

Purpose

Recent studies have suggested that specific single-nucleotide polymorphisms (SNPs) contribute to the clinical features of benign prostatic hyperplasia (BPH). In this study, we investigated the relationships of genetic polymorphisms of the epidermal growth factor (EGF) gene and the epidermal growth factor receptor (EGFR) gene with BPH.

Methods

A total of 218 patients with BPH were enrolled in this study. We evaluated the relationship between eight SNPs in the EGF and EGFR genes and prostate volume, prostate-specific antigen (PSA), and International Prostate Symptom Score of BPH patients. Each SNP was genotyped by direct sequencing. Statistical analysis applying codominant, dominant, recessive, and log-additive models was performed via logistic regression.

Results

The rs11568943 and rs11569017 SNPs in the EGF gene showed significant associations with prostate volume (rs11568943: P=0.038 in the log-additive model, P=0.024 in the allele distribution; rs11569017, P=0.031 in the dominant model, P=0.028 in the log-additive model, P=0.020 in the allele distribution). Additionally, the rs3756261, rs11568943, and rs11569017 SNPs of the EGF gene and the rs2293347 SNP of the EGFR gene were associated with PSA levels (P<0.05 in each model, respectively).

Conclusions

These results suggest that the EGF gene may affect prostate volume. In addition, the EGF and EGFR genes may be associated with PSA levels in patients with BPH.

Coexpression and expression quantitative trait loci analyses of the angiogenesis gene-gene interaction network in prostate cancer

BACKGROUND

Prostate cancer (PCa) shows a substantial clinical heterogeneity. The existing risk classification for PCa prognosis based on clinical factors is not sufficient. Although some biomarkers for PCa aggressiveness have been identified, their underlying functional mechanisms are still unclear. We previously reported a gene-gene interaction network associated with PCa aggressiveness based on single nucleotide polymorphism (SNP)-SNP interactions in the angiogenesis pathway. The goal of this study is to investigate potential functional evidence of the involvement of the genes in this gene-gene interaction network.

METHODS

A total of 11 angiogenesis genes were evaluated. The crosstalks among genes were examined through coexpression and expression quantitative trait loci (eQTL) analyses. The study population is 352 Caucasian PCa patients in the Cancer Genome Atlas (TCGA) study. The pairwise coexpressions among the genes of interest were evaluated using the Spearman coefficient. The eQTL analyses were tested using the Kruskal-Wallis test.

RESULTS

Among all within gene and 55 possible pairwise gene evaluations, 12 gene pairs and one gene (MMP16) showed strong coexpression or significant eQTL evidence. There are nine gene pairs with a strong correlation (Spearman correlation ≥0.6, P<1×10^-13). The top coexpressed gene pairs are EGFR-SP1 (r=0.73), ITGB3-HSPG2 (r=0.71), ITGB3-CSF1 (r=0.70), MMP16-FBLN5 (r=0.68), ITGB3-MMP16 (r=0.65), ITGB3-ROBO1 (r=0.62), CSF1-HSPG2 (r=0.61), CSF1-FBLN5 (r=0.6), and CSF1-ROBO1 (r=0.60). One cis-eQTL in MMP16 and five trans-eQTLs (MMP16-ESR1, ESR1-ROBO1, CSF1-ROBO1, HSPG2-ROBO1, and FBLN5-CSF1) are significant with a false discovery rate q value less than 0.2.

CONCLUSIONS

These findings provide potential biological evidence for the gene-gene interactions in this angiogenesis network. These identified interactions between the angiogenesis genes not only provide information for PCa etiology mechanism but also may serve as integrated biomarkers for building a risk prediction model for PCa aggressiveness.

SNP-SNP Interaction Analysis on Soybean Oil Content under Multi-Environments

Soybean oil content is one of main quality traits. In this study, we used the multifactor dimensionality reduction (MDR) method and a soybean high-density genetic map including 5,308 markers to identify stable single nucleotide polymorphism (SNP)—SNP interactions controlling oil content in soybean across 23 environments. In total, 36,442,756 SNP-SNP interaction pairs were detected, 1865 of all interaction pairs associated with soybean oil content were identified under multiple environments by the Bonferroni correction with p <3.55×10⁻¹¹. Two and 1863 SNP-SNP interaction pairs detected stable across 12 and 11 environments, respectively, which account around 50% of total environments. Epistasis values and contribution rates of stable interaction (the SNP interaction pairs were detected in more than 2 environments) pairs were detected by the two way ANOVA test, the available interaction pairs were ranged 0.01 to 0.89 and from 0.01 to 0.85, respectively. Some of one side of the interaction pairs were identified with previously research as a major QTL without epistasis effects. The results of this study provide insights into the genetic architecture of soybean oil content and can serve as a basis for marker-assisted selection breeding.

A role for matrix remodelling proteins in invasive and malignant meningiomas

AIMS

Meningiomas are one of the most common brain tumours in adults. Invasive and malignant meningiomas present a significant therapeutic challenge due to high recurrence rates and invasion into surrounding bone, brain, neural and soft tissues. Understanding the molecular mechanism of invasion could help in designing novel therapeutic approaches in order to prevent the need for repeat surgery, decrease morbidity and improve patient survival. The aim of this study was to identify the key factors and underlying mechanisms which govern invasive properties of meningiomas.

METHODS

Formalin-fixed paraffin-embedded (FFPE) as well as frozen tumour tissues from bone-invasive, non-invasive and malignant meningiomas were used for RNA microarray, quantitative real-time PCR or Western blot analyses. Malignant meningioma cell lines (F5) were subject to MMP16 downregulation or overexpression and used for in vitro and in vivo functional assays. Subdural xenograft meningioma tumours were generated to study the invasion of tumour cells into brain parenchyma using cell lines with altered MMP16 expression.

RESULTS

We establish that the expression level of MMP16 was significantly elevated in both bone-invasive and brain invasive meningiomas. Gain- and loss-of-function experiments indicated a role for MMP16 in meningioma cell movement, invasion and tumour cell growth. Furthermore, MMP16 was shown to positively regulate MMP2, suggesting this mechanism may modulate meningioma invasion in invasive meningiomas.

CONCLUSIONS

Overall, the results support a role for MMP16 in promoting invasive properties of the meningioma tumours. Further studies to explore the potential value for clinical use of matrix metalloproteinases inhibitors are warranted.

Prognostic role of genetic biomarkers in clinical progression of prostate cancer

The aim of this study was to analyze the use of 12 single-nucleotide polymorphisms in genes ELAC2, RNASEL and MSR1 as biomarkers for prostate cancer (PCa) detection and progression, as well as perform a genetic classification of high-risk patients. A cohort of 451 men (235 patients and 216 controls) was studied. We calculated means of regression analysis using clinical values (stage, prostate-specific antigen, Gleason score and progression) in patients and controls at the basal stage and after a follow-up of 72 months. Significantly different allele frequencies between patients and controls were observed for rs1904577 and rs918 (MSR1 gene) and for rs17552022 and rs5030739 (ELAC2). We found evidence of increased risk for PCa in rs486907 and rs2127565 in variants AA and CC, respectively. In addition, rs627928 (TT-GT), rs486907 (AG) and rs3747531 (CG-CC) were associated with low tumor aggressiveness. Some had a weak linkage, such as rs1904577 and rs2127565, rs4792311 and rs17552022, and rs1904577 and rs918. Our study provides the proof-of-principle that some of the genetic variants (such as rs486907, rs627928 and rs2127565) in genes RNASEL, MSR1 and ELAC2 can be used as predictors of aggressiveness and progression of PCa. In the future, clinical use of these biomarkers, in combination with current ones, could potentially reduce the rate of unnecessary biopsies and specific treatments.

Machine learning for biomarker identification in cancer research - developments toward its clinical application

The patterns identified from the systematically collected molecular profiles of patient tumor samples, along with clinical metadata, can assist personalized treatments for effective management of cancer patients with similar molecular subtypes. There is an unmet need to develop computational algorithms for cancer diagnosis, prognosis and therapeutics that can identify complex patterns and help in classifications based on plethora of emerging cancer research outcomes in public domain. Machine learning, a branch of artificial intelligence, holds a great potential for pattern recognition in cryptic cancer datasets, as evident from recent literature survey. In this review, we focus on the current status of machine learning applications in cancer research, highlighting trends and analyzing major achievements, roadblocks and challenges toward its implementation in clinics.

Combined effects of six cytokine gene polymorphisms and SNP-SNP interactions on hepatocellular carcinoma risk in Southern Guangxi, China

Cytokine gene single nucleotide polymorphisms (SNPs) are involved in the genesis and progression of hepatocellular carcinoma (HCC). We hypothesized that combined effects of cytokine gene SNPs and SNP-SNP interactions are associated with HCC risk. Six SNPs in cytokine genes (IL-2, IFN-γ, IL-1β, IL-6, and IL-10) were genotyped in a study of 720 Chinese HCC cases and 784 cancer-free controls. Although none of these SNPs individually had a significant effect on the risk of HCC, we found that the combined effects of these six SNPs may contribute to HCC risk (OR=1.821, 95% CI=1.078-3.075). This risk was pronounced among smokers, drinkers, and hepatitis B virus carriers. A SNP-SNP interaction between IL-2-330 and IFN-γ-1615 was associated with an increased HCC risk (OR=1.078, 95% CI=1.022-1.136). In conclusion, combined effects of SNPs and SNP-SNP interactions in cytokine genes may contribute to HCC risk.

Replication and heritability of prostate cancer risk variants: impact of population-specific factors

BACKGROUND

Prostate cancer incidence and mortality rates vary across populations, with African American men exhibiting the highest rates. To date, genome-wide association studies have identified 104 SNPs independently associated with prostate cancer in men of European ancestry.

METHODS

We investigated whether the ability to replicate findings for these 104 SNPs in African American, Asian, and Latino populations depends on variation in risk allele frequencies (RAF), strength of associations, and/or patterns of linkage disequilibrium (LD) at the associated loci. We extracted estimates of effect from the literature, and determined RAF and LD information across the populations from the 1000 Genomes Project.

RESULTS

Risk variants were largely replicated across populations. Relative to Europeans, 83% had smaller effect sizes among African Americans and 73% demonstrated smaller effect sizes among Latinos. Among Asians, however, 56% showed larger effect sizes than among Europeans. The largest difference in RAFs was observed between European and African ancestry populations, but this difference did not impact our ability to replicate. The extent of LD within 250 kb of risk loci in Asian ancestry populations was suggestively lower for variants that did not replicate (P = 0.013).

CONCLUSIONS

Despite substantial overlap in prostate cancer risk SNPs across populations, the variation in prostate cancer incidence among different populations may still in part reflect unique underlying genetic architectures.

IMPACT

Studying different ancestral populations is crucial for deciphering the genetic basis of prostate cancer.

Research on single nucleotide polymorphisms interaction detection from network perspective

Single Nucleotide Polymorphisms (SNPs) found in Genome-Wide Association Study (GWAS) mainly influence the susceptibility of complex diseases, but they still could not comprehensively explain the relationships between mutations and diseases. Interactions between SNPs are considered so important for deeply understanding of those relationships that several strategies have been proposed to explore such interactions. However, part of those methods perform poorly when marginal effects of disease loci are weak or absent, others may lack of considering high-order SNPs interactions, few methods have achieved the requirements in both performance and accuracy. Considering the above reasons, not only low-order, but also high-order SNP interactions as well as main-effect SNPs, should be taken into account in detection methods under an acceptable computational complexity. In this paper, a new pairwise (or low-order) interaction detection method IG (Interaction Gain) is introduced, in which disease models are not required and parallel computing is utilized. Furthermore, high-order SNP interactions were proposed to be detected by finding closely connected function modules of the network constructed from IG detection results. Tested by a wide range of simulated datasets and four WTCCC real datasets, the proposed methods accurately detected both low-order and high-order SNP interactions as well as disease-associated main-effect SNPS and it surpasses all competitors in performances. The research will advance complex diseases research by providing more reliable SNP interactions.

miRNA in Prostate Cancer: New Prospects for Old Challenges

Prostate cancer (PCa) is one of the most commonly diagnosed cancers among men but has limited prognostic biomarkers available for follow up. MicroRNAs (miRNAs) are small non-coding RNAs that regulate expression of their target genes. Accumulating experimental evidence reports differential miRNA expression in PCa, and that miRNAs are actively involved in the pathogenesis and progression of PCa. miRNA and androgen receptor signaling cross-talk is an established factor in PCa pathogenesis. Differential miRNA expression was found between patients with high versus low Gleason scores, and was also observed in patients with biochemical failure, hormone-resistant cancer and in metastasis. Metastasis requires epithelial-mesenchymal transition which shares many cancer stem cell biological characteristics and both are associated with miRNA dysregulation. In the era of personalized medicine, there is a broad spectrum of potential clinical applications of miRNAs. These applications can significantly improve PCa management including their use as diagnostic and/or prognostic markers, or as predictive markers for treatment efficiency. Preliminary evidence demonstrates that miRNAs can also be used for risk stratification. Circulatory miRNAs can serve as non-invasive biomarkers in urine and/or serum of PCa patients. More recently, analysis of miRNAs and circulating tumor cells are gaining significant attention. Moreover, miRNAs represent an attractive new class of therapeutic targets for PCa. Here, we summarize the current knowledge and the future prospects of miRNAs in PCa, their advantages, and potential challenges as tissue and circulating biomarkers. Prostate cancer (PCa) is the most commonly diagnosed cancer among men in western populations. The American Cancer Society estimated 239, 590 new cases and 29, 720 expected deaths in the USA in 2013. One in every six men are at risk of developing PCa during their lifetime (1). Currently, the standard biomarker for PCa diagnosis is prostate-specific antigen (PSA), which has its limitations, leading to the risks of PCa over diagnosis and harmful overtreatment. The prognostic value of PSA is also questionable (2). Stepping into the new epoch of personalized medicine, molecular markers are urgently needed to improve the different aspects of PCa management (3). miRNAs represent an attractive class of emerging biomarkers that can help in this regard (4;5).

Association of genetic and non-genetic risk factors with the development of prostate cancer in Malaysian men

There is growing global interest to stratify men into different levels of risk to developing prostate cancer, thus it is important to identify common genetic variants that confer the risk. Although many studies have identified more than a dozen common genetic variants which are highly associated with prostate cancer, none have been done in Malaysian population. To determine the association of such variants in Malaysian men with prostate cancer, we evaluated a panel of 768 SNPs found previously associated with various cancers which also included the prostate specific SNPs in a population based case control study (51 case subjects with prostate cancer and 51 control subjects) in Malaysian men of Malay, Chinese and Indian ethnicity. We identified 21 SNPs significantly associated with prostate cancer. Among these, 12 SNPs were strongly associated with increased risk of prostate cancer while remaining nine SNPs were associated with reduced risk. However, data analysis based on ethnic stratification led to only five SNPs in Malays and 3 SNPs in Chinese which remained significant. This could be due to small sample size in each ethnic group. Significant non-genetic risk factors were also identified for their association with prostate cancer. Our study is the first to investigate the involvement of multiple variants towards susceptibility for PC in Malaysian men using genotyping approach. Identified SNPs and non-genetic risk factors have a significant association with prostate cancer.