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Manavalan R, Priya S. Genetic interactions effects for cancer disease identification using computational models: a review. Med Biol Eng Comput 2021; 59:733-758. [PMID: 33839998 DOI: 10.1007/s11517-021-02343-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 03/10/2021] [Indexed: 11/29/2022]
Abstract
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.
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Affiliation(s)
- R Manavalan
- Department of Computer Science, Arignar Anna Government Arts College, Villupuram, Tamil Nadu, 605602, India.
| | - S Priya
- Computer Science, Arignar Anna Government Arts College, Villupuram, Tamil Nadu, India
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Chen PH, Chuang LY, Wu KC, Wang YH, Shieh TY, Sheu JJ, Chang HW, Yang CH. Application of simulation-based CYP26 SNP-environment barcodes for evaluating the occurrence of oral malignant disorders by odds ratio-based binary particle swarm optimization: A case-control study in the Taiwanese population. PLoS One 2019; 14:e0220719. [PMID: 31465460 DOI: 10.1371/journal.pone.0220719] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 07/22/2019] [Indexed: 12/15/2022] Open
Abstract
Introduction Genetic polymorphisms and social factors (alcohol consumption, betel quid (BQ) usage, and cigarette consumption), both separately or jointly, play a crucial role in the occurrence of oral malignant disorders such as oral and pharyngeal cancers and oral potentially malignant disorders (OPMD). Material and methods Simultaneous analyses of multiple single nucleotide polymorphisms (SNPs) and environmental effects on oral malignant disorders are essential to examine, albeit challenging. Thus, we conducted a case-control study (N = 576) to analyze the risk of occurrence of oral malignant disorders by using binary particle swarm optimization (BPSO) with an odds ratio (OR)-based method. Results We demonstrated that a combination of SNPs (CYP26B1 rs887844 and CYP26C1 rs12256889) and socio-demographic factors (age, ethnicity, and BQ chewing), referred to as the combined effects of SNP-environment, correlated with maximal risk diversity of occurrence observed between the oral malignant disorder group and the control group. The risks were more prominent in the oral and pharyngeal cancers group (OR = 10.30; 95% confidence interval (CI) = 4.58–23.15) than in the OPMD group (OR = 5.42; 95% CI = 1.94–15.12). Conclusions Simulation-based “SNP-environment barcodes” may be used to predict the risk of occurrence of oral malignant disorders. Applying simulation-based “SNP-environment barcodes” may provide insight into the importance of screening tests in preventing oral and pharyngeal cancers and OPMD.
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Yang CH, Lin YD, Chuang LY, Chen JB, Chang HW. Joint Analysis of SNP-SNP-Environment Interactions for Chronic Dialysis by an Improved Branch and Bound Algorithm. J Comput Biol 2017; 24:1212-1225. [PMID: 28876085 DOI: 10.1089/cmb.2017.0090] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
In previous studies, both single-nucleotide polymorphism (SNP)-SNP or gene-gene (G × G) interactions and SNP-environmental factor (G × E) interactions were reported to partially account for "missing" heritability. However, (G × G) × E interactions were less commonly addressed. The purpose of this study was to develop a novel strategy to evaluate possible (G × G) × E interactions in D-loop-based chronic dialysis association. Using values from our previously published data set (704 controls and 193 cases) of 77 D-loop SNPs and 7 environmental factors (coronary heart disease, hypertension, diabetes mellitus, triglyceride, cholesterol, blood thiol, and TBARS levels), we compared the performances of G, G × G, G × E, and (G × G) × E. We found that the interactions of four individual SNPs previously associated with a significantly high risk of chronic dialysis [odds ratio (OR) = 1.56-4.93] with environmental factors (G × E) increased the risk of chronic dialysis (maximum OR = 35.43). We then used an improved branch and bound algorithm to identify combinations of two to four SNPs that were most highly associated with chronic dialysis (OR = 9.27-34.39). When the interactions of the two- and three-SNP combinations with environmental factors were evaluated, we found that the (G × G) × E effects increased the risk of chronic dialysis (maximum OR = 8.32-57.54 and OR = 12.52-57.81, respectively; adjusted OR = 8.67-81.81 and OR = 12.29-81.95, respectively). Taken together, the (G × G) × E interactions identified chronic dialysis-associated SNPs that would not have been found using G × G or G × E interactions, suggesting that (G × G) × E interactions may be helpful to solve the problems of missing heritability in association studies.
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Affiliation(s)
- Cheng-Hong Yang
- 1 Department of Electronic Engineering, National Kaohsiung University of Applied Sciences , Kaohsiung, Taiwan .,2 Graduate Institute of Clinical Medicine, Kaohsiung Medical University , Kaohsiung, Taiwan
| | - Yu-Da Lin
- 1 Department of Electronic Engineering, National Kaohsiung University of Applied Sciences , Kaohsiung, Taiwan
| | - Li-Yeh Chuang
- 3 Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University , Kaohsiung, Taiwan
| | - Jin-Bor Chen
- 4 Division of Nephrology, Department of Internal Medicine, Mitochondrial Research Unit, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine , Kaohsiung, Taiwan
| | - Hsueh-Wei Chang
- 5 Institute of Medical Science and Technology, National Sun Yat-Sen University , Kaohsiung, Taiwan .,6 Department of Medical Research, Kaohsiung Medical University Hospital , Kaohsiung, Taiwan .,7 Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University , Kaohsiung, Taiwan
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Chen WC, Wang WC, Okada Y, Chang WP, Chou YH, Chang HH, Huang JD, Chen DY, Chang WC. rs2841277 ( PLD4) is associated with susceptibility and rs4672495 is associated with disease activity in rheumatoid arthritis. Oncotarget 2017; 8:64180-64190. [PMID: 28969061 PMCID: PMC5609993 DOI: 10.18632/oncotarget.19419] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Accepted: 06/05/2017] [Indexed: 12/16/2022] Open
Abstract
Rheumatoid arthritis (RA) is one of the most common autoimmune diseases, can lead to long-term joint damage, chronic pain, and loss of motor function in the hands, and may share some common genetic factors with other autoimmune disorders, such as ankylosing spondylitis (AS). Many single-nucleotide polymorphisms (SNPs) were reported by genome-wide association studies (GWASs) of RA, but some of them have not been examined in the Taiwanese population. In this study, for 15 SNPs reported in previous RA and AS GWASs, we investigated their association with RA in a Taiwanese population. Based on 334 RA patients recruited from the Taichung Veterans General Hospital and 16,036 healthy subjects from the Taiwan Biobank (TWB) project, we observed that subjects having minor allele C at rs2841277 (phospholipase D family, member 4 (PLD4)) have lower susceptibility of RA, compare to those having genotype TT (Odds ratio (OR) = 0.6, p = 3.0 × 10−6). Among the RA patients, we observed that subjects having GG at rs4672495 have a lower proportion of severe RA, compare to other subjects (OR = 0.09, p = 5.6 × 10−3). Results of a bioinformatics approach showed that rs2841277 is able to influence expression of LINC00638 and AHNAK2 and rs4672495 is able to influence the expression of B3GNT2. In summary, this study replicated an association of rs2841277 with RA susceptibility and showed an AS-associated SNP, rs4672495, is associated with RA activity in the Taiwanese population.
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Affiliation(s)
- Wei-Chiao Chen
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Wen-Chang Wang
- The Ph.D. Program for Translational Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Yukinori Okada
- Department of Statistical Genetics, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Wei-Pin Chang
- School of Health Care Administration, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Yii-Her Chou
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.,Department of Urology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Hui-Hua Chang
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,School of Pharmacy, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Jin-Ding Huang
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Der-Yuan Chen
- Department of Internal Medicine and Medical Education, Taichung Veterans General Hospital, Taichung, Taiwan.,Faculty of Medicine, National Yang Ming University, Taipei, Taiwan.,Ph.D. Program in Translational Medicine, National Chung Hsing University, Taichung, Taiwan.,Institute of Biomedical Science and Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Wei-Chiao Chang
- The Ph.D. Program for Translational Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, School of Pharmacy, Taipei Medical University, Taipei, Taiwan.,Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan.,Center for Biomarkers and Biotech Drugs, Kaohsiung Medical University, Kaohsiung, Taiwan.,Department of Pharmacy, Taipei Medical University-Wanfang Hospital, Taipei, Taiwan
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D'Souza W, Pradhan S, Saranath D. Multiple single nucleotide polymorphism analysis and association of specific genotypes in FHIT, SAMD4A, and ANKRD17 in Indian patients with oral cancer. Head Neck 2017; 39:1586-1595. [DOI: 10.1002/hed.24798] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Revised: 01/13/2017] [Accepted: 02/22/2017] [Indexed: 12/22/2022] Open
Affiliation(s)
- Wendy D'Souza
- Department of Biological Sciences; Sunandan Divatia School of Science, SVKM's NMIMS (Deemed-to-be) University, Vile Parle; Mumbai 400056 India
| | | | - Dhananjaya Saranath
- Department of Biological Sciences; Sunandan Divatia School of Science, SVKM's NMIMS (Deemed-to-be) University, Vile Parle; Mumbai 400056 India
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Yang CH, Moi SH, Chuang LY, Yuan SF, Hou MF, Lee YC, Chang HW. Interaction of MRE11 and Clinicopathologic Characteristics in Recurrence of Breast Cancer: Individual and Cumulated Receiver Operating Characteristic Analyses. Biomed Res Int 2017; 2017:2563910. [PMID: 28133604 DOI: 10.1155/2017/2563910] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 11/28/2016] [Indexed: 12/28/2022]
Abstract
The interaction between the meiotic recombination 11 homolog A (MRE11) oncoprotein and breast cancer recurrence status remains unclear. The aim of this study was to assess the interaction between MRE11 and clinicopathologic variables in breast cancer. A dataset for 254 subjects with breast cancer (220 nonrecurrent and 34 recurrent) was used in individual and cumulated receiver operating characteristic (ROC) analyses of MRE11 and 12 clinicopathologic variables for predicting breast cancer recurrence. In individual ROC analysis, the area under curve (AUC) for each predictor of breast cancer recurrence was smaller than 0.7. In cumulated ROC analysis, however, the AUC value for each predictor improved. Ten relevant variables in breast cancer recurrence were used to find the optimal prognostic indicators. The presence of any six of the following ten variables had a high (79%) sensitivity and a high (70%) specificity for predicting breast cancer recurrence: tumor size ≥ 2.4 cm, tumor stage II/III, therapy other than hormone therapy, age ≥ 52 years, MRE11 positive cells > 50%, body mass index ≥ 24, lymph node metastasis, positivity for progesterone receptor, positivity for epidermal growth factor receptor, and negativity for estrogen receptor. In conclusion, this study revealed that these 10 clinicopathologic variables are the minimum discriminators needed for optimal discriminant effectiveness in predicting breast cancer recurrence.
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Yang CH, Lin YD, Chuang LY, Chang HW. Analysis of high-order SNP barcodes in mitochondrial D-loop for chronic dialysis susceptibility. J Biomed Inform 2016; 63:112-119. [PMID: 27507088 DOI: 10.1016/j.jbi.2016.08.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Revised: 06/26/2016] [Accepted: 08/05/2016] [Indexed: 12/18/2022]
Abstract
OBJECTIVES Positively identifying disease-associated single nucleotide polymorphism (SNP) markers in genome-wide studies entails the complex association analysis of a huge number of SNPs. Such large numbers of SNP barcode (SNP/genotype combinations) continue to pose serious computational challenges, especially for high-dimensional data. METHODS We propose a novel exploiting SNP barcode method based on differential evolution, termed IDE (improved differential evolution). IDE uses a "top combination strategy" to improve the ability of differential evolution to explore high-order SNP barcodes in high-dimensional data. RESULTS We simulate disease data and use real chronic dialysis data to test four global optimization algorithms. In 48 simulated disease models, we show that IDE outperforms existing global optimization algorithms in terms of exploring ability and power to detect the specific SNP/genotype combinations with a maximum difference between cases and controls. In real data, we show that IDE can be used to evaluate the relative effects of each individual SNP on disease susceptibility. CONCLUSION IDE generated significant SNP barcode with less computational complexity than the other algorithms, making IDE ideally suited for analysis of high-order SNP barcodes.
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Affiliation(s)
- Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.
| | - Li-Yeh Chuang
- Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan.
| | - Hsueh-Wei Chang
- Institute of Medical Science and Technology, National Sun Yat-Sen University, Kaohsiung, Taiwan; Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan.
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Fu OY, Chang HW, Lin YD, Chuang LY, Hou MF, Yang CH. Breast cancer-associated high-order SNP-SNP interaction of CXCL12/CXCR4-related genes by an improved multifactor dimensionality reduction (MDR-ER). Oncol Rep 2016; 36:1739-47. [PMID: 27461876 DOI: 10.3892/or.2016.4956] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 03/03/2016] [Indexed: 11/06/2022] Open
Abstract
In association studies, the combined effects of single nucleotide polymorphism (SNP)-SNP interactions and the problem of imbalanced data between cases and controls are frequently ignored. In the present study, we used an improved multifactor dimensionality reduction (MDR) approach namely MDR-ER to detect the high order SNP‑SNP interaction in an imbalanced breast cancer data set containing seven SNPs of chemokine CXCL12/CXCR4 pathway genes. Most individual SNPs were not significantly associated with breast cancer. After MDR‑ER analysis, six significant SNP‑SNP interaction models with seven genes (highest cross‑validation consistency, 10; classification error rates, 41.3‑21.0; and prediction error rates, 47.4‑55.3) were identified. CD4 and VEGFA genes were associated in a 2‑loci interaction model (classification error rate, 41.3; prediction error rate, 47.5; odds ratio (OR), 2.069; 95% bootstrap CI, 1.40‑2.90; P=1.71E‑04) and it also appeared in all the best 2‑7‑loci models. When the loci number increased, the classification error rates and P‑values decreased. The powers in 2‑7‑loci in all models were >0.9. The minimum classification error rate of the MDR‑ER‑generated model was shown with the 7‑loci interaction model (classification error rate, 21.0; OR=15.282; 95% bootstrap CI, 9.54‑23.87; P=4.03E‑31). In the epistasis network analysis, the overall effect with breast cancer susceptibility was identified and the SNP order of impact on breast cancer was identified as follows: CD4 = VEGFA > KITLG > CXCL12 > CCR7 = MMP2 > CXCR4. In conclusion, the MDR‑ER can effectively and correctly identify the best SNP‑SNP interaction models in an imbalanced data set for breast cancer cases.
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Affiliation(s)
- Ou-Yang Fu
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, R.O.C
| | - Hsueh-Wei Chang
- Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, R.O.C
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan, R.O.C
| | - Li-Yeh Chuang
- Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I‑Shou University, Kaohsiung 84001, Taiwan, R.O.C
| | - Ming-Feng Hou
- Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, R.O.C
| | - Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan, R.O.C
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Gao J, Qiu X, Wang X, Peng C, Zheng F. Associations of ChREBP and Global DNA Methylation with Genetic and Environmental Factors in Chinese Healthy Adults. PLoS One 2016; 11:e0157128. [PMID: 27281235 PMCID: PMC4900669 DOI: 10.1371/journal.pone.0157128] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 05/25/2016] [Indexed: 12/26/2022] Open
Abstract
Age, gender, diet, gene and lifestyle have been reported to affect metabolic status and disease susceptibility through epigenetic pathway. But it remains indistinct that which factors account for certain epigenetic modifications. Our aim was to identify the influencing factors on inter-individual DNA methylation variations of carbohydrate response element binding protein (ChREBP) and global genome in peripheral blood leucocytes (PBLs). ChREBP DNA methylation was determined by bisulfite sequencing, and genomic 5mdC contents were quantified by capillary hydrophilic-interaction liquid chromatography/ in-source fragmentation/ tandem mass spectrometry system in about 300 healthy individuals. Eleven single nucleotide polymorphisms (SNPs) spanning ChREBP and DNA methyltransferase 1 (DNMT1) were genotyped by high resolution melting or PCR-restriction fragment length polymorphism. DNMT1 mRNA expression was analyzed by quantitative PCR. We found ChREBP DNA methylation levels were statistically associated with age (Beta (B) = 0.028, p = 0.006) and serum total cholesterol concentrations (TC) (B = 0.815, p = 0.010), independent of sex, concentrations of triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol (LDL-C), fasting blood glucose and systolic blood pressure, diastolic blood pressure, PBLs counts and classifications. The DNMT1 haplotypes were related to ChREBP (odds ratio (OR) = 0.668, p = 0.029) and global (OR = 0.450, p = 0.015) DNA methylation as well as LDL-C, but not DNMT1 expression. However, only the relation to LDL-C was robust to correction for multiple testing (ORFDR = 1.593, pFDR = 0.013). These results indicated that the age and TC were independent influential factors of ChREBP methylation and DNMT1 variants could probably influence LDL-C to further modify ChREBP DNA methylation. Certainly, sequential comprehensive analysis of the interactions between genetic variants and blood lipid levels on ChREBP and global DNA methylation was required.
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Affiliation(s)
- Jiajia Gao
- Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xueping Qiu
- Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xuebin Wang
- Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Chunyan Peng
- Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Fang Zheng
- Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University, Wuhan, China
- * E-mail:
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Yang CH, Lin YD, Yen CY, Chuang LY, Chang HW. A systematic gene-gene and gene-environment interaction analysis of DNA repair genes XRCC1, XRCC2, XRCC3, XRCC4, and oral cancer risk. OMICS 2016; 19:238-47. [PMID: 25831063 DOI: 10.1089/omi.2014.0121] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Oral cancer is the sixth most common cancer worldwide with a high mortality rate. Biomarkers that anticipate susceptibility, prognosis, or response to treatments are much needed. Oral cancer is a polygenic disease involving complex interactions among genetic and environmental factors, which require multifaceted analyses. Here, we examined in a dataset of 103 oral cancer cases and 98 controls from Taiwan the association between oral cancer risk and the DNA repair genes X-ray repair cross-complementing group (XRCCs) 1-4, and the environmental factors of smoking, alcohol drinking, and betel quid (BQ) chewing. We employed logistic regression, multifactor dimensionality reduction (MDR), and hierarchical interaction graphs for analyzing gene-gene (G×G) and gene-environment (G×E) interactions. We identified a significantly elevated risk of the XRCC2 rs2040639 heterozygous variant among smokers [adjusted odds ratio (OR) 3.7, 95% confidence interval (CI)=1.1-12.1] and alcohol drinkers [adjusted OR=5.7, 95% CI=1.4-23.2]. The best two-factor based G×G interaction of oral cancer included the XRCC1 rs1799782 and XRCC2 rs2040639 [OR=3.13, 95% CI=1.66-6.13]. For the G×E interaction, the estimated OR of oral cancer for two (drinking-BQ chewing), three (XRCC1-XRCC2-BQ chewing), four (XRCC1-XRCC2-age-BQ chewing), and five factors (XRCC1-XRCC2-age-drinking-BQ chewing) were 32.9 [95% CI=14.1-76.9], 31.0 [95% CI=14.0-64.7], 49.8 [95% CI=21.0-117.7] and 82.9 [95% CI=31.0-221.5], respectively. Taken together, the genotypes of XRCC1 rs1799782 and XRCC2 rs2040639 DNA repair genes appear to be significantly associated with oral cancer. These were enhanced by exposure to certain environmental factors. The observations presented here warrant further research in larger study samples to examine their relevance for routine clinical care in oncology.
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Affiliation(s)
- Cheng-Hong Yang
- 1 Department of Electronic Engineering, National Kaohsiung University of Applied Sciences , Kaohsiung, Taiwan
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Adams LJ, Bello G, Dumancas GG. Development and Application of a Genetic Algorithm for Variable Optimization and Predictive Modeling of Five-Year Mortality Using Questionnaire Data. Bioinform Biol Insights 2015; 9:31-41. [PMID: 26604716 PMCID: PMC4639510 DOI: 10.4137/bbi.s29469] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 09/22/2015] [Indexed: 12/31/2022] Open
Abstract
The problem of selecting important variables for predictive modeling of a specific outcome of interest using questionnaire data has rarely been addressed in clinical settings. In this study, we implemented a genetic algorithm (GA) technique to select optimal variables from questionnaire data for predicting a five-year mortality. We examined 123 questions (variables) answered by 5,444 individuals in the National Health and Nutrition Examination Survey. The GA iterations selected the top 24 variables, including questions related to stroke, emphysema, and general health problems requiring the use of special equipment, for use in predictive modeling by various parametric and nonparametric machine learning techniques. Using these top 24 variables, gradient boosting yielded the nominally highest performance (area under curve [AUC] = 0.7654), although there were other techniques with lower but not significantly different AUC. This study shows how GA in conjunction with various machine learning techniques could be used to examine questionnaire data to predict a binary outcome.
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Affiliation(s)
- Lucas J Adams
- Department of Chemistry, Oklahoma Baptist University, Shawnee, OK, USA
| | - Ghalib Bello
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Gerard G Dumancas
- Department of Chemistry, Oklahoma Baptist University, Shawnee, OK, USA
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Ou-Yang F, Lin YD, Chuang LY, Chang HW, Yang CH, Hou MF. The Combinational Polymorphisms of ORAI1 Gene Are Associated with Preventive Models of Breast Cancer in the Taiwanese. Biomed Res Int 2015; 2015:281263. [PMID: 26380267 DOI: 10.1155/2015/281263] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Accepted: 01/21/2015] [Indexed: 11/26/2022]
Abstract
The ORAI calcium release-activated calcium modulator 1 (ORAI1) has been proven to be an important gene for breast cancer progression and metastasis. However, the protective association model between the single nucleotide polymorphisms (SNPs) of ORAI1 gene was not investigated. Based on a published data set of 345 female breast cancer patients and 290 female controls, we used a particle swarm optimization (PSO) algorithm to identify the possible protective models of breast cancer association in terms of the SNPs of ORAI1 gene. Results showed that the PSO-generated models of 2-SNP (rs12320939-TT/rs12313273-CC), 3-SNP (rs12320939-TT/rs12313273-CC/rs712853-(TT/TC)), 4-SNP (rs12320939-TT/rs12313273-CC/rs7135617-(GG/GT)/rs712853-(TT/TC)), and 5-SNP (rs12320939-TT/rs12313273-CC/rs7135617-(GG/GT)/rs6486795-CC/rs712853-(TT/TC)) displayed low values of odds ratios (0.409–0.425) for breast cancer association. Taken together, these results suggested that our proposed PSO strategy is powerful to identify the combinational SNPs of rs12320939, rs12313273, rs7135617, rs6486795, and rs712853 of ORAI1 gene with a strongly protective association in breast cancer.
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He N, Liu L, Duan X, Wang L, Yuan D, Jin T, Kang L. Identification of a shared protective genetic susceptibility locus for colorectal cancer and gastric cancer. Tumour Biol 2015; 37:2443-8. [PMID: 26383524 DOI: 10.1007/s13277-015-4070-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Accepted: 09/13/2015] [Indexed: 11/25/2022] Open
Abstract
Risk of both colorectal cancer (CRC) and gastric cancer (GC) is considered to be heritable with mounting evidence for their genetic susceptibility. However, it remains unknown whether a shared genetic background is underlying these two cancers. A total of ten single nucleotide polymorphisms (SNPs) associated with digestive system cancers risk were selected from previous genome-wide association studies. All SNPs were genotyped in 449 CRC cases, 588 GC cases, and 703 controls using Sequenom Mass-ARRAY technology. Odds ratios (ORs) and 95 % confidence intervals (95 % CIs) were estimated using unconditional logistic regression analysis with adjustment for age and gender, and evaluated their association with both cancers in a Han Chinese population using chi-squared (χ (2)) test and genetic model analysis. By χ (2) test, we found that rs2057314 (p = 0.028; OR = 1.21) was significantly associated with an increased risk of CRC, rs7758229 (p = 0.005; OR = 0.77) was significantly associated with a decreased risk of GC. Furthermore, a shared susceptibility locus rs9502893 was found to have significant protective effect against CRC (p = 0.010; OR = 0.80) and GC (p = 0.0003; OR = 0.74). Our findings could provide insight into the underlying shared a partly overlapping genetic aspect of CRC and GC in a Chinese population. Additional studies are required to verify and discover more common genetic variants associated with risk for digestive system cancers.
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Affiliation(s)
- Na He
- Key Laboratory of High Altitude Environment and Genes Related to Diseases of the Tibet Autonomous Region, School of Medicine, Xizang Minzu University, #6 East Wenhui Road, Xianyang, Shaanxi, 712082, China.,Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Diseases of the Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, China
| | - Lijun Liu
- Key Laboratory of High Altitude Environment and Genes Related to Diseases of the Tibet Autonomous Region, School of Medicine, Xizang Minzu University, #6 East Wenhui Road, Xianyang, Shaanxi, 712082, China.,Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Diseases of the Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, China
| | - Xianglong Duan
- Department of General Surgery, People's Hospital of Shaanxi Province, Xi'an, Shaanxi, 710068, China
| | - Li Wang
- Key Laboratory of High Altitude Environment and Genes Related to Diseases of the Tibet Autonomous Region, School of Medicine, Xizang Minzu University, #6 East Wenhui Road, Xianyang, Shaanxi, 712082, China.,Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Diseases of the Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, China
| | - Dongya Yuan
- Key Laboratory of High Altitude Environment and Genes Related to Diseases of the Tibet Autonomous Region, School of Medicine, Xizang Minzu University, #6 East Wenhui Road, Xianyang, Shaanxi, 712082, China.,Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Diseases of the Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, China
| | - Tianbo Jin
- Key Laboratory of High Altitude Environment and Genes Related to Diseases of the Tibet Autonomous Region, School of Medicine, Xizang Minzu University, #6 East Wenhui Road, Xianyang, Shaanxi, 712082, China. .,Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Diseases of the Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, China. .,School of Life Sciences, Northwest University, Xi'an, Shaanxi, 710069, China.
| | - Longli Kang
- Key Laboratory of High Altitude Environment and Genes Related to Diseases of the Tibet Autonomous Region, School of Medicine, Xizang Minzu University, #6 East Wenhui Road, Xianyang, Shaanxi, 712082, China. .,Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Diseases of the Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, China.
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Farooqi AA, Yaylim I, Ozkan NE, Zaman F, Halim TA, Chang HW. Restoring TRAIL mediated signaling in ovarian cancer cells. Arch Immunol Ther Exp (Warsz) 2014; 62:459-74. [PMID: 25030086 DOI: 10.1007/s00005-014-0307-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2013] [Accepted: 06/26/2014] [Indexed: 02/08/2023]
Abstract
Ovarian cancer has emerged as a multifaceted and genomically complex disease. Genetic/epigenetic mutations, suppression of tumor suppressors, overexpression of oncogenes, rewiring of intracellular signaling cascades and loss of apoptosis are some of the deeply studied mechanisms. In vitro and in vivo studies have highlighted different molecular mechanisms that regulate tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) mediated apoptosis in ovarian cancer. In this review, we bring to limelight, expansion in understanding systematical characterization of ovarian cancer cells has led to the rapid development of new drugs and treatments to target negative regulators of TRAIL mediated signaling pathway. Wide ranging synthetic and natural agents have been shown to stimulate mRNA and protein expression of death receptors. This review is compartmentalized into programmed cell death protein 4, platelet-derived growth factor signaling and miRNA control of TRAIL mediated signaling to ovarian cancer. Mapatumumab and PRO95780 have been tested for efficacy against ovarian cancer. Use of high-throughput screening assays will aid in dissecting the heterogeneity of this disease and increasing a long-term survival which might be achieved by translating rapidly accumulating information obtained from molecular and cellular studies to clinic researches.
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Affiliation(s)
- Ammad Ahmad Farooqi
- Laboratory for Translational Oncology and Personalized Medicine, RLMC, 35 km Ferozepur Road, Lahore, Pakistan,
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Chang WC, Fang YY, Chang HW, Chuang LY, Lin YD, Hou MF, Yang CH. Identifying association model for single-nucleotide polymorphisms of ORAI1 gene for breast cancer. Cancer Cell Int 2014; 14:29. [PMID: 24685237 PMCID: PMC3994227 DOI: 10.1186/1475-2867-14-29] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2013] [Accepted: 03/07/2014] [Indexed: 12/20/2022] Open
Abstract
Background ORAI1 channels play an important role for breast cancer progression and metastasis. Previous studies indicated the strong correlation between breast cancer and individual single nucleotide polymorphisms (SNPs) of ORAI1 gene. However, the possible SNP-SNP interaction of ORAI1 gene was not investigated. Results To develop the complex analyses of SNP-SNP interaction, we propose a genetic algorithm (GA) to detect the model of breast cancer association between five SNPs (rs12320939, rs12313273, rs7135617, rs6486795 and rs712853) of ORAI1 gene. For individual SNPs, the differences between case and control groups in five SNPs of ORAI1 gene were not significant. In contrast, GA-generated SNP models show that 2-SNP (rs12320939-GT/rs6486795-CT), 3-SNP (rs12320939-GT/rs12313273-TT/rs6486795-TC), 5-SNP (rs12320939-GG/rs12313273-TC/rs7135617-TT/rs6486795-TT/rs712853-TT) have higher risks for breast cancer in terms of odds ratio analysis (1.357, 1.689, and 13.148, respectively). Conclusion Taken together, the cumulative effects of SNPs of ORAI1 gene in breast cancer association study were well demonstrated in terms of GA-generated SNP models.
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Affiliation(s)
- Wei-Chiao Chang
- Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, School of Pharmacy, Taipei Medical University, Taipei, Taiwan.,Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan.,Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.,Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yong-Yuan Fang
- Labor Safety and Health Office, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan
| | - Hsueh-Wei Chang
- Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.,Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan.,Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Li-Yeh Chuang
- Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
| | - Ming-Feng Hou
- Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.,Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.,Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan
| | - Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
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Chuang LY, Lane HY, Lin YD, Lin MT, Yang CH, Chang HW. Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm. Ann Gen Psychiatry 2014; 13:15. [PMID: 24955105 PMCID: PMC4050220 DOI: 10.1186/1744-859x-13-15] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2014] [Accepted: 04/23/2014] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Facial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear. METHODS This study compared patients with high and low FEP performances (n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes (i.e., the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups). RESULTS The analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance. CONCLUSIONS This study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion.
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Affiliation(s)
- Li-Yeh Chuang
- Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung 84001, Taiwan
| | - Hsien-Yuan Lane
- Institute of Clinical Medical Science, China Medical University, Taichung 40402, Taiwan ; Department of Psychiatry, China Medical University Hospital, Taichung 40402, Taiwan
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan
| | - Ming-Teng Lin
- Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung 84001, Taiwan ; Department of Psychiatry, Taipei Veterans General Hospital, Hsinchu Branch, Hsinchu 31064, Taiwan
| | - Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan
| | - Hsueh-Wei Chang
- Cancer Center, Translational Research Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan ; Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80424, Taiwan ; Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
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Yang CH, Lin YD, Chuang LY, Chen JB, Chang HW. MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction. PLoS One 2013; 8:e79387. [PMID: 24236125 PMCID: PMC3827354 DOI: 10.1371/journal.pone.0079387] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Accepted: 09/20/2013] [Indexed: 12/25/2022] Open
Abstract
Background Determining the complex relationship between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has proven capable of effectively detecting statistical patterns of epistasis. However, MDR has its weakness in accurately assigning multi-locus genotypes to either high-risk and low-risk groups, and does generally not provide accurate error rates when the case and control data sets are imbalanced. Consequently, results for classification error rates and odds ratios (OR) may provide surprising values in that the true positive (TP) value is often small. Methodology/Principal Findings To address this problem, we introduce a classifier function based on the ratio between the percentage of cases in case data and the percentage of controls in control data to improve MDR (MDR-ER) for multi-locus genotypes to be classified correctly into high-risk and low-risk groups. In this study, a real data set with different ratios of cases to controls (1∶4) was obtained from the mitochondrial D-loop of chronic dialysis patients in order to test MDR-ER. The TP and TN values were collected from all tests to analyze to what degree MDR-ER performed better than MDR. Conclusions/Significance Results showed that MDR-ER can be successfully used to detect the complex associations in imbalanced data sets.
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Affiliation(s)
- Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
| | - Li-Yeh Chuang
- Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
- * E-mail: (L-YC); (H-WC)
| | - Jin-Bor Chen
- Division of Nephrology, Department of Internal Medicine, Mitochondrial Research Unit, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Hsueh-Wei Chang
- Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Taiwan
- Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- * E-mail: (L-YC); (H-WC)
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Chen JB, Chuang LY, Lin YD, Liou CW, Lin TK, Lee WC, Cheng BC, Chang HW, Yang CH. Preventive SNP–SNP interactions in the mitochondrial displacement loop (D-loop) from chronic dialysis patients. Mitochondrion 2013; 13:698-704. [DOI: 10.1016/j.mito.2013.01.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2012] [Revised: 01/24/2013] [Accepted: 01/31/2013] [Indexed: 12/01/2022]
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Chen JB, Chuang LY, Lin YD, Liou CW, Lin TK, Lee WC, Cheng BC, Chang HW, Yang CH. Genetic algorithm-generated SNP barcodes of the mitochondrial D-loop for chronic dialysis susceptibility. ACTA ACUST UNITED AC 2013; 25:231-7. [DOI: 10.3109/19401736.2013.796513] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Wu SJ, Chuang LY, Lin YD, Ho WH, Chiang FT, Yang CH, Chang HW. Particle swarm optimization algorithm for analyzing SNP-SNP interaction of renin-angiotensin system genes against hypertension. Mol Biol Rep 2013; 40:4227-33. [PMID: 23695493 DOI: 10.1007/s11033-013-2504-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2013] [Accepted: 04/27/2013] [Indexed: 11/24/2022]
Abstract
Most non-significant individual single nucleotide polymorphisms (SNPs) were undiscovered in hypertension association studies. Their possible SNP-SNP interactions were usually ignored and leaded to missing heritability. In present study, we proposed a particle swarm optimization (PSO) algorithm to analyze the SNP-SNP interaction associated with hypertension. Genotype dataset of eight SNPs of renin-angiotensin system genes for 130 non-hypertension and 313 hypertension subjects were included. Without SNP-SNP interaction, most individual SNPs were non-significant difference between the hypertension and non-hypertension groups. For SNP-SNP interaction, PSO can select the SNP combinations involving different SNP numbers, namely the best SNP barcodes, to show the maximum frequency difference between non-hypertension and hypertension groups. After computation, the best PSO-generated SNP barcodes were dominant in non-hypertension in terms of the occurrences of frequency differences between non-hypertension and hypertension groups. The OR values of the best SNP barcodes involving 2-8 SNPs were 0.705-0.334, suggesting that these SNP barcodes were protective against hypertension. In conclusion, this study demonstrated that non-significant SNPs may generate the joint effect in association study. Our proposed PSO algorithm is effective to identify the best protective SNP barcodes against hypertension.
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Affiliation(s)
- Shyh-Jong Wu
- Department of Medical Laboratory Science and Biotechnology, Kaohsiung Medical University, Kaohsiung, Taiwan
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Yang CH, Lin YD, Chuang LY, Chang HW. Evaluation of breast cancer susceptibility using improved genetic algorithms to generate genotype SNP barcodes. IEEE/ACM Trans Comput Biol Bioinform 2013; 10:361-371. [PMID: 23929860 DOI: 10.1109/tcbb.2013.27] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Genetic association is a challenging task for the identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases. To fully execute genetic studies of complex diseases, modern geneticists face the challenge of detecting interactions between loci. A genetic algorithm (GA) is developed to detect the association of genotype frequencies of cancer cases and noncancer cases based on statistical analysis. An improved genetic algorithm (IGA) is proposed to improve the reliability of the GA method for high-dimensional SNP-SNP interactions. The strategy offers the top five results to the random population process, in which they guide the GA toward a significant search course. The IGA increases the likelihood of quickly detecting the maximum ratio difference between cancer cases and noncancer cases. The study systematically evaluates the joint effect of 23 SNP combinations of six steroid hormone metabolisms, and signaling-related genes involved in breast carcinogenesis pathways were systematically evaluated, with IGA successfully detecting significant ratio differences between breast cancer cases and noncancer cases. The possible breast cancer risks were subsequently analyzed by odds-ratio (OR) and risk-ratio analysis. The estimated OR of the best SNP barcode is significantly higher than 1 (between 1.15 and 7.01) for specific combinations of two to 13 SNPs. Analysis results support that the IGA provides higher ratio difference values than the GA between breast cancer cases and noncancer cases over 3-SNP to 13-SNP interactions. A more specific SNP-SNP interaction profile for the risk of breast cancer is also provided.
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Affiliation(s)
- Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan.
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Ding M, Wang H, Chen J, Shen B, Xu Z. Identification and functional annotation of genome-wide ER-regulated genes in breast cancer based on ChIP-Seq data. Comput Math Methods Med 2012; 2012:568950. [PMID: 23346221 DOI: 10.1155/2012/568950] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2012] [Accepted: 12/18/2012] [Indexed: 11/18/2022]
Abstract
Estrogen receptor (ER) is a crucial molecule symbol of breast cancer. Molecular interactions between ER complexes and DNA regulate the expression of genes responsible for cancer cell phenotypes. However, the positions and mechanisms of the ER binding with downstream gene targets are far from being fully understood. ChIP-Seq is an important assay for the genome-wide study of protein-DNA interactions. In this paper, we explored the genome-wide chromatin localization of ER-DNA binding regions by analyzing ChIP-Seq data from MCF-7 breast cancer cell line. By integrating three peak detection algorithms and two datasets, we localized 933 ER binding sites, 92% among which were located far away from promoters, suggesting long-range control by ER. Moreover, 489 genes in the vicinity of ER binding sites were identified as estrogen response elements by comparison with expression data. In addition, 836 single nucleotide polymorphisms (SNPs) in or near 157 ER-regulated genes were found in the vicinity of ER binding sites. Furthermore, we annotated the function of the nearest-neighbor genes of these binding sites using Gene Ontology (GO), KEGG, and GeneGo pathway databases. The results revealed novel ER-regulated genes pathways for further experimental validation. ER was found to affect every developed stage of breast cancer by regulating genes related to the development, progression, and metastasis. This study provides a deeper understanding of the regulatory mechanisms of ER and its associated genes.
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Chen JB, Yang YH, Lee WC, Liou CW, Lin TK, Chung YH, Chuang LY, Yang CH, Chang HW. Sequence-based polymorphisms in the mitochondrial D-loop and potential SNP predictors for chronic dialysis. PLoS One 2012; 7:e41125. [PMID: 22815937 PMCID: PMC3399812 DOI: 10.1371/journal.pone.0041125] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2012] [Accepted: 06/21/2012] [Indexed: 01/12/2023] Open
Abstract
Background The mitochondrial (mt) displacement loop (D-loop) is known to accumulate structural alterations and mutations. The aim of this study was to investigate the prevalence of single nucleotide polymorphisms (SNPs) within the D-loop among chronic dialysis patients and healthy controls. Methodology and Principal Findings We enrolled 193 chronic dialysis patients and 704 healthy controls. SNPs were identified by large scale D-loop sequencing and bioinformatic analysis. Chronic dialysis patients had lower body mass index, blood thiols, and cholesterol levels than controls. A total of 77 SNPs matched with the positions in reference of the Revised Cambridge Reference Sequence (CRS) were found in the study population. Chronic dialysis patients had a significantly higher incidence of 9 SNPs compared to controls. These include SNP5 (16108Y), SNP17 (16172Y), SNP21 (16223Y), SNP34 (16274R), SNP35 (16278Y), SNP55 (16463R), SNP56 (16519Y), SNP64 (185R), and SNP65 (189R) in D-loop of CRS. Among these SNPs with genotypes, SNP55-G, SNP56-C, and SNP64-A were 4.78, 1.47, and 5.15 times more frequent in dialysis patients compared to controls (P<0.05), respectively. When adjusting the covariates of demographics and comorbidities, SNP64-A was 5.13 times more frequent in dialysis patients compared to controls (P<0.01). Furthermore, SNP64-A was found to be 35.80, 3.48, 4.69, 5,55, and 4.67 times higher in female patients and in patients without diabetes, coronary artery disease, smoking, and hypertension in an independent significance manner (P<0.05), respectively. In patients older than 50 years or with hypertension, SNP34-A and SNP17-C were found to be 7.97 and 3.71 times more frequent (P<0.05) compared to patients younger than 50 years or those without hypertension, respectively. Conclusions and Significance The results of large-scale sequencing suggest that specific SNPs in the mtDNA D-loop are significantly associated with chronic dialysis. These SNPs can be considered as potential predictors for chronic dialysis.
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Affiliation(s)
- Jin-Bor Chen
- Division of Nephrology, Department of Internal Medicine, Mitochondrial Research Unit, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Yi-Hsin Yang
- School of Pharmacy, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Wen-Chin Lee
- Division of Nephrology, Department of Internal Medicine, Mitochondrial Research Unit, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chia-Wei Liou
- Department of Neurology and Mitochondrial Research Unit, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Tsu-Kung Lin
- Department of Neurology and Mitochondrial Research Unit, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Yueh-Hua Chung
- Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan
| | - Li-Yeh Chuang
- Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
| | - Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
- * E-mail: (HWC); (CHY)
| | - Hsueh-Wei Chang
- Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Taiwan
- Center of Excellence for Environmental Medicine, Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- * E-mail: (HWC); (CHY)
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Abstract
Background Possible single nucleotide polymorphism (SNP) interactions in breast cancer are usually not investigated in genome-wide association studies. Previously, we proposed a particle swarm optimization (PSO) method to compute these kinds of SNP interactions. However, this PSO does not guarantee to find the best result in every implement, especially when high-dimensional data is investigated for SNP–SNP interactions. Methodology/Principal Findings In this study, we propose IPSO algorithm to improve the reliability of PSO for the identification of the best protective SNP barcodes (SNP combinations and genotypes with maximum difference between cases and controls) associated with breast cancer. SNP barcodes containing different numbers of SNPs were computed. The top five SNP barcode results are retained for computing the next SNP barcode with a one-SNP-increase for each processing step. Based on the simulated data for 23 SNPs of six steroid hormone metabolisms and signalling-related genes, the performance of our proposed IPSO algorithm is evaluated. Among 23 SNPs, 13 SNPs displayed significant odds ratio (OR) values (1.268 to 0.848; p<0.05) for breast cancer. Based on IPSO algorithm, the jointed effect in terms of SNP barcodes with two to seven SNPs show significantly decreasing OR values (0.84 to 0.57; p<0.05 to 0.001). Using PSO algorithm, two to four SNPs show significantly decreasing OR values (0.84 to 0.77; p<0.05 to 0.001). Based on the results of 20 simulations, medians of the maximum differences for each SNP barcode generated by IPSO are higher than by PSO. The interquartile ranges of the boxplot, as well as the upper and lower hinges for each n-SNP barcode (n = 3∼10) are more narrow in IPSO than in PSO, suggesting that IPSO is highly reliable for SNP barcode identification. Conclusions/Significance Overall, the proposed IPSO algorithm is robust to provide exact identification of the best protective SNP barcodes for breast cancer.
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Affiliation(s)
- Li-Yeh Chuang
- Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
| | - Hsueh-Wei Chang
- Department of Biomedical Science and Environmental Biology, Center of Excellence for Environmental Medicine, Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- * E-mail: (HWC); (CHY)
| | - Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
- * E-mail: (HWC); (CHY)
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Abstract
Background Genomic islands play an important role in medical, methylation and biological studies. To explore the region, we propose a CpG islands prediction analysis platform for genome sequence exploration (CpGPAP). Results CpGPAP is a web-based application that provides a user-friendly interface for predicting CpG islands in genome sequences or in user input sequences. The prediction algorithms supported in CpGPAP include complementary particle swarm optimization (CPSO), a complementary genetic algorithm (CGA) and other methods (CpGPlot, CpGProD and CpGIS) found in the literature. The CpGPAP platform is easy to use and has three main features (1) selection of the prediction algorithm; (2) graphic visualization of results; and (3) application of related tools and dataset downloads. These features allow the user to easily view CpG island results and download the relevant island data. CpGPAP is freely available at http://bio.kuas.edu.tw/CpGPAP/. Conclusions The platform's supported algorithms (CPSO and CGA) provide a higher sensitivity and a higher correlation coefficient when compared to CpGPlot, CpGProD, CpGIS, and CpGcluster over an entire chromosome.
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Affiliation(s)
- Li-Yeh Chuang
- Department of Chemical Engineering, Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
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Abstract
Cytokines are low molecular weight regulatory proteins or glycoprotein that modulates the intensity and duration of immune response by stimulating or inhibiting the activation, proliferation, and/or differentiation of target cells. Different cytokines are known to have diverse role in breast cancer initiation and progression. Interleukin-10 (IL-10), a pleiotropic anti-inflammatory cytokine, induces immunosuppression and assists in escape from tumor immune surveillance. Like several other cytokines, IL-10 also can exert dual proliferative and inhibitory effect on breast tumor cells indicating a complex role of IL-10 in breast cancer initiation and progression. In this review, we tried to put together a comprehensive current view on significance of IL-10 in promotion, inhibition, and importance as prognosticator in breast cancer based on in vitro, in vivo, and clinical evidences. For literature collection, we conducted PubMed search with keywords "IL-10" and "breast cancer".
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