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Application of back propagation artificial neural network on genetic variants in adiponectin ADIPOQ, peroxisome proliferator-activated receptor-?, and retinoid X receptor-? genes and type 2 diabetes risk in a Chinese Han population.

ABSTRACT: AIMS: Our study was designed to explore the applied characteristics of the back propagation artificial neural network (BPANN) on studying the genetic variants in adipnectin ADIPOQ, peroxisome proliferator-activated receptor (PPAR)-?, and retinoid X receptor-? (RXR-?) genes and type 2 diabetes mellitus (T2DM) risks in a Chinese Han population. SUBJECTS AND METHODS: We used BPANN as the fitting model based on data gathered from T2DM patients (n=913) and normal controls (n=1,001). The mean impact value (MIV) for each input variables were calculated, and the sequence of the factors according to their absolute MIVs was sorted. RESULTS: The results from BPANN were compared with multiple logistic regression analysis, and the generalized multifactor dimensionality reduction (GMDR) method was used to calculate the joint effects of ADIPOQ, PPAR-?, and RXR-? genes. By BPANN analysis, the sequence according to the importance of the T2DM risk factors was in the order of serum adiponectin level, rs3856806, rs7649121, hypertension, rs3821799, rs17827276, rs12495941, rs4240711, age, rs16861194, waist circumference, rs2241767, rs2920502, rs1063539, alcohol drinking, smoking, hyperlipoproteinemia, gender, rs3132291, T2DM family history, rs4842194, rs822394, rs1801282, rs1045570, rs16861205, rs6537944, body mass index, rs266729, and rs1801282. However, compared with multiple logistic regression analysis, only 11 factors were statistically significant. After overweight and obesity were taken as environment adjustment factors into the analysis, model A2 B4 C5 C6 C8 (rs3856806, rs4240711, rs7649121, rs3821799, rs12495941) was the best model (coefficient of variation consistency=10/10, P=0.0107) in the GMDR method. CONCLUSIONS: These results suggested the interactions of ADIPOQ, PPAR-?, and RXR-? genes might play a role in susceptibility to T2DM. BPANN could be used to analyze the risk factors of diseases and provide more complicated relationships between inputs and outputs.

SUBMITTER: Shi H

PROVIDER: S-EPMC3284696 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Application of back propagation artificial neural network on genetic variants in adiponectin ADIPOQ, peroxisome proliferator-activated receptor-γ, and retinoid X receptor-α genes and type 2 diabetes risk in a Chinese Han population.

Shi Hui H Lu Ying Y Du Juan J Du Wencong W Ye Xinhua X Yu Xiaofang X Ma Jianhua J Cheng Jinluo J Gao Yanqin Y Cao Yuanyuan Y Zhou Ling L Li Qian Q

Diabetes technology & therapeutics 20111024 3

<h4>Aims</h4>Our study was designed to explore the applied characteristics of the back propagation artificial neural network (BPANN) on studying the genetic variants in adipnectin ADIPOQ, peroxisome proliferator-activated receptor (PPAR)-γ, and retinoid X receptor-α (RXR-α) genes and type 2 diabetes mellitus (T2DM) risks in a Chinese Han population.<h4>Subjects and methods</h4>We used BPANN as the fitting model based on data gathered from T2DM patients (n=913) and normal controls (n=1,001). The ...[more]

PMID: 22023374

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Project description:BackgroundBRCA1 mutated breast cancers are commonly diagnosed as negative for classical hormone receptors i.e. estrogen receptor, progesterone receptor and/or Her2. Due to these common targets being absent the application of anti-endocrine therapies is rather limited and a certain focus has been set on discovering alternative target molecules. We recently highlighted thyroid hormone receptors (TRs) to predict prognosis in breast cancer patients that had been diagnosed a BRCA1 germline mutation. Vitamin D Receptor (VDR), Retinoid X Receptor (RXR) and Peroxisome Proliferator-activated Receptor γ (PPARγ) are known to interact with TRs by forming functional heterodimers. Whether VDR, RXR or PPARγ are expressed in BRCA1 mutated breast cancer or may even be present in case of triple negativity is not known. Hence the current study aimed to investigate VDR, RXR and PPARγ in BRCA1 mut breast cancer and to test whether any of the three may be associated with clinico-pathological criteria including overall survival.MethodsThis study analyzed VDR, RXR and PPARγ by immunohistochemistry in BRCA1 associated (n = 38) and sporadic breast cancer (n = 79). Receptors were quantified by applying an established scoring system (IR-score) and were tested for association with clinico-pathological variables.ResultsVDR, RXR and PPARγ were detected in over 90% of triple negative BRCA1 mut breast cancer and were significantly (VDR: p < 0.001, RXR: p = 0.010, PPARγ: p < 0.001) overexpressed in BRCA1 mutated as compared to sporadic cancer cases. VDR and RXR positivity predicted prolonged overall survival only in BRCA1 mutated cases while such association was not observed in sporadic breast cancer.ConclusionsIn conclusion, this is the first study to describe VDR, RXR and PPARγ in BRCA1 mutated breast cancer. Based on the data presented here these receptors may be hypothesized to potentially evolve as interesting markers or even targets in hereditary breast cancer. However, independent studies are indispensable thus to confirm this hypothesis.

Project description:ContextCurrent epidemiological evidence suggests an association between obesity, hyperinsulinemia, and colorectal cancer risk. Adiponectin is a hormone secreted by the adipose tissue, and serum levels are inversely correlated with obesity and hyperinsulinemia. While there is evidence of an association between circulating adiponectin levels and colorectal cancer risk, no association between genes of the adiponectin pathway and colorectal cancer have been reported to date.ObjectiveTo determine the association of 10 haplotype-tagging single-nucleotide polymorphisms (SNPs) of the adiponectin (ADIPOQ) and adiponectin receptor 1 (ADIPOR1) genes with colorectal cancer risk.Design, setting, and patientsTwo case-control studies including patients with a diagnosis of colorectal cancer and controls were recruited between 2000 and 2007. Case-control study 1 included a total of 441 patients with a diagnosis of colorectal cancer and 658 controls; both groups were of Ashkenazi Jewish ancestry and from New York, New York. Case-control study 2 included 199 patients with a diagnosis of colorectal cancer and 199 controls from Chicago, Illinois, matched 1:1 for sex, age, and ethnicity.Main outcome measuresADIPOQ and ADIPOR1 SNP frequency among cases and controls.ResultsIn study 1, after adjustment for age, sex, and SNPs from the same gene, 3 ADIPOQ SNPs and 1 ADIPOR1 SNP were associated with colorectal cancer risk: rs266729 (adjusted odds ratio [AOR], 0.72; 95% confidence interval [CI], 0.55-0.95) and rs822396 (AOR, 0.37; 95% CI, 0.14-1.00) were associated with decreased risk whereas rs822395 (AOR, 1.76; 95% CI, 1.09-2.84) and rs1342387 (AOR, 1.79; 95% CI, 1.18-2.72) were associated with increased risk. In study 2, after adjustment for age, sex, race, and SNPs from the same gene, the ADIPOQ SNP rs266729 was associated with a decreased colorectal cancer risk of similar magnitude as in study 1 (AOR, 0.52; 95% CI, 0.34-0.78). Combined analysis of both studies shows an association of rs266729 with decreased colorectal cancer risk (AOR, 0.73; 95% CI, 0.53-0.99).ConclusionThe SNP rs266729, which tags the 5' flanking region of the ADIPOQ gene, is associated with decreased colorectal cancer risk.

Dataset Information

Application of back propagation artificial neural network on genetic variants in adiponectin ADIPOQ, peroxisome proliferator-activated receptor-?, and retinoid X receptor-? genes and type 2 diabetes risk in a Chinese Han population.

Publications

Application of back propagation artificial neural network on genetic variants in adiponectin ADIPOQ, peroxisome proliferator-activated receptor-γ, and retinoid X receptor-α genes and type 2 diabetes risk in a Chinese Han population.

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