Statistical Modeling of MicroRNA Expression with Human Cancers

MicroRNAs (miRNAs) are small non-coding RNAs (containing about 22 nucleotides) that regulate gene expression. MiRNAs are involved in many different biological processes such as cell proliferation, differentiation, apoptosis, fat metabolism, and human cancer genes; while miRNAs may function as candidates for diagnostic and prognostic biomarkers and predictors of drug response. This paper emphasizes the statistical methods in the analysis of the associations of miRNA gene expression with human cancers and related clinical phenotypes: 1) simple statistical methods include chi-square test, correlation analysis, t-test and one-way ANOVA; 2) regression models include linear and logistic regression; 3) survival analysis approaches such as non-parametric Kaplan-Meier method and log-rank test as well as semi-parametric Cox proportional hazards models have been used for time to event data; 4) multivariate method such as cluster analysis has been used for clustering samples and principal component analysis (PCA) has been used for data mining; 5) Bayesian statistical methods have recently made great inroads into many areas of science, including the assessment of association between miRNA expression and human cancers; and 6) multiple testing

Alcohol Consumption, Depression, Insomnia and Colorectal Cancer Screening: Racial Differences

Background: Mortality from colorectal cancer (CRC) can be reduced drastically by early detection and early treatment. However, uptake of CRC screening is relatively low, about 50% for those whom the test is highly recommended. Objectives: We examined the influence of and racial differences in depression, insomnia, alcohol use, and tobacco use on CRC screening uptake in the US. Patients and Methods: Analysis of the 2012 National Health Information Survey data was conducted. Both weighted univariate and multiple logistic regression analyses were performed in SAS to estimate the odds ratios (ORs) and their 95% confidence intervals (CIs). A total of 21511 participants were included in the analysis. Results: Prevalence of CRC screening in the participants was 19%. Adjusting for all factors, insomnia (OR = 1.18, 95%CI = 1.06 - 1.32), moderate alcohol drinking (OR = 1.16, 95%CI = 1.01 - 1.30), past smoking (OR = 1.17, 95%CI = 1.04 - 1.32), depression (OR = 1.37, 95%CI = 1.18 - 1.58), African American (AA) race, and cancer history were positively associated with CRC screening. Females and Single were inversely associated with CRC screening prevalence. In stratified analysis by races (White and AA), depression was associated with CRC screening in both races. Marital status, smoking, cancer history and insomnia were associated with CRC screening in Whites only; while alcohol use was associated with CRC screening in AAs only. Conclusions: We have found significant associations between lifestyle factors (alcohol consumption and smoking) and mental health problems (depression and insomnia) and CRC screening uptake. To improve overall CRC screening uptake in the US, it is important to consider racial differences in predictors and tailor appropriate interventions to each racial/ethnic group

Bayesian Survival Analysis of Genetic Variants in PTPRN2 Gene for Age at Onset of Cancer

Background: The protein tyrosine phosphatase, receptor type, N polypeptide 2 (PTPRN2) gene may play a role in cancer; however, no study has focused on the associations of genetic variants within the PTPRN2 gene with age at onset (AAO) of cancer. Methods: This study examined 220 single nucleotide polymorphisms (SNPs) within the PTPRN2 gene in the Marshfield sample with 716 cancer cases (any diagnosed cancer, excluding minor skin cancer) and 2,848 non-cancer controls. Multiple logistic regression model and linear regression model in PLINK software were used to examine the association of each SNP with the risk of cancer and AAO, respectively. For survival analysis of AAO, both classic Cox regression and Bayesian survival analysis using the Cox proportional hazards model in SAS v. 9.4 were applied to detect the association of each SNP with AAO. The hazards ratios (HRs) with 95% confidence intervals (CIs) were estimated. Results: Single marker analysis identified 10 SNPs associated with the risk of cancer and 9 SNPs associated with AAO (p \u3c 0.05). SNP rs7783909 revealed the strongest association with cancer (p = 6.52x10-3); while the best signal for AAO was rs4909140 (p = 6.18x10-4), which was also associated with risk of cancer (p = 0.0157). Classic Cox regression model showed that 11 SNPs were associated with AAO (top SNP rs4909140 with HR = 1.38, 95%CI = 1.11-1.71, p = 3.3x10-3). Bayesian Cox regression model showed similar results to those using the classic Cox regression (top SNP rs4909140 with HR = 1.39, 95%CI = 1.1-1.69). Conclusions: This study provides evidence of several genetic variants within the PTPRN2 gene influencing the risk of cancer and AAO, and will serve as a resource for replication in other populations

Two-dimensional thermoelastic contact problem of functionally graded materials involving frictional heating

AbstractThe two-dimensional thermoelastic sliding frictional contact of functionally graded material (FGM) coated half-plane under the plane strain deformation is investigated in this paper. A rigid punch is sliding over the surface of the FGM coating with a constant velocity. Frictional heating, with its value proportional to contact pressure, friction coefficient and sliding velocity, is generated at the interface between the punch and the FGM coating. The material properties of the coating vary exponentially along the thickness direction. In order to solve the heat conduction equation analytically, the homogeneous multi-layered model is adopted for treating the graded thermal diffusivity coefficient with other thermomechanical properties being kept as the given exponential forms. The transfer matrix method and Fourier integral transform technique are employed to convert the problem into a Cauchy singular integral equation which is then solved numerically to obtain the unknown contact pressure and the in-plane component of the surface stresses. The effects of the gradient index, Peclet number and friction coefficient on the thermoelastic contact characteristics are discussed in detail. Numerical results show that the distribution of the contact stress can be altered and therefore the thermoelastic contact damage can be modified by adjusting the gradient index, Peclet number and friction coefficient

Evaluating outlier loci and their effect on the identification of pedigree errors

Homozygosity outlier loci, which show patterns of variation that are extremely divergent from the rest of the genome, can be evaluated by comparison of the homozygosity under Hardy-Weinberg proportions (the sum of the squares of allele frequencies) with the expected homozygosity under neutrality. Such outlier loci are potentially under selection (balancing selection or directional selection) when genome-wide effects (such as bottleneck and rapid population growth) are excluded. Outlier loci show skewed allele frequencies with respect to neutrality and may therefore affect the identification of pedigree errors. However, choosing neutral markers (excluding outlier loci) for the identification of pedigree errors has been neglected thus far. Our results showed that 4.1%, 5.5%, and 1.5% of the microsatellite markers, Illumina single-nucleotide polymorphisms (SNPs), and Affymetrix SNPs, respectively, on the autosomes appear to be under balancing selection (p ≤ 0.01) while 0.8% of the Affymetrix SNPs are consistent with directional selection. On the X-chromosome, 7.7%, 3.2%, and 0.4% of the microsatellite markers, Illumina SNPs, and Affymetrix SNPs, respectively, appear to be under balancing selection. 9.3% of Illumina SNPs and 6.7% of Affymetrix SNPs which have high minor allele frequency (≥40%) appear to be under balancing selection. Pedigree structure errors in 15 of 143 pedigrees were detected using microsatellite markers from the autosomes and/or selected SNPs from chromosomes 1 to 18 of the Illumina and/or selected SNPs from chromosomes 1 to 16 of the Affymetrix. Outlier loci did not make a major difference to the identification of pedigree errors. The Collaborative Study on the Genetics of Alcoholism data has pedigree errors and some of them may be due to sample mix up