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

Revisiting the Ω(2012)\Omega(2012) as a hadronic molecule and its strong decays

Recently, the Belle collaboration measured the ratios of the branching fractions of the newly observed $\Omega(2012)$ excited state. They did not observe significant signals for the $\Omega(2012) \to \bar{K} \Xi^*(1530) \to \bar{K} \pi \Xi$ decay, and reported an upper limit for the ratio of the three body decay to the two body decay mode of $\Omega(2012) \to \bar{K} \Xi$. In this work, we revisit the newly observed $\Omega(2012)$ from the molecular perspective where this resonance appears to be a dynamically generated state with spin-parity $3/2^-$ from the coupled channels interactions of the $\bar{K} \Xi^*(1530)$ and $\eta \Omega$ in $s$-wave and $\bar{K} \Xi$ in $d$-wave. With the model parameters for the $d$-wave interaction, we show that the ratio of these decay fractions reported recently by the Belle collaboration can be easily accommodated.Comment: Published version. Published in Eur.\ Phys.\ J.\ C {\bf 80}, 361 (2020

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

Jakość środowiska, korupcja i rozwój przemysłu: perspektywa globalna

This paper investigates the causal relationship between environmental quality and corruption for 129 countries, using the panel cointegration and panel-based error correction models for the period 2002-2015. In the paper, we use EPI, EHI, and EVI to represent environmental quality, which are more reasonable and comprehensive. We further take industry growth into consideration and investigate its impact on environmental quality. Our results corroborate that there exists a long-term equilibrium cointegrated relationship among the variables, both of corruption and industry growth have a negative effect on environmental quality and the corruption can seriously decrease environmental quality in the long term, while industry growth weakens environmental quality no matter in the short or long run.W artykule zbadano związek przyczynowy między jakością środowiska a korupcją w 129 krajach, wykorzystując modele kointegracji panelowej i panelowej korekcji błędów za lata 2002-2015. W pracy używamy EPI, EHI i EVI do wyznaczania jakości środowiska, które wydają się najbardziej sensowne i wszechstronne. Ponadto bierzemy pod uwagę rozwój branży i badamy jego wpływ na jakość środowiska. Nasze wyniki potwierdzają, że istnieje długoterminowa stabilna skointegorowana relacja pomiędzy zmiennymi, zarówno korupcja, jak i rozwój przemysłu mają negatywny wpływ na jakość środowiska, a korupcja może poważnie obniżyć jakość środowiska w dłuższej perspektywie, podczas gdy wzrost przemysłu osłabia jakość środowiska zarówno w krótkim, jak i długim okresie