IL1RL1 polymorphisms rs12479210 and rs1420101 are associated with increased lung cancer risk in the Chinese Han population

Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms

Background and Purpose

Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors, and genetic factors are important determinants of COPD. This study focuses on screening the best predictive models for assessing COPD-associated SNPs and then using the best models to predict potential risk factors for COPD.

Methods

Healthy subjects (n=290) and COPD patients (n=233) were included in this study, the Agena MassARRAY platform was applied to genotype the subjects for SNPs. The selected sample loci were first screened by logistic regression analysis, based on which the key SNPs were further screened by LASSO regression, RFE algorithm and Random Forest algorithm, and the ROC curves were plotted to assess the discriminative performance of the models to screen the best prediction model. Finally, the best prediction model was used for the prediction of risk factors for COPD.

Results

One-way logistic regression analysis screened 44 candidate SNPs from 146 SNPs, on the basis of which 44 SNPs were screened or feature ranked using LASSO model, RFE-Caret, RFE-Lda, RFE-lr, RFE-nb, RFE-rf, RFE-treebag algorithms and random forest model, respectively, and obtained ROC curve values of 0.809, 0.769, 0.798, 0.743, 0.686, 0.766, 0.743, 0.719, respectively, so we selected the lasso model as the best model, and then constructed a column-line graph model for the 25 SNPs screened in it, and found that rs12479210 might be the potential risk factors for COPD.

Conclusion

The LASSO model is the best predictive model for COPD and rs12479210 may be a potential risk locus for COPD.

Genetic Associations of Plasminogen Activator Inhibitor-1-Related miRNA Variants with Coronary Artery Disease

Coronary artery disease (CAD) is one of the most common types of cardiovascular disease and can lead to a heart attack as plaque gradually builds up inside the coronary arteries, blocking blood flow. Previous studies have shown that polymorphisms in the PAI-1 gene are associated with CAD; however, studies of the PAI-1 3'-untranslated region, containing a miRNA binding site, and the miRNAs that interact with it, are insufficient. To investigate the association between miRNA polymorphisms and CAD in the Korean population based on post-transcriptional regulation, we genotyped five polymorphisms in four miRNAs targeting the 3'-untranslated region of PAI-1 using real-time PCR and TaqMan assays. We found that the mutant genotype of miR-30c rs928508 A > G was strongly associated with increased CAD susceptibility. In a genotype combination analysis, the combination of the homozygous mutant genotype (GG) of miR-30c rs928508 with the wild-type genotype (GG) of miR-143 rs41291957 resulted in increased risk for CAD. Also, in an allele combination analysis, the combination of the mutant allele (G) of miR-30c rs928508 and the wild-type allele (G) of miR-143 rs41291957 resulted in increased risk for CAD. Furthermore, metabolic syndrome and diabetes mellitus showed synergistic effects on CAD risk when combined with miR-30c rs928508. These results can be applied to identify CAD prognostic biomarkers among miRNA polymorphisms and various clinical factors.

Genetic polymorphisms in CYP2B6 may be associated with lung cancer risk in the Chinese Han population

BACKGROUND

Our study aimed to elucidate the association between single nucleotide polymorphisms (SNPs) in CYP2B6 gene and susceptibility to lung cancer (LC).

METHODS

Five SNPs in CYP2B6 were genotyped in Chinese Han population (507 cases and 505 controls) utilizing Agena MassARRAY. The relationship between these SNPs and LC susceptibility was assessed using odds ratios, 95% confidence intervals, and χ2 tests. Additionally, multifactor dimensionality reduction was employed to analyze SNP-SNP interactions. Bioinformatics methods were applied to investigate the function of these SNPs.

RESULTS

We found that rs2099361 was associated with an increased susceptibility to LC in the codominant model (OR = 1.31, p = 0.045). Stratification analysis revealed the allele G at rs4803418 and the allele T at rs4803420 of CYP2B6 (BMI >24 kg/m2) were significantly linked to decreased susceptibility of LC. Conversely, the allele C at rs12979270 (BMI >24 kg/m2) showed increased susceptibility to LC. Moreover, a robust redundant relationship between rs12979270 and rs4803420 was identified in the study. According to the VannoPortal database, we found that rs4803420, rs12979270 and rs2099361 may modulate the binding affinity of LMNB1, SP1 and HDAC2, respectively.

CONCLUSIONS

Our results suggest that SNPs in the CYP2B6 gene play crucial roles in LC susceptibility.