Association of miR-155 and Angiotensin Receptor Type 1 Polymorphisms with the Risk of Ischemic Stroke in a Chinese Population

Association between miR-365 polymorphism and ischemic stroke in a Chinese population

Background

Ischemic stroke (IS) represents a major cause of morbidity and mortality across the globe. The aberrant expression of miR-365 has been found to be implicated in a wide array of human diseases, including atherosclerosis and cancer. Studies on single-nucleotide polymorphisms (SNPs) in miRNA genes can help gain insight into the susceptibility to the condition. This study aimed to examine the relationship between miR-365 SNPs and the risk of IS.

Methods

The study recruited 215 IS patients and 220 controls. The SNPscans genotyping was employed to genotype three polymorphic loci (rs121224, rs30230, and rs178553) of miR-365. The relative expression of miR-365 in peripheral blood mononuclear cells of the patients and controls was determined by using real-time quantitative PCR.

Results

The miR-365 rs30230 polymorphism exhibited a significant association with the risk of developing IS (TC vs. CC: adjusted OR = 0.55, 95% CI: 0.33-0.92, P = 0.022; TT vs. CC: adjusted OR = 0.34, 95% CI: 0.14-0.85, P = 0.021; TC +TT vs. CC: adjusted OR = 0.51, 95% CI: 0.31-0.83, P = 0.007; T vs. C: adjusted OR = 0.57, 95% CI: 0.39-0.83, P = 0.004). Haplotype analysis revealed that the C-T-G haplotype was associated with a decreased risk of IS (OR = 0.68, 95% CI: 0.46-1.00, P = 0.047). Furthermore, miR-365 expression was significantly higher in IS patients than in controls (P < 0.001). Interestingly, patients with rs30230 TC or TT genotypes had lower miR-365 levels compared to their counterparts with CC genotypes (P < 0.001).

Conclusions

The miR-365 rs30230 polymorphism might bear an association with IS susceptibility in the Chinese population, and the rs30230 TC/TT genotype might be a protective factor against IS.

FeCO3, constructing the network biomarkers using the inter-feature correlation coefficients and its application in detecting high-order breast cancer biomarkers

Aims:

This study aims to formulate the inter-feature correlation as the engineered features.

Background:

Modern biotechnologies tend to generate a huge number of characteristics of a sample, while an OMIC dataset usually has a few dozens or hundreds of samples due to the high costs of generating the OMIC data. So many bio-OMIC studies assumed the inter-feature independence and selected a feature with a high phenotype-association.

Objective:

However, many features are closely associated with each other due to their physical or functional interactions, which may be utilized as a new view of features.

Method:

This study proposed a feature engineering algorithm based on the correlation coefficients (FeCO3) by utilizing the correlations between a given sample and a few reference samples. A comprehensive evaluation was carried out for the proposed FeCO3 network features using 24 bio-OMIC datasets.

Result:

The experimental data suggested that the newly calculated FeCO3 network features tended to achieve better classification performances than the original features, using the same popular feature selection and classification algorithms. The FeCO3 network features were also consistently supported by the literature. FeCO3 was utilized to investigate the high-order engineered biomarkers of breast cancer, and detected the PBX2 gene (Pre-B-Cell Leukemia Transcription Factor 2) as one of the candidate breast cancer biomarkers. Although the two methylated residues cg14851325 (Pvalue=8.06e-2) and cg16602460 (Pvalue=1.19e-1) within PBX2 did not have statistically significant association with breast cancers, the high-order inter-feature correlations showed a significant association with breast cancers.

Conclusion:

The proposed FeCO3 network features calculated the high-order inter-feature correlations as novel features, and may facilitate the investigations of complex diseases from this new perspective. The source code is available in FigShare at 10.6084/m9.figshare.13550051 or the web site http://www.healthinformaticslab.org/supp/ .

miRetrieve-an R package and web application for miRNA text mining

microRNAs (miRNAs) regulate gene expression and thereby influence biological processes in health and disease. As a consequence, miRNAs are intensely studied and literature on miRNAs has been constantly growing. While this growing body of literature reflects the interest in miRNAs, it generates a challenge to maintain an overview, and the comparison of miRNAs that may function across diverse disease fields is complex due to this large number of relevant publications. To address these challenges, we designed miRetrieve, an R package and web application that provides an overview on miRNAs. By text mining, miRetrieve can characterize and compare miRNAs within specific disease fields and across disease areas. This overview provides focus and facilitates the generation of new hypotheses. Here, we explain how miRetrieve works and how it is used. Furthermore, we demonstrate its applicability in an exemplary case study and discuss its advantages and disadvantages.