Study and Characterization of Long Non-coding RUNX1-IT1 among Large Artery Atherosclerosis Stroke Patients Based on the ceRNA Hypothesis

Determinant-based grouping of SNPs and its application for detecting disease-associated genomic loci

Groups of single nucleotide polymorphisms (SNPs) are more effective than individual SNPs in identifying genetic loci associated with diseases. However, an optimal method for grouping SNPs remains an open question. Here, we introduce a novel approach for SNP grouping, leveraging the determinant of linkage disequilibrium (LD) matrices as a comprehensive metric of multicollinearity. This method builds on the established use of determinants in regression analysis as an aggregate measure of variable interdependence. We proposed that SNPs be grouped by evaluating the determinant of their LD matrices, with the approach validated using both synthetic genotype-phenotype data and real-world data from genome-wide association studies (GWAS) of ischemic stroke. Application of this method identified two previously known and five novel candidate genes associated with the onset of disease. Additionally, we developed a straightforward procedure to estimate a critical parameter for the model: the minimal determinant value for an LD matrix to be considered singular. In summary, the determinant of the LD matrix serves as a robust integrative measure for assessing SNP group quality. This metric underpins a bioinformatics workflow capable of identifying genomic loci associated with disease onset, offering a valuable tool for advancing genetic association studies.

Multi-omics research strategies in ischemic stroke: A multidimensional perspective

Ischemic stroke (IS) is a multifactorial and heterogeneous neurological disorder with high rate of death and long-term impairment. Despite years of studies, there are still no stroke biomarkers for clinical practice, and the molecular mechanisms of stroke remain largely unclear. The high-throughput omics approach provides new avenues for discovering biomarkers of IS and explaining its pathological mechanisms. However, single-omics approaches only provide a limited understanding of the biological pathways of diseases. The integration of multiple omics data means the simultaneous analysis of thousands of genes, RNAs, proteins and metabolites, revealing networks of interactions between multiple molecular levels. Integrated analysis of multi-omics approaches will provide helpful insights into stroke pathogenesis, therapeutic target identification and biomarker discovery. Here, we consider advances in genomics, transcriptomics, proteomics and metabolomics and outline their use in discovering the biomarkers and pathological mechanisms of IS. We then delineate strategies for achieving integration at the multi-omics level and discuss how integrative omics and systems biology can contribute to our understanding and management of IS.

LncRNA RUNX1-IT1 affects the differentiation of Th1 cells by regulating NrCAM transcription in Graves' disease

Graves' disease (GD) is a kind of autoimmune diseases. The development of GD is closely related to the imbalance of Th1/Th2 generated by the differentiation of CD4⁺ T cells. This study was sought to clarify the role of lncRNA RUNX1-IT1 and explore the mechanism of its function. The expressions of RUNX1-IT1 and Neural cell adhesion molecule (NrCAM) in the peripheral blood of GD patients were detected by qRT-PCR and Western blot. We performed RNA pull down, RIP, and ChIP experiments to verify the correlation between p53 and RUNX1-IT1, p53 and NrCAM. The levels of Th1 cells differentiation markers were detected by Flow cytometry assay and ELISA. The expressions of lncRNA RUNX1-IT1 and NrCAM were most significantly up-regulated in CD4⁺ T cells of GD patients, and NrCAM expression was significantly positively correlated with RUNX1-IT1 expression. Furthermore, p53 was a potential transcription factor of NrCAM, which could interact with NrCAM. NrCAM level was up-regulated after the overexpression of p53 in CD4⁺ T cells, while knockdown of RUNX1-IT1 reversed this effect. Down-regulation of NrCAM and RUNX1-IT1 could decrease the mRNA and protein levels of transcriptional regulator T-bet and CXC chemokine ligand 10 (CXCL10) in CD4⁺ T cells. Our results suggested that RUNX1-IT1 regulated the expressions of the important Th1 factor T-bet, CXCL10, and interferon γ (IFN-γ) by regulating NrCAM transcription, thus participating in the occurrence and development of specific autoimmune disease GD.