Current Genomics - Current Issue

Recent investigations have underscored the importance of long non-coding RNAs (lncRNAs), which exhibit more specific expression in tissues and cells than mRNA and are involved in gene regulation during development, pathology, and other processes through various mechanisms. Despite the predominant focus on the role of lncRNA Dio3os in cancer research, there has been relatively limited exploration of its potential involvement in glycolipid metabolism. Therefore, this study aims to consolidate existing knowledge on the function of Dio3os in glycolipid metabolism and calls for a broader investigation into its physiological roles.

This review synthesizes available literature to detail the gene characteristics of lncRNA Dio3os and its expression patterns. It also compiles recent insights and mechanisms pertaining to Dio3os's involvement in glycolipid metabolism, particularly its participation in the ceRNA regulatory network.

Recent studies demonstrate that lncRNA Dio3os regulates glycolysis in cancer cells and impacts obesity, potentially serving as an indicator for diabetic peripheral neuropathy. Furthermore, its diminished expression has been noted in atherosclerotic plaques.

lncRNA Dio3os exerts a significant regulatory influence on glycolipid metabolism, with variations in its expression levels potentially affecting disease presentations. Further investigations are warranted to elucidate the precise relationship between lncRNA Dio3os and its associated pathologies.

Retinal Vein Occlusion (RVO) is a common and main cause of blindness. Causal, possible risk variables must be identified to develop preventative strategies for RVO. Thus, we decided to evaluate whether smoking, alcohol, obesity, sedentary behaviour, hypertension, and hyperglycemia are associated with increased risk of RVO.

The data sources of Mendelian Randomization (MR) study included FinnGen consortium and the original GWAS article. A total of 130,604 cases with RVO from FinnGen consortium and 12,136 cases with RVO from the original GWAS article. The exposures of this MR study included smoking, alcoholic consumption, obesity, sedentariness, hypertension, and hyperglycemia. The outcome of this MR study was RVO.

Genetic predispositions to alcohol consumption (OR (odds ratio), 1.124; 95%CI, 1.007-1.254; P=0.037) and hyperglycemia (OR, 1.108; 95%CI, 1.023-1.200; P=0.012) were associated with increased risks of RVO in FinnGen. There were no significant associations of genetically predicted consumption of smoking (OR, 1.037; 95%CI, 0.341-3.155; P=0.949), obesity (OR, 1.045; 95%CI, 0.975-1.119; P=0.213), sedentariness (OR, 1.022; 95%CI, 0.753-1.38-; P=0.888), or hypertension (OR, 0.944; 95%CI, 0.848-1.051; P=0.290) with RVO.

This MR analysis provides genetic evidence that increased alcohol consumption and hyperglycemia may be causal risk factors for RVO. In addition, no genetic evidence in this MR analysis supported that there were causal associations between smoking, sedentariness, obesity and hypertension with RVO.

Ubiquitination, a unique post-translational modification, plays a cardinal role in diverse cellular functions such as protein degradation, signal transduction, DNA repair, and regulation of cell cycle. Thus, accurate prediction of potential ubiquitination sites is an urgent requirement for exploring the ubiquitination mechanism as well as the disease pathogenesis associated with ubiquitination processes.

This study introduces a novel deep learning architecture, ResUbiNet, which utilized a protein language model (ProtTrans), amino acid properties, and BLOSUM62 matrix for sequence embedding and multiple state-of-the-art architectural components, i.e., transformer, multi-kernel convolution, residual connection, and squeeze-and-excitation for feature extractions.

The results of cross-validation and external tests showed that the ResUbiNet model achieved better prediction performances in comparison with the available hCKSAAP_UbSite, RUBI, MDCapsUbi, and MusiteDeep models.

ResUbiNet’s integration of advanced features and architectures significantly enhances prediction performance, aiding in understanding ubiquitination mechanisms and related diseases.

Yin Yang 2 (YY2) plays a pivotal role in various tumorigenic processes; however, its specific involvement in esophageal carcinoma (ESCA) remains elusive. This study aims to investigate the expression and potential functional significance of YY2 in ESCA.

The expression and functions of YY2 in ESCA were analyzed using a broad range of bioinformatics databases and tools, including TCGA, TIMER, TISIDB, QUANTISEQ, cBioPortal, DNMIVD, LinkedOmics, DAVID, GSEA, GEPIA2, LASSO, miRWalk, miRDB, and TargetScan. Furthermore, RT-qPCR, immunohistochemical staining, western blot, CCK8 assay, and wound healing assay were employed to validate the involvement of YY2 in ESCA pathogenesis.

Bioinformatics analyses revealed that the YY2 gene is upregulated in ESCA tissues, with its high expression significantly associated with poor prognosis and elevated levels of M2 macrophages, NK cells, Tregs, CTLA4, TIGIT, and Siglec-15. Validating the ESCA samples demonstrated that knockdown of YY2 effectively inhibited cell proliferation and migration in ESCA cells. The biological functions of YY2 and its co-expressed genes were primarily associated with transcriptional regulation, DNA methylation, glycometabolism, and ubiquitination. Moreover, the regulatory network of YY2 in the glycolysis pathway was found to involve multiple genes and miRNAs. Finally, a prognostic model based on YY2 and its associated glycolysis genes revealed a strong inverse correlation between higher risk scores and lower survival rates in esophageal adenocarcinoma (EAC).

YY2 may serve as a promising prognostic biomarker and an innovative therapeutic target for patients with ESCA, regulating cell proliferation, migration, immune microenvironment, and glycolysis.