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Next, we highlight recent findings regarding the presence of the NTT-MMP-2 isoform in renal dysfunction, in kidney and cardiac diseases, including damage observed in aging, acute ischemia-reperfusion injury (IRI), chronic kidney disease, diabetic nephropathy, and human renal transplants with delayed graft function. Finally, we briefly discuss how our insights may guide further experimental and clinical studies that are needed to elucidate the underlying mechanisms and the role of the NTT-MMP-2 isoform in renal dysfunction, which may help to establish it as a potential therapeutic target in kidney diseases.Familial hypercholesterolemia (FH) is a common, yet underdiagnosed, genetic disorder characterized by lifelong elevated low-density lipoprotein cholesterol levels, which can increase the risk of early-onset coronary artery disease (CAD). In the present study, we screened the nucleotide variations of the LDLR and PCSK9 genes, as well as a part of the APOB gene, in Iranian patients with FH and premature CAD to find the genetic cause of the disorder. Fifteen unrelated individuals with a clinical diagnosis of FH and premature CAD were recruited. Direct DNA sequencing was applied to screen the whole coding exons and exon-intron boundaries of the LDLR and PCSK9 genes and the main parts of their introns, together with exon 26 of the APOB gene. The pathogenicity of the identified mutations was investigated via either segregation analyses in the family or in silico predictive software. Six different point mutations (p.Cys148Tyr, p.Cys216Tyr, p.Cys302Trp, p.Cys338Trp, p.Leu479Gln, and p.G593Afs∗72) in LDLR and a double in this study.N6-methyladenosine (m6A) is an abundant modification on mRNA that plays an important role in regulating essential RNA activities. Several wet lab studies have identified some RNA binding proteins (RBPs) that are related to m6A's regulation. The objective of this study was to identify potential m6A-associated RBPs using an integrative computational framework. The framework was composed of an enrichment analysis and a classification model. Utilizing RBPs' binding data, we analyzed reproducible m6A regions from independent studies using this framework. The enrichment analysis identified known m6A-associated RBPs including YTH domain-containing proteins; it also identified RBM3 as a potential m6A-associated RBP for mouse. Furthermore, a significant correlation for the identified m6A-associated RBPs is observed at the protein expression level rather than the gene expression level. On the other hand, a Random Forest classification model was built for the reproducible m6A regions using RBPs' binding data. The RBP-based predictor demonstrated not only competitive performance when compared with sequence-based predictions but also reflected m6A's action of repelling against RBPs, which suggested that our framework can infer interaction between m6A and m6A-associated RBPs beyond sequence level when utilizing RBPs' binding data. In conclusion, we designed an integrative computational framework for the identification of known and potential m6A-associated RBPs. We hope the analysis will provide more insights on the studies of m6A and RNA modifications.Long non-coding RNAs (LncRNAs) can bind to other proteins or RNAs to regulate gene expression, and its role in tumors has been extensively studied. A common RNA binding protein, UPF1, is also a key factor in a variety of RNA decay pathways. RNA decay pathways serve to control levels of particular RNA molecules. The expression of UPF1 is often dysregulated in tumors, an observation which suggests that UPF1 contributes to development of a variety of tumors. Herein, we review evidence from studies of fourteen lncRNAs interact with UPF1. The interaction between lncRNA and UPFI provide fundamental basis for cell transformation and tumorigenic growth.Glioma is the common histological subtype of malignancy in the central nervous system, with high morbidity and mortality. Glioma cancer stem cells (CSCs) play essential roles in tumor recurrence and treatment resistance. Thus, exploring the stem cell-related genes and subtypes in glioma is important. In this study, we collected the RNA-sequencing (RNA-seq) data and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. With the differentially expressed genes (DEGs) and weighted gene correlation network analysis (WGCNA), we identified 86 mRNA expression-based stemness index (mRNAsi)-related genes in 583 samples from TCGA RNA-seq dataset. Furthermore, these samples from TCGA database could be divided into two significantly different subtypes with different prognoses based on the mRNAsi corresponding gene, which could also be validated in the CGGA database. The clinical characteristics and immune cell infiltrate distribution of the two stemness subtypes are different. Then, functional enrichment analyses were performed to identify the different gene ontology (GO) terms and pathways in the two different subtypes. Moreover, we constructed a stemness subtype-related risk score model and nomogram to predict the prognosis of glioma patients. buy UK 5099 Finally, we selected one gene (ETV2) from the risk score model for experimental validation. The results showed that ETV2 can contribute to the invasion, migration, and epithelial-mesenchymal transition (EMT) process of glioma. In conclusion, we identified two distinct molecular subtypes and potential therapeutic targets of glioma, which could provide new insights for the development of precision diagnosis and prognostic prediction for glioma patients.Non-obstructive azoospermia (NOA) is one of the most important causes of male infertility. It is mainly characterized by the absence of sperm in semen repeatedly or the number of sperm is small and not fully developed. At present, its pathogenesis remains largely unknown. The goal of this study is to identify hub genes that might affect biomarkers related to spermatogenesis. Using the clinically significant transcriptome and single-cell sequencing data sets on the Gene Expression Omnibus (GEO) database, we identified candidate hub genes related to spermatogenesis. Based on them, we performed Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses, protein-protein interaction (PPI) network analysis, principal component analysis (PCA), cell cluster analysis, and pseudo-chronological analysis. We identified a total of 430 differentially expressed genes, of which three have not been reported related to spermatogenesis (C22orf23, TSACC, and TTC25), and the expression of these three hub genes was different in each type of sperm cells.
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