ABSTRACT. The aim of this study was to identify differentially expressed genes (DEGs) in renal medullary hypertension and reveal their pathogenic mechanisms. We downloaded the gene expression profile of GSE28360 from the Gene Expression Omnibus database. The profile included 14 samples (5 normal and 9 hypertension). The DEGs in normal and disease samples were distinguished with a false-discovery rate threshold of <0.05 and a fold-change value of >2 or <-2. We put the selected genes into the online program String 8.3 to obtain the protein-protein interaction network and selected the hub proteins. These hub proteins were then placed in the PANTHER database to determine hub protein-related pathways and explain their functions. Finally, we cleared up the singlenucleotide polymorphisms (SNPs) of the hub genes via combing with the National Center for Biotechnology SNP database. A total of 13 genes were identified as DEGs between normal and disease samples. Five selected hub proteins, B-cell translocation gene 2 (BTG2), FBJ murine osteosarcoma viral oncogene homolog (FOS), nuclear receptor subfamily 4, group A, member 1 (NR4A1), NR4A member 2 (NR4A2), and NR4A member 3 (NR4A3), were mainly related to angiogenesis and B-cell activation. After SNP analysis, 103, 103, 595, 150, and 493 SNPs were found to correspond to BTG2, FOS, NR4A1, NR4A2, and NR4A3, respectively. Our results suggest that pathways of angiogenesis and B-cell activation may involve in the progression of renal medulla hypertension.
Background Lung adenocarcinoma (LUAD) is a lung cancer subtype with poor prognosis. We investigated the prognostic value of methylation‐ and homologous recombination deficiency (HRD)‐associated gene signatures in LUAD. Methods Data on RNA sequencing, somatic mutations, and methylation were obtained from TCGA database. HRD scores were used to stratify patients with LUAD into high and low HRD groups and identify differentially mutated and expressed genes (DMEGs). Pearson correlation analysis between DMEGs and methylation yielded methylation‐associated DMEGs. Cox regression analysis was used to construct a prognostic model, and the distribution of clinical features in the high‐ and low‐risk groups was compared. Results Patients with different HRD scores showed different DNA mutation patterns. There were 272 differentially mutated genes and 6294 differentially expressed genes. Fifty‐seven DMEGs were obtained; the top 10 upregulated genes were COL11A1, EXO1, ASPM, COL12A1, COL2A1, COL3A1, COL5A2, DIAPH3, CAD, and SLC25A13, while the top 10 downregulated genes were C7, ERN2, DLC1, SCN7A, SMARCA2, CARD11, LAMA2, ITIH5, FRY, and EPHB6. Forty‐two DMEGs were negatively correlated with 259 methylation sites. Gene ontology and pathway enrichment analysis of the DMEGs revealed enrichment of loci involved in extracellular matrix‐related remodeling and signaling. Six out of the 42 methylation‐associated DMEGs were significantly associated with LUAD prognosis and included in the prognostic model. The model effectively stratified high‐ and low‐risk patients, with the high‐risk group having more patients with advanced stage disease. Conclusion We developed a novel prognostic model for LUAD based on methylation and HRD. Methylation‐associated DMEGs may function as biomarkers and therapeutic targets for LUAD. Further studies are needed to elucidate their roles in LUAD carcinogenesis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.