This study aimed to identify differentially expressed genes (DEGs) of unruptured intracranial aneurysms (IAs) and provide beneficial information for early diagnosis and treatment of IAs. The gene expression profile GSE26969 from the Gene Expression Omnibus database was downloaded, which included six human IA samples: three intracranial arterial aneurysm samples and three normal superficial temporal artery samples (control). Based on these data, we identified the DEGs between normal and disease samples with packages in the R language.
Microarray data of astrocytes extracted from the optic nerves of donors with and without glaucoma were analyzed to screen for differentially expressed genes (DEGs). Functional exploration with bioinformatic tools was then used to understand the roles of the identified DEGs in glaucoma. Microarray data were downloaded from the Gene Expression Omnibus (GEO) database, which contains 13 astrocyte samples, 6 from healthy subjects and 7 from patients suffering from glaucoma. Data were pre-processed, and DEGs were screened out using R software packages.
This study aimed to identify marker genes in diabetic wounds using a dataset based on a DNA microarray of dermal lymphatic endothelial cells, and our results provide a basic understanding of diabetic wounds through further study of these differentially expressed genes (DEGs). From the Gene Expression Omnibus database, we downloaded a gene expression microarray (GSE38396) that includes 8 samples: 4 normal controls and 4 disease samples (type II diabetes).
The aim of this study was to identify feature genes that are associated with hereditary hemochromatosis (HHC; iron overload) in cardiac and skeletal muscle of mice. First, the expression profile GSE9726 was downloaded from Gene Expression Omnibus database which included 12 samples. Then the differentially expressed genes (DEGs) were identified by R language. Furthermore, the KUPS software was used to identify relationships in interactions among common DEGs in the cardiac and skeletal muscles. We then used the EASE software to obtain enriched pathways in a gene interaction network.