mProX™ Human GPR183 Stable Cell Line

Shi W, et al. "Bioinformatics approach to identify the hub gene associated with COVID-19 and ." IET systems biology, 2023.
The coronavirus disease 2019 (COVID-19) has become a global health crisis, with pulmonary fibrosis emerging as a major complication of SARS-CoV-2 infection. As this is a new and rapidly evolving disease, there are still many unknown aspects surrounding it. To address this, researchers analyzed datasets from the Gene Expression Omnibus for COVID-19 and idiopathic pulmonary fibrosis. Using the Random Forest algorithm, six potential hub genes associated with the severity of COVID-19 in patients were identified. A risk prediction model was then developed and validated using another dataset. Of the six genes, S100A8 was found to be a significant target gene in the development of pulmonary fibrosis in severe COVID-19 patients. Additionally, a neural network model was successfully constructed to predict patient prognosis. As more data becomes available, bioinformatic techniques have the potential to identify key targets for diagnosis, treatment, and prognosis of COVID-19, as well as guide the development of clinical interventions such as drugs and vaccines.
Pubmed: 37814484   DOI: 10.1049/syb2.12080

Zhao X, et al. "Analysis of Prospective Genetic Indicators for Prenatal Exposure to Arsenic in ." Biological trace element research, 2023.
In this study, the researchers aimed to identify potential biomarkers for prenatal arsenic exposure in newborn cord blood using machine learning techniques. Two datasets (GSE48354 and GSE7967) from the Gene Expression Omnibus (GEO) database were retrieved and merged for analysis. The "limma" package in R was used to identify differentially expressed genes (DEGs), which were then narrowed down using the LASSO regression and SVM-RFE algorithms. A receiver operating characteristic (ROC) curve was used to evaluate the efficacy of the biomarkers. The researchers also investigated the proportion of invading immune cells in each sample and their relationship with the biomarkers using the CIBERSORT algorithm and Spearman approach, respectively. Through their analysis, the team identified 28 DEGs, with the main biomarkers being DENND2D, OLIG1, RGS18, CXCL16, DDIT4, FOS, G0S2, GPR183, JMJD6, and SOCS3. Additionally, an immune infiltration analysis and correlation analysis revealed that these biomarkers were closely associated with invading immune cells. Ultimately, the study determined that ten genes, including some of the aforementioned biomarkers, were important indicators of cord blood exposure to arsenic and that immune cells may play a crucial role in this process.
Pubmed: 37740142   DOI: 10.1007/s12011-023-03863-1