Chronic kidney disease (CKD) is a complex and prevalent condition that is characterized by impaired renal function, persistent inflammation and immune dysfunction. CKD is a significant public health concern globally and is associated with increased morbidity, mortality, and healthcare costs. The prevalence of CKD is expected to rise given its association with aging, diabetes, and hypertension.
Despite the importance of early detection and management of CKD, there is a lack of reliable diagnostic biomarkers and effective therapeutic targets for this condition. This presents a significant clinical challenge to clinicians and researchers alike. Therefore, there is an urgent need to explore new strategies for the diagnosis and management of CKD.
Integrating bioinformatics and pharmacological approaches offer a promising avenue for identifying effective biomarkers and therapeutic targets for CKD. Integrated analyses of data from various sources such as microarray and metabolomics data can provide a comprehensive overview of the molecular changes associated with CKD. Bioinformatics analyses such as GO analysis, KEGG pathway analysis, and WGCNA can identify key signaling pathways and potential biomarkers associated with CKD development and progression.
Pharmacological approaches can provide additional insights through the analysis of drug-gene interactions and the identification of potential therapeutic targets for CKD. A better understanding of these interactions can inform the development of targeted therapies that can improve the diagnosis and management of CKD.
This article collection aims to identify effective biomarkers for the diagnosis and treatment of CKD by integrating bioinformatics and pharmacological approaches, by gathering manuscripts addressing, but not limited to, the following topics:
· Conducting bioinformatics analyses, including GO analysis, KEGG pathway analysis, and WGCNA, to identify key signaling pathways and potential biomarkers
· Exploring potential pharmacological implications by analyzing drug-gene interactions and identifying potential therapeutic targets
· Examining immune infiltration features and related hub genes that might play a role in CKD development
Chronic kidney disease (CKD) is a complex and prevalent condition that is characterized by impaired renal function, persistent inflammation and immune dysfunction. CKD is a significant public health concern globally and is associated with increased morbidity, mortality, and healthcare costs. The prevalence of CKD is expected to rise given its association with aging, diabetes, and hypertension.
Despite the importance of early detection and management of CKD, there is a lack of reliable diagnostic biomarkers and effective therapeutic targets for this condition. This presents a significant clinical challenge to clinicians and researchers alike. Therefore, there is an urgent need to explore new strategies for the diagnosis and management of CKD.
Integrating bioinformatics and pharmacological approaches offer a promising avenue for identifying effective biomarkers and therapeutic targets for CKD. Integrated analyses of data from various sources such as microarray and metabolomics data can provide a comprehensive overview of the molecular changes associated with CKD. Bioinformatics analyses such as GO analysis, KEGG pathway analysis, and WGCNA can identify key signaling pathways and potential biomarkers associated with CKD development and progression.
Pharmacological approaches can provide additional insights through the analysis of drug-gene interactions and the identification of potential therapeutic targets for CKD. A better understanding of these interactions can inform the development of targeted therapies that can improve the diagnosis and management of CKD.
This article collection aims to identify effective biomarkers for the diagnosis and treatment of CKD by integrating bioinformatics and pharmacological approaches, by gathering manuscripts addressing, but not limited to, the following topics:
· Conducting bioinformatics analyses, including GO analysis, KEGG pathway analysis, and WGCNA, to identify key signaling pathways and potential biomarkers
· Exploring potential pharmacological implications by analyzing drug-gene interactions and identifying potential therapeutic targets
· Examining immune infiltration features and related hub genes that might play a role in CKD development