Identifying effective diagnostic biomarkers and immune infiltration features in chronic kidney disease by bioinformatics and validation

Abstract

Background: Chronic kidney disease (CKD), characterized by sustained inflammation and immune dysfunction, is highly prevalent and can eventually progress to end-stage kidney disease. However, there is still a lack of effective and reliable diagnostic markers and therapeutic targets for CKD.Methods: First, we merged data from GEO microarrays (GSE104948 and GSE116626) to identify differentially expressed genes (DEGs) in CKD and healthy patient samples. Then, we conducted GO, KEGG, HPO, and WGCNA analyses to explore potential functions of DEGs and select clinically significant modules. Moreover, STRING was used to analyse protein-protein interactions. CytoHubba and MCODE algorithms in the cytoscape plug-in were performed to screen hub genes in the network. We then determined the diagnostic significance of the obtained hub genes by ROC and two validation datasets. Meanwhile, the expression level of the biomarkers was verified by IHC. Furthermore, we examined immunological cells’ relationships with hub genes. Finally, GSEA was conducted to determine the biological functions that biomarkers are significantly enriched. STITCH and AutoDock Vina were used to predict and validate drug–gene interactions.Results: A total of 657 DEGs were screened and functional analysis emphasizes their important role in inflammatory responses and immunomodulation in CKD. Through WGCNA, the interaction network, ROC curves, and validation set, four hub genes (IL10RA, CD45, CTSS, and C1QA) were identified. Furthermore, IHC of CKD patients confirmed the results above. Immune infiltration analysis indicated that CKD had a significant increase in monocytes, M0 macrophages, and M1 macrophages but a decrease in regulatory T cells, activated dendritic cells, and so on. Moreover, four hub genes were statistically correlated with them. Further analysis exhibited that IL10RA, which obtained the highest expression level in hub genes, was involved in abnormalities in various immune cells and regulated a large number of immune system responses and inflammation-related pathways. In addition, the drug–gene interaction network contained four potential therapeutic drugs targeting IL10RA, and molecular docking might make this relationship viable.Conclusion: IL10RA and its related hub molecules might play a key role in the development of CKD and could be potential biomarkers in CKD.

Identification of Protein Quality Markers in Toad Venom from Bufo gargarizans

“…However, the enormous complexity and considerable amount of proteins present great challenges to the study of toad secretions [11], and the identity and actual function of toad venom proteins are still quite controversial [4]. In combination with proteomics and transcriptomics [38,39], the composition of active proteins in toad venom was clarified to further investigate their potential as quality markers. We focused on eDEPs because toad venom is a secretory substance.…”

Study of molecular patterns associated with ferroptosis in Parkinson’s disease and its immune signature

“…The Pick-Soft-Threshold function is then used to calculate the adjacency values [ 69 ]. Second, the neighbor-joining values are converted into a topological overlap matrix (TOM) to measure the average network connectivity of each gene [ 70 ]. The dissimilarity between genes is calculated (1-TOM).…”

Multi-omics analysis of kidney tissue metabolome and proteome reveals the protective effect of sheep milk against adenine-induced chronic kidney disease in mice

“…Food & Function 115 markedly downregulated proteins and 38 markedly upregulated proteins; however, one protein (ADGRE1) was downregulated in both groups. The upregulated proteins included ANXA1, CD14, CD44, CD74, CHI3L, C1QA, C1QB, S100A8, and S100A9, which are associated with renal inflammation [24][25][26][27][28][29][30][31] (Fig. 3E), and collagen proteins (COL1A1, COL1A2, COL5A1, COL5A2 and COL4A2), MFAP4, SPARC, MRC1 and MRC2, which are related to renal fibrosis [32][33][34][35][36] (Fig.…”