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

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.

Comprehensive Analysis of Inhibitor of Apoptosis Protein Expression and Prognostic Significance in Non-Small Cell Lung Cancer

Inhibitors of apoptosis proteins (IAPs) have been associated with tumor development and progression by affecting apoptosis through cell death signaling pathways. To date, eight IAPs (BIRC1-8) have been identified in mammalian cells. However, the role of IAPs in non-small cell lung cancer (NSCLC) development and progression has not been explored in depth. In this study, we used public datasets and bioinformatics tools to compare the expression, prognostic significance, and function of IAPs in NSCLC and its subtypes. Expression of IAPs in cancer and normal tissues and at different stages of NSCLC was compared with gene expression profiling interactive analysis, and their prognostic significance was analyzed with the Kaplan-Meier Plotter database. The correlations among IAPs were analyzed with the STRING database and SPSS19.0. Functional annotation of IAPs was analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment on the basis of the DAVID tool. Among patients with lung adenocarcinoma (LUAD), the expression level of BIRC5 was higher than that in normal samples, and the expression of BIRC1 and BIRC5 significantly varied in different stages. Moreover, the BIRC1-3 and BIRC5 mRNA levels were associated with overall survival (OS), and the BIRC1-2 and BIRC5-6 mRNA levels were associated with progression-free survival (PFS). Among patients with lung squamous cell carcinoma (LUSC), the expression level of BIRC1 was lower and that of BIRC5 was higher than those in normal tissues, and BIRC5 expression significantly varied in different stages. BIRC1 expression was associated with OS, whereas BIRC2 and BIRC6 expression was associated with PFS. Enrichment analysis showed that most IAPs are associated with ubiquitin- and apoptosis-related pathways. Collectively, this study suggests BIRC5 as a potential diagnostic and staging marker, BIRC1 as a potential marker of OS, and BIRC2 and BIRC6 as potential PFS markers for patients with NSCLC. These highlight new targets for the early detection, treatment, and management of NSCLC.

Identifying Immune Cell Infiltration and Effective Diagnostic Biomarkers in Rheumatoid Arthritis by Bioinformatics Analysis

Background

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by inflammatory cell infiltration, leading to persistent synovitis and joint destruction. The pathogenesis of RA remains unclear. This study aims to explore the immune molecular mechanism of RA through bioinformatics analysis.

Methods

Five microarray datasets and a high throughput sequencing dataset were downloaded. CIBERSORT algorithm was performed to evaluate immune cell infiltration in synovial tissues between RA and healthy control (HC). Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were conducted to identify the significantly different infiltrates of immune cells. Differentially expressed genes (DEGs) were screened by "Batch correction" and "RobustRankAggreg" methods. Functional correlation of DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Candidate biomarkers were identified by cytoHubba of Cytoscape, and their diagnostic effectiveness was predicted by Receiver Operator Characteristic Curve (ROC) analysis. The association of the identified biomarkers with infiltrating immune cells was explored using Spearman's rank correlation analysis in R software.

Results

Ten significantly different types of immune cells between RA and HC were identified. A total of 202 DEGs were obtained by intersection of DEGs screened by two methods. The function of DEGs were significantly associated with immune cells. Five hub genes (CXCR4, CCL5, CD8A, CD247, and GZMA) were screened by R package "UpSet". CCL5+CXCR4 and GZMA+CD8A were verified to have the capability to diagnose RA and early RA with the most excellent specificity and sensitivity, respectively. The correlation between immune cells and biomarkers showed that CCL5 was positively correlated with M1 macrophages, CXCR4 was positively correlated with memory activated CD4+ T cells and follicular helper T (Tfh) cells, and GZMA was positively correlated with Tfh cells.

Conclusions

CCL5, CXCR4, GZMA, and CD8A can be used as diagnostic biomarker for RA. GZMA-Tfh cells, CCL5-M1 macrophages, and CXCR4- memory activated CD4+ T cells/Tfh cells may participate in the occurrence and development of RA, especially GZMA-Tfh cells for the early pathogenesis of RA.