Identification and Validation of Prognostic Biomarkers Specifically Expressed in Macrophage in IgA Nephropathy Patients Based on Integrated Bioinformatics Analyses

Unveiling biomarkers and therapeutic targets in IgA nephropathy through large-scale blood transcriptome analysis

INTRODUCTION

IgA nephropathy (IgAN) is the most prevalent form of glomerulonephritis. Unfortunately, molecular biomarkers for IgAN derived from omics studies are still lacking. This research aims to identify critical genes associated with IgAN through large-scale blood transcriptome analysis.

METHODS

We constructed novel blood transcriptome profiles from peripheral blood mononuclear cells (PBMCs) of 53 Chinese IgAN patients and 28 healthy individuals. Our analysis included GO, KEGG, and GSEA for biological pathways. We analyzed immune cell profiles with CIBERSORT and constructed PPI networks with STRING, visualized in Cytoscape. Key differentially expressed genes (DEGs) were identified using CytoHubba and MCODE. We assessed the correlation between gene expressions and clinical data to evaluate clinical significance and identified hub genes through machine learning, validated with an open-access dataset. Potential drugs were explored using the CMap database.

RESULTS

We identified 333 DEGs between IgAN patients and healthy controls, mainly related to immune response and inflammation. Key pathways included NK cell mediated cytotoxicity, complement and coagulation cascades, antigen processing, and B cell receptor signaling. Cytoscape revealed 16 clinically significant genes (including KIR2DL1, KIR2DL3, VISIG4, C1QB, and C1QC, associated with sub-phenotype and prognosis). Machine learning identified two hub genes (KLRC1 and C1QB) for a diagnostic model of IgAN with 0.92 accuracy, validated at 1.00 against the GSE125818 dataset. Sirolimus, calcifediol, and efaproxiral were suggested as potential therapeutic agents.

CONCLUSION

Key DEGs, particularly VISIG4, KLRC1, and C1QB, emerge as potential specific markers for IgAN, paving the way for future targeted personalized treatment options.

Exploration of potential biomarkers and therapeutic targets for trauma-related acute kidney injury

PURPOSE

Acute kidney injury (AKI) is one of the most common functional injuries observed in trauma patients. However, certain trauma medications may exacerbate renal injury. Therefore, the early detection of trauma-related AKI holds paramount importance in improving trauma prognosis.

METHODS

Qualified datasets were selected from public databases, and common differentially expressed genes related to trauma-induced AKI and hub genes were identified through enrichment analysis and the establishment of protein-protein interaction (PPI) networks. Additionally, the specificity of these hub genes was investigated using the sepsis dataset and conducted a comprehensive literature review to assess their plausibility. The raw data from both datasets were downloaded using R software (version 4.2.1) and processed with the "affy" package19 for correction and normalization.

RESULTS

Our analysis revealed 585 upregulated and 629 downregulated differentially expressed genes in the AKI dataset, along with 586 upregulated and 948 downregulated differentially expressed genes in the trauma dataset. Concurrently, the establishment of the PPI network and subsequent topological analysis highlighted key hub genes, including CD44, CD163, TIMP metallopeptidase inhibitor 1, cytochrome b-245 beta chain, versican, membrane spanning 4-domains A4A, mitogen-activated protein kinase 14, and early growth response 1. Notably, their receiver operating characteristic curves displayed areas exceeding 75%, indicating good diagnostic performance. Moreover, our findings postulated a unique molecular mechanism underlying trauma-related AKI.

CONCLUSION

This study presents an alternative strategy for the early diagnosis and treatment of trauma-related AKI, based on the identification of potential biomarkers and therapeutic targets. Additionally, this study provides theoretical references for elucidating the mechanisms of trauma-related AKI.

A comprehensive bioinformatics analysis identifies mitophagy biomarkers and potential Molecular mechanisms in hypertensive nephropathy

Mitophagy, the selective removal of damaged mitochondria, plays a critical role in kidney diseases, but its involvement in hypertensive nephropathy (HTN) is not well understood. To address this gap, we investigated mitophagy-related genes in HTN, identifying potential biomarkers for diagnosis and treatment. Transcriptome datasets from the Gene Expression Omnibus database were analyzed, resulting in the identification of seven mitophagy related differentially expressed genes (MR-DEGs), namely PINK1, ULK1, SQSTM1, ATG5, ATG12, MFN2, and UBA52. Further, we explored the correlation between MR-DEGs, immune cells, and inflammatory factors. The identified genes demonstrated a strong correlation with Mast cells, T-cells, TGFβ3, IL13, and CSF3. Machine learning techniques were employed to screen important genes, construct diagnostic models, and evaluate their accuracy. Consensus clustering divided the HTN patients into two mitophagy subgroups, with Subgroup 2 showing higher levels of immune cell infiltration and inflammatory factors. The functions of their proteins primarily involve complement, coagulation, lipids, and vascular smooth muscle contraction. Single-cell RNA sequencing revealed that mitophagy was most significant in proximal tubule cells (PTC) in HTN patients. Pseudotime analysis of PTC confirmed the expression changes observed in the transcriptome. Intercellular communication analysis suggested that mitophagy might regulate PTC's participation in intercellular crosstalk. Notably, specific transcription factors such as HNF4A, PPARA, and STAT3 showed strong correlations with mitophagy-related genes in PTC, indicating their potential role in modulating PTC function and influencing the onset and progression of HTN. This study offers a comprehensive analysis of mitophagy in HTN, enhancing our understanding of the pathogenesis, diagnosis, and treatment of HTN.Communicated by Ramaswamy H. Sarma.

Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis

Objective

To identify potential diagnostic markers of lupus nephritis (LN) based on bioinformatics and machine learning and to explore the significance of immune cell infiltration in this pathology.

Methods

Seven LN gene expression datasets were downloaded from the GEO database, and the larger sample size was used as the training group to obtain differential genes (DEGs) between LN and healthy controls, and to perform gene function, disease ontology (DO), and gene set enrichment analyses (GSEA). Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify candidate biomarkers. The diagnostic value of LN diagnostic gene biomarkers was further evaluated in the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from LN patients and to analyze their correlation with diagnostic markers.

Results

Thirty and twenty-one DEGs were screened in kidney tissue and peripheral blood, respectively. Both of which covered macrophages and interferons. The disease enrichment analysis of DEGs in kidney tissues showed that they were mainly involved in immune and renal diseases, and in peripheral blood it was mainly enriched in cardiovascular system, bone marrow, and oral cavity. The machine learning algorithm combined with external dataset validation revealed that C1QA(AUC = 0.741), C1QB(AUC = 0.758), MX1(AUC = 0.865), RORC(AUC = 0.911), CD177(AUC = 0.855), DEFA4(AUC= 0.843)and HERC5(AUC = 0.880) had high diagnostic value and could be used as diagnostic biomarkers of LN. Compared to controls, pathways such as cell adhesion molecule cam, and systemic lupus erythematosus were activated in kidney tissues; cell cycle, cytoplasmic DNA sensing pathways, NOD-like receptor signaling pathways, proteasome, and RIG-1-like receptors were activated in peripheral blood. Immune cell infiltration analysis showed that diagnostic markers in kidney tissue were associated with T cells CD8 and Dendritic cells resting, and in blood were associated with T cells CD4 memory resting, suggesting that CD4 T cells, CD8 T cells and dendritic cells are closely related to the development and progression of LN.

Conclusion

C1QA, C1QB, MX1, RORC, CD177, DEFA4 and HERC5 could be used as new candidate molecular markers for LN. It may provide new insights into the diagnosis and molecular treatment of LN in the future.