Transcriptome meta-analysis and validation to discovery of hub genes and pathways in focal and segmental glomerulosclerosis

Abstract

Background: Focal segmental glomerulosclerosis (FSGS), a histologic pattern of injury in the glomerulus, is one of the leading glomerular causes of end-stage renal disease (ESRD) worldwide. Despite extensive research, the underlying biological alterations causing FSGS remain poorly understood. Studying variations in gene expression profiles offers a promising approach to gaining a comprehensive understanding of FSGS molecular pathogenicity and identifying key elements as potential therapeutic targets. This work is a meta-analysis of gene expression profiles from glomerular samples of FSGS patients. The main aims of this study are to establish a consensus list of differentially expressed genes in FSGS, validate these findings, understand the disease's pathogenicity, and identify novel therapeutic targets.

Methods: After a thorough search in the GEO database and subsequent quality control assessments, seven gene expression datasets were selected for the meta-analysis: GSE47183 (GPL14663), GSE47183 (GPL11670), GSE99340, GSE108109, GSE121233, GSE129973, and GSE104948. The random effect size method was applied to identify differentially expressed genes (meta-DEGs), which were then used to construct a regulatory network (STRING, MiRTarBase, and TRRUST) and perform various pathway enrichment analyses. The expression levels of several meta-DEGs, specifically ADAMTS1, PF4, EGR1, and EGF, known as angiogenesis regulators, were analyzed using quantitative reverse transcription polymerase chain reaction (RT-qPCR).

Results: The identified 2,898 meta-DEGs, including 665 downregulated and 669 upregulated genes, were subjected to various analyses. A co-regulatory network comprising 2,859 DEGs, 2,688 microRNAs (miRNAs), and 374 transcription factors (TFs) was constructed, and the top molecules in the network were identified based on degree centrality. Part of the pathway enrichment analysis revealed significant disruption in the angiogenesis regulatory pathways in the FSGS kidney. The RT-qPCR results confirmed an imbalance in angiogenesis pathways by demonstrating the differential expression levels of ADAMTS1 and EGR1, two key angiogenesis regulators, in the FSGS condition.

Conclusion: In addition to presenting a consensus list of differentially expressed genes in FSGS, this meta-analysis identified significant distortions in angiogenesis-related pathways and factors in the FSGS kidney. Targeting these factors may offer a viable strategy to impede the progression of FSGS.

Keywords: Angiogenesis; Focal and segmental glomerulosclerosis; Meta-analysis; Therapeutic target; Transcriptome.

Fig. 1

Flow diagram representing different steps of the present meta-analysis

Fig. 2

Flow diagram of data selection. Different steps of microarray dataset selection including identification, screening, and eligibility are shown in this flow diagram

Fig. 3

Data preprocessing and meta-analysis; (A) PCA plots before and after batch effect removal showing the differences and similarities between the samples and density plots against log2 of read counts, before and after batch effect removal depicting the relative distribution of different counts in each group. (B) The UpSet diagram representing the intersection of DEGs in datasets. (C) The heatmap of top 50 DEGs according to the adjusted p-value

Fig. 4

The network of connected significant enriched biological process terms for the FSGS meta-DEGs. Each node represents a GO biological process, colored to indicate GO groups. Edges between nodes signify relationships based on the similarity of associated genes

Fig. 5

(A) The PPI network of FSGS meta-DEGs. The square includes all the DEGs involved in the regulation of angiogenesis. The DEGs in the small rectangle are the top 20 DEGs based on degree centrality in the network. Interestingly, most of the top DEGs (based on degree centrality) are involved in angiogenesis regulation. The labeled (star) DEGs were selected for expressional analysis in the FSGS tissue samples. (B) The expression analysis results of 4 involved DEGs in the angiogenesis. ADAMTS1 and EGR1 showed a significant dysregulation in the FSGS samples