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Identification of Crucial Degs and Hub Genes in Focal Segmental Glomerulosclerosis: A Bioinformatics Study

Author(s): Bhuvnesh Rai, Prabhakar Mishra, Mehar Hasan Asif and Swasti Tiwari

Aims

In individuals with focal segmental glomerulosclerosis, identify the important differentially expressed genes (DEGs) relative to healthy control, in kidney tissue samples (glomeruli and tubulointerstitium tissue) and examine their probable role in the molecular mechanism and pathogenesis process of disease.

Methods

From the Gene Expression Omnibus (GEO) database, raw microarray data generated from kidney tissues from focal segmental glomerulosclerosis patients, and healthy controls patients (GSE121233, GSE125779, GSE129973) were retrieved. Transcription analysis console 4.0 was used to identify DEGs. FUNRICH (Functional enrichment analysis tools) and Enrichr were used to perform functional gene enrichment analysis. Then, Search Tool for Retrieval Interacting Genes (STRING) 10.0 for PPI analysis and cyto scape's for network visualization was used. Further hub genes were identified using the cytohubbaalogorithm plug-in. Then KEGG and REACTOME databases were integrated with Shiny Go and FUNRICH to perform pathway analysis. We used GSEA analysis and associated pathway enrichment by metascape analysis utilizing the molecular complex identification (MCODE) algorithm to discover densely linked network components to further elucidate the likely mechanism of action of related genes in FSGS. The MCODE networks identified for individual gene lists have been gathered. Pathway and process enrichment analysis has been applied to each MCODE component independently, and the three best-scoring terms by p-value have been retained as the functional description of the corresponding components, shown in the tables underneath corresponding network plots further cross validation was done by ORA (over representation analysis) for the commonly (up-regulated) genes including hubgene from all three datasets was done by webgestalt. Finally, we check the raw expression level of all up regulated DEGs, including hub genes, in one main data set (GSE121233) and one validation dataset (GSE129973) to validate the expression of genes identified. We also checked the gene expression level in ERCB (RNA-seq) datasets for various kidney diseases with diabetes including FSGS and other demographic parameters such as GFR and proteinuria, as well as gene expression in gender by using nephroseq.

Results

85 DEGs were co-expressed in the three datasets, out of which 16 genes were up-regulated and 69 genes were down regulated in FSGS. DEGs are mostly involved in extracellular matrix organization (biological process), extracellular matrix proteoglycans and integrins cell surface interaction (biological pathways). Protein–protein interaction (PPI) network of 16 upregulated degs in FSGS, identified 43 co expressed genes out of which 20 genes as hub gene where identified by cytohubba, four high ranked hubgenes with greater degree of interaction were (Fibronectin-1, Complement C3 & collagen, type IV, alpha 1, Integrin β6). Metascape analysis results suggests extracellular matrix organization and the genes involved to regulated this biological process are (COL4A1, FN1, ITGB6, LUM, ADAMTS1) shows highest interactions with ECM proteoglycans regulated by genes (COL4A1, FN1, ITGB6, LUM) and integrin mediated signaling pathways regulated by genes (COL4A1, FN1, ITGB6, LUM, ADAMTS1) with enrichment score 134 and 120 with p value p<0.01 and z score 21 and 14. Over representation analysis suggests FN1, C3, ITGB6, COL4A1, C7 and LUMgene is enriched in above stated pathways identified with enrichment ratio more than 50% with p value P<0.0001, GSEA suggests higher expression of genes in these pathways. Three datasets showed the same expression in average as well as raw signals, of upregulated genes including hub gene (LUM,ABCG1, ADAMTS1, C3, MIR4521, PIGR, FN1, COL4A1, MYOF, ITGB6, CLDN1, REN, CDH6, C7, HAVCR1, NR1D1) further each identified genes are checked in nephroseq database at RNA-seq levels for different demographics and other diabetes associated diseases, shown by box plot and bar graph as in our bioinformatics analysis.

Conclusion

In FSGS, bioinformatics analysis revealed 16 upregulated genes, including 11 crucial genes (HAVCR1, COL4A1, ABCG1, C3, ITGB6, LUM, MYOF, PIGR, C7, FN1, ADAMTS1) and 4 hub-genes (FN1, C3, ITGB6, COL4A1) with high rank and high degree of interaction from three FSGS datasets included in the study, indicating their involvement in FSGS pathways and suggesting they could be potential targets in the disease molecular mechanism.

    Editor In Chief

    Jean-Marie Exbrayat

  • General Biology-Reproduction and Comparative Development,
    Lyon Catholic University (UCLy),
    Ecole Pratique des Hautes Etudes,
    Lyon, France

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