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Fig. 4 | Journal of Translational Medicine

Fig. 4

From: Incorporating knowledge of disease-defining hub genes and regulatory network into a machine learning-based model for predicting treatment response in lupus nephritis after the first renal flare

Fig. 4

Regulatory-level activity specific for LN. A Box plots show differences in ssGSEA scores between LN samples and controls in tissues (GSE32591 and GSE112943) and peripheral blood (GSE81622 and GSE72326). Wilcoxon’s rank sum test P values are shown. B Box plots show differences in ssGSEA scores between LN and other chronic renal diseases in GSE60861 (upper panel) and GSE69438 (lower panel). Wilcoxon’s rank sum test P values are shown between LN and other renal diseases. DM: diabetes mellitus. FSGN: focal segmental glomerulonephritis. HTN: hypertension. MCD: minimal change disease. MN: membranous nephropathy. VL: vasculitis. CKD: chronic kidney disease. MGN: membranous glomerulonephritis. C Heatmap of correlation coefficients calculated between the ssGSEA scores of hub genes and immunology panel genes in tissue (GSE32591 and GSE112943) and blood samples (GSE81622 and GSE72326). GSE32591 is divided according to tissue origins to evaluate the expression patterns in two tissue compartments. Hierarchical clustering based on the K-means method is performed. Color bar indicates Spearman’s correlation coefficient. Cluster numbers are shown on the right. DDC: disease-defining cluster. D Dot plots show enriched GO terms (biological process and molecular function) and KEGG pathways for each eight cluster. –Log10-transformed multiple testing-adjusted P value is shown for each enriched term. E Pie plots reveal distribution of DEGs between responders and non-responders in GSE200306 according to the DDCs in renal biopsies obtained at first RF (left panel) and after treatment (right panel). DDC with the largest percentage is annotated. The percentages of genes up-regulated in non-responders are shown in this DDC

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