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Gene expression fingerprints in human tubulointerstitial inflammation and fibrosis as prognostic markers of disease progression

2004; Elsevier BV; Volume: 65; Issue: 3 Linguagem: Inglês

10.1111/j.1523-1755.2004.00499.x

1523-1755

Anna Henger, Matthias Kretzler, Peter Doran, Mahnaz Bonrouhi, Holger Schmid, Éva Kiss, Clemens D. Cohen, Stephen F. Madden, Štefan Porubský, Elisabeth Gröne, Detlef Schlöndorff, Peter J. Nelson, Hermann-Josef Gröne,

Advanced Biosensing Techniques and Applications

Gene expression fingerprints in human tubulointerstitial inflammation and fibrosis as prognostic markers of disease progression.BackgroundGene expression profiling of nephropathies may facilitate development of diagnostic strategies for complex renal diseases as well as provide insight into the molecular pathogenesis of kidney diseases. To test molecular based renal disease categorization, differential gene expression profiles were compared between control and hydronephrotic kidneys showing varying degrees of inflammation and fibrosis.MethodsRNA expression profiles from 9 hydronephrotic and 3 control kidneys were analyzed using small macroarrays dedicated to genes involved in cell-cell contact, matrix turnover, and inflammation. In parallel, the degree of tubulointerstitial inflammation, fibrosis, and tubular atrophy using light microscopy and quantitative immunohistochemical parameters was determined.ResultsHierarchic clustering and self-organizing maps led to a gene expression dendrogram with three distinct nodes representing the control group, four kidneys with high inflammation, and five kidneys giving high fibrosis scores. To evaluate the clinical applicability of the marker set, the expression of nine genes (6Ckine, IL-8, MMP-9, MMP-3, MMP-7, urokinase R, CXCR5, integrin-β4, and pleiotrophin) was tested in tubulointerstitial samples from routine renal biopsies. Seven mRNA markers showed differential regulation in inflammation and fibrosis in the biopsy population. Clinical follow-up revealed stringent correlation between gene expression data and progression of renal disease, and allowed segregation of the biopsies into progressive or stable disease course based on gene expression profiles.ConclusionThis study suggests the feasibility of gene expression–based disease categorization in human nephropathies based on the extraction of marker gene sets. Gene expression fingerprints in human tubulointerstitial inflammation and fibrosis as prognostic markers of disease progression. Gene expression profiling of nephropathies may facilitate development of diagnostic strategies for complex renal diseases as well as provide insight into the molecular pathogenesis of kidney diseases. To test molecular based renal disease categorization, differential gene expression profiles were compared between control and hydronephrotic kidneys showing varying degrees of inflammation and fibrosis. RNA expression profiles from 9 hydronephrotic and 3 control kidneys were analyzed using small macroarrays dedicated to genes involved in cell-cell contact, matrix turnover, and inflammation. In parallel, the degree of tubulointerstitial inflammation, fibrosis, and tubular atrophy using light microscopy and quantitative immunohistochemical parameters was determined. Hierarchic clustering and self-organizing maps led to a gene expression dendrogram with three distinct nodes representing the control group, four kidneys with high inflammation, and five kidneys giving high fibrosis scores. To evaluate the clinical applicability of the marker set, the expression of nine genes (6Ckine, IL-8, MMP-9, MMP-3, MMP-7, urokinase R, CXCR5, integrin-β4, and pleiotrophin) was tested in tubulointerstitial samples from routine renal biopsies. Seven mRNA markers showed differential regulation in inflammation and fibrosis in the biopsy population. Clinical follow-up revealed stringent correlation between gene expression data and progression of renal disease, and allowed segregation of the biopsies into progressive or stable disease course based on gene expression profiles. This study suggests the feasibility of gene expression–based disease categorization in human nephropathies based on the extraction of marker gene sets.