ArticleScientific reports2024
Integrative analysis of multi-omics data reveals importance of collagen and the PI3K AKT signalling pathway in CAKUT.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multi-organ single-cell analysis of preferential expression of CAKUT genes.BMC nephrology · 2026Article
- Coordinated DNA methyltransferase 3A and methyltransferase-like 7A activity reprograms the tumor microenvironment through discoidin domain receptor 1 signaling.Cancer biology & medicine · 2026Article
- From Genes to Malformations: Molecular Mechanisms Driving the Pathogenesis of Congenital Anomalies of the Kidney and Urinary Tract.International journal of molecular sciences · 2025Review
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15 authors.
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Abstract
Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) is the leading cause of childhood chronic kidney failure and a significant cause of chronic kidney disease in adults. Genetic and environmental factors are known to influence CAKUT development, but the currently known disease mechanism remains incomplete. Our goal is to identify affected pathways and networks in CAKUT, and thereby aid in getting a better understanding of its pathophysiology. With this goal, the miRNome, peptidome, and proteome of over 30 amniotic fluid samples of patients with non-severe CAKUT was compared to patients with severe CAKUT. These omics data sets were made findable, accessible, interoperable, and reusable (FAIR) to facilitate their integration with external data resources. Furthermore, we analysed and integrated the omics data sets using three different bioinformatics strategies: integrative analysis with mixOmics, joint dimensionality reduction and pathway analysis. The three bioinformatics analyses provided complementary features, but all pointed towards an important role for collagen in CAKUT development and the PI3K-AKT signalling pathway. Additionally, several key genes (CSF1, IGF2, ITGB1, and RAC1) and microRNAs were identified. We published the three analysis strategies as containerized workflows. These workflows can be applied to other FAIR data sets and help gaining knowledge on other rare diseases.
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