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Updated: Oct 26, 2026

Quantitative Real-Time PCR Evaluation of microRNA Expressions in Mouse Kidney with Unilateral Ureteral Obstruction
Published on: August 27, 2020
Multi-method Machine-Learning Identification of IL11 as a Key Gene in Kidney Stone Disease: Mechanistic Clues and
Wenlong Yu1, Lei Xie1, Jiajie Zhang1
1Urology, Guilin Hospital of Traditional Chinese Medicine, Guilin, CHN.
Background:
Randall's plaque (RP) is a key initiating lesion of idiopathic calcium stones at the renal papilla, and transcriptomic profiling of human RP has revealed inflammation- and matrix-remodeling-related changes, including upregulation of interleukin 11 (IL11). However, under a pure kidney-stone phenotype, a rigorous framework combining multi-algorithm consensus screening with fuller diagnostic appraisal (calibration, internal validation, and decision-curve analysis (DCA)) remains needed.
Methods:
GSE73680 (RP, n=29; normal papilla, n=33) was obtained from the Gene Expression Omnibus (GEO). Exploratory differential expression (limma) and weighted gene co-expression network analysis (WGCNA) were intersected, followed by Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) protein-protein interaction (PPI) analysis. Consensus genes were defined by five feature-selection algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), random forest, XGBoost, and Boruta. For IL11, we assessed expression, built a penalized logistic nomogram with receiver operating characteristic (ROC), calibration, and patient-blocked internal validation, and performed Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) deconvolution and correlation-ranked KEGG gene set enrichment analysis (GSEA).
Results:
The intersection of 39 exploratory differentially expressed genes (DEGs) with the WGCNA green module yielded 23 candidates, of which IL11 was the sole five-method consensus gene and was significantly upregulated in RP (Wilcoxon P=8.9×10⁻⁵). The single-gene model achieved an apparent area under the curve (AUC) of 0.782 (95% confidence interval (CI) 0.663-0.901) and an out-of-bag (OOB) AUC of 0.756. PPI linked IL11 to SPP1 and TNFRSF11B, and GSEA enriched lysosome, cell adhesion molecules, and leukocyte transendothelial migration pathways.
Conclusions:
Building on prior studies, this work strengthens the evidence chain for IL11 as a priority target under a pure kidney-stone/RP phenotype through five-algorithm consensus, complete diagnostic appraisal, and adhesion/migration-related mechanistic clues.