Multi-method Machine-Learning Identification of IL11 as a Key Gene in Kidney Stone Disease: Mechanistic Clues and Diagnostic Potential.
- 1Urology, Guilin Hospital of Traditional Chinese Medicine, Guilin, CHN.
Abstract
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.
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BibTeX
@article{yu2026multimethod,
title = {{Multi-method Machine-Learning Identification of IL11 as a Key Gene in Kidney Stone Disease: Mechanistic Clues and Diagnostic Potential.}},
author = {Wenlong Yu and Lei Xie and Jiajie Zhang and Yongqiang Liang},
journal = {Cureus},
year = {2026},
month = sep,
doi = {10.7759/cureus.116828},
url = {https://europepmc.org/article/MED/42927360},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multi-method Machine-Learning Identification of IL11 as a Key Gene in Kidney Stone Disease: Mechanistic Clues and Diagnostic Potential.
AU - Wenlong Yu
AU - Lei Xie
AU - Jiajie Zhang
AU - Yongqiang Liang
PY - 2026
DA - 2026/09/24
JO - Cureus
DO - 10.7759/cureus.116828
UR - https://europepmc.org/article/MED/42927360
AB - 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.
SN - 2168-8184
ER -