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Identification of AKI signatures and classification patterns in ccRCC based on machine learning
Li Wang1, Fei Peng2, Zhen Hua Li3
1Department of Nephrology, Changzheng Hospital, Naval Medical University, Shanghai, China.
Frontiers in Medicine
|June 9, 2023
Summary
This study identifies seven novel biomarkers for early acute kidney injury (AKI) prediction using machine learning, offering a new nomogram for risk stratification and insights into AKI's role in clear cell renal cell carcinoma (ccRCC) prognosis.
Area of Science:
- Biomarkers and Machine Learning
- Renal Cell Carcinoma Research
- Immunology and Oncology
Background:
- Early detection of acute kidney injury (AKI) is crucial for mitigation, yet predictive biomarkers are limited.
- The interplay between AKI and clear cell renal cell carcinoma (ccRCC) requires further elucidation.
- Novel biomarkers are needed to improve AKI prediction and understand its relationship with ccRCC.
Purpose of the Study:
- To identify novel biomarkers for predicting AKI using machine learning algorithms.
- To investigate the correlation between AKI biomarkers and ccRCC subtypes.
- To develop a predictive model for AKI risk stratification.
Main Methods:
- Downloaded and analyzed four public AKI datasets (GSE126805, GSE139061, GSE30718, GSE90861) and one validation dataset (GSE43974) from the Gene Expression Omnibus (GEO) database.
- Utilized the R package limma to identify differentially expressed genes (DEGs) between AKI and normal kidney tissues.
- Employed four machine learning algorithms to identify novel AKI biomarkers and calculated correlations with immune cells using ggcor.
- Identified and verified two distinct ccRCC subtypes based on AKI signatures.
Main Results:
- Identified seven robust AKI signatures using four machine learning methods.
- Revealed significantly higher immune cell infiltration in the AKI cluster, including CD4 T cells, natural killer cells, and neutrophils.
- Developed a nomogram for AKI risk prediction with high discrimination (AUC 0.919 training, 0.945 testing) and good calibration.
- Demonstrated that AKI signatures could differentiate ccRCC subtypes (CS1) with improved survival and drug sensitivity.
Conclusions:
- Successfully identified seven AKI-related biomarkers and developed a nomogram for stratified AKI risk prediction.
- Confirmed the utility of AKI signatures in predicting ccRCC prognosis and patient outcomes.
- Provided novel insights into the early prediction of AKI and its association with ccRCC, highlighting potential therapeutic targets.
Keywords:
acute kidney injuryclear cell renal cell carcinomaimmunitymachine learningmolecular subtypesMore Related Videos
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