# Identification and Verification of Biomarkers Related to Polyamine Metabolism in Diabetic Nephropathy

## TL;DR

This study identifies three genes linked to polyamine metabolism that may serve as biomarkers for diabetic nephropathy, offering new insights for diagnosis and treatment.

## Contribution

The study identifies and experimentally verifies novel biomarkers (KAZALD1, GLCE, RPRD1B) related to polyamine metabolism in diabetic nephropathy.

## Key findings

- KAZALD1, GLCE, and RPRD1B were identified as potential biomarkers with high diagnostic accuracy (AUC > 0.8).
- The biomarkers are involved in pathways like amino acid degradation and immune cell interactions.
- The biomarkers were confirmed in clinical samples using RT-qPCR and IHC.

## Abstract

Kidney damage in chronic kidney disease patients is affected by the degradation products of polyamines. However, the effect of polyamine metabolism–related genes (PM‐RGs) in diabetic nephropathy (DN) is not clear. The objective of this study is to elucidate the potential correlation between PM‐RGs and DN.

DN‐related datasets and 59 PM‐RGs were obtained from the public database. Then, Differentially Expressed Gene 1 (DEG 1) related to DN in GSE142153 and DEG 2 related to PM‐RGs were crossed to obtain intersection genes. The gene with the same expression trend in DEG 3 obtained in GSE185011 and DEG 1 was overlapped with the intersection gene to obtain the candidate genes. Thereafter, two machine learning algorithms and ROC curves were adopted to select biomarkers. Moreover, enrichment analysis, immune infiltration analysis, and drug prediction were implemented to further study the biomarkers. Finally, the expressions of biomarkers were analyzed in clinical samples assessed by RT‐qPCR and IHC.

KAZALD1, GLCE, and RPRD1B were identified as biomarkers for DN, with their area under the curve values being greater than 0.8. They were involved in multiple biological pathways, such as valine, leucine, and isoleucine degradation, cytokine–cytokine receptor interaction, and peroxisome. Furthermore, immune cells were found to correlate with biomarkers. For instance, the expression of KAZALD1 and RPRD1B showed positive correlations with naive CD8 T cells and M1 macrophages among other immune cells while exhibiting negative correlations with CD8 T cells, B cells, T helper cells, and others. Additionally, based on three biomarkers, 11 drugs (benzopyrene, Bisphenol A, ethinyl estradiol, etc.) were predicted. KAZALD1 and RPRD1B were notably highly expressed in clinical DN samples in RT‐qPCR and IHC.

The research pinpointed KAZALD1, GLCE, and RPRD1B as biomarkers for DN, offering a novel target reference for diagnosing and treating DN.