Front. Endocrinol. Frontiers in Endocrinology Front. Endocrinol. 1664-2392 Frontiers Media S.A. 10.3389/fendo.2025.1732027 Correction Correction: Bioinformatic analysis identifies LPL as a critical gene in diabetic kidney disease via lipoprotein metabolism DongQian † Conceptualization Data curation Formal analysis Supervision Writing – original draft Writing – review & editing XuHuan † Conceptualization Data curation Formal analysis Investigation Methodology Writing – original draft Writing – review & editing XuPengjie Formal analysis Investigation Software Visualization Writing – review & editing LiuJiang Formal analysis Resources Validation Writing – review & editing ShenZhouji * Conceptualization Funding acquisition Methodology Project administration Writing – review & editing Department of Nephrology, The Affiliated Lihuili Hospital of Ningbo University, Ningbo, China Correspondence: Zhouji Shen, lhlshenzhouji@nbu.edu.cn

These authors have contributed equally to this work

 14 11 2025 2025 16 1732027 25 10 2025 29 10 2025 Copyright © 2025 Dong, Xu, Xu, Liu and Shen. 2025 Dong, Xu, Xu, Liu and Shen https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. A Correction on Bioinformatic analysis identifies LPL as a critical gene in diabetic kidney disease via lipoprotein metabolism By Dong Q, Xu H, Xu P, Liu J and Shen Z (2025) Front. Endocrinol. 16:1620032. doi: 10.3389/fendo.2025.1620032 diabetic kidney disease lipoprotein lipase immune cell infiltration lipid metabolism bioinformatic section-at-acceptance Renal Endocrinology

The Figure 5 caption were in the wrong order. The labels for panels (A) and (B) have been swapped. The order has now been corrected.

Consensus clustering analysis of five hub genes in DKD patients. (A) Cumulative Distribution Function (CDF) curve for consensus clustering with different values of k. (B) Consensus matrix for clustering, showing optimal stability at k=2, resulting in two molecular subtypes (Cluster A and Cluster B) based on the expression of hub genes. (C) Expression levels of the hub genes (LPL, BCAM, SERPINE2, GCNT3, and CTNNBIP1) in the two identified subtypes. ***P<0.001. (D) Heatmap of gene expression between Cluster A and Cluster B **P<0.01.

(A) Line graph showing consensus cumulative distribution functions (CDF) for clusters 2 through 9, indicating cluster stability. (B) Heat map and dendrogram of the consensus matrix for k=2, displaying two distinct clusters. (C) Box plots comparing gene expression levels of five genes between Cluster A and Cluster B, with significant differences marked by asterisks. (D) Heat map illustrating gene expression patterns across samples divided into Cluster A and Cluster B with a dendrogram on the side.

The error made was a directional error in the interpretation of the statistical result for the gene CTNNBIP1.

A correction has been made to the section Results, Consensus cluster analysis, Paragraph 2:

“There were significant differences in the expression levels of core genes between the two subtypes (Figures 5C, D). Specifically, compared to Cluster B, Cluster A exhibited significantly reduced expression of LPL (P < 0.01) and CTNNBIP1 (p < 0.001), along with lower expression of BCAM. In contrast, Cluster A showed significantly elevated expression of SERPINE2 (p < 0.01) and higher expression of GCNT3.”

The pathway names and their corresponding P-values have been corrected to match the data presented in Table 1.

A correction has been made to the section Results, Hub gene enrichment analysis, Paragraph 1:

“The results showed that LPL was significantly associated with Cholesterol metabolism (P = 0.012), Glycerolipid metabolism (P = 0.015) and PPAR signaling pathway (P = 0.018, Table 1). These findings suggest that LPL may play a crucial role in lipid metabolism disorders, and through regulating lipidmetabolism and inflammatory responses, it could contribute to the progression of DKD.”

The original version of this article has been updated.

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Edited and reviewed by: Yao-Wu Liu, Xuzhou Medical University, China