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ORIGINAL RESEARCH article

Front. Endocrinol.

Sec. Systems Endocrinology

Volume 16 - 2025 | doi: 10.3389/fendo.2025.1570332

This article is part of the Research TopicIntegrated Diagnostics and Biomarker Discovery in Endocrinology and Biomedical Sciences: Volume IIView all 7 articles

The biological and immunological significance of the estrogen-related gene IER3 in diabetes

Provisionally accepted
Da  KeDa KeXian  HeXian HeWenzhe  LiWenzhe LiHongyan  WuHongyan WuYaling  SunYaling Sun*Jie  TanJie Tan*Ya  WangYa Wang*
  • The First Affiliated Hospital of Yangtze University, Jingzhou, China

The final, formatted version of the article will be published soon.

Background: Diabetes Mellitus (DM) is a complex metabolic disorder characterized by hyperglycemia, primarily arising from insufficient insulin secretion or the development of insulin resistance. Estrogen plays a significant role in regulating the occurrence and progression of DM. This study aims to investigate the role of estrogen-related genes in diabetes, focusing on identifying potential biomarkers and therapeutic targets for the disease.Methods: We initially obtained gene expression datasets related to type 2 diabetes mellitus (T2DM) from the GEO database. A systematic and coherent series of methodologies was then implemented in a structured manner. First, Principal Component Analysis (PCA) was employed for preliminary data exploration and dimensionality reduction. Next, we identified Differentially Expressed Genes (DEGs). Subsequently, we conducted Weighted Gene Co-expression Network Analysis (WGCNA) to uncover gene modules associated with DM. This was followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the biological functions and pathways associated with the identified genes. To enhance the precision of biomarker identification, we applied three distinct machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF), for further refined selection. This comprehensive approach ultimately identified the estrogen-related gene IER3 as a promising biomarker for DM. Furthermore, correlation analyses focusing on immune cell infiltration were conducted to clarify the immunological role of IER3 in DM.Our findings revealed a significant downregulation of IER3 in DM patients, accompanied by an AUC value of 0.723 in the diagnostic curve ROC, indicating its considerable diagnostic and prognostic potential for DM. Furthermore, the expression levels of IER3 exhibited a strong correlation with variations in the proportions of diverse immune cell types, suggesting that it may play a pivotal role in the immunoregulatory mechanisms underlying DM.In conclusion, our findings reveal that the estrogen-related gene IER3 is significantly downregulated in patients with DM, highlighting its potential as a diagnostic and prognostic marker for the disease. Therefore, IER3 may serve as a promising biomarker and therapeutic target for DM.

Keywords: Diabetes Mellitus, Glycometabolism, estrogen, Bioinformatics analysis, machine learning, Ier3

Received: 03 Feb 2025; Accepted: 18 Aug 2025.

Copyright: © 2025 Ke, He, Li, Wu, Sun, Tan and Wang. 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) or licensor 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.

* Correspondence:
Yaling Sun, The First Affiliated Hospital of Yangtze University, Jingzhou, China
Jie Tan, The First Affiliated Hospital of Yangtze University, Jingzhou, China
Ya Wang, The First Affiliated Hospital of Yangtze University, Jingzhou, China

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