Hub biomarkers and their clinical relevance in glycometabolic disorders: A comprehensive bioinformatics and machine learning approach.
10.1097/CM9.0000000000003525
- Author:
Liping XIANG
1
;
Bing ZHOU
1
;
Yunchen LUO
2
;
Hanqi BI
3
;
Yan LU
3
;
Jian ZHOU
1
Author Information
1. Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China.
2. Department of Endocrinology and Metabolism, Shanghai General Hospital Affiliated to Shanghai Jiao Tong University, Shanghai 200080, China.
3. Institute of Metabolism and Regenerative Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China.
- Publication Type:Journal Article
- Keywords:
Biomarkers;
Cytokines;
Gluconeogenesis;
Immune infiltration;
Oxidative stress
- MeSH:
Machine Learning;
Humans;
Computational Biology/methods*;
Biomarkers/metabolism*;
Diabetes Mellitus, Type 2/genetics*;
Animals;
Mice;
Gluconeogenesis/physiology*;
Gene Expression Profiling;
Transcriptome/genetics*;
Gene Regulatory Networks/genetics*;
Clinical Relevance
- From:
Chinese Medical Journal
2025;138(16):2016-2027
- CountryChina
- Language:English
-
Abstract:
BACKGROUND:Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic disorders. This study aimed to identify hub biomarkers of these disorders to provide a theoretical foundation for enhancing diagnosis and treatment.
METHODS:Gene expression profiles from liver tissues of three well-characterized gluconeogenesis mouse models were analyzed to identify commonly differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA), machine learning techniques, and diagnostic tests on transcriptome data from publicly available datasets of type 2 diabetes mellitus (T2DM) patients were employed to assess the clinical relevance of these DEGs. Subsequently, we identified hub biomarkers associated with gluconeogenesis-related glycometabolic disorders, investigated potential correlations with immune cell types, and validated expression using quantitative polymerase chain reaction in the mouse models.
RESULTS:Only a few common DEGs were observed in gluconeogenesis-related glycometabolic disorders across different contributing factors. However, these DEGs were consistently associated with cytokine regulation and oxidative stress (OS). Enrichment analysis highlighted significant alterations in terms related to cytokines and OS. Importantly, osteomodulin ( OMD ), apolipoprotein A4 ( APOA4 ), and insulin like growth factor binding protein 6 ( IGFBP6 ) were identified with potential clinical significance in T2DM patients. These genes demonstrated robust diagnostic performance in T2DM cohorts and were positively correlated with resting dendritic cells.
CONCLUSIONS:Gluconeogenesis-related glycometabolic disorders exhibit considerable heterogeneity, yet changes in cytokine regulation and OS are universally present. OMD , APOA4 , and IGFBP6 may serve as hub biomarkers for gluconeogenesis-related glycometabolic disorders.
