Bioinformatics analysis identifies potential ferroptosis key genes in the pathogenesis of diabetic peripheral neuropathy

Ming Tian; Jin Yong Zhi; Fan Pan; Yong Zhu Chen; Ai Zhong Wang; Hui Ying Jia; Rong Huang; Wen Hui Zhong

doi:10.3389/fendo.2023.1048856

Bioinformatics analysis identifies potential ferroptosis key genes in the pathogenesis of diabetic peripheral neuropathy

Front Endocrinol (Lausanne). 2023 May 12:14:1048856. doi: 10.3389/fendo.2023.1048856. eCollection 2023.

Authors

Ming Tian¹, Jin Yong Zhi², Fan Pan³, Yong Zhu Chen³, Ai Zhong Wang³, Hui Ying Jia^{4

5

6}, Rong Huang², Wen Hui Zhong³

Affiliations

¹ Burns Department, Shanghai Jiao Tong University Affiliated Ruijin Hospital, Shanghai, China.
² Department of General Surgery, Putuo Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China.
³ Department of Anesthesiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
⁴ Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁵ Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the People's Republic (PR) China, Shanghai, China.
⁶ Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Background: Diabetic peripheral neuropathy (DPN) is a serious complication in Diabetes Mellitus (DM) patients and the underlying mechanism is yet unclear. Ferroptosis has been recently intensively researched as a key process in the pathogenesis of diabetes but there yet has been no related bioinformatics-based studies in the context of DPN.

Methods: We used data mining and data analysis techniques to screen differentially expressed genes (DEGs) and immune cell content in patients with DPN, DM patients and healthy participants (dataset GSE95849). These DEGs were then intersected with the ferroptosis dataset (FerrDb) to obtain ferroptosis DEGs and the associated key molecules and miRNAs interactions were predicted.

Results: A total of 33 ferroptosis DEGs were obtained. Functional pathway enrichment analysis revealed 127 significantly related biological processes, 10 cellular components, 3 molecular functions and 30 KEGG signal pathways. The biological processes that were significantly enriched were in response to extracellular stimulus and oxidative stress. Key modules constructed by the protein-protein interaction network analysis led to the confirmation of the following genes of interest: DCAF7, GABARAPL1, ACSL4, SESN2 and RB1. Further miRNA interaction prediction revealed the possible involvement of miRNAs such as miR108b-8p, miR34a-5p, mir15b-5p, miR-5838-5p, miR-192-5p, miR-222-3p and miR-23c. Immune-environment content of samples between DM and DPN patients revealed significant difference in the levels of endothelial cells and fibroblasts, which further speculates their possible involvement in the pathogenesis of DPN.

Conclusion: Our findings could provide insight for investigations about the role of ferroptosis in the development of DPN.

Keywords: bioinformatics; diabetic peripheral neuropathy; ferroptosis; immune-environment; oxidative stress.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology
Diabetes Mellitus*
Diabetic Neuropathies* / genetics
Endothelial Cells
Ferroptosis* / genetics
Humans
MicroRNAs* / genetics
Sestrins

Substances

MicroRNAs
SESN2 protein, human
Sestrins

Grants and funding

This study has been supported by the following funds: (1) Pudong New Area Science and Technology Development Fund (PKJ2020-Y10) (2) Shanghai Sixth People's Hospital Scientific research Fund (YNXG202212) (3) National Natural Science Foundation of China No.81670752 (4) Putuo District Clinical Characteristic Special Disease Construction Project- Lower Limbs Arteriosclerosis obliterans (2020TZSB01).