loading page

Identification of Ferroptosis-Related Genes in Type 2 Diabetes Mellitus Based on the Machine Learning
  • +3
  • Sen Wang,
  • yongpan lu,
  • Yixin Zhang,
  • Yuli Zhao,
  • Huimin Guo,
  • Li Feng
Sen Wang
Shandong Provincial Qianfoshan Hospital
Author Profile
yongpan lu
Shandong Provincial Qianfoshan Hospital
Author Profile
Yixin Zhang
Shandong Provincial Qianfoshan Hospital
Author Profile
Yuli Zhao
Shandong Provincial Qianfoshan Hospital
Author Profile
Huimin Guo
Shandong Provincial Qianfoshan Hospital
Author Profile
Li Feng
Shandong Provincial Qianfoshan Hospital

Corresponding Author:qfsfengli@163.com

Author Profile

Abstract

Background: Type 2 diabetes mellitus (T2DM), which has a high incidence and several harmful consequences, poses a severe danger to human health. More research is being done on ferroptosis’ function in T2DM. This study uses a bioinformatics technique to look for new diagnostic T2DM biomarkers associated with ferroptosis. Methods: In order to identify ferroptosis-related genes (DEGs) that are differently expressed between T2DM patients and healthy individuals, we first obtained T2DM sequencing data and ferroptosis-related genes (FRGs) from the Gene Expression Omnibus (GEO) database and FerrDb database. Then, drug-gene interaction networks and ceRNA networks linked to the marker genes were built after marker genes were filtered by two machine learning algorithms (LASSO and SVM-RFE algorithms). Finally, to confirm the expression of marker genes, the GSE76895 dataset was utilized. The protein expression of some marker genes between T2DM and non-diabetic tissues was also examined by Western Blotting, Immunohistochemistry (IHC) and Immunofluorescence (IF), respectively. Results: We obtained 58 DEGs associated with ferroptosis. GO and KEGG enrichment analysis showed that these DGEs were significantly enriched in hypoxia and ferroptosis. Subsequently, eight marker genes (SCD, CD44, HIF1A, BCAT2, MTF1, HILPDA, NR1D2 and MYCN) were screened by LASSO and SVM- RFE machine learning algorithms, and a model was constructed based on these eight genes. These newly discovered marker genes may be linked to alterations in the immune microenvironment in T2DM patients. In addition, based on these 8 genes, we obtained 48 drugs and a complex ceRNA network map. Finally, Western Blotting, IHC and IF results of clinical samples further confirmed the results of public databases. Conclusions: The diagnosis and etiology of T2DM can be greatly aided by eight ferroptosis-related genes, opening up novel therapeutic avenues.
10 Jun 2023Submitted to Immunity, Inflammation and Disease
13 Jun 2023Submission Checks Completed
13 Jun 2023Assigned to Editor
13 Jun 2023Review(s) Completed, Editorial Evaluation Pending
20 Jun 2023Reviewer(s) Assigned
19 Aug 2023Editorial Decision: Revise Major
03 Sep 20231st Revision Received
08 Sep 2023Submission Checks Completed
08 Sep 2023Assigned to Editor
08 Sep 2023Review(s) Completed, Editorial Evaluation Pending
08 Sep 2023Reviewer(s) Assigned
17 Sep 2023Editorial Decision: Accept
Oct 2023Published in Immunity, Inflammation and Disease volume 11 issue 10. https://doi.org/10.1002/iid3.1036