Conclusion
This study revealed the potential molecular mechanisms of m6A-related ferroptosis genes in T2DM, which could provide novel insights for the clinical diagnosis and treatment of T2DM.
Material and methods
Firstly, differentially expressed m6A-FRGs (DEm6A-FRGs) were obtained by intersecting the differentially expressed genes (DEGs) and the m6A-related ferroptosis genes (m6A-FRGs). After enrichment analysis of DEm6A-FRGs, artificial neural network (ANN) and nomogram models were constructed using 4 biomarkers. Moreover, the gene set enrichment analysis of biomarkers was performed. Furthermore, the transcription factors (TF)-mRNA and competing endogenous RNAs (ceRNA) regulatory networks were constructed to reveal the potential regulation of biomarkers at molecular level. In addition, the targeted drugs of biomarkers were predicted, and the molecular docking was used to study the inter-molecular interactions between biomarkers and targeted drugs by "AutoDockvina".
Methods
Firstly, differentially expressed m6A-FRGs (DEm6A-FRGs) were obtained by intersecting the differentially expressed genes (DEGs) and the m6A-related ferroptosis genes (m6A-FRGs). After enrichment analysis of DEm6A-FRGs, artificial neural network (ANN) and nomogram models were constructed using 4 biomarkers. Moreover, the gene set enrichment analysis of biomarkers was performed. Furthermore, the transcription factors (TF)-mRNA and competing endogenous RNAs (ceRNA) regulatory networks were constructed to reveal the potential regulation of biomarkers at molecular level. In addition, the targeted drugs of biomarkers were predicted, and the molecular docking was used to study the inter-molecular interactions between biomarkers and targeted drugs by "AutoDockvina".
Purpose
This study is aims to explore the role of ferroptosis genes regulated by N6-methyladenosine (m6A) in Type 2 diabetes mellitus (T2DM). Material and
Results
Totals of 10 DEm6A-FRGs were obtained by intersecting the 402 DEGs and 299 m6A-FRGs. Moreover, the ANN model and nomogram model were constructed with 4 biomarkers including CDKN1A, MIOX, MYCN and CD82, among them, CDKN1A was the most important biomarker for forecasting T2DM. Notably, the function of extracellular matrix structural constituent was low expression in CD82 and MIOX, the function of mitochondrial protein-containing complex was high expression in CD82 and CDKN1A. Furthermore, TP63 could regulate CD82, CDKN1A and MIOX, GATA3 could regulate CD82, CDKN1A and MYCN at the same time. The ceRNA network was constructed with 4 mRNAs, 51 miRNAs and 37 lncRNAs, among them, XIST was a key lncRNA that associated with 12 miRNAs, which could influence CDKN1A. In addition, bisphenol A was associated with CD82 and MYCN, CGP 25608 was associated with CDKN1A and MIOX.