Identification of ferroptosis-related molecular clusters and genes for diabetic osteoporosis based on the machine learning

Frontiers in Endocrinology

Endocrinology

Xueling Qu¹  Shouyu Wang²  Mingzhi Song²  Linxuan Zou²  Junwei Zong²  Lin Zhao³  Xin Han⁴  Zhuqiang Jia⁵  Lei Meng⁶  Ming Lu⁷  Xingkai Wang⁸  Juewei Zhang⁹  Zitong Zhao^1,10 

[1] Changjianglu Pelvic Floor Repair Center, Dalian Women and Children’s Medical Group, Dalian, China;Department of Orthopaedic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China;Department of Quality Management, Dalian Municipal Central Hospital, Dalian, China;Department of Surgery, Naqu People's Hospital, Tibet, China;Department of Orthopaedic Surgery, The Second Affiliated Hospital of Dalian Medical University, Dalian, China;Department of Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China;Department of Surgery, Naqu People's Hospital, Tibet, China;Department of Surgery, The First Affiliated Hospital of Nanhua Medical University, Hengyang, China;Department of Trauma and Tissue Repair Surgery, Dalian Municipal Central Hospital, Dalian, China;Department of Trauma and Tissue Repair Surgery, Dalian Municipal Central Hospital, Dalian, China;Department of Orthopaedic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China;Health Inspection and Quarantine, College of Medical Laboratory, Dalian Medical University, Dalian, China;International Department, Beijing No.80 High School, Beijing, China;

关键词: diabetic osteoporosis;    ferroptosis;    molecular clusters;    machine learning;    prediction model;   

DOI  :  10.3389/fendo.2023.1189513

 received in 2023-03-19, accepted in 2023-06-21,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundDiabetic osteoporosis exhibits heterogeneity at the molecular level. Ferroptosis, a controlled form of cell death brought on by a buildup of lipid peroxidation, contributes to the onset and development of several illnesses. The aim was to explore the molecular subtypes associated with ferroptosis in diabetic osteoporosis at the molecular level and to further elucidate the potential molecular mechanisms.MethodsIntegrating the CTD, GeneCards, FerrDb databases, and the microarray data of GSE35958, we identified ferroptosis-related genes (FRGs) associated with diabetic osteoporosis. We applied unsupervised cluster analysis to divide the 42 osteoporosis samples from the GSE56814 microarray data into different subclusters based on FRGs. Subsequently, FRGs associated with two ferroptosis subclusters were obtained by combining database genes, module-related genes of WGCNA, and differentially expressed genes (DEGs). Eventually, the key genes from FRGs associated with diabetic osteoporosis were identified using the least absolute shrinkage and selection operator (LASSO), Boruta, support vector machine recursive feature elimination (SVM ­ RFE), and extreme gradient boosting (XGBoost) machine learning algorithms. Based on ROC curves of external datasets (GSE56815), the model’s efficiency was examined.ResultsWe identified 15 differentially expressed FRGs associated with diabetic osteoporosis. In osteoporosis, two distinct molecular clusters related to ferroptosis were found. The expression results and GSVA analysis indicated that 15 FRGs exhibited significantly different biological functions and pathway activities in the two ferroptosis subclusters. Therefore, we further identified 17 FRGs associated with diabetic osteoporosis between the two subclusters. The results of the comprehensive analysis of 17 FRGs demonstrated that these genes were heterogeneous and had a specific interaction between the two subclusters. Ultimately, the prediction model had a strong foundation and excellent AUC values (0.84 for LASSO, 0.84 for SVM ­ RFE, 0.82 for Boruta, and 0.81 for XGBoost). IDH1 is a common gene to all four algorithms thus being identified as a key gene with a high AUC value (AUC = 0.698).ConclusionsAs a ferroptosis regulator, IDH1 is able to distinguish between distinct molecular subtypes of diabetic osteoporosis, which may offer fresh perspectives on the pathogenesis of the disease’s clinical symptoms and prognostic heterogeneity.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Meng, Zhang, Zhao, Zou, Jia, Han, Zhao, Song, Zong, Wang, Qu and Lu