AUTHOR=Hu Yunxiang , Han Jun , Ding Shengqiang , Liu Sanmao , Wang Hong 

TITLE=Identification of ferroptosis-associated biomarkers for the potential diagnosis and treatment of postmenopausal osteoporosis

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.986384

DOI=10.3389/fendo.2022.986384

ISSN=1664-2392

ABSTRACT=Objective
Postmenopausal osteoporosis (PMOP) is one of the most commonly occurring conditions worldwide and is characterized by estrogen deficiency as well as persistent calcium loss with age. The aim of our study was to identify significant ferroptosis-associated biomarkers for PMOP.
Methods and materials
We obtained our training dataset from the Gene Expression Omnibus (GEO) database using GSE56815 expression profiling data. Meanwhile we extracted ferroptosis-associated genes for further analysis. Differentially expressed ferroptosis-associated genes (DEFAGs) between PMOP patients and normal controls were selected using the “limma” package. We established a ferroptosis-associated gene signature using training models, specifically, random forest (RF) and support vector machine (SVM) models. Using consensus clustering, the PMOP patient subtypes were identified. A ferroptosis associated gene (FAG)-Scoring scheme was developed by PCA. The important candidate genes associated with OP were also compared between different ferrclusters and geneclusters.
Results
There were significant DEFAGs acquired, of which five (HMOX1, HAMP, LPIN1, MAP3K5, FLT3) were selected for establishing a ferroptosis-associated gene signature based on the most appropriate training model, namely, the RF model. Based on the expression levels of the five DEFAGs, a clinical application nomogram was established. The PMOP patients were divided into two subtypes (ferrclusterA,B and geneclusterA,B, respectively) according to the consensus clustering method based on DEFAGs and differentially expressed genes (DEGs). FerrclusterB and geneclusterB had higher ferroptosis score than ferrclusterA and geneclusterA, respectively. The expression of COL1A1 gene was significantly higher in ferrclusterB and genclusterB, while there is no statistical significance in term of VDR gene, COL1A2 genes, and PTH gene expressions.
Conclusions
On the basis of five explanatory variables (HMOX1, HAMP, LPIN1, MAP3K5 and FLT3), we developed a diagnostic ferroptosis-associated gene signature and identified two differently categorized PMOP subtypes that may potentially be applied in the diagnosis and individualized treatment of PMOP. The ER gene, VDR gene, IL-6 gene, COL1A1 and COL1A2 genes, and PTH gene are important candidate gene of OP, however, more studies are still anticipated to further elucidate the relationship between these genes and ferroptosis in OP.