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Bioinformatics-based analysis of key genes in steroid-induced osteonecrosis of the femoral head that are associated with copper metabolism
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Abstract
Background: The copper metabolism may play a role in angiogenesis and osteogenesis. However, the relationship between copper metabolism and steroid-induced osteonecrosis of the femoral head (SONFH) still remains unclear.Methods: The GSE123568 dataset and 2062 copper metabolism-related genes (CMRGs) were downloaded from the Gene Expression Omnibus (GEO) and GeneCards databases, respectively. The differentially expressed copper metabolism-related SONFH genes (DE-CMR-SONFHGs) were identified by differential analysis and weighted gene coexpression network analysis (WGCNA). The Least-Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) algorithms were employed to screen key DE-CMR-SONFHGs. Receiver operating characteristic (ROC) analysis and functional enrichment analysis were performed on the key genes. Targeting drugs were predicted with The Drug Gene Interaction Database (DGIdb). The correlation between key SONFH genes and copper death-related genes was calculated by Pearson method. The result of bioinformatics analysis were confirmed by quantitative real-time polymerase chain reaction (qRT‒PCR).Results: A total of 106 DE-CMR-SONFHGs were identified. Then, 2 key genes (PNP and SLC2A1) were identified by LASSO and SVM algorithms. The ROC curves of PNP and SLC2A1 indicated that they had diagnostic value in distinguishing SONFH from control samples. Additionally, 20 immune cell populations had notably different abundances between the SONFH and control samples. PNP showed the most significant correlations with plasmacytoid dendritic cells and CD56bright natural killer cells, and SLC2A1 expression showed the most significant correlations with T follicular helper cells and T helper 17 cells. Moreover, 11 drugs that target PNP and 5 drugs that target SLC2A1 were identified. Four copper death-related genes (LIPT1, DLD, PDHB, and MTF1) were upregulated in the disease samples. The qRT‒PCR results showed that PNP and SLC2A1 expression was downregulated and that the copper death-related genes DLD, PDHB and MTF1, which are closely related to these two key genes, were notably highly expressed in the peripheral blood of SONFH patients. These findings were consistent with the bioinformatics analysis results.Conclusions: Copper homeostatic imbalance may be involved in the process of SONFH. PNP and SLC2A1 were confirmed to be key genes related to SONFH, and these genes may provide insights for the treatment of SONFH.
Springer Science and Business Media LLC
Title: Bioinformatics-based analysis of key genes in steroid-induced osteonecrosis of the femoral head that are associated with copper metabolism
Description:
Abstract
Background: The copper metabolism may play a role in angiogenesis and osteogenesis.
However, the relationship between copper metabolism and steroid-induced osteonecrosis of the femoral head (SONFH) still remains unclear.
Methods: The GSE123568 dataset and 2062 copper metabolism-related genes (CMRGs) were downloaded from the Gene Expression Omnibus (GEO) and GeneCards databases, respectively.
The differentially expressed copper metabolism-related SONFH genes (DE-CMR-SONFHGs) were identified by differential analysis and weighted gene coexpression network analysis (WGCNA).
The Least-Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) algorithms were employed to screen key DE-CMR-SONFHGs.
Receiver operating characteristic (ROC) analysis and functional enrichment analysis were performed on the key genes.
Targeting drugs were predicted with The Drug Gene Interaction Database (DGIdb).
The correlation between key SONFH genes and copper death-related genes was calculated by Pearson method.
The result of bioinformatics analysis were confirmed by quantitative real-time polymerase chain reaction (qRT‒PCR).
Results: A total of 106 DE-CMR-SONFHGs were identified.
Then, 2 key genes (PNP and SLC2A1) were identified by LASSO and SVM algorithms.
The ROC curves of PNP and SLC2A1 indicated that they had diagnostic value in distinguishing SONFH from control samples.
Additionally, 20 immune cell populations had notably different abundances between the SONFH and control samples.
PNP showed the most significant correlations with plasmacytoid dendritic cells and CD56bright natural killer cells, and SLC2A1 expression showed the most significant correlations with T follicular helper cells and T helper 17 cells.
Moreover, 11 drugs that target PNP and 5 drugs that target SLC2A1 were identified.
Four copper death-related genes (LIPT1, DLD, PDHB, and MTF1) were upregulated in the disease samples.
The qRT‒PCR results showed that PNP and SLC2A1 expression was downregulated and that the copper death-related genes DLD, PDHB and MTF1, which are closely related to these two key genes, were notably highly expressed in the peripheral blood of SONFH patients.
These findings were consistent with the bioinformatics analysis results.
Conclusions: Copper homeostatic imbalance may be involved in the process of SONFH.
PNP and SLC2A1 were confirmed to be key genes related to SONFH, and these genes may provide insights for the treatment of SONFH.
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