Validating core therapeutic targets for osteoporosis treatment based on integrating network pharmacology and
Shiyang Weng1, Huichao Fu1, Shengxiang Xu2
1Department of Trauma Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201600, China.
Objective:
Our goal was to find metabolism-related lncRNAs that were associated with osteoporosis (OP) and construct a model for predicting OP progression using these lncRNAs.
Methods:
The GEO database was employed to obtain gene expression profiles. The WGCNA technique and differential expression analysis were used to identify hypoxia-related lncRNAs. A Lasso regression model was applied to select 25 hypoxia-related genes, from which a classification model was created. Its robust classification performance was confirmed with an area under the ROC curve close to 1, as verified on the validation set. Concurrently, we constructed a ceRNA network based on these genes to unveil potential regulatory processes. Biologically active compounds of STZYD were identified using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP) database. BATMAN was used to identify its targets, and we obtained OP-related genes from Malacards and DisGeNET, followed by identifying intersection genes with metabolism-related genes. A pharmacological network was then constructed based on the intersecting genes. The pharmacological network was further integrated with the ceRNA network, resulting in the creation of a comprehensive network that encompasses herb-active components, pathways, lncRNAs, miRNAs, and targets. Expression levels of hypoxia-related lncRNAs in mononuclear cells isolated from peripheral blood of OP and normal patients were subsequently validated using quantitative real-time PCR (qRT-PCR). Protein levels of RUNX2 were determined through a western blot assay.
Results:
CBFB, GLO1, NFKB2 and PIK3CA were identified as central therapeutic targets, and ADD3-AS1, DTX2P1-UPK3BP1-PMS2P11, TTTY1B, ZNNT1 and LINC00623 were identified as core lncRNAs.
Conclusions:
Our work uncovers a possible therapeutic mechanism for STZYD, providing a potential therapeutic target for OP. In addition, a prediction model of metabolism-related lncRNAs of OP progression was constructed to provide a reference for the diagnosis of OP patients.
Insights
This study identified core long non-coding RNAs (lncRNAs) associated with osteoporosis (OP) progression. A predictive model using these metabolism-related lncRNAs was developed, offering potential diagnostic references for OP patients.
Area of Science:
- Genomics
- Molecular Biology
- Pharmacology
Background:
- Osteoporosis (OP) is a significant skeletal disorder characterized by low bone mass and microarchitectural deterioration.
- Identifying reliable biomarkers for OP progression is crucial for timely diagnosis and effective treatment.
Purpose of the Study:
- To identify metabolism-related long non-coding RNAs (lncRNAs) associated with osteoporosis (OP).
- To construct a predictive model for OP progression using identified lncRNAs.
- To explore the therapeutic mechanism of STZYD for OP.
Main Methods:
- Gene expression profiles were analyzed using the GEO database and Weighted Gene Co-expression Network Analysis (WGCNA).
- A Lasso regression model selected key hypoxia-related lncRNAs for a classification model.
- A comprehensive network integrating Traditional Chinese Medicine (TCM) active compounds, targets, and OP-related genes was constructed.
- Quantitative real-time PCR (qRT-PCR) and western blot assays validated key lncRNA and protein expression.
Main Results:
- The study identified five core lncRNAs (ADD3-AS1, DTX2P1-UPK3BP1-PMS2P11, TTTY1B, ZNNT1, and LINC00623) associated with OP.
- A robust classification model for OP prediction was developed with high accuracy (ROC curve close to 1).
- CBFB, GLO1, NFKB2, and PIK3CA were identified as central therapeutic targets.
Conclusions:
- The identified lncRNAs and the predictive model offer potential diagnostic tools for OP.
- The study suggests a therapeutic mechanism for STZYD, highlighting potential therapeutic targets for OP treatment.
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