Validating core therapeutic targets for osteoporosis treatment based on integrating network pharmacology and informatics
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 a...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | SLAS Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2472630324000049 |
| _version_ | 1797220269278887936 |
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| author | Shiyang Weng Huichao Fu Shengxiang Xu Jieruo Li |
| author_facet | Shiyang Weng Huichao Fu Shengxiang Xu Jieruo Li |
| author_sort | Shiyang Weng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T19:09:22Z |
| format | Article |
| id | doaj.art-71ad62cf1f154b54bdb052dd5abfa0a4 |
| institution | Directory Open Access Journal |
| issn | 2472-6303 |
| language | English |
| last_indexed | 2024-04-24T12:46:51Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SLAS Technology |
| spelling | doaj.art-71ad62cf1f154b54bdb052dd5abfa0a42024-04-07T04:36:06ZengElsevierSLAS Technology2472-63032024-04-01292100122Validating core therapeutic targets for osteoporosis treatment based on integrating network pharmacology and informaticsShiyang Weng0Huichao Fu1Shengxiang Xu2Jieruo Li3Department of Trauma Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201600, ChinaDepartment of Trauma Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201600, ChinaDepartment of Orthopedic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang 310009, China; Corresponding authors.Department of Sport Medicine, Institute of Orthopedics Diseases and Center for Joint Surgery and Sports Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China; Corresponding authors.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.http://www.sciencedirect.com/science/article/pii/S2472630324000049OsteoporosisMetabolism-related lncRNAsTCMSP databaseWGCNAMolecular docking |
| spellingShingle | Shiyang Weng Huichao Fu Shengxiang Xu Jieruo Li Validating core therapeutic targets for osteoporosis treatment based on integrating network pharmacology and informatics SLAS Technology Osteoporosis Metabolism-related lncRNAs TCMSP database WGCNA Molecular docking |
| title | Validating core therapeutic targets for osteoporosis treatment based on integrating network pharmacology and informatics |
| title_full | Validating core therapeutic targets for osteoporosis treatment based on integrating network pharmacology and informatics |
| title_fullStr | Validating core therapeutic targets for osteoporosis treatment based on integrating network pharmacology and informatics |
| title_full_unstemmed | Validating core therapeutic targets for osteoporosis treatment based on integrating network pharmacology and informatics |
| title_short | Validating core therapeutic targets for osteoporosis treatment based on integrating network pharmacology and informatics |
| title_sort | validating core therapeutic targets for osteoporosis treatment based on integrating network pharmacology and informatics |
| topic | Osteoporosis Metabolism-related lncRNAs TCMSP database WGCNA Molecular docking |
| url | http://www.sciencedirect.com/science/article/pii/S2472630324000049 |
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