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ORIGINAL ARTICLE
Year : 2020  |  Volume : 16  |  Issue : 4  |  Page : 867-873

Utilizing benchmarked dataset and gene regulatory network to investigate hub genes in postmenopausal osteoporosis


1 Departments of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
2 Department of Orthopedics, The Third Affiliated Hospital of Anhui Medical University, Hefei 230031, Anhui Province, China
3 Departments of Joint Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China

Correspondence Address:
Zong-Sheng Yin
Department of Joint Surgery, The First Affiliated Hospital of Anhui Medical University, No. 218, Jixi Road, Hefei 230032, Anhui Province
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1482.204842

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Objective: The objective of this paper was to investigate hub genes of postmenopausal osteoporosis (PO) utilizing benchmarked dataset and gene regulatory network (GRN). Materials and Methods: To achieve this goal, the first step was to benchmark the dataset downloaded from the ArrayExpress database by adding local noise and global noise. Second, differentially expressed genes (DEGs) between PO and normal controls were identified using the Linear Models for Microarray Data package based on benchmarked dataset. Third, five kinds of GRN inference methods, which comprised Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT), and GEne Network Inference with Ensemble of trees (Genie3), were described and evaluated by receiver operating characteristic (ROC) and precision and recall (PR) curves. Finally, GRN constructed according to the method with best performance was implemented to conduct topological centrality (closeness) for the purpose of investigate hub genes of PO. Results:A total of 236 DEGs were obtained based on benchmarked dataset of 20,554 genes. By assessing Zscore, GeneNet, CLR, PCIT, and Genie3 on the basis of ROC and PR curves, Genie3 had a clear advantage than others and was applied to construct the GRN which was composed of 236 nodes and 27,730 edges. Closeness centrality analysis of GRN was carried out, and we identified 14 hub genes (such as TTN, ACTA1, and MYBPC1) for PO. Conclusion: In conclusion, we have identified 14 hub genes (such as TN, ACTA1, and MYBPC1) based on benchmarked dataset and GRN. These genes might be potential biomarkers and give insights for diagnose and treatment of PO.


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