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Copyright (c) 2024 Xiaofeng Zhou
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The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.Identification of important genes for human periodontal ligament cells in response to mechanical force based on WGCNA
Corresponding Author(s) : Xiaofeng Zhou
Cellular and Molecular Biology,
Vol. 70 No. 3: Issue 3
Abstract
To identify the differentially important genes of human periodontal ligament cells (PDLC) in response to different types of force, the dataset with regard to human PDLC in response to force was retrieved from the GEO. Differentially expressed genes (DEG) analysis between mechanical force (MF) and the control group was conducted. The gene set enrichment analysis (GSEA) was applied to identify the functional enrichment in different MF groups. Weighted gene co-expression network analysis (WGCNA) of transcriptomic data was performed to identify the highly correlated genes in human PDLC in response to MF. The Lasso regression model was applied to screen the key genes. Results showed A total of 2861 DEGs were identified between the MF group and control group, including 1470 up-regulated DEGs and 1391 down-regulated DEGs. Different biological processes were enriched between the static group and the intermittent group. The Myc targets, TGF-β signaling pathway and PI3K/AKT/MTOR signaling pathway were enriched in intermittent-MF and static-MF groups. The turquoise module (including 386 hub genes) in WGCNA was highly correlated with intermittent traits and the black module (including 33 hub genes) was positively correlated with static traits. The lasso analysis result showed that the CLIC4, NPLOC4 and PRDX6 had the greatest impact on the human PDLC with mechanic stimuli with good predictive efficiency. In conclusion, we developed important genes for human PDLC in response to MF, which might be potential markers for orthodontic tooth movement.
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