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pmid:36160115

Potential Biomarkers and Signaling Pathways Associated with the Pathogenesis of Primary Ameloblastoma: A Systems Biology Approach.

Zeynab Bayat · Azin Mirzaeian · Amir Taherkhani

International journal of dentistry · 2022 doi:10.1155/2022/3316313

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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title
Potential Biomarkers and Signaling Pathways Associated with the Pathogenesis of Primary Ameloblastoma: A Systems Biology Approach.
doi
10.1155/2022/3316313
pmid
36160115
authors array · 3 items
[0]
Zeynab Bayat
[1]
Azin Mirzaeian
[2]
Amir Taherkhani
journal
International journal of dentistry
year
2022
abstract
Objective: Ameloblastoma is a benign odontogenic tumor that may lead to ameloblastic carcinoma. This study aimed to determine potential signaling pathways and biological processes, critical genes and their regulating transcription factors (TFs), and miRNAs, as well as protein kinases involved in the etiology of primary ameloblastoma. Methods: The dataset GSE132472 was obtained from the GEO database, and multivariate statistical analyses were applied to identify differentially expressed genes (DEGs) in primary ameloblastoma tissues compared to the corresponding normal gingiva samples. A protein-protein interaction (PPI) map was built using the STRING database. The Cytoscape software identified significant modules and the hub genes within the PPI network. Gene Ontology annotation and signaling pathway analyses were executed by employing the DAVID and Reactome databases, respectively. Significant TFs and miRNAs acting on the hub genes were identified using the iRegulon plugin and MiRWalk 2.0 database, respectively. A protein kinase enrichment analysis was conducted using the online Kinase Enrichment Analysis 2 (KEA2) web server. The approved drugs acting on the hub genes were also found. Results: A total of 1,629 genes were differentially expressed in primary ameloblastoma (P value <0.01 and |Log2FC| > 1). HRAS, CDK1, MAPK3, ERBB2, COL1A1, CYCS, and BRCA1 demonstrated high degree and betweenness centralities in the PPI network. E2F4 was the most significant TF acting on the hub genes. BTK was the protein kinase significantly enriched by the TFs. Cholesterol biosynthesis was considerably involved in primary ameloblastoma. Conclusions: This study provides an intuition into the potential mechanisms involved in the etiology of ameloblastoma.
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Subsequently, the supervised Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) [14] was used to identify DEGs between early-stage ameloblastoma and healthy tissue samples.
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sections[4] "2.2. Multivariate Statistical Analyses" paragraph 2
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Subsequently, the supervised Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) [14] was used to identify DEGs between early-stage ameloblastoma and healthy tissue samples.
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The study identified 1,629 differentially expressed genes in primary ameloblastoma tissues compared with corresponding normal gingiva.
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A total of 1,629 genes were differentially expressed in primary ameloblastoma (P value <0.01 and |Log2FC| > 1).
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sections[0] "Abstract" paragraph 3
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A total of 1,629 genes were differentially expressed in primary ameloblastoma (P value <0.01 and |Log2FC| > 1).
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The study identified 1,629 differentially expressed genes in primary ameloblastoma tissues compared with corresponding normal gingiva.
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Gene expression profile in patient-derived ameloblastoma tumor
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Subsequently, the supervised Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) [14] was used to identify DEGs between early-stage ameloblastoma and healthy tissue samples.
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Complete file
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This study aimed to determine potential signaling pathways and biological processes, critical genes and their regulating transcription factors (TFs), and miRNAs, as well as protein kinases involved in the etiology of primary ameloblastoma.\nMethods: The dataset GSE132472 was obtained from the GEO database, and multivariate statistical analyses were applied to identify differentially expressed genes (DEGs) in primary ameloblastoma tissues compared to the corresponding normal gingiva samples. A protein-protein interaction (PPI) map was built using the STRING database. The Cytoscape software identified significant modules and the hub genes within the PPI network. Gene Ontology annotation and signaling pathway analyses were executed by employing the DAVID and Reactome databases, respectively. Significant TFs and miRNAs acting on the hub genes were identified using the iRegulon plugin and MiRWalk 2.0 database, respectively. 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