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Copyright (c) 2023 Jiang Su, Wei Zhou, Huajie Yuan, Hui Wang, Hua Zhang
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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 and functional analysis of novel biomarkers in adenoid cystic carcinoma
Corresponding Author(s) : Hua Zhang
Cellular and Molecular Biology,
Vol. 69 No. 6: Issue 6
Abstract
To explore the key genes associated with the development and progression of adenoid cystic carcinoma (ACC), with the aim of exploring novel biomarkers that can better diagnose ACC, and thus better improve patient prognosis. The GSE59701 and GSE88804 datasets (containing transcriptomic data for a total of 19 normal samples and 20 tumor samples) were downloaded from the Gene Expression Omnibus (GEO) database, combined into one dataset and used to remove batch effects using the SVA algorithm. A total of 711 differentially expressed genes (DEGs) were screened by using the limma package. The metscape database (www. metascape.org) was used for gene ontology (GO) analysis and gene-specific Kyoto Genome Encyclopedia (KEGG) pathway analysis, which showed that the main enriched pathways of DEGs were kinase activity, fertility properties, extracellular matrix structural components, tryptophan metabolism, cancer pathway, PI3K-Akt signaling pathway. The STRING database was used to construct protein-protein interaction (PPI) networks for DEGs, and Cytoscape software was used to visualize the result. Lasso regression and SVM algorithm screened 3 key genes: GABBR1, ITGA9 and MLKL. The results of GSEA on key genes showed that they are mainly enriched in pathways such as cell cycle, and taste transduction mechanisms. CIBERSORT algorithm was used to analyze immune cell infiltration, the "corrplot" package was used to analyze the interaction relationships between immune cells. Spearman correlation analysis demonstrated that GABBR1, ITGA9 and MLKL were all strongly correlated with differentially expressed immune cells. Moreover, correlation analysis of key and differentially regulated genes showed that GABBR1 and MLKL were significantly correlated with MYB and TP53, respectively. In conclusion, GABBR1, ITGA9 and MLKL affect the progression of ACC, where GABBR1 and MLKL may regulate ACC through MYB and TP53, and the relationship between ITGA9 and ECM and PI3K-Akt may have some influence on the development of ACC.
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