[Screening of characteristic genes of salivary gland adenoid cystic carcinoma based on weighted co-expression network and machine learning].

Q4 Medicine

上海口腔医学 Pub Date : 2024-12-01

Wen-Chao Bu, Shi-Xin Chen, Yin-Hua Jiang, Ming-Guo Cao, Xin-Ru Wu, Yun-Qian Guan, Si-Yuan Xie

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引用次数: 0

Abstract

Purpose: To identify potential biomarkers of salivary gland adenoid cystic carcinoma to further understand the potential pathogenesis of adenoid cystic carcinoma.

Methods: Two microarray datasets (GSE59701, GSE88804) were downloaded from NCBI GEO database. LIMMA software package was used to screen SACC differentially expressed genes. WGCNAs were used to find the important module genes that were most associated with SACC. Two machine learning methods(LASSO and SVM-RFE) were used to identify Hub genes. Subsequently, ROC curve used to predict SACC was developed to determine the diagnostic effect. R4.2.1 software was used for statistical analysis.

Results: Three hub genes(GABBR1, EN1 and LINC01296) were identified, and a ROC curve with high predictive performance (AUC, 1.000-1.000) was established.

Conclusions: Three hub genes(GABBR1, EN1 and LINC01296) were obtained by WGCNA, LASSO, SVM-RFE as potential biomarkers of SACC, and the findings of this study provide a foothold for future research on potential key genes of SACC, and a target basis for the early diagnosis and treatment of SACC.

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来源期刊

上海口腔医学 Medicine-Medicine (all)

CiteScore

0.30

自引率

0.00%

发文量

5299

期刊介绍： "Shanghai Journal of Stomatology (SJS)" is a comprehensive academic journal of stomatology directed by Shanghai Jiao Tong University and sponsored by the Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. The main columns include basic research, clinical research, column articles, clinical summaries, reviews, academic lectures, etc., which are suitable for reference by clinicians, scientific researchers and teaching personnel at all levels engaged in oral medicine.