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Analysis of Breast Cancer Differences Between China and Western Countries Based on Radiogenomics

Overview
Journal Genes (Basel)
Publisher MDPI
Date 2022 Dec 23
PMID 36553681
Authors
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Abstract

Using radiogenomics methods, the differences between tumor imaging data and genetic data in Chinese and Western breast cancer (BC) patients were analyzed, and the correlation between phenotypic data and genetic data was explored. In this paper, we analyzed BC patients' image characteristics and transcriptome data separately, then correlated the magnetic resonance imaging (MRI) phenotype with the transcriptome data through a computational method to develop a radiogenomics feature. The data was fed into the designed random forest (RF) model, which used the area under the receiver operating curve (AUC) as the evaluation index. Next, we analyzed the hub genes in the differentially expressed genes (DEGs) and obtained seven hub genes, which may cause Chinese and Western BC patients to behave differently in the clinic. We demonstrated that combining relevant genetic data and imaging features could better classify Chinese and Western patients than using genes or imaging characteristics alone. The AUC values of 0.74, 0.81, and 0.95 were obtained separately using the image characteristics, DEGs, and radiogenomics features. We screened SYT4, GABRG2, CHGA, SLC6A17, NEUROG2, COL2A1, and MATN4 and found that these genes were positively or negatively correlated with certain imaging characteristics. In addition, we found that the SLC6A17, NEUROG2, CHGA, and MATN4 genes were associated with clinical features.

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References
1.
Kurian A, Gong G, Chun N, Mills M, Staton A, Kingham K . Performance of BRCA1/2 mutation prediction models in Asian Americans. J Clin Oncol. 2008; 26(29):4752-8. PMC: 2653135. DOI: 10.1200/JCO.2008.16.8310. View

2.
Zhao H, Zou L, Geng X, Zheng S . Limitations of mammography in the diagnosis of breast diseases compared with ultrasonography: a single-center retrospective analysis of 274 cases. Eur J Med Res. 2015; 20:49. PMC: 4406115. DOI: 10.1186/s40001-015-0140-6. View

3.
Pinker K, Shitano F, Sala E, Do R, Young R, Wibmer A . Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging. 2017; 47(3):604-620. PMC: 5916793. DOI: 10.1002/jmri.25870. View

4.
Yu G, Wang L, Han Y, He Q . clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012; 16(5):284-7. PMC: 3339379. DOI: 10.1089/omi.2011.0118. View

5.
Young M, Wakefield M, Smyth G, Oshlack A . Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010; 11(2):R14. PMC: 2872874. DOI: 10.1186/gb-2010-11-2-r14. View