Identification of Genes Predicting Poor Response of Trastuzumab in Human Epidermal Growth Factor Receptor 2 Positive Breast Cancer. (27th July 2022)

• Record Type: Journal Article
• Authors: Dong, Xinrui; Dai, Huijuan; Sun, Aijun; Yu, Zhenfeng; Du, Yueyao
• Other Names: Meng Lingzhang Academic Editor.
• Is Part Of: Journal of immunology research. Volume 2022(2022)
• Publication Date: 2022-07-27
• Subjects: Immunology -- Periodicals; Immunology -- Research -- Periodicals; 616.07905
• DOI: 10.1155/2022/9529114
• Languages: English
• ISSNs: 2314-8861

Abstract

Abstract : Objective . To identify trastuzumab-resistant genes predicting drug response and poor prognosis in human epidermal growth factor receptor 2 positive (HER2+) breast cancer. Methods . Gene expression profiles from the GEO (Gene Expression Omnibus) database were obtained and analyzed. Differentially expressed genes (DEGs) between the pathological complete response (pCR) group and non-pCR group in a trastuzumab neoadjuvant therapy cohort and DEGs between Herceptin-resistant and wild-type cell lines were detected and evaluated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were performed to select the functional hub genes. The hub genes' prognostic power was validated by another trastuzumab adjuvant treatment cohort. Results . Fifty upregulated overlapping DEGs were identified by analyzing two trastuzumab resistance-related GEO databases. Functional analysis picked out ten hub genes enriched in mitochondrial function and metabolism pathways: ASCL1, CPT2, DLD, ELVOL7, GAMT, NQO1, SLC23A1, SPR, UQCRB, and UQCRQ. These hub genes could distinguish patients with trastuzumab resistance from the sensitive ones. Further survival analysis of hub genes showed that DLD overexpression was significantly associated with an unfavorable prognosis in HER2+ breast cancer patients. Conclusion . Ten novel trastuzumab resistance-related genes were discovered, of which DLD could be used for trastuzumab response prediction and prognostic prediction in HER2+ breast cancer.