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. 2022 Jul 27:2022:9529114.
doi: 10.1155/2022/9529114. eCollection 2022.

Identification of Genes Predicting Poor Response of Trastuzumab in Human Epidermal Growth Factor Receptor 2 Positive Breast Cancer

Xinrui Dong  1 , Huijuan Dai  1 , Aijun Sun  2 , Zhenfeng Yu  3 , Yueyao Du  1
Affiliations

Identification of Genes Predicting Poor Response of Trastuzumab in Human Epidermal Growth Factor Receptor 2 Positive Breast Cancer

Xinrui Dong et al. J Immunol Res. .

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.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Flow chart of data collection and screening of trastuzumab-resistant gene DLD. GSE62327 and GSE15043 were selected to find potential trastuzumab resistance genes. Then, we carried out the functional analysis and figured out ten hub genes. GSE58984 was chosen as the prognostic validation cohort of hub genes. Finally, DLD was discovered. The detail of data collection was demonstrated in the methods part.
Figure 2
Figure 2
Demonstration of the DEGs in 2 GEO datasets and identification of overlapping genes. (a) Volcano plot of DEGs in GSE62327 and GSE5043. Red dots: upregulation; blue dots: downregulation; grey dots: non-differentially expressed genes. (b) Venn diagram to identify the common upregulated and downregulated DEGs in two cohorts. (c) Pie charts to compare expression in tumor tissues and adjacent nontumorous tissues in 50 upregulated DEGs and 38 downregulated DEGs separately. (d) Forest plots to demonstrate the univariate Cox regression analysis results between DEGs expression and OS. The most significant 15 DEGs were shown. DEGs: differentially expressed genes.
Figure 3
Figure 3
Construction of DEGs' GO and KEGG network and identification of hub genes. (a) Top 5 terms in GO pathway enrichment results of upregulated and downregulated DEGs separately. Blue charts: downregulated DEGs enrichments. Red charts: upregulated DEGs enrichments. (b) Top 5 terms in KEGG pathway enrichments results of upregulated DEGs. (c) A total of 10 hub genes via GO and KEGG network. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; BP: biological process; CC: cellular component; MF: molecular function.
Figure 4
Figure 4
The distant disease-free survival (DDFS) analysis of 10 hub genes in GSE58984. Only patients with overexpression of DLD had a reduced DDFS compared to the low expression group (P < 0.05).
Figure 5
Figure 5
Exploration of DLD in clinical characteristics, survival analysis and immune analysis. (a) The expression level of DLD between tumor and paratumorous tissues. (b) The expression level of DLD among different PAM50 subtypes. (c) The expression level of DLD between patients ≤60 years and >60 years. (d) The expression level of DLD between IDC and ILC. (e) The overall survival analysis and disease-specific survival of DLD in BRCA of TCGA cohort. (f) The overall survival analysis, disease-specific survival, and progress-free interval of DLD in HER2+ BRCA of TCGA cohort. (g) The immune landscape of DLD in HER2+ BRCA based on EPIC immune algorithm. (h) The correlation between DLD expression and NK cells, CD4+ T cells, and CD8+ T cells. IDC: invasive ductal carcinoma; ILC: invasive lobular carcinoma; TPM: transcripts per million; ns: nonsense; ∗P < 0.05; ∗∗P < 0.01; and ∗∗∗P < 0.001.
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