Identification of key pathways and genes in response to trastuzumab treatment in breast cancer using bioinformatics analysis

Abstract

Breast cancer (BC) is one of the leading causes of death among women worldwide. The gene expression profile GSE22358 was downloaded from the Gene Expression Omnibus (GEO) database, which included 154 operable early-stage breast cancer samples treated with neoadjuvant capecitabine plus docetaxel, with (34) or without trastuzumab (120), to identify gene signatures during trastuzumab treatment and uncover their potential mechanisms. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and a protein-protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed by Cytoscape software. There were 2284 DEGs, including 1231 up-regulated genes enriched in DNA replication, protein N-linked glycosylation via asparagine, and response to toxic substances, while 1053 down-regulated genes were enriched in axon guidance, protein localization to plasma membrane, protein stabilization, and protein glycosylation. Eight hub genes were identified from the PPI network, including GSK3B, RAC1, PXN, ERBB2, HSP90AA1, FGF2, PIK3R1 and RAC2. Our experimental results showed that GSK3B was also highly expressed in breast cancer tissues and was associated with poor survival, as was β-catenin. In conclusion, the present study indicated that the identified DEGs and hub genes further our understanding of the molecular mechanisms underlying trastuzumab treatment in BC and highlighted GSK3B, which might be used as a molecular target for the treatment of BC.

Keywords: bioinformatics analysis; breast cancer; differentially expressed gene; microarray; trastuzumab.

Figure 1. Heat map of the top 100 differentially expressed genes

Hierarchical clustering analysis to categorize the data into two groups that had similar expression patterns with or without trastuzumab Red: up-regulation, N = 50; Purple: down-regulation, N = 50.

Figure 2. Top 3 modules from the protein–protein interaction network

(A) module 1, (B) module 2, (C) module 3 were analyzed using the plug-in Molecular Complex Detection in Cytoscape based on the information in the STRING database, (D) the enriched pathways and functional annotation of the genes involved in the modules of 1, 2, and 3.

Figure 3. Cross-cancer alteration summary for GSK3B (166 studies/1 gene)

GSK3B was analyzed from the cBioPortal for Cancer Genomics (http://www.cbioportal.org). The red column indicates the amplification pattern.

Figure 4. GSK3B is upregulated in breast cancer

(A) Hematoxylin and eosin staining of breast cancer tissue. (B) and (C) Typical western blots and statistical data of GSK3B and β-catenin from normal and breast cancer tissue. N = 5, data are expressed as the mean ± s.e.m.; ^**P < 0.01. ^*P < 0.05, normal compared to breast cancer; Student’s t-test. (D) Expression level of GSK3B and CNNTB1 in cancer and normal tissues. BLCA: Bladder Carcinoma; BRCA: Breast Carcinoma. (E) The pair-wise gene expression correlation analysis for GSK3B and CNNTB1 in breast cancer. GSK3B and CNNTB1 are positively correlated. (F) Statistical analysis of cell number in MDA-MB-231 cells infected with Ad-GSK3B (MOI 25) or Ad-CTNNB1 (MOI 25). Results are expressed as mean ± SEM. ^**p < 0.01, control vs Ad- GSK3B; ^##p < 0.01, control vs Ad-CTNNB1 (n = 4).

Figure 5. Prognostic value of GSK3B and CTNNB1

(A) and (B) Kaplan plot for GSK3B (A), CTNNB1 (B) in breast cancer. (C) Statistical data of GSK3B (N = 50) and CTNNB1 (N = 120). The data are expressed as the mean ± s.e.m., low expression compared to high; Student’s t-test.