Screening and predicted value of potential biomarkers for breast cancer using bioinformatics analysis

Abstract

Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women. Increasing molecular targets have been discovered for breast cancer prognosis and therapy. However, there is still an urgent need to identify new biomarkers. Therefore, we evaluated biomarkers that may aid the diagnosis and treatment of breast cancer. We searched three mRNA microarray datasets (GSE134359, GSE31448 and GSE42568) and identified differentially expressed genes (DEGs) by comparing tumor and non-tumor tissues using GEO2R. Functional and pathway enrichment analyses of the DEGs were performed using the DAVID database. The protein-protein interaction (PPI) network was plotted with STRING and visualized using Cytoscape. Module analysis of the PPI network was done using MCODE. The associations between the identified genes and overall survival (OS) were analyzed using an online Kaplan-Meier tool. The redundancy analysis was conducted by DepMap. Finally, we verified the screened HUB gene at the protein level. A total of 268 DEGs were identified, which were mostly enriched in cell division, cell proliferation, and signal transduction. The PPI network comprised 236 nodes and 2132 edges. Two significant modules were identified in the PPI network. Elevated expression of the genes Discs large-associated protein 5 (DLGAP5), aurora kinase A (AURKA), ubiquitin-conjugating enzyme E2 C (UBE2C), ribonucleotide reductase regulatory subunit M2(RRM2), kinesin family member 23(KIF23), kinesin family member 11(KIF11), non-structural maintenance of chromosome condensin 1 complex subunit G (NCAPG), ZW10 interactor (ZWINT), and denticleless E3 ubiquitin protein ligase homolog(DTL) are associated with poor OS of breast cancer patients. The enriched functions and pathways included cell cycle, oocyte meiosis and the p53 signaling pathway. The DEGs in breast cancer have the potential to become useful targets for the diagnosis and treatment of breast cancer.

Figure 1

Identification of DEGs in the indicated breast cancer datasets. (A) Three online-available expression profiling datasets (GSE134359, GSE31448, GSE42568) were analyzed using GEO2R, and genes differentially expressed in breast tumor and peri-tumor samples (adj. P < 0.05 and |log₂ FC |> 1.5) were defined as DEGs, followed by Venn diagram of DEGs. (B) Heatmap of top DEGs (adj.P < 0.05 and |log₂ FC|> 3) in datasets GSE134359, GSE31448 and GSE42568.

Figure 2

GO and KEGG analyses of DEGs. (A) GO analysis with up-regulated (red) and down-regulated (green) DEGs. Enriched GO items with P < 0.01 are shown, including biological process, cellular component, and molecular function. (B) KEGG analysis with up-regulated (red) and down-regulated (green) DEGs. Enriched KEGG pathways (P < 0.01) are shown.

Figure 3

PPI and MCODE analyses of DEGs. (A) Protein–protein interaction network of 268 DEGs. (B) A significant module, containing 53 up-regulated proteins, was selected from protein–protein interaction network. (C) Another module selected from protein–protein interaction network. (D) GO analysis of MCODE genes. Enriched GO items with P < 0.01 are shown. (E) KEGG pathway analysis of MCODE genes. Enriched pathways with P < 0.05 are shown. For (A–C), red nodes are up-regulated proteins, and green nodes are down-regulated proteins. The lines represent the interaction relationship between nodes.

Figure 4

Prognostic estimation of the top 10 HUB genes. The top 10 HUB genes including ZWINT, DLGAP5, DTL, NCAPG, CCNB1, AURKA, KIF23, KIF11, RRM2 and UBE2C, were identified by cytoHubba, followed by survival analysis. Breast cancer patients were divided into two groups according to auto select best cutoff. Low, patients with gene expression lower than best cutoff; high, patients with gene expression higher than best cutoff.

Figure 5

Redundancy analysis of the top 10 HUB genes. The essential role of indicated HUB genes in breast cancer cell survival was analyzed via DepMap, (https://depmap.org/portal/), which was established from CRISPR and RNAi screening data. (A) Redundancy analysis of ten genes in total cells lines. (B) The CERES dependency score of ten genes in breast cancer cells. A lower CERES score indicates a higher likelihood that the gene of interest is essential in a given cell line. A score of 0 indicates a gene is not essential (dotted line); − 1 is comparable to the median of all pan-essential genes (red line).

Figure 6

Protein expression of the top 10 HUB genes. The top 10 HUB genes, including ZWINT, DLGAP5, DTL, NCAPG, CCNB1, AURKA, KIF23, KIF11, RRM2 and UBE2C, were identified by cytoHubba, verified at the protein level by Ualcan. Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.