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.

Environmental Phenol and Paraben Exposure Risks and Their Potential Influence on the Gene Expression Involved in the Prognosis of Prostate Cancer

“…We used Cytoscape, a web-based bioinformatics software, to visualize molecular interaction networks. The CytoHubba plugin screened for core genes following a PCa prognosis analysis that was comparable to previous studies [ 46 , 48 , 74 , 83 ]. Through the CytoHubba and PPI network, CCND1, VEGFA, EGF, MYC, CASP3, IGF1, STAT3, TP53, ESR1, and CDH1 were identified as hub genes and considered to be playing an important role(s) in PCa progression.…”

“…The androgen receptor is the typical target for PCa detection and therapy, and estrogens and their receptors have also been implicated in the development of PCa [ 46 ]. The RNA polymerase II promoter is important for the transcription of DNA [ 49 ] and was identified in various studies for being involved in BP enrichment analysis in different cancers, such as breast cancer [ 74 ], ovarian cancer [ 75 ], and liver cancer [ 76 ]. Our results showed, for the KEGG signaling pathway analysis: the genes markedly enriched cancer, PCa, Hepatitis B, colorectal cancer, and the HIF-1 signaling pathway.…”

“…[ 84 ], it was reported that UBE2C, TOP2A, and CCNB1 were associated with PCa prognosis and higher expression in PCa samples than in normal tissues. RRM2 is associated with poor overall survival of breast cancer patients and can become a useful target for diagnosing and treating patients [ 74 ]. Ubiquitin-conjugating enzyme E2C (UBE2C) was highly expressed in PCa compared with normal tissue in TCGA; the expression was also more highly associated with the Gleason score (>7) of PCa, which plays as an independent prognostic factor of PCa, and recreated a critical role in the pathway of PCa (WNT-β-catenin signaling pathway and NOTCH signaling pathway) [ 84 ].…”

“…Moreover, UBE2C was reported high in breast cancer, indicated to be an independent prognostic factor connected with the recurrence and death, and associated with a shorter survival period of breast cancer patients. Therefore, it is regarded as a possible therapeutic target for breast cancer [ 74 ]. Centromere protein F (CENPF) gene encodes a protein that associates with the nuclear matrix component during the G2 phase, including cell growth and protein synthesis during mitosis.…”

Biomarkers for Breast Adenocarcinoma Using In Silico Approaches

“…Vascular endothelial growth factor (VEGF) has also been identified as a key target for antiangiogenic remedy, and its inhibitors sorafenib, sunitinib, and bevacizumab are also used for breast cancer therapy. However, due to tumor heterogeneity, low ratios of responders, and relapse, there is an urgent need to perceive new biomarkers, which can usefully resource the analysis and treatment of breast cancer [ 15 ].…”