Identification of potential immunotherapy biomarkers for breast cancer by bioinformatics analysis

Abstract

Breast cancer is a serious malignancy with a high incidence worldwide and a tendency to relapse. The aim of the present study was to identify potential biomarkers in breast cancer using integrated bioinformatics analysis. Microarray data for 127 breast tumor samples and 23 non-tumor samples were downloaded from the Gene Expression Omnibus database; 121 differentially expressed genes were identified. Functional analysis using DAVID revealed that these genes were significantly enriched in endodermal cell differentiation and proteinaceous extracellular matrix. Five bioactive compounds (prostaglandin J2, tanespimycin, semustine, 5182598, and flunarizine) were identified using Connectivity Map. Cytoscape software and the STRING database were used to construct a protein–protein interaction network. Kaplan–Meier Plotter, ONCOMINE, and Gene Expression Profiling Interactive Analysis showed that overexpression of CD24, MMP1, SDC1, and SPP1 was related to poor prognosis in breast cancer patients. The immunohistochemistry microarray was used to further confirm that protein expression of CD24, MMP1, SDC1, and SPP1 was much higher in tumor tissues than in non-tumor tissues. Expression of hub genes at the protein level was correlated with subtype and grade of breast cancer. Furthermore, immunity analysis indicated that the hub genes were significantly correlated with the infiltration of five immune cell types (CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells). Thus, the results of this study indicate potential biomarkers that could have applications in the development of immune therapy for breast cancer. However, more in vivo, and in vitro studies are required to further verify these results.

Important oncogenic and immunogenic roles of SPP1 and CSF1 in hepatocellular carcinoma

“…In melanoma patients, SPP1 expression was positively correlated with melanoma progression, and higher SPP1 expression indicated a worse prognosis, whereas silencing SPP1 suppressed melanoma cell proliferation, and migration [ 25 ]. In breast cancer, the level of SPP1 was significantly higher in tumors than in adjacent tissues and was positively correlated with tumor grade and subtype [ 26 ]. In gastric cancer cell lines, the elevated expression of SPP1 was a critical determinant of poor prognosis [ 27 ].…”

Mutational synergy withCREBBPloss in lymphomagenesis identified through forward insertional mutagenesis in a new DLBCL mouse model

“…Interestingly the B220low/CD19+ neoplastic cells also enhanced CD24 expression on their cell surface. CD24 overexpression has been reported in several solid tumours [54,55,56] and haematological malignancies [57]. In recent years, it has emerged as a novel ‘don’t eat me’ signal, which modulates anti-tumour immunity through its binding to the inhibitory receptor Siglec-10 expressed by macrophages [56].…”

Bioinformatics and network biology approach to identifying type 2 diabetes genes and pathways that influence the progression of breast cancer

“…Tamoxifen inhibits the effects of estrogen by inhibiting DNA synthesis by binding to estrogen receptors on tumors and other tissue targets. SPP1 is significantly more abundant in breast carcinoma tissue, making it a potential option for immunotherapy and a novel prognostic biomarker [136] [137] . The overexpression of PEG10 (Paternally Expressed Imprinted Gene 10) in breast cancer cells promotes migration and invasion as well as cell cycle, clone formation and cell proliferation [138] .…”