Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis

Xiaoying Zhou,Meng Wang,Weike Sun,Tong Han,Liang Li,Shusheng Qiu,Chuanguang Xiao,Jun Chu,Lili Lin

doi:10.1186/s12957-020-02026-z

Abstract

BackgroundThis study intended to determine important genes related to the prognosis and recurrence of breast cancer.MethodsGene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free survival (DFS) group, while samples without recurrence and survival beyond 5 years were defined as better DFS group. Another gene expression profile dataset (GSE45725) of breast cancer was downloaded as the validation data. Differentially expressed genes (DEGs) were screened between better and poor DFS groups, which were then performed function enrichment analysis. The DEGs that were enriched in the GO function and KEGG signaling pathway were selected for cox regression analysis and Logit regression (LR) model analysis. Finally, correlation analysis between LR model classification and survival prognosis was analyzed.ResultsBased on the breast cancer gene expression profile data in TCGA, 540 DEGs were screened between better DFS and poor DFS groups, including 177 downregulated and 363 upregulated DEGs. A total of 283 DEGs were involved in all GO functions and KEGG signaling pathways. Through LR model screening, 10 important feature DEGs were identified and validated, among which, ABCA3, CCL22, FOXJ1, IL1RN, KCNIP3, MAP2K6, and MRPL13, were significantly expressed in both groups in the two data sets. ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 were good prognostic factors, while KCNIP3 and MRPL13 were poor prognostic factors.ConclusionABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 may serve as good prognostic factors, while KCNIP3 and MRPL13 may be poor prognostic factors for the prognosis of breast cancer.

Highlights

This study intended to determine important genes related to the prognosis and recurrence of breast cancer
We aimed to further determine important genes related to the prognosis and recurrence of breast cancer by analyzing the breast cancer gene expression profile in TCGA database based on Logit regression (LR) model analysis and survival analysis
Differentially expressed genes (DEGs) screening According to the DEG screening threshold (FDR < 0.05 and |log2FC| > 1), a total of 540 DEGs were screened, including 177 significantly downregulated and 363 significantly upregulated DEGs (Fig. 1a)

Summary

Introduction

This study intended to determine important genes related to the prognosis and recurrence of breast cancer. The most widely used prognostic factors for the recurrence of breast cancer included tumor size, histologic grade, and the number of axillary lymph nodes with metastasis [4, 5]. These prognostic factors can supply independent prognostic information for patients with breast cancer, whereas they are not suitable for optimal patient management, especially as we move towards the era of personalized treatment [6]. A recent study has suggested that a variety of gene expression changes have occurred in early or precancerous breast cancer, which often precede the appearance of clinical symptoms and can serve as molecular biomarkers of early breast cancer [7]. Our understanding of the molecular mechanisms of breast cancer recurrence is far from clear because of the molecular heterogeneity of breast cancer

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