An mRNA characterization model predicting survival in patients with invasive breast cancer based on The Cancer Genome Atlas database

Article type: Research Article

Authors: Li, Huayao^a | Gao, Chundi^a | Zhuang, Jing^b | Liu, Lijuan^b | Yang, Jing^b | Liu, Cun^a | Zhou, Chao^a | Feng, Fubin^b | Liu, Ruijuan^b | Sun, Changgang^{a; b; *}

Affiliations: [a] Shandong University of Traditional Chinese Medicine, Shandong, China | [b] Weifang Traditional Chinese Hospital, Shandong, China

Correspondence: [*] Corresponding author: Changgang Sun, Shandong University of Traditional Chinese Medicine, Jinan 250014, Shandong Jinan 25000, China. E-mail: zhongliuyike@163.com.

Abstract: BACKGROUND: Invasive breast cancer is a highly heterogeneous tumor, although there have been many prediction methods for invasive breast cancer risk prediction, the prediction effect is not satisfactory. There is an urgent need to develop a more accurate method to predict the prognosis of patients with invasive breast cancer. OBJECTIVE: To identify potential mRNAs and construct risk prediction models for invasive breast cancer based on bioinformatics METHODS: In this study, we investigated the differences in mRNA expression profiles between invasive breast cancer and normal breast samples, and constructed a risk model for the prediction of prognosis of invasive breast cancer with univariate and multivariate Cox analyses. RESULTS:We constructed a risk model comprising 8 mRNAs (PAX7, ZIC2, APOA5, TP53AIP1,MYBPH, USP41, DACT2, and POU3F2) for the prediction of invasive breast cancer prognosis. We used the 8-mRNA risk prediction model to divide 1076 samples into high-risk groups and low-risk groups, the Kaplan-Meier curve showed that the high-risk group was closely related to the poor prognosis of overall survival in patients with invasive breast cancer. The receiver operating characteristic curve revealed an area under the curve of 0.773 for the 8 mRNA model at 3-year overall survival, indicating that this model showed good specificity and sensitivity for prediction of prognosis of invasive breast cancer. CONCLUSIONS: The study provides an effective bioinformatic analysis for the better understanding of the molecular pathogenesis and prognosis risk assessment of invasive breast cancer.

Keywords: Invasive breast cancer, univariate and multivariate Cox analyses, bioinformatic analysis, 8-mRNA model

DOI: 10.3233/CBM-201684

Journal: Cancer Biomarkers, vol. 30, no. 4, pp. 417-428, 2021