Three inflammation‐related genes could predict risk in prognosis and metastasis of patients with breast cancer. (11th January 2019)

• Record Type: Journal Article
• Authors: Zhao, Shuangtao; Shen, Wenzhi; Du, Renle; Luo, Xiaohe; Yu, Jiangyong; Zhou, Wei; Dong, Xiaoli; Gao, Ruifang; Wang, Chaobin; Yang, Houpu; Wang, Shu
• Is Part Of: Cancer medicine. Volume 8:Number 2(2019:Feb.)
• Page Start: 593
• Page End: 605
• Publication Date: 2019-01-11
• Subjects: breast cancer -- CYCS -- prognosis -- TBX21 -- TGIF2; 616.994005
• DOI: 10.1002/cam4.1962
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract: Background: Current predictive model is not developed by inflammation‐related genes to evaluate clinical outcome of breast cancer patients. Methods: With mRNA expression profiling, we identified 3 mRNAs with significant expression between 15 normal samples and 669 breast cancer patients. Using 7 cell lines and 150 paraffin‐embedded specimens, we verified the expression pattern by bio‐experiments. Then, we constructed a three‐mRNA model by Cox regression method and approved its predictive accuracy in both training set (n = 1095) and 4 testing sets (n = 703). Results: We developed a three‐mRNA ( TBX21, TGIF2, and CYCS ) model to stratify patients into high‐ and low‐risk subgroup with significantly different prognosis. In training set, 5‐year OS rate was 84.5% (78.8%‐90.5%) vs 73.1% (65.9%‐81.2%) for the low‐ and high‐risk group (HR = 1.573 (1.090‐2.271); P = 0.016). The predictive value was similar in four independent testing sets (HR>1.600; P < 0.05). This model could assess survival independently with better predictive power compared with single clinicopathological risk factors and any of the three mRNAs. Patients with both low‐risk values and any poor prognostic factors had more favorable survival from nonmetastatic status (HR = 1.740 (1.028‐2.945), P = 0.039). We established two nomograms for clinical application that integrated this model and another three significant risk factors to forecast survival rates precisely in patients with or without metastasis. Conclusions: This model is a dependable tool to predict the disease recurrence precisely and could improve the predictive accuracy of survival probability for breast cancer patients with or without metastasis. Abstract : This study revealed an important novel finding by reporting a mRNA‐based classifier developed by Cox proportional hazards regression algorithm with a three‐mRNA signature to predict prognosis of breast cancer with or without metastasis in 1938 patients. The robustness and the independence of the prognostic model were successfully validated in training and test datasets.