Format

Send to

Choose Destination
Cancer Med. 2019 Feb;8(2):593-605. doi: 10.1002/cam4.1962. Epub 2019 Jan 11.

Three inflammation-related genes could predict risk in prognosis and metastasis of patients with breast cancer.

Author information

1
Breast Disease Center, Peking University People's Hospital, Peking University, Beijing, China.
2
Department of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
3
Department of Pathology and Institute of Precision Medicine, Jining Medical University, Jining, China.
4
The School of Medicine, Nankai University, Tianjin, China.

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.

KEYWORDS:

CYCS; TBX21; TGIF2; breast cancer; prognosis

Supplemental Content

Full text links

Icon for Wiley Icon for PubMed Central
Loading ...
Support Center