Establishment and Validation of a Model for Disease-Free Survival Rate Prediction Using the Combination of microRNA-381 and Clinical Indicators in Patients with Breast Cancer

Abstract

Objective

We have found that miR-381 can regulate the proliferation of breast cancer cells by regulating TWIST protein, it can serve as a potential marker for the tumor progression. Thus, we herein establishment and validation of a model for predicting disease progression in patients with breast cancer using a combination of microRNA-381 (miR-381) and clinical indicators.

Methods

Data from 160 breast cancer patients in the First People’s Hospital of Lianyungang were collected, The relationship between miR-381 expression and tumor subtype was analyzed. The Kaplan–Meier (K–M) curve method was used to investigate the disease-free survival rate, while multivariate Cox regression analysis was used to investigate the risk factors affecting the prognosis of the patients. A model for predicting disease progression was subsequently established and validated.

Results

The miR-381 was significantly higher in the stage I patients than stage II/III patients. The miR-381 level of triple-negative breast cancer (TNBC) type was significantly decreased. The miR-381 could be used to effectively predict the prognosis, using cut-off value of 0.2515, with a sensitivity of 65.38% (51.8–76.85%), specificity of 75.00% (46.77–91.11%). The K–M survival curve indicated that the patients with higher miR-381 expression had a better prognosis. The miR-381+Ki-67+TN model and TN (T and N in TNM staging) model were established and subsequently compared. The TN model had an area under the curve (AUC) of 0.479 (95% CI 0.329, 0.629); in comparison, the our model had an AUC of 0.719 (95% CI 0.580, 0.857), showing better performance.

Conclusion

The miR-381 expression was correlated with different (TNM) stages and tumor subtypes. The higher the TNM stage, the lower the miR-381 expression in the tumor tissue, while it was significantly decreased in TNBC. A prediction model consisting of combination of miR-381 and Ki-67 and TN indicators could predict disease progression more effectively.

Keywords:

• miR-381
• breast cancer
• prognosis
• nomogram
• predictive model

Data Sharing Statement

All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.

Ethics Approval and Consent to Participate

The study was conducted in accordance with the Declaration of Helsinki (as was revised in 2013). The study was approved by Ethics Committee of the First People’s Hospital of Lianyungang (KY-20220426003-01). Written informed consent was obtained from all participants.

Acknowledgments

We are particularly grateful to all the people who have given us help on our article.

Disclosure

The authors declare that they have no competing interests in this work.

Additional information

Funding

There is no funding to report.