4.7 Article

Cumulative receiver operating characteristics for analyzing interaction between tissue visfatin and clinicopathologic factors in breast cancer progression

Journal

CANCER CELL INTERNATIONAL
Volume 18, Issue -, Pages -

Publisher

BMC
DOI: 10.1186/s12935-018-0517-z

Keywords

Visfatin; Progression; Breast cancer; Receiver operating characteristic (ROC); Interaction

Categories

Funding

  1. Ministry of Science and Technology [MOST 104-2320-B-037-013-MY3, MOST 103-2221-E-151-029-MY3]
  2. National Sun Yat-sen University-KMU Joint Research Project [NSYSU-KMU 107-p001]
  3. Kaohsiung Medical University Hospital [KMUH105-5M24]
  4. Kaohsiung Medical University [KMU-M106028]
  5. Health and welfare surcharge of tobacco products
  6. Ministry of Health and Welfare, Taiwan, Republic of China [MOHW106-TDU-B-212-144007, MOHW105-TDU-B-212-134007, MOHW104-TD-B-111-05, MOHW104-TDU-B-212-124-003]

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Background: Visfatin has been reported to be associated with breast cancer progression, but the interaction between the visfatin and clinicopathologic factors in breast cancer progression status requires further investigation. To address this problem, it is better to simultaneously consider multiple factors in sensitivity and specificity assays. Methods: In this study, a dataset for 105 breast cancer patients (84 disease-free and 21 progressing) were chosen. Individual and cumulative receiver operating characteristics (ROC) were used to analyze the impact of each factor along with interaction effects. Results: In individual ROC analysis, only 3 of 13 factors showed better performance for area under curve (AUC), i.e., AUC > 7 for hormone therapy (HT), tissue visfatin, and lymph node (LN) metastasis. Under our proposed scoring system, the cumulative ROC analysis provides higher AUC performance (0.746-0.886) than individual ROC analysis in predicting breast cancer progression. Considering the interaction between these factors, a minimum of six factors, including HT, tissue visfatin, LN metastasis, tumor stage, age, and tumor size, were identified as being highly interactive and associated with breast cancer progression, providing potential and optimal discriminators for predicting breast cancer progression. Conclusion: Taken together, the cumulative ROC analysis provides better prediction for breast cancer progression than individual ROC analysis.

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