BMC Cancer (Sep 2024)

Adipogenesis biomarkers as the independent predictive factors for breast cancer recurrence: a systematic review and meta-analysis

  • Shihang Hu,
  • Sze Keong Tey,
  • Ava Kwong

DOI
https://doi.org/10.1186/s12885-024-12931-1
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 12

Abstract

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Abstract Background Comprehensive analysis of clinical evidence for breast cancer adipogenesis with prognosis is lacking. This study aims to consolidate the latest evidence on the relationship between adipogenesis and breast cancer outcomes. Data sources : Medline, Web of Science, Embase, Scopus, Clinicaltrials.gov, Cochrane library. Methods A systematic review was conducted according to the PRISMA guidelines. Studies that reported the correlation between tumor adipogenesis and cancer recurrence or empirical pathological markers were included for meta-analysis. The standard reference for pathological markers determination was set as histopathological examination. The PROSPERO ID was CRD489135. Results Eleven studies were included in this systematic review and meta-analysis. Several adipogenesis biomarkers involved in the synthesis, elongation, and catabolism of fatty acids, such as FASN, Spot 14, pS6K1, lipin-1, PLIN2, Elovl6, and PPARγ, were identified as the potential biomarkers for predicting outcomes. Through meta-analysis, the predictive value of adipogenesis biomarkers for 5-year recurrence rate was calculated, with a pooled predictive risk ratio of 2.19 (95% CI: 1.11–4.34). In terms of empirical pathological markers, a negative correlation between adipogenesis biomarkers and ki-67 was observed (RR: 0.69, 95% CI: 0.61–0.79). However, no significant correlation was found between the adipogenesis and ER, PR, HER2, or p53 positivity. Conclusions Biomarker of adipogenesis in breast cancer is a significant predictor of long-term recurrence, and this prediction is independent of HR, HER2, and ki-67. The diverse roles of adipogenesis in different breast cancer subtypes highlight the need for further research to uncover specific biomarkers that can used for diagnosis and prediction. Protocol registration PROSPERO ID: CRD489135.

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