Author
Listed:
- Lei Dong
- Can Liu
- Lixin Li
- Xiaotong Zhang
- Wei Xu
- Gaosai Dai
Abstract
Breast cancer (BC) represents the most prevalent malignant tumor among women globally, with its pronounced heterogeneity presenting significant challenges for precision diagnosis and treatment. Aberrant lipid metabolism, particularly the ratio of low-density lipoprotein to high-density lipoprotein (LHR), has garnered considerable interest in relation to BC development and progression. Nevertheless, the association between LHR and clinicopathological characteristics remains inadequately explored. To address this gap, the present retrospective study analyzed 583 BC patients who underwent surgical treatment at Qilu Hospital of Shandong University. Preoperative blood lipid profiles and clinicopathological data were systematically collected to investigate the association between LHR and critical features, including molecular subtypes. The findings indicated that LHR levels were significantly elevated in patients aged 46 years or older, postmenopausal women, and individuals with a body mass index of 25 kg/m2 or higher. Subgroup analyses incorporating age, menopausal status, and BMI suggested potential interactions between these clinical factors and LHR. Although univariate analysis demonstrated an association between LHR and certain clinicopathological features, multivariate logistic regression analysis revealed that menopausal status, rather than LHR, was the sole independent predictor of ER-positive and PR-positive status. Therefore, LHR should not be considered an independent predictive biomarker for receptor status. In conclusion, although the LHR demonstrates statistically significant associations with certain clinicopathological characteristics, its modest effect sizes and discriminative ability close to chance indicate limited clinical utility as an independent biomarker. These preliminary findings highlight the need for future research focused on identifying reliable metabolic signatures. Such studies should consider utilizing multi-marker panels instead of relying exclusively on individual lipid metrics for risk stratification in breast cancer.
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