Objectives: The purpose of this study was to evaluate the relationship between body mass index and lipid profiles with breast cancer prognosis together with the relationship of these parametres with known breast cancer prognostic indices including c-erbB2 expression.
Patients And Methods: Four hundred and thirty-three patients diagnosed with breast cancer at Ankara University, Faculty of Medicine, Department of Medical Oncology made up the study population. The primary endpoints were relapse and death. Body mass index at the time of diagnosis, lipid levels at the time of diagnosis, estrogen receptor status, progesterone receptor status, c-erbB2 expression, tumor grade, patient age, axillary lymph node involvement level, tumor stage, menopausal status and surgery details were taken into account.
Results: The mean body mass indices were similar in the remission, relapse and mortality groups. Patients with body mass indices higher than 30 kg/m² had a lower incidence of c-erbB2 expression when compared to patients with body mass indices < 18.5 kg/m(2) (19 vs. 50 %, p = 0.009). Survival analysis revealed that patients with body mass indices < 18.5 kg/m(2) had significantly shorter disease free survivals when compared to patients with body mass indices between 25 and 29.9 kg/m(2). Mean serum lipid levels were similar in the remission, relapse and mortality groups. A trend toward relapse was shown in patients with total cholesterol > 240 mg/dl, but this was statistically insignificant. Survival analysis revealed that patients with triglyceride levels lower than 150 mg/dl had a statistically significant longer disease-free survival when compared to the other groups. Again a trend towards shorter overall survival was seen in patients with total cholesterol > 240 mg/dl, but this relationship was also statistically insignificant.
Conclusion: Most large previous studies reported adverse breast cancer outcome with obesity. However in our study, patients with lower body weight had a shorter disease-free survival. This could be explained by the low number of patients in this study, genetic profile of the patient population, possible weight changes after treatment and the inverse relationship between body mass index and c-erbB2 expression.
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http://dx.doi.org/10.1007/s12094-012-0878-z | DOI Listing |
Adv Sci (Weinh)
January 2025
Department of Radiology, The Second Hospital of Tianjin Medical University, Tianjin, 300211, P. R. China.
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January 2025
Translational Research Support Section, National Cancer Center Hospital East, Chiba, Japan.
Early cancer detection substantially improves the rate of patient survival; however, conventional screening methods are directed at single anatomical sites and focus primarily on a limited number of cancers, such as gastric, colorectal, lung, breast, and cervical cancer. Additionally, several cancers are inadequately screened, hindering early detection of 45.5% cases.
View Article and Find Full Text PDFBreast Cancer Res Treat
January 2025
Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Cell Mol Biol (Noisy-le-grand)
January 2025
Department of Integrative Medicine, Huashan Hospital, Fudan University, Shanghai, China.
Mitochondrial ribosomal protein S23 (MRPS23), encoded by a nuclear gene, is a well-known driver of proliferation in cancer. It participates in mitochondrial protein translation, and its expression association has been explored in many types of cancer. However, MRPS23 expression associations are rarely reported in breast cancer (BC).
View Article and Find Full Text PDFCell Mol Biol (Noisy-le-grand)
January 2025
Istanbul University, Faculty of Science, Department of Biology, Istanbul, Türkiye.
In this study, the effects of histone deacetylase inhibitor CI-994 and nanotechnological drug liposomal cisplatin LipoPlatin on Luminal A breast cancer and triple-negative breast cancer were explored using agents alone and in combination. MCF-7 and MDA-MB-231 cell lines were used. Cell viability, and cell index values obtained from xCELLigence System, MI, BrdU LI and AI were evaluated in experiments.
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