Molecular Subtyping: status of the molecular factors in the locally advanced breast cancer and its correlation with risk factors
DOI:
https://doi.org/10.3329/bsmmuj.v14i3.56599Keywords:
Breast cancer, locally advanced breast cancer, molecular subtypes, Estrogen Receptor, Progesterone receptor, Human Epidermal Growth Factor Receptor -2, ki67Abstract
Molecular factors play an important role in the management and treatment outcome of breast cancer. Molecular subtyping has been developed depending upon estrogen and progesterone receptors, human epidermal growth factor receptor-2 and ki76 level. This cross-sectional study was done to assess the molecular subtypes of locally advanced breast cancer and its associated risk factors. Total 94 patients with locally advanced breast cancer were included in the study. The mean age was 42.6 years with a standard deviation of 9.56. In total, 91.5 percent of respondents had menarche at or after the age of 12, and 26.6 % had used hormonal contra- ception in the past. Tobacco users and positive family history were found in 21.2% and 5.35% of the cases. Among the patients, 3.4 % of cases had their first childbirth after the age of 30 and 95.5% of patients feed their babies from their both breasts. Among 94 cases 5 did not have any child. Estrogen receptor was found positive in 35% of cases, progesterone receptor-positive patient was 33% and HER-2 was found positive in 39.4% of cases. Ki-67 level was found high in 66% of cases. Among the 94 cases, the Luminal A subtype was found in 18% and the Lumi- nal B subtype was found in 27.7% cases. The human epidermal growth factor receptor-2 subtype was found relatively less frequent than Luminal type B (24.5% vs. 27.7%). Triple-neg- ative breast cancer was most commonly diagnosed among the patients (almost 30%). The increased number of triple-negative variants signifies poor prognostic outcomes. The risk factor of breast cancer did not show any statistical correlation with molecular subtypes.
BSMMU J 2021; 14(3): 57-61
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Copyright (c) 2021 Hasan Shahrear Ahmed, Krisna Rani Majumder, Mohammad Jahan Shams, AFM Anwar Hossain, Md Mizanur Rahman
This work is licensed under a Creative Commons Attribution 4.0 International License.