乳腺癌亚型在不同种族和孟加拉国妇女中的流行:人口统计学、临床病理学和综合癌症信息学分析。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Diganta Islam, Md Shihabul Islam, Sanjida Islam Dorin, Jesmin
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引用次数: 0

摘要

背景:乳腺癌的分子分型与雌激素受体(ER)、孕激素受体(PR)和人表皮生长因子受体2 (HER2)有关。本研究旨在系统分析不同民族和孟加拉乳腺癌(BC)患者中ER、PR、HER2的表达、功能、预后价值及其流行情况。方法:本研究纳入25例BC患者和25例健康对照,年龄在25 ~ 70岁之间。采用方差分析和卡方检验比较研究特征。此外,对来自TCGA的775例BC患者的多组学数据集进行了乳腺癌亚型的ER、PR和HER2分析,并在不同种族之间进行了比较。结果:对于大多数BD乳腺癌病例,诊断时的年龄为大于或等于40岁,只有组织病理学诊断(p值0.004),没有乳房x光检查或其他病理检查的历史。在治疗方面,只有化疗(p值0.004)和没有激素治疗(p值60%)的ii期癌症和TNBC亚型(p值40%)。BC种族分层数据显示,ER、PR和HER2在所有种族之间具有很强的相关性(p值4.99e-35;假定值3.79 e-18)。亚型分层数据显示,白种人中Luminal a(58.3%)的比例较高,而亚洲人中Luminal B(24.3%)和HER2(25.2%)亚型的比例较高,非洲人中TNBC(36.0%)的比例较高。然而,在BD患者中发现TNBC的频率(52.2%)明显高于亚洲人(14.8%)(p值p值0.005),HER2富集(p值0.015)明显更具侵袭性,在亚洲人群中占主导地位。结论:不同种族的BC亚型与孟加拉国妇女之间存在显著关联,这些发现可能有助于在不久的将来预防、管理和提高对危险因素的认识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prevalence of Breast Cancer Subtypes Among Different Ethnicities and Bangladeshi Women: Demographic, Clinicopathological, and Integrated Cancer Informatics Analysis.

Prevalence of Breast Cancer Subtypes Among Different Ethnicities and Bangladeshi Women: Demographic, Clinicopathological, and Integrated Cancer Informatics Analysis.

Prevalence of Breast Cancer Subtypes Among Different Ethnicities and Bangladeshi Women: Demographic, Clinicopathological, and Integrated Cancer Informatics Analysis.

Prevalence of Breast Cancer Subtypes Among Different Ethnicities and Bangladeshi Women: Demographic, Clinicopathological, and Integrated Cancer Informatics Analysis.

Background: The molecular subtyping of breast cancer is related to estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). The present study aimed to systematically analyze the expression, function, and prognostic value of ER, PR, HER2, and their prevalence in different ethnic groups and among Bangladeshi breast cancer (BC) patients.

Method: This study included 25 BC patients and 25 healthy controls, aged between 25 and 70 years. The study characteristics were compared using the ANOVA and Chi-square tests. Also, the multi-Omics dataset of 775 BC patients from TCGA was analyzed for ER, PR, and HER2 in breast cancer subtypes and compared among different ethnicities.

Results: For most BD breast cancer cases, the age at diagnosis was ⩾40 years, had only a histopathological diagnosis (P-value .004), and no history of mammography or other pathological tests. For treatment, had only chemotherapy (P-value .004) and no hormone therapy (P-value <.001). The majority of patients (>60%) were of stage-II cancer and TNBC (40%) subtype. The BC ethnicity-stratified data of ER, PR, and HER2 indicated a strong correlation across all ethnicities (P-value 4.99e-35; P-value 3.79e-18). The subtypes stratified data indicated a higher percentage of Luminal A (58.3%) in Caucasians whereas Luminal B (24.3%) and HER2 (25.2%) subtypes were found higher in Asians and TNBC (36.0%) were found in Africans. However, a significantly higher frequency of TNBC (52.2%) compared to Asians (14.8%) was found in BD patients (P-value <.001). The overall survival analysis of BC subtypes demonstrated that Luminal B (P-value .005) and HER2 enriched (P-value .015) were significantly more aggressive and were dominant in the Asian population.

Conclusion: A significant association was found between BC subtypes with different ethnicities and Bangladeshi women and these findings might aid in the prevention, management, and raising of awareness against risk factors in the near future.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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