Clement G Yedjou, William Johnson, Solange S Tchounwou, Shaloam Dasari, Sylvianne Njiki, Paul B Tchounwou
Background: Prostate cancer (PCa) is one of the common cancers in males and its incidence keeps increasing globally. Approximately 81% of PCa is diagnosed during the early stage of the disease. The treatment options for prostate care include surgery, radiotherapy, and chemotherapy, but these treatments often have side effects that may lead to issues such as impotence or decreased bowel function. Our central goal is to test the apoptotic effects of Vernonia amygdalina Delile (an edible medicinal plant that is relatively inexpensive, nontoxic, and virtually without side effects) for the prevention of PCa using human adenocarcinoma (PC-3) cells as a test model.
Methods: To address our central goal, PC-3 cells were treated with Vernonia amygdalina Delile (VAD). Cell cycle arrest and cell apoptosis were evaluated by Flow Cytometry assessment. Nucleosomal DNA fragmentation was detected by agarose gel electrophoresis.
Results: Flow cytometry data showed that VAD induced cell cycle arrest at the G0/G1 checkpoint and significantly upregulated caspase-3 in treated cells compared to the control cells. Agarose gel electrophoresis resulted in the formation of DNA ladders in VAD-treated cells.
Conclusions: These results suggest that inhibition of cancer cell growth, induction of cell cycle arrest, and apoptosis through caspase-3 activation and nucleosomal DNA fragmentation are involved in the therapeutic mechanisms of VAD as a candidate drug towards the prevention and/or treatment of PCa.
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Clement G Yedjou, Solange S Tchounwou, Jameka Grigsby, Kearra Johnson, Paul B Tchounwou
Breast cancer (BC) is the most common malignancy in women worldwide. In the United States, the lifetime risk of developing an invasive form of breast cancer is 12.5% among women. BC arises in the lining cells (epithelium) of the ducts or lobules in the glandular tissue of the breast. The goal of the present study was to use machine learning (ML) as a novel technology to assess and compare the invasive forms of BC including, infiltrating ductal carcinoma, infiltrating lobular carcinoma, and mucinous carcinoma. To achieve this goal, we used ML algorithms and collected a dataset of 334 BC patients available at https://www.kaggle.com/amandam1/breastcancerdataset and interpreted this dataset based on the form of BC, age, sex, tumor stages, surgery type, and survival rate. Among the 334 patients, 70% were diagnosed with infiltrating ductal carcinoma, 27% with infiltrating lobular carcinoma, and 3% with mucinous carcinoma. Overall, out of 334 BC patients: 64 (19.16%) were in stage I, 189 (56.59%) in stage II, and 81 (24.25%) in stage III. Sixty-six, 67, 96, and 105 patients underwent lumpectomy, simple mastectomy, modified radical mastectomy, and other types of surgery, respectively. The survival rates were 83.4% for stage I, 79.1% for stage II, and 77% for stage III. Findings from the present study demonstrated that ML provides an important tool to curate large amount of BC data, as well as a scientific means to improve BC outcomes.
乳腺癌（BC）是全球女性最常见的恶性肿瘤。在美国，女性一生中患浸润性乳腺癌的风险为 12.5%。乳腺癌产生于乳腺腺体组织中导管或小叶的内膜细胞（上皮）。本研究的目的是利用机器学习（ML）这一新型技术来评估和比较浸润性乳腺癌，包括浸润性导管癌、浸润性小叶癌和粘液腺癌。为了实现这一目标，我们使用了 ML 算法，并从 https://www.kaggle.com/amandam1/breastcancerdataset 收集了 334 名 BC 患者的数据集，并根据 BC 的形式、年龄、性别、肿瘤分期、手术类型和存活率对数据集进行了解读。在 334 名患者中，70% 被诊断为浸润性导管癌，27% 被诊断为浸润性小叶癌，3% 被诊断为粘液腺癌。总体而言，在 334 名 BC 患者中，有 64 人（19.16%）被确诊为浸润性导管癌：64人（19.16%）处于I期，189人（56.59%）处于II期，81人（24.25%）处于III期。接受肿块切除术、单纯乳房切除术、改良根治性乳房切除术和其他类型手术的患者分别为 66 人、67 人、96 人和 105 人。I 期患者的存活率为 83.4%，II 期患者的存活率为 79.1%，III 期患者的存活率为 77%。本研究的结果表明，ML 是整理大量乳腺癌数据的重要工具，也是改善乳腺癌预后的科学手段。
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