Antiestrogenic Activity and Possible Mode of Action of Certain New Nonsteroidal Coumarin-4-acetamides

Identification of Candidate Genes in Breast Cancer Induced by Estrogen Plus Progestogens Using Bioinformatic Analysis

Menopausal hormone therapy (MHT) was widely used to treat menopause-related symptoms in menopausal women. However, MHT therapies were controversial with the increased risk of breast cancer because of different estrogen and progestogen combinations, and the molecular basis behind this phenomenon is currently not understood. To address this issue, we identified differentially expressed genes (DEGs) between the estrogen plus progestogens treatment (EPT) and estrogen treatment (ET) using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data. As a result, a total of 96 upregulated DEGs were first identified. Seven DEGs related to the cell cycle (CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3) were validated by RT-qPCR. Specifically, these seven DEGs were increased in EPT compared to ET (p < 0.05) and had higher expression levels in breast cancer than adjacent normal tissues (p < 0.05). Next, we found that estrogen receptor (ER)-positive breast cancer patients with a higher CNNE2 expression have a shorter overall survival time (p < 0.05), while this effect was not observed in the other six DEGs (p > 0.05). Interestingly, the molecular docking results showed that CCNE2 might bind to 17β-estradiol (−6.791 kcal/mol), progesterone (−6.847 kcal/mol), and medroxyprogesterone acetate (−6.314 kcal/mol) with a relatively strong binding affinity, respectively. Importantly, CNNE2 protein level could be upregulated with EPT and attenuated by estrogen receptor antagonist, acolbifene and had interactions with cancer driver genes (AKT1 and KRAS) and high mutation frequency gene (TP53 and PTEN) in breast cancer patients. In conclusion, the current study showed that CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3 might contribute to EPT-related tumorigenesis in breast cancer, with CCNE2 might be a sensitive risk indicator of breast cancer risk in women using MHT.

Indocyanine Green-Camptothecin Co-Loaded Perfluorocarbon Double-Layer Nanocomposite: A Versatile Nanotheranostics for Photochemotherapy and FDOT Diagnosis of Breast Cancer

Breast cancer remains the most frequently diagnosed cancer and is the leading cause of neoplastic disease burden for females worldwide, suggesting that effective therapeutic and/or diagnostic strategies are still urgently needed. In this study, a type of indocyanine green (ICG) and camptothecin (CPT) co-loaded perfluorocarbon double-layer nanocomposite named ICPNC was developed for detection and photochemotherapy of breast cancer. The ICPNCs were designed to be surface modifiable for on-demand cell targeting and can serve as contrast agents for fluorescence diffuse optical tomography (FDOT). Upon near infrared (NIR) irradiation, the ICPNCs can generate a significantly increased production of singlet oxygen compared to free ICG, and offer a comparable cytotoxicity with reduced chemo-drug dosage. Based on the results of animal study, we further demonstrated that the ICPNCs ([ICG]/[CPT] = 40-/7.5-μM) in association with 1-min NIR irradiation (808 nm, 6 W/cm²) can provide an exceptional anticancer effect to the MDA-MB-231 tumor-bearing mice whereby the tumor size was significantly reduced by 80% with neither organ damage nor systemic toxicity after a 21-day treatment. Given a number of aforementioned merits, we anticipate that the developed ICPNC is a versatile theranostic nanoagent which is highly promising to be used in the clinic.

Comparing Machine Learning Models for Aromatase (P450 19A1)

Aromatase, or cytochrome P450 19A1, catalyzes the aromatization of androgens to estrogens within the body. Changes in the activity of this enzyme can produce hormonal imbalances that can be detrimental to sexual and skeletal development. Inhibition of this enzyme can occur with drugs and natural products as well as environmental chemicals. Therefore, predicting potential endocrine disruption via exogenous chemicals requires that aromatase inhibition be considered in addition to androgen and estrogen pathway interference. Bayesian machine learning methods can be used for prospective prediction from the molecular structure without the need for experimental data. Herein, the generation and evaluation of multiple machine learning models utilizing different sources of aromatase inhibition data are described. These models are applied to two test sets for external validation with molecules relevant to drug discovery from the public domain. In addition, the performance of multiple machine learning algorithms was evaluated by comparing internal five-fold cross-validation statistics of the training data. These methods to predict aromatase inhibition from molecular structure, when used in concert with estrogen and androgen machine learning models, allow for a more holistic assessment of endocrine-disrupting potential of chemicals with limited empirical data and enable the reduction of the use of hazardous substances.