Inflammatory Cytokines Associated with Obesity, Type-2 Diabetes, and Hypertension Exacerbate Breast Cancer Risk in Underserved African American and Latin American Women.

Background: Comorbid chronic diseases, such as obesity, Type-2 Diabetes (T2D), and hypertension (HTN), are major public health issues and highly prevalent among underserved African Americans (AA) and Latin Americans (LA). Elevated inflammatory cytokines are underlying processes in comorbidities (obesity, T2D, and HTN) that could contribute to tumorigenesis and adverse cancer outcomes. Methods: A panel of 19 cytokines was measured by Luminex assay from 570 AA and LA women's serum samples. The comorbidities and breast cancer information were extracted from our existing clinical database. Comorbidity-associated cytokines were identified by linear regression analysis, and the odds ratios of increasing cytokines for breast cancer were evaluated by Logistic regression. Results: Women with obesity, T2D, and HTN elevated specific groups of cytokines. EGF, MCP1, MDC, MIP-1b, and Groα were independent of T2D and HTN significantly associated with obesity. TGFß1 and TGFß2 were T2D-associated cytokines, and MIB-1b, TNFα, and VEGFα were HTN-associated cytokines. Among those comorbidity-associated cytokines, CXCL1, CCL4, CXCL10, TNFα, TGFß1, and TGFß2 were also significantly associated with breast cancer diagnosed at age < 50. Two or more comorbidities further increased the levels of Groα, MIP-1b, TNFα, and TGFßs. Conclusions: Comorbidity-associate cytokines could augment the risk of breast cancer for AA and LA women.

Advancements in small molecule drug design: A structural perspective.

In this review, we outline recent advancements in small molecule drug design from a structural perspective. We compare protein structure prediction methods and explore the role of the ligand binding pocket in structure-based drug design. We examine various structural features used to optimize drug candidates, including functional groups, stereochemistry, and molecular weight. Computational tools such as molecular docking and virtual screening are discussed for predicting and optimizing drug candidate structures. We present examples of drug candidates designed based on their molecular structure and discuss future directions in the field. By effectively integrating structural information with other valuable data sources, we can improve the drug discovery process, leading to the identification of novel therapeutics with improved efficacy, specificity, and safety profiles.

Roles of Protein Disulfide Isomerase in Breast Cancer.

Protein disulfide isomerase (PDI) is the endoplasmic reticulum (ER)'s most abundant and essential enzyme and serves as the primary catalyst for protein folding. Due to its apparent role in supporting the rapid proliferation of cancer cells, the selective blockade of PDI results in apoptosis through sustained activation of UPR pathways. The functions of PDI, especially in cancers, have been extensively studied over a decade, and recent research has explored the use of PDI inhibitors in the treatment of cancers but with focus areas of other cancers, such as brain or ovarian cancer. In this review, we discuss the roles of PDI members in breast cancer and PDI inhibitors used in breast cancer research. Additionally, a few PDI members may be suggested as potential molecular targets for highly metastatic breast cancers, such as TNBC, that require more attention in future research.