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BackgroundKi67 is a biomarker of proliferation to be used in immunohistochemistry (IHC)-based surrogate assay to determine the necessity of cytotoxic therapy for Luminal-like breast cancer patients. cyclinD1 is another frequently used biomarker of proliferation. A retrospective study was performed here to investigate if these two biomarkers may be combined to improve the prognosis of Luminal-like patients.MethodsBoth Ki67 and cyclinD1 protein levels were measured absolutely and quantitatively using Quantitative Dot Blot method in 143 Luminal-like specimens. Optimized cutoffs for these two biomarkers were developed to evaluate their prognostic roles using Kaplan–Meier overall survival (OS) analysis.ResultscyclinD1 was found as an independent prognostic factor from Ki67 in univariate and multivariate OS analyses. At optimized cutoffs (cyclinD1 at 0.44 μmol/g and Ki67 at 2.31 nmol/g), the subgroup with both biomarkers below the cutoffs (n = 65) had 10-year survival probability at 90% in comparison to those with both biomarkers above the cutoffs (n = 18) with 8-year survival probability at 26% (log-rank test, p

BackgroundImmunohistochemistry (IHC)-based surrogate assay is the prevailing method in daily clinical practice to determine the necessity of chemotherapy for Luminal-like breast cancer patients worldwide. It relies on Ki67 scores to separate Luminal A-like from Luminal B-like breast cancer subtypes. Yet, IHC-based Ki67 assessment is known to be plagued with subjectivity and inconsistency to undermine the performance of the surrogate assay. A novel method needs to be explored to improve the clinical utility of Ki67 in daily clinical practice.Materials and MethodsThe Ki67 protein levels in a cohort of 253 specimens were assessed with IHC and quantitative dot blot (QDB) methods, respectively, and used to assign these specimens into Luminal A-like and Luminal B-like subtypes accordingly. Their performances were compared with the Kaplan–Meier, univariate, and multivariate survival analyses of the overall survival (OS) of Luminal-like patients.ResultsThe surrogate assay based on absolutely quantitated Ki67 levels (cutoff at 2.31 nmol/g) subtyped the Luminal-like patients more effectively than that based on Ki67 scores (cutoff at 14%) (Log rank test, p = 0.00052 vs. p = 0.031). It is also correlated better with OS in multivariate survival analysis [hazard ratio (HR) at 6.89 (95% CI: 2.66–17.84, p = 0.0001) vs. 2.14 (95% CI: 0.89–5.11, p = 0.087)].ConclusionsOur study showed that the performance of the surrogate assay may be improved significantly by measuring Ki67 levels absolutely, quantitatively, and objectively using the QDB method.

Epigenetics, including DNA methylation, histone modification, and noncoding RNA regulation, are physiological regulatory changes that affect gene expression without modifying the DNA sequence. Although epigenetic disorders are considered a sign of cell carcinogenesis and malignant events that affect tumor progression and drug resistance, in view of the reversible nature of epigenetic modifications, clinicians believe that associated mechanisms can be a key target for cancer prevention and treatment. In contrast, epidemiological and preclinical studies indicated that the epigenome is constantly reprogrammed by intake of natural organic compounds and the environment, suggesting the possibility of utilizing natural compounds to influence epigenetics in cancer therapy. Flavonoids, although not synthesized in the human body, can be consumed daily and are common in medicinal plants, vegetables, fruits, and tea. Recently, numerous reports provided evidence for the regulation of cancer epigenetics by flavonoids. Considering their origin in natural and food sources, few side effects, and remarkable biological activity, the epigenetic antitumor effects of flavonoids warrant further investigation. In this article, we summarized and analyzed the multi-dimensional epigenetic effects of all 6 subtypes of flavonoids (including flavonols, flavones, isoflavones, flavanones, flavanols, and anthocyanidin) in different cancer types. Additionally, our report also provides new insights and a promising direction for future research and development of flavonoids in tumor prevention and treatment via epigenetic modification, in order to realize their potential as cancer therapeutic agents.

Background:Astragalus polysaccharides (APS), natural plant compounds, have recently emerged as a promising strategy for cancer treatment, but little is known concerning their effects on breast cancer (BC) tumorigenesis.Methods: We obtained breast cancer genetic data from The Cancer Genome Atlas (TCGA) database, network pharmacology to further clarify its biological properties. Survival analysis and molecular docking techniques were implemented for the final screening to obtain key target information. Our experiments focused on the detection of intervention effects of APS on BC cells (MCF-7 and MDA-MB-231), and quantitative RT-PCR (qRT-PCR) was used to assess the expression of key targets.Results: A total of 1,439 differentially expressed genes (DEGs) were identified by TCGA and used to build disease networks. Module analysis, gene ontology and pathway analysis revealed characteristic of the DEGs network. Topological properties were used to identify key targets, survival analysis and molecular docking finally found that the targets of APS regulation of BC cells may be CCNB1, CDC6, and p53. Through cell viability, migration and invasion assays, we found that APS interferes with the development of breast cancer in MCF7 and MDA-MB-231 cells in a dose-dependent manner. Furthermore, qRT-PCR verification suggested that the expression of CCNB1 and CDC6 in breast cancer cells was significantly downregulated in response to APS, while expression of the tumor suppressor gene P53 was significantly increased.Conclusion: Results of this study suggest therapeutic potential for APS in BC treatment, possibly through interventions with CCNB1, CDC6, and P53. Furthermore, these findings illustrate the feasibility of using network pharmacology to connect large-scale target data as a way to discover the mechanism of natural products interfering with disease.