Dataset Information

Effects of Luteolin on Human Breast Cancer Using Gene Expression Array: Inferring Novel Genes.

ABSTRACT: Taraxacum officinale (dandelion) is often used in traditional Chinese medicine for the treatment of cancer; however, the downstream regulatory genes and signaling pathways mediating its effects on breast cancer remain unclear. The present study aimed to explore the effects of luteolin, the main biologically active compound of T. officinale, on gene expression profiles in MDA-MB-231 and MCF-7 breast cancer cells. The results revealed that luteolin effectively inhibited the proliferation and motility of the MDA-MB-231 and MCF-7 cells. The mRNA expression profiles were determined using gene expression array analysis and analyzed using a bioinformatics approach. A total of 41 differentially expressed genes (DEGs) were found in the luteolin-treated MDA-MB-231 and MCF-7 cells. A Gene Ontology analysis revealed that the DEGs, including AP2B1, APP, GPNMB and DLST, mainly functioned as oncogenes. The human protein atlas database also found that AP2B1, APP, GPNMB and DLST were highly expressed in breast cancer and that AP2B1 (cut-off value, 75%) was significantly associated with survival rate (p = 0.044). In addition, a Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that the DEGs were involved in T-cell leukemia virus 1 infection and differentiation. On the whole, the findings of the present study provide a scientific basis that may be used to evaluate the potential benefits of luteolin in human breast cancer. Further studies are required, however, to fully elucidate the role of the related molecular pathways.

SUBMITTER: Wang SH

PROVIDER: S-EPMC9164068 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Effects of Luteolin on Human Breast Cancer Using Gene Expression Array: Inferring Novel Genes.

Wang Shih-Ho SH Wu Chin-Hu CH Tsai Chin-Chuan CC Chen Tai-Yu TY Tsai Kuen-Jang KJ Hung Chao-Ming CM Hsu Chia-Yi CY Wu Chia-Wei CW Hsieh Tsung-Hua TH

Current issues in molecular biology 20220509 5

<i>Taraxacum officinale</i> (dandelion) is often used in traditional Chinese medicine for the treatment of cancer; however, the downstream regulatory genes and signaling pathways mediating its effects on breast cancer remain unclear. The present study aimed to explore the effects of luteolin, the main biologically active compound of <i>T. officinale</i>, on gene expression profiles in MDA-MB-231 and MCF-7 breast cancer cells. The results revealed that luteolin effectively inhibited the prolifera ...[more]

PMID: 35678671

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Project description:The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of biotechnological processes that use complex, largely uncharacterized media, such as biofuel production.

Dataset Information

Effects of Luteolin on Human Breast Cancer Using Gene Expression Array: Inferring Novel Genes.

Publications

Effects of Luteolin on Human Breast Cancer Using Gene Expression Array: Inferring Novel Genes.

Similar Datasets

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