Dataset Information

Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties.

ABSTRACT: Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural products and obtained key structural properties, such as chemical bond or group, with the purpose of ascertaining their effect on a single cell line or a single tissue type. Here, for the first time, we develop a machine learning method to comprehensively predict natural products responses against a panel of cancer cell lines based on both the gene expression and the chemical properties of natural products. The results on two datasets, training set and independent test set, show that this proposed method yields significantly better prediction accuracy. In addition, we also demonstrate the predictive power of our proposed method by modeling the cancer cell sensitivity to two natural products, Curcumin and Resveratrol, which indicate that our method can effectively predict the response of cancer cell lines to these two natural products. Taken together, the method will facilitate the identification of natural products as cancer therapies and the development of precision medicine by linking the features of patient genomes to natural product sensitivity.

SUBMITTER: Yue Z

PROVIDER: S-EPMC4671159 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties.

Yue Zhenyu Z Zhang Wenna W Lu Yongming Y Yang Qiaoyue Q Ding Qiuying Q Xia Junfeng J Chen Yan Y

PeerJ 20151126

Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural products and obtained key structural properties, such as chemical bond or group, with the purpose of ascertaining their effect on a single cell line or a single tissue type. Here, for the first time, we ...[more]

PMID: 26644976

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Project description:Background: Gastric cancer is one of the most important malignancies with poor prognosis. Ferroptosis and cuproptosis are newly discovered metal-dependent types of programmed cell death, which may directly affect the outcome of gastric cancer. Long noncoding RNAs (lncRNAs) can affect the prognosis of cancer with stable structures, which could be potential prognostic prediction factors for gastric cancer. Methods: Differentially expressed metal-dependent programmed cell death (PCD)-related lncRNAs were identified with DESeq2 and Pearson's correlation analysis. Through GO and KEGG analyses and GSEA , we identified the potential effects of metal-dependent PCD-related lncRNAs on prognosis. Using Cox regression analysis with the LASSO method, we constructed a 12-lncRNA prognostic signature model. Also, we evaluated the prognostic efficiency with Kaplan-Meier (K-M) survival curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) methods. The sensitivities for antitumor drugs were then predicted with the pRRophetic method. Also, we discuss Chinese patent medicines and plant extracts that could induce metal-dependent programmed cell death. Results: We constructed a metal-dependent PCD-related lncRNA-gene co-expression network. Also, a metal-dependent PCD-related gastric cancer prognostic signature model including 12 lncRNAs was constructed. The K-M survival curve revealed a poor prognosis in the high-risk group. ROC curve analysis shows that the AUC of our model is 0.766, which is better than that of other published models. Moreover, the half-maximum inhibitory concentration (IC50) for dasatinib, lapatinib, sunitinib, cytarabine, saracatinib, and vinorelbine was much lower among the high-risk group. Conclusion: Our 12 metal-dependent PCD-related lncRNA prognostic signature model may improve the OS prediction for gastric cancer. The antitumor drug sensitivity analysis results may also be helpful for individualized chemotherapy regimen design.

Dataset Information

Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties.

Publications

Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties.

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