Potential Compounds for Oral Cancer Treatment: Resveratrol, Nimbolide, Lovastatin, Bortezomib, Vorinostat, Berberine, Pterostilbene, Deguelin, Andrographolide, and Colchicine

Bundela, Saurabh; Sharma, Anjana; Bisen, Prakash S.

doi:10.1371/journal.pone.0141719

Cited by 38 publications

(24 citation statements)

References 49 publications

(71 reference statements)

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“…There are many off‐target effects that the drugs utilized could have; hence an inducible system with upregulated Bax may be exploited in OSCC cell lines. Importantly, the present study lends credence to the inclusion of nimbolide as a natural compound with potential therapeutic efficacy against oral cancer, as well as a promising candidate agent for overcoming cisplatin resistance.…”

Section: Discussionsupporting

confidence: 56%

Role and regulation of proapoptotic Bax in oral squamous cell carcinoma and drug resistance

et al. 2018

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Background: Bax, a proapoptotic protein but its regulation during oral cancer progression and resistance remains elusive. Methods: A total of 127 samples including adjacent normal, primary tumor, and resistance to chemoradiation therapy (RCRT) samples from oral squamous cell carcinoma (OSCC) patients were used. The status of Bax was analyzed at DNA/mRNA/protein levels and the results were correlated with p53 and Akt expression in tissue samples/ cisplatin-resistant oral tongue SCC (SCC9/SCC4-CisR) cell line. Results: Frequent progressive decrease of Bax expression with infrequent promoter methylation, polymorphisms G(-248)A, and mutations was observed in OSCC progression/resistance. Furthermore, by targeting Akt pathway, induction of Bax-dependent cell death was observed and this was further enhanced with nimbolide treatment in SCC9/SCC4-CisR cells. Conclusion: Hence, the Bax gene alteration and its deregulation through p53/Akt pathway are important for OSCC progression and drug resistance. Akt Inhibitor VIII and nimbolide synergistically induce Bax, and it is therefore beneficial for chemosensitizing cisplatin-resistant human OSCC.

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Section: Discussionsupporting

confidence: 56%

Role and regulation of proapoptotic Bax in oral squamous cell carcinoma and drug resistance

et al. 2018

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“…The drug profile of 24 anticancer drugs was tested against a large number of cell lines in order to understand the relation between drug resistance and altered genomic features of a cancer cell line. Bundela et al (45) built an SVM-RBF model to identify potential therapeutic compounds for oral cancer from the large pool of compounds from the publicly available compound databases.…”

Section: Selecting Anticancer Drugs Warmuth Et Al (43) Used Activementioning

confidence: 99%

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

2018

CGP

732

347

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Abstract. Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that hasMachine learning (ML) "learns" a model from past data in order to predict future data (1). The key process is the learning which is one of the artificial intelligences. Many different statistical, probabilistic, and optimization techniques can be implemented as the learning methods such as the logistic regression, artificial neural networks (ANN), K-nearest neighbor (KNN), decision trees (DT) and Naive Bayes. There are two main types of ML learning -supervised learning and unsupervised learning. The supervised learning builds a model by learning from known classes (labeled training data). In contrast, unsupervised learning methods learn the common features from unknown class data (unlabeled training data).ML algorithms have been used for key feature training and recognition and for group classification. The strength of ML methods is it could detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to complex genomic data, especially in cancer studies. For example, ANN and DT have been used in cancer detection and diagnosis for nearly 20 years (2-3). The clinical implication of cancer heterogeneity and various cancer genomic data available motivate the applications of ML for cancer classification using genomic data.SVM learning is one of many ML methods. Compared to the other ML methods SVM is very powerful at recognizing subtle patterns in complex datasets (4). SVM can be used to recognize handwriting, recognize fraudulent credit cards, identify a speaker, as well as detect face (5). Cancer is a genetic disease where the genomic feature patterns or feature function patterns may represent the cancer subtypes, the outcome prognosis, drug benefit prediction, tumorigenesis drivers, or a tumor-specific biological process. Therefore, the Artificial Intelligence of SVM can help us in recognizing these patterns in a variety of applications. SVM ModelSVM is a powerful method for building a classifier. It aims to create a decision boundary between two classes that enables the prediction of labels from one or more feature vectors (6). This decision boundary, known as the hyperplane, is orientated in such a way that it is as far as 41

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“…PT can penetrate the blood-brain barrier and reduce c-Met signaling in breast cancer cells, thereby promoting metastasis by inducing the secretion of the inflammatory cytokines IL8 and CXCL1, and can trigger vascular reprogramming by activating tumor-associated astrocytes to secrete the c-Met ligand HGF [22] . PT also represents a potential drug for treating other cancers, including ovarian cancer, prostate can- cer, oral cancer, and melanoma [23][24][25][26] . Here, we demonstrate the powerful cytotoxicity of PT on EC cells by triggering caspase-dependent apoptosis.…”

Section: Discussionmentioning

confidence: 99%

Pterostilbene suppresses human endometrial cancer cells in vitro by down-regulating miR-663b

Wang

Shen

et al. 2017

Acta Pharmacol Sin

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Resveratrol has long been known as an antioxidant and a chemopreventive agent. Similar to resveratrol, pterostilbene (PT) is also a phenolic compound extracted from the Vitis species. However, there are few studies on the antitumor effect of PT. Thus, we investigated the effects of PT on the endometrial cancer (EC) cells in vitro and the related molecular mechanisms. Treatment of EC cell lines HTB-111 and Ishikawa with PT (25-100 µmol/L) dose-dependently suppressed the cell viability and induced apoptosis. Using miR microarrays, we examined the miR expression profile in Ishikawa cells with or without PT, and revealed that miR-663b was the most decreased in PT-treated Ishikawa cells. Furthermore, we predicted and verified that the pro-apoptosis factor BCL2L14 is the direct target of miR-663b. Over-expression of miR-663b and knock-down of BCL2L14 counteracted the suppressing effects of PT on HTB-111 and Ishikawa cells. In addition, we evaluated the miR-663b levels in EC tissues of 51 patients using an in situ hybridization technique. With the median of the score of miR-663b as a cut-off value, these EC patients were divided into two groups, and the patients with high miR-663b expression had significantly poor prognosis.

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Potential Compounds for Oral Cancer Treatment: Resveratrol, Nimbolide, Lovastatin, Bortezomib, Vorinostat, Berberine, Pterostilbene, Deguelin, Andrographolide, and Colchicine

Cited by 38 publications

References 49 publications

Role and regulation of proapoptotic Bax in oral squamous cell carcinoma and drug resistance

Role and regulation of proapoptotic Bax in oral squamous cell carcinoma and drug resistance

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

Pterostilbene suppresses human endometrial cancer cells in vitro by down-regulating miR-663b

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