Pterostilbene leads to DNMT3B-mediated DNA methylation and silencing of OCT1-targeted oncogenes in breast cancer cells

Beetch, Megan; Boycott, Cayla; Harandi-Zadeh, Sadaf; Yang, T.Y.; Martin, Bernice M.; Dixon-McDougall, Thomas; Ren, Kevin; Gacad, Allison; Dupuis, John H.; Ullmer, Melissa; Lubecka, Katarzyna; Yada, Rickey Y.; Brown, Carolyn J.; Howe, LeAnn; Stefańska, Barbara

doi:10.1016/j.jnutbio.2021.108815

Cited by 16 publications

(10 citation statements)

References 70 publications

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“…Comparison with the list of genes in CGC showed an overlap of 228, 169, and 188 genes for BRCA, COAD, and LUAD, respectively. The list of top genes also consists of genes, such as PRKCA ( Kelemen et al, 2009 ; Lin et al, 2017 ; Pham et al, 2017 ; Beetch et al, 2021 ), SOX9 ( Lü et al, 2008 ; Carrasco-Garcia et al, 2016 ; Lizarraga et al, 2019 ), and NFKBIA ( Furukawa et al, 2013 ), that are not present in CGC but found in the literature for their role in respective cancer types. We also identify a large number of rare driver genes identified in as few as one sample in BRCA ( Supplementary Table S6 ), COAD ( Supplementary Table S7 ), and LUAD ( Supplementary Table S8 ).…”

Section: Resultsmentioning

confidence: 99%

Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes

2022

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The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumor progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalized driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumor suppressor gene (TSG), oncogene (OG), or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalized Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalized driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labeling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared with mutation and expression data, achieving an accuracy ≥0.99 for BRCA, LUAD, and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD, and LUAD cancer types reveal commonly altered genes such as TP53 and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9, and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalized driver genes as TSGs and OGs and also identified rare driver genes.

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Section: Resultsmentioning

confidence: 99%

Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes

2022

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“…Comparison with the list of genes in CGC showed an overlap of 228, 169, and 188 genes for BRCA, COAD and LUAD respectively. The list of top genes also consists of genes, such as PRKCA (Lin et al, 2017; Pham et al, 2017; Beetch et al, 2021; Kelemen et al, 2009), SOX9 (Lizarraga et al, 2019; Carrasco-Garcia et al, 2016; Lü et al, 2008) and NFKBIA (Furukawa et al, 2013), that are not present in CGC but found in the literature for their role in respective cancer types. We also identify a large number of rare driver genes identified in as few as one sample in BRCA (Supplementary Table 6), COAD (Supplementary Table 7) and LUAD (Supplementary Table 8).…”

Section: Resultsmentioning

confidence: 99%

Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

Sudhakar

Rengaswamy

Raman

2022

Preprint

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The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

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“…These compounds have demonstrated promising effects against tumors, along with possessing antioxidant, anti-inflammatory, and immune-enhancing properties in various cancer types. 75 - 78 Bidirectional interactions have been observed between gut microbiota and flavonoid, stilbenoid, and diarylheptanoid compounds, and these interactions manifest through direct and indirect mechanisms, encompassing gut microbiota modulation, T cell differentiation regulation, intestinal barrier function enhancement, inflammatory pathway regulation, and reduction of oxidative stress. 79 - 81 Bile acids, as metabolites of the intestinal microbiota, have been shown to modulate antitumor immunity and regulate immunological homeostasis.…”

Section: Discussionmentioning

confidence: 99%

Clinical Efficacy and Gut Microbiota Regulating-Related Effect of Si-Jun-Zi Decoction in Postoperative Non-Small Cell Lung Cancer Patients: A Prospective Observational Study

He,

Qi,

et al. 2024

Integr Cancer Ther

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Background: Postoperative non-small cell lung cancer (NSCLC) patients frequently encounter a deteriorated quality of life (QOL), disturbed immune response, and disordered homeostasis. Si-Jun-Zi Decoction (SJZD), a well-known traditional Chinese herbal formula, is frequently employed in clinical application for many years. Exploration is underway to investigate the potential therapeutic effect of SJZD for treating postoperative NSCLC. Objective: To assess the efficacy of SJZD on QOLs, hematological parameters, and regulations of gut microbiota in postoperative NSCLC patients. Methods: A prospective observational cohort study was conducted, enrolling 65 postoperative NSCLC patients between May 10, 2020 and March 15, 2021 in Yueyang Hospital, with 33 patients in SJZD group and 32 patients in control (CON) group. The SJZD group comprised of patients who received standard treatments and the SJZD decoction, while the CON group consisted of those only underwent standard treatments. The treatment period was 4 weeks. The primary outcome was QOL. The secondary outcomes involved serum immune cell and inflammation factor levels, safety, and alterations in gut microbiota. Results: SJZD group showed significant enhancements in cognitive functioning ( P = .048) at week 1 and physical functioning ( P = .019) at week 4. Lung cancer-specific symptoms included dyspnea ( P = .001), coughing ( P = .008), hemoptysis ( P = .034), peripheral neuropathy ( P = .019), and pain (arm or shoulder, P = .020, other parts, P = .019) eased significantly in the fourth week. Anemia indicators such as red blood cell count ( P = .003 at week 1, P = .029 at week 4) and hemoglobin ( P = .016 at week 1, P = .048 at week 4) were significantly elevated by SJZD. SJZD upregulated blood cell cluster differentiation (CD)3+ ( P = .001 at week 1, P < .001 at week 4), CD3+CD4+ ( P = .012 at week 1), CD3+CD8+ ( P = .027 at week 1), CD19+ ( P = .003 at week 4), increased anti-inflammatory interleukin (IL)-10 ( P = .004 at week 1, P = .003 at week 4), and decreased pro-inflammatory IL-8 ( P = .004 at week 1, p = .005 at week 4). Analysis of gut microbiota indicated that SJZD had a significant impact on increasing microbial abundance and diversity, enriching probiotic microbes, and regulating microbial biological functions. Conclusions: SJZD appears to be an effective and safe treatment for postoperative NSCLC patients. As a preliminary observational study, this study provides a foundation for further research.

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Pterostilbene leads to DNMT3B-mediated DNA methylation and silencing of OCT1-targeted oncogenes in breast cancer cells

Cited by 16 publications

References 70 publications

Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes

Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes

Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

Clinical Efficacy and Gut Microbiota Regulating-Related Effect of Si-Jun-Zi Decoction in Postoperative Non-Small Cell Lung Cancer Patients: A Prospective Observational Study

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