Integrative Bioinformatics Analysis Reveals Potential Target Genes and TNFα Signaling Inhibition by Brazilin in Metastatic Breast Cancer Cells

Hermawan, Adam; Putri, Herwandhani

doi:10.31557/apjcp.2020.21.9.2751

Cited by 10 publications

(10 citation statements)

References 97 publications

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“…These findings were in agreement with similar studies, so that cancer-related terms such as collagen fibril organization, extracellular matrix organization [ 65 ], cellular response to amino acid stimulus, platelet activation [ 66 ], tissue homeostasis [ 67 ], regulation of immune response, skin development [ 68 ], platelet-derived growth factor binding, extracellular matrix structural constituent, SMAD binding [ 66 ], extracellular matrix [ 65 ], collagen trimmer, and endoplasmic reticulum lumen [ 66 ] were enriched for 21 selected genes by the RF method. Moreover KEGG pathway enrichment analysis indicated that cancer related pathways such as protein digestion and absorption, platelet activation [ 65 ], focal adhesion, Amoebiasis [ 66 ], human papillomavirus infection [ 69 ], AGE-RAGE signaling pathway in diabetic complications [ 70 ], and relaxin signaling pathway [ 71 ] were significantly enriched which was in agreement with the results of other studies.…”

Section: Discussionsupporting

confidence: 87%

Identification of gene profiles related to the development of oral cancer using a deep learning technique

et al. 2023

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Background Oral cancer (OC) is a debilitating disease that can affect the quality of life of these patients adversely. Oral premalignant lesion patients have a high risk of developing OC. Therefore, identifying robust survival subgroups among them may significantly improve patient therapy and care. This study aimed to identify prognostic biomarkers that predict the time-to-development of OC and survival stratification for patients using state-of-the-art machine learning and deep learning. Methods Gene expression profiles (29,096 probes) related to 86 patients from the GSE26549 dataset from the GEO repository were used. An autoencoder deep learning neural network model was used to extract features. We also used a univariate Cox regression model to select significant features obtained from the deep learning method (P < 0.05). High-risk and low-risk groups were then identified using a hierarchical clustering technique based on 100 encoded features (the number of units of the encoding layer, i.e., bottleneck of the network) from autoencoder and selected by Cox proportional hazards model and a supervised random forest (RF) classifier was used to identify gene profiles related to subtypes of OC from the original 29,096 probes. Results Among 100 encoded features extracted by autoencoder, seventy features were significantly related to time-to-OC-development, based on the univariate Cox model, which was used as the inputs for the clustering of patients. Two survival risk groups were identified (P value of log-rank test = 0.003) and were used as the labels for supervised classification. The overall accuracy of the RF classifier was 0.916 over the test set, yielded 21 top genes (FUT8-DDR2-ATM-CD247-ETS1-ZEB2-COL5A2-GMAP7-CDH1-COL11A2-COL3A1-AHR-COL2A1-CHORDC1-PTP4A3-COL1A2-CCR2-PDGFRB-COL1A1-FERMT2-PIK3CB) associated with time to developing OC, selected among the original 29,096 probes. Conclusions Using deep learning, our study identified prominent transcriptional biomarkers in determining high-risk patients for developing oral cancer, which may be prognostic as significant targets for OC therapy. The identified genes may serve as potential targets for oral cancer chemoprevention. Additional validation of these biomarkers in experimental prospective and retrospective studies will launch them in OC clinics.

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Section: Discussionsupporting

confidence: 87%

Identification of gene profiles related to the development of oral cancer using a deep learning technique

et al. 2023

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“…The two most widely studied proteins in the ERBB family: ERBB1 (HER1 or EGFR) and ERBB2 (HER2), was profoundly known to lead to the progression of cancer, which is also crucial to initiate metastasis, as was notable in breast cancer (Hsu and Hung 2016). An other study by Hermawan and Putri (2020) explored the target of brazilin (one of the major compounds in C. sap pan L.) in the TNBC metastatic breast cancer model re vealed that the inhibition of brazilin was possible through blocking in the TNFα pathway. Different features partly influence this distinct mechanism from TNBC with any other subtypes (including ER+ and HER2+).…”

Section: Discussionmentioning

confidence: 99%

Ethanolic extract of sappan wood (Caesalpinia sappan L.) inhibits MCF-7 and MCF-7/HER2 mammospheres' formation: an in vitro and bioinformatic study

Novitasari

Muntafiah

Sari

et al. 2021

Indones. J. Biotechnol.

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One of the mechanisms of cancer cell resistance toward chemotherapy is through cancer stem cells (CSCs), which are characterized by excessive activation of regulator proteins such as human epidermal receptor 2 (HER2). Sappan wood (Caesalpinia sappan L.) contains brazilin and brazilein that exhibit cytotoxic effects on several cancer cell lines. We aimed to explore the potency of the ethanolic extract of sappan (EES) in CSCs through bioinformatic analyses and by using a three-dimensional (3D) breast cancer stem cells (BCSCs) for in vitro assay with two different models (i.e., BCSCs and HER2-BCSCs) in order to identify the potential therapeutic targets of genes (PTTGs). Bioinformatic analyses identiﬁed PTTGs, which were further analyzed by gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, protein-protein interaction (PPI) networks, and hub protein selection. Mammospheres were cultured under conditioned media. The cytotoxic effects of EES were then measured by direct counting and based on the mammosphere-forming potential (MFP). Bioinformatic analysis disclosed PIK3CA and TP53 as PTTGs in BCSCs and HER2-BCSCs, respectively. In addition, the KEGG pathway analyses also demonstrated that PTTGs could regulate the ERBB pathway. EES thus demonstrated cytotoxicity and inhibited the formation of mammospheres. Collectively, EES exhibited excellent potential for further development as an inhibitor of cancer stem cells in breast cancer.

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“…The microarray data of the two GSE codes were further analyzed using the GEO2R to identify differentially expressed genes (DEGs) (Zhang et al, 2020). The adjusted p-value < 0.05 and Iog fold change > 1 were used to select the significant (Hermawan and Putri, 2020a). The two samples derived from the GSE code were then analyzed using Venny 2.1.0 to obtain a Venn diagram (Liu et al, 2019).…”

Section: Data Miningmentioning

confidence: 99%

Genomic Understanding Reveals the Important Role of FGFR2 as Paeoniflorin Target for Circumventing Breast Cancer Resistance to Tamoxifen

Wibowo

Nugroho

Hermawan

2021

Asian Pac J Cancer Prev

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Objectives: Paeoniflorin (PF), a compound found in Paeonia lactiflora and Paeonia suffruticosa, has anticancer potential, particularly in inhibiting migration and invasion, the resistant cancer cells hallmarks. To date, the mechanism of overcoming tamoxifen resistance in breast cancer is not yet elucidated. This research aims to explore the potential target of PF as a co-treatment for circumventing breast cancer resistance to tamoxifen with a genomic understandingbioinformatics. Methods: Microarray data originating from GSE67916 and GSE85871 in the NCBI GEO database was analyzed to obtain differentially expressed genes (DEGs). Further analyses were performed on DEGs using the DAVID v6.8, STRING-DB v11.0, the Cytoscape, and cBioportal. Gene expression analysis validation in breast cancer cells and tamoxifen-resistant breast cancer cells was accomplished using GEPIA and ONCOMINE databases. Survival rate analysis of selected genes was conducted using Kaplan-Meier. Results: We obtained 175 DEGs from the two samples (tamoxifen-resistant and paeoniflorin-treated). DEG involves in 70 biological processes, 26 cellular components, and 18 molecular functions, and three pathways relevant to breast cancer. The PPI network analysis and hub genes selection obtained 10 genes with the highest degree scores. Genetic changes for selected genes, including IFNB1, CDK6, FGFR2, OAS1, BCL2, and STAT2 were found from 0.5% to 7% of the case population per patient case. Additional analysis using cBioportal revealed FGFR signaling pathway through Ras is important for the PF mechanism in circumventing breast cancer resistance to tamoxifen. ONCOMINE and GEPIA analysis emphasized the importance of selected genes in the tamoxifen-resistance mechanism. Conclusion: PF has potential to be used as a co-treatment for circumventing breast cancer resistance to tamoxifen by targeting FGFR2 signaling, but further validation is needed.

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Integrative Bioinformatics Analysis Reveals Potential Target Genes and TNFα Signaling Inhibition by Brazilin in Metastatic Breast Cancer Cells

Abstract: which leads to relapse and metastasis (Rivera and Gomez, 2010). Developing a strategy that can overcome the progression and metastasis of breast cancer cells is necessary, including the development of new drugs from natural products.

Cited by 10 publications

References 97 publications

Identification of gene profiles related to the development of oral cancer using a deep learning technique

Identification of gene profiles related to the development of oral cancer using a deep learning technique

Ethanolic extract of sappan wood (Caesalpinia sappan L.) inhibits MCF-7 and MCF-7/HER2 mammospheres' formation: an in vitro and bioinformatic study

Genomic Understanding Reveals the Important Role of FGFR2 as Paeoniflorin Target for Circumventing Breast Cancer Resistance to Tamoxifen

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