Integrated use of bioinformatic resources reveals that co-targeting of histone deacetylases, IKBK and SRC inhibits epithelial-mesenchymal transition in cancer

Barneh, Farnaz; Mirzaie, Mehdi; Nickchi, Payman; Tan, Tuan Zea; Thiery, Jean Paul; Piran, Mehran; Salimi, Mona; Goshadrou, Fatemeh; Aref, Amir Reza; Jafari, Mohieddin

doi:10.1093/bib/bby030

Cited by 18 publications

(18 citation statements)

References 70 publications

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“…EMT therapeutics that repurpose existing FDA-approved drugs is most exciting, as it significantly reduces the translational time of such therapeutics. A comprehensive bioinformatics analysis of pre-approved drugs has proposed and validated various drug combinations, like IKBK and SRC kinase inhibition together with HDAC inhibition, to hamper EMT (73). Often, scientists researching novel EMT drug candidates focus on preventing an epithelial cells transition to an invasive mesenchymal cell, or inducing MET to revert to a normal epithelial state.…”

Section: Thinking Outside the Cell-novel Emt Stimulimentioning

confidence: 99%

Unresolved Complexity in the Gene Regulatory Network Underlying EMT

Lavin

Tiwari

2020

Front. Oncol.

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Epithelial to mesenchymal transition (EMT) is the process whereby a polarized epithelial cell ceases to maintain cell-cell contacts, loses expression of characteristic epithelial cell markers, and acquires mesenchymal cell markers and properties such as motility, contractile ability, and invasiveness. A complex process that occurs during development and many disease states, EMT involves a plethora of transcription factors (TFs) and signaling pathways. Whilst great advances have been made in both our understanding of the progressive cell-fate changes during EMT and the gene regulatory networks that drive this process, there are still gaps in our knowledge. Epigenetic modifications are dynamic, chromatin modifying enzymes are vast and varied, transcription factors are pleiotropic, and signaling pathways are multifaceted and rarely act alone. Therefore, it is of great importance that we decipher and understand each intricate step of the process and how these players at different levels crosstalk with each other to successfully orchestrate EMT. A delicate balance and fine-tuned cooperation of gene regulatory mechanisms is required for EMT to occur successfully, and until we resolve the unknowns in this network, we cannot hope to develop effective therapies against diseases that involve aberrant EMT such as cancer. In this review, we focus on data that challenge these unknown entities underlying EMT, starting with EMT stimuli followed by intracellular signaling through to epigenetic mechanisms and chromatin remodeling.

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Section: Thinking Outside the Cell-novel Emt Stimulimentioning

confidence: 99%

Unresolved Complexity in the Gene Regulatory Network Underlying EMT

Lavin

Tiwari

2020

Front. Oncol.

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“…For example, Barneh et al constructed a drug-target network and further developed its projected version called Drug Similarity Network (DSN) and Target Similarity Network (TSN), which can be used for drugtarget prediction (Barneh et al, 2016). Recently, they have applied the method to predict drug combinations, and confirmed them experimentally (Barneh et al, 2018;Barneh et al, 2019). To facilitate drug repositioning, network topological similaritybased inference (NTSIM) and its classification-equipped version, i.e., NTSIM-C methods were also proposed to unveil novel drugdisease associations (Zhang et al, 2018).…”

Section: A B Cmentioning

confidence: 99%

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine

et al. 2020

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The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patientderived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.

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“…Furthermore, they demonstrated the disregulation of hormonal factors affecting genome instability and the decrease of immune surveillance in breast cancer [31]. We know EMT is a complex process through which tumor cells facilitate their dissemination and acquire stemness characteristics [14,32]. Not only signaling pathways of stemness but also in the stimulation of self-renewing pathways in tumor cells are essential in embryogenesis [14,33].…”

Section: Distant Metastasis-free-survival and Overall Survival Analmentioning

confidence: 99%

“…We know EMT is a complex process through which tumor cells facilitate their dissemination and acquire stemness characteristics [14,32]. Not only signaling pathways of stemness but also in the stimulation of self-renewing pathways in tumor cells are essential in embryogenesis [14,33]. Such several metastatic prone pathways and the interplay among all of them activated in circulating tumor cells ( Figure 2c and Figure 3).…”

Section: Distant Metastasis-free-survival and Overall Survival Analmentioning

confidence: 99%

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Metastasis Progression Through the Interplay Between the Immune System and Epithelial-mesenchymal-transition in Circulating Breast Tumor Cells

Khoshbakht

Jamalkandi²,

Masudi-Nejad

2020

Preprint

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BackgroundCirculating tumor cells (CTCs) are the critical initiators of distant metastasis formation. In which, the reciprocal interplay among different metastatic pathways which promote survival of CTCs, is not well introduced, using network approaches. CTC cells include single and cluster cells, in which cluster cells revealed 23-50 fold more metastatic potentials.Here, to investigate the unknown pathways of single/cluster CTCs, the co-expression network reconstructed, using WGCNA (Weighted Correlation Network Analysis) method. Having used the hierarchical clustering, we detected the Immune-response and EMT subnetworks. The metastatic potential of genes was assessed and validated through the support vector machine (SVM), neural network, and decision tree methods on two external datasets. To identify the active signaling pathways in CTCs, we reconstructed a casual network. The Log-Rank test and Kaplan-Meier curve were applied to detect prognostic gene signatures for metastasis-free survival. Finally, a predictive model was developed for metastasis risk of patients, using VIF-stepwise feature selection. ResultsOur results showed the crosstalk among EMT, the immune system, menstrual cycles, and the stemness pathway in CTCs. In which, fluctuation of menstrual cycles is a new detected pathway in breast cancer CTCs. The reciprocal association between immune responses and EMT was identified in single/cluster CTCs. The SVM model indicated a high metastatic potential of EMT subnetwork (accuracy, sensitivity, and specificity scores were 87%). The distant-metastasis-free-survival model was identified to predict patients’ metastasis risks. (c-index=0.8). Finally, novel metastatic biomarkers including PTCRA, F13A1, ICAM2, and SNRPC were detected in breast cancer.Conclusions In conclusion, the reciprocal interplay among critical pathways in CTCs enhances their survival and metastatic potentials. Such findings may help to develop more precise predictive metastatic-risk models or detect novel biomarkers.

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Integrated use of bioinformatic resources reveals that co-targeting of histone deacetylases, IKBK and SRC inhibits epithelial-mesenchymal transition in cancer

Cited by 18 publications

References 70 publications

Unresolved Complexity in the Gene Regulatory Network Underlying EMT

Unresolved Complexity in the Gene Regulatory Network Underlying EMT

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine

Metastasis Progression Through the Interplay Between the Immune System and Epithelial-mesenchymal-transition in Circulating Breast Tumor Cells

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