The retinal determination gene network: from developmental regulator to cancer therapeutic target

Kong, Deguang; Liu, Yu; Liu, Qian; Han, Na; Zhang, Cuntai; Pestell, Richard G.; Wu, Kongming; Wu, Gaosong

doi:10.18632/oncotarget.9394

Cited by 33 publications

(35 citation statements)

References 95 publications

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“…Exciting outcome of molecular targeting therapy is driving the need for better understanding the tumor biological features of each patient (7). Recent evidence has linked RDGN family with the progression of various cancers, indicating it might be a potential therapeutic target for precision medicine (11)(12)(13)(14)(15)(16)(17)(18)25). In the present study, our results first showed that SIX1 protein elevated in thyroid malignant tumor.…”

Section: Discussionsupporting

confidence: 58%

“…Most recent study found the microRNA-548a-3p/SIX1 axis strongly linked aerobic glycolysis to carcinogenesis (17). SIX1 and EYA can integrate with other signal pathways to modulate the apoptosis, cell proliferation and tumor growth (18). To date, the profiles of SIX1 and EYA1 have been independently identified as a prognostic biomarker in breast cancer (13,19).…”

Section: Introductionmentioning

confidence: 99%

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SIX1 Activates STAT3 Signaling to Promote the Proliferation of Thyroid Carcinoma via EYA1

Kong

Liu

et al. 2019

Front. Oncol.

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As a critical member of the Retinal Determination Gene Network (RDGN), SIX1 has been regarded as a tumor promoter in various types of cancer. However, its role in papillary thyroid carcinoma (PTC) has never been investigated. In this study, thyroid carcinoma tissue microarray staining was employed to identify the expression patterns of SIX1 and its co-activator EYA1. Papillary thyroid cancer cell lines, BCPAP, and TPC-1 cells were used to investigate the potential mechanism of SIX1 in vitro and in vivo. Flow cytometry analysis, MTT assay, the growth curve assay, colony formation assay, EdU incorporation and xenograft assay were performed to demonstrate the role of SIX1 in the malignant change of PTC cells. Western blot and Real-time PCR were used to detect the interaction among the SIX1, EYA1, and STAT3 signaling. In comparison with normal tissue, high expressions of SIX1 and EYA1 were associated with a malignant tumor. Importantly, SIX1 strongly correlated with EYA1 in thyroid carcinoma tissue microarray. Functional assays indicated SIX1 increased EYA1 expression by stabilizing EYA1 at the post-transcriptional level. Besides, SIX1 promoted the proliferation and invasion of thyroid carcinoma via activation of STAT3 signaling and its downstream targets in an EYA1-dependent manner. SIX1 can integrate with EYA1 to contribute to PTC development via activation of the classical STAT3 signaling. These data suggested targeting the abnormal activation of the SIX1/EYA1 complex may represent a novel therapeutic strategy for advanced PTC patients.

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Section: Discussionsupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

SIX1 Activates STAT3 Signaling to Promote the Proliferation of Thyroid Carcinoma via EYA1

Kong

Liu

et al. 2019

Front. Oncol.

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“…DACH1 is a key member of the retinal determination gene network, a group which affects cell cycle regulation and cancer cell growth, EMT, invasion, and migration in a context‐specific manner . Its reduced expression is observed in several types of cancer, mostly associated with poor prognosis . Our results also point that these TFs' inferred activity is repressed in lung adenocarcinoma, similarly to previous lung cancer studies .…”

Section: Discussionsupporting

confidence: 88%

Integrated transcriptomics reveals master regulators of lung adenocarcinoma and novel repositioning of drug candidates

Bastiani

Klamt

2019

Cancer Medicine

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Background Lung adenocarcinoma is the major cause of cancer‐related deaths in the world. Given this, the importance of research on its pathophysiology and therapy remains a key health issue. To assist in this endeavor, recent oncology studies are adopting Systems Biology approaches and bioinformatics to analyze and understand omics data, bringing new insights about this disease and its treatment. Methods We used reverse engineering of transcriptomic data to reconstruct nontumorous lung reference networks, focusing on transcription factors (TFs) and their inferred target genes, referred as regulatory units or regulons. Afterwards, we used 13 case‐control studies to identify TFs acting as master regulators of the disease and their regulatory units. Furthermore, the inferred activation patterns of regulons were used to evaluate patient survival and search drug candidates for repositioning. Results The regulatory units under the influence of ATOH8, DACH1, EPAS1, ETV5, FOXA2, FOXM1, HOXA4, SMAD6, and UHRF1 transcription factors were consistently associated with the pathological phenotype, suggesting that they may be master regulators of lung adenocarcinoma. We also observed that the inferred activity of FOXA2, FOXM1, and UHRF1 was significantly associated with risk of death in patients. Finally, we obtained deptropine, promazine, valproic acid, azacyclonol, methotrexate, and ChemBridge ID compound 5109870 as potential candidates to revert the molecular profile leading to decreased survival. Conclusion Using an integrated transcriptomics approach, we identified master regulator candidates involved with the development and prognostic of lung adenocarcinoma, as well as potential drugs for repurposing.

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“…These genes may control the expression levels of other variegation modifiers. In addition, both Six4 and Tsh are members of the “retinal determination network (RDN),” a collection of genes whose roles were originally characterized in studies of Drosophila eye development (Datta et al 2011) but which is now known to control cell specification and cell proliferation in multiple developing epithelia in Drosophila and mammals (Kong et al 2016). Thus, changes in the expression of tsh or Six4 in follicle progenitors either directly or indirectly affect progenitor proliferation and differentiation.…”

Section: Discussionmentioning

confidence: 99%

Identification of Genes MediatingDrosophilaFollicle Cell Progenitor Differentiation by Screening for Modifiers of GAL4::UAS Variegation

Lee¹,

Skora²,

Spradling

2017

G3 Genes|Genomes|Genetics

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The Drosophila melanogaster ovarian follicle cell lineage provides a powerful system for investigating how epigenetic changes contribute to differentiation. Downstream from an epithelial stem cell, follicle progenitors undergo nine mitotic cell cycles before transitioning to the endocycle and initiating differentiation. During their proliferative phase, follicle progenitors experience Lsd1-dependent changes in epigenetic stability that can be monitored using GAL4::UAS variegation. Eventually, follicle progenitors acquire competence to respond to Delta, a Notch ligand present in the environment, which signals them to cease division and initiate differentiation. The time required to acquire competence determines the duration of mitotic cycling and hence the final number of follicle cells. We carried out a screen for dominant modifiers of variegation spanning nearly 70% of Drosophila euchromatin to identify new genes influencing follicle progenitor epigenetic maturation. The eight genes found include chromatin modifiers, but also cell cycle regulators and transcription factors. Five of the modifier genes accelerate the acquisition of progenitor competence and reduce follicle cell number, however, the other three genes affect follicle cell number in an unexpected manner.

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The retinal determination gene network: from developmental regulator to cancer therapeutic target

Cited by 33 publications

References 95 publications

SIX1 Activates STAT3 Signaling to Promote the Proliferation of Thyroid Carcinoma via EYA1

SIX1 Activates STAT3 Signaling to Promote the Proliferation of Thyroid Carcinoma via EYA1

Integrated transcriptomics reveals master regulators of lung adenocarcinoma and novel repositioning of drug candidates

Identification of Genes MediatingDrosophilaFollicle Cell Progenitor Differentiation by Screening for Modifiers of GAL4::UAS Variegation

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