Competitive endogenous RNA is an intrinsic component of EMT regulatory circuits and modulates EMT

Liu, Yuwei; Xue, Mengzhu; Du, Shaowei; Feng, Wan-wan; Zhang, Ke; Zhang, Liwen; Liu, Haiyue; Jia, Guoyi; Wu, Lingshuang; Hu, Xin; Chen, Luonan; Wang, Peng

doi:10.1038/s41467-019-09649-1

Cited by 88 publications

(75 citation statements)

References 48 publications

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“…FN1, derived from tumor-associated fibroblasts, stimulates cancer cell invasion after assembly [14]. In addition, as an intrinsic component of epithelialmesenchymal transition (EMT) regulatory circuit, FN1 is a potential target for interfering EMT during tumorigenesis 15 BioMed Research International [15]. Here, we suspect that FN1 is related to the pathway of ECM-receptor interaction.…”

Section: Discussionmentioning

confidence: 99%

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets

Shen

Dong

Liu

et al. 2020

BioMed Research International

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Background. The molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms. Methods. The GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression. Results. By integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database. Conclusion. The identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.

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Section: Discussionmentioning

confidence: 99%

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets

Shen

Dong

Liu

et al. 2020

BioMed Research International

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“…We were able to distinguish low-risk and high-risk samples with a relatively high prognosis accuracy consisting of these six mRNAs. Among these mRNAs, TGFBI was investigated as a highly induced transcript during EMT in A549 cells, acting as the ceRNA for miR-21 to modulate EMT [36]. SFRP1 exhibited a tumor-promoting function by selectively activating TGFβ signaling in gastric cancer cells and thus activating EMT progress [37].…”

Section: Discussionmentioning

confidence: 99%

A novel six-gene signature for prognosis prediction in ovarian cancer

Pan

2020

Preprint

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Ovarian cancer (OC) is the most malignant tumor in the female reproductive tract. Although abundant molecular biomarkers have been identified, a robust and accurate gene expression signature is still essential to assist oncologists in evaluating the prognosis of ovarian cancer patients. In the current study, 367 patients were adopted through The Cancer Genome Atlas (TCGA) database, and mRNA expression profiling was performed. Then, we used a gene set enrichment analysis (GSEA) to screen genes correlated with the epithelial-to-mesenchymal transition (EMT) process and identify these genes with the Cox proportional regression model. Six genes (TGFBI, SFRP1, COL16A1, THY1, PPIB, BGN) associated with overall survival (OS) were used to construct a risk assessment model, by which the patients were divided into high-risk and low-risk groups. The six-gene signature was identified as an independent prognostic biomarker of the OS of ovarian cancer patients via multivariate Cox regression analysis. Besides, the six-gene model was also validated significantly by Gene Expression Omnibus (GEO) database. In summary, we established a six-gene signature relevant to the prognosis of ovarian cancer, which might become a therapeutic target with clinical usefulness in the future.

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“…Epithelial-mesenchymal transition (EMT) plays an essential role in embryonic development and the transformation of early-stage tumors into invasive malignancies (4,5). Previous studies have demonstrated that TGF-β1 signaling is a potent inducer of EMT in various types of cancer, including NSCLC (6)(7)(8). It is well known that transforming growth factor β1 (TGF-β1) plays crucial roles in cell differentiation, proliferation, apoptosis and angiogenesis (9)(10)(11).…”

Section: Introductionmentioning

confidence: 99%

Neuropilin 1 modulates TGF‑β1‑induced epithelial‑mesenchymal transition in non‑small cell lung cancer

Ding

et al. 2019

Int J Oncol

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Previously, the authors reported that neuropilin-1 (NRP1) was significantly increased and acted as a vital promoter in the metastasis of non-small cell lung cancer (NSCLC). However, the regulatory mechanism of NRP1 in NSCLC cell migration and invasion remained unclear. The present study aimed to explore the regulatory mechanism of NRP1 in the transforming growth factor-β (TGF-β) 1-induced migration and invasion of NSCLC cells. The expression level of NRP1 was determined by RT-qPCR analysis in human tissue samples with or without lymph node metastasis. Transwell assay and wound healing assay were conducted to determine the cell migration. Lentivirus-mediated stable knockdown and overexpression of NRP1 cell lines were constructed. Exogenous TGF-β1 stimulation, SIS3 treatment, western blot analysis and in vivo metastatic model were utilized to clarify the underlying regulatory mechanisms. The results demonstrated that the expression of NRP1 was increased in metastatic NSCLC tissues. NRP1 promoted NSCLC metastasis in vitro and in vivo. The Transwell assays, wound healing assays and western blot analysis revealed that the knockdown of NRP1 significantly inhibited TGF-β1-mediated EMT and migratory and invasive capabilities of NSCLC. Furthermore, the overexpression of NRP1 weakened the inhibitory effect of SIS3 on the NSCLC migration and invasion. Co-IP assay revealed that NRP1 interacted with TGFβRII to induce EMT. On the whole, the findings of this study demonstrated that NRP1 was overexpressed in metastatic NSCLC tissues. NRP1 could contributes to TGF-β1-induced EMT and metastasis in NSCLC by binding with TGFβRII.

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Competitive endogenous RNA is an intrinsic component of EMT regulatory circuits and modulates EMT

Cited by 88 publications

References 48 publications

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets

A novel six-gene signature for prognosis prediction in ovarian cancer

Neuropilin 1 modulates TGF‑β1‑induced epithelial‑mesenchymal transition in non‑small cell lung cancer

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