&lt;p&gt;EFEMP2 Suppresses the Invasion of Lung Cancer Cells by Inhibiting Epithelial-Mesenchymal Transition (EMT) and Down-Regulating MMPs&lt;/p&gt;

Song, Lingyun; Li, Xiangxin; Liu, Xiangyan; Wang, Zhou; Yu, Yang; Shi, Mo; Jiang, Bin; He, Xi

doi:10.2147/ott.s236111

Cited by 16 publications

(16 citation statements)

References 43 publications

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“…EFEMP2 is an extracellular matrix protein necessary for elastic fiber formation and connective tissue development, processes that are highly associated with tumor invasion and metastasis (42). The expression of EFEMP2 in bladder cancer tissues was significantly lower than that in normal tissues in previous study (43).…”

Section: Discussionmentioning

confidence: 85%

Bioinformatic screening and identification of downregulated hub genes in adrenocortical carcinoma

Zhang

Miao

et al. 2020

Exp Ther Med

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The molecular mechanisms of adrenocortical carcinoma (ACC) carcinogenesis and progression remain unclear. In the present study, three microarray datasets from the Gene Expression Omnibus database were screened, which identified a total of 96 differentially expressed genes (DEGs). A protein-protein interaction network (PPI) was established for these DEGs and module analysis was performed using STRING and Cytoscape. A total of eight hub genes were identified from the most significant module; namely, calponin 1 (CNN1), myosin light chain kinase (MYLK), cysteine and glycine rich protein 1 (CSRP1), myosin heavy chain 11 (MYH11), fibulin extracellular matrix protein 2 (EFEMP2), fibulin 1 (FBLN1), microfibril associated protein 4 (MFAP4) and fibulin 5 (FBLN5). The biological functions of these hub genes were analyzed using the DAVID online tool. Changes in the expression of hub genes did not affect overall survival; however, downregulated EFEMP2 decreased disease-free survival. CSRP1 and MFAP4 expression levels were associated with adverse clinicopathological features. In conclusion, although all eight hub genes were downregulated in ACC, they appeared to have important functions in ACC carcinogenesis and progression. Identification of these genes complements the genetic expression profile of ACC and provides insight for the diagnosis, treatment and prognosis of ACC.

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Section: Discussionmentioning

confidence: 85%

Bioinformatic screening and identification of downregulated hub genes in adrenocortical carcinoma

Zhang

Miao

et al. 2020

Exp Ther Med

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“…Many of these genes influence the capability of cells to migrate or to respond to mitogenic stimuli, are involved in extracellular matrix homeostasis [ 81 , 82 ] and can either promote or decrease EMT/mesenchymal characteristics depending on the cell type and disease state (cancerous as opposed to normal cells). For instance, TIMP3 and EFEMP2 suppress EMT in cancer cells and therefore tumour invasiveness through inhibition of metalloproteinase (MMP) genes ( Figure 5 C) [ 83 , 84 ], while elevation of TPM1/2 (encoding tropomyosins) in lens epithelial cells is associated with induction of EMT following injury through increased TGFβ (transforming growth factor) signalling [ 85 ], one of the main pathways to induce EMT [ 86 ]. Interestingly, in diabetic α-cells both EMT and TGFβ signalling marker genes are upregulated ( Figure 5 D).…”

Section: Resultsmentioning

confidence: 99%

Single-cell transcriptomics of human islet ontogeny defines the molecular basis of β-cell dedifferentiation in T2D

Avrahami

Wang

Schug

et al. 2020

Molecular Metabolism

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Objective Dedifferentiation of pancreatic β-cells may reduce islet function in type 2 diabetes (T2D). However, the prevalence, plasticity and functional consequences of this cellular state remain unknown. Methods We employed single-cell RNAseq to detail the maturation program of α- and β-cells during human ontogeny. We also compared islets from non-diabetic and T2D individuals. Results Both α- and β-cells mature in part by repressing non-endocrine genes; however, α-cells retain hallmarks of an immature state, while β-cells attain a full β-cell specific gene expression program. In islets from T2D donors, both α- and β-cells have a less mature expression profile, de-repressing the juvenile genetic program and exocrine genes and increasing expression of exocytosis, inflammation and stress response signalling pathways. These changes are consistent with the increased proportion of β-cells displaying suboptimal function observed in T2D islets. Conclusions These findings provide new insights into the molecular program underlying islet cell maturation during human ontogeny and the loss of transcriptomic maturity that occurs in islets of type 2 diabetics.

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“…Another report showed that DDR2 mediates stromal and cancer cell interaction in mesenchymal stem cells and metastasis growth in breast cancer (17,18). EFEMP2 significantly inhibits the invasion and metastasis of tumor cells and the process of epithelial-mesenchymal transition (EMT) through the Wnt/bcatenin pathway in lung, bladder, and breast cancers (19)(20)(21). Similarly, ITGBL1 is involved in tumor cell invasion and metastasis through the KRAS/EMT pathway (22).…”

Section: Discussionmentioning

confidence: 99%

A Weighted Gene Co-Expression Network Analysis–Derived Prognostic Model for Predicting Prognosis and Immune Infiltration in Gastric Cancer

et al. 2021

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BackgroundGastric cancer (GC) is a major public health problem worldwide. In recent decades, the treatment of gastric cancer has improved greatly, but basic research and clinical application of gastric cancer remain challenges due to the high heterogeneity. Here, we provide new insights for identifying prognostic models of GC.MethodsWe obtained the gene expression profiles of GSE62254 containing 300 samples for training. GSE15459 and TCGA-STAD for validation, which contain 200 and 375 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules. We performed Lasso regression and Cox regression analyses to identify the most significant five genes to develop a novel prognostic model. And we selected two representative genes within the model for immunohistochemistry staining with 105 GC specimens from our hospital to verify the prediction efficiency. Moreover, we estimated the correlation coefficient between our model and immune infiltration using the CIBERSORT algorithm. The data from GSE15459 and TCGA cohort validated the robustness and predictive accuracy of this prognostic model.ResultsOf the 12 gene modules identified, 1,198 green-yellow module genes were selected for further analysis. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using the Cox proportional hazards regression model. Finally, we constructed a five gene prognostic model: Risk Score = [(-0.7547) * Expression (ARHGAP32)] + [(-0.8272) * Expression (KLF5)] + [1.09 * Expression (MAMLD1)] + [0.5174 * Expression (MATN3)] + [1.66 * Expression (NES)]. The prognosis of samples in the high-risk group was significantly poorer than that of samples in the low-risk group (p = 6.503e-11). The risk model was also regarded as an independent predictor of prognosis (HR, 1.678, p < 0.001). The observed correlation with immune cells suggested that this risk model could potentially predict immune infiltration.ConclusionThis study identified a potential risk model for prognosis and immune infiltration prediction in GC using WGCNA and Cox regression analysis.

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<p>EFEMP2 Suppresses the Invasion of Lung Cancer Cells by Inhibiting Epithelial-Mesenchymal Transition (EMT) and Down-Regulating MMPs</p>

Cited by 16 publications

References 43 publications

Bioinformatic screening and identification of downregulated hub genes in adrenocortical carcinoma

Bioinformatic screening and identification of downregulated hub genes in adrenocortical carcinoma

Single-cell transcriptomics of human islet ontogeny defines the molecular basis of β-cell dedifferentiation in T2D

A Weighted Gene Co-Expression Network Analysis–Derived Prognostic Model for Predicting Prognosis and Immune Infiltration in Gastric Cancer

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