scholarly journals Gene Expression Analysis of Two Epithelial-mesenchymal Transition-related Genes: Long Noncoding RNA-ATB and SETD8 in Gastric Cancer Tissues

2018 ◽  
Vol 7 (1) ◽  
pp. 42 ◽  
Author(s):  
Parvaneh Nikpour ◽  
Nooshin Nourbakhsh ◽  
Modjtaba Emadi-Baygi ◽  
Rasoul Salehi
Aging ◽  
2016 ◽  
Vol 8 (9) ◽  
pp. 2023-2038 ◽  
Author(s):  
Hu Zhou ◽  
Fubing Wang ◽  
Hao Chen ◽  
Qian Tan ◽  
Shili Qiu ◽  
...  

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Aline Simoneti Fonseca ◽  
Anelisa Ramão ◽  
Matheus Carvalho Bürger ◽  
Jorge Estefano Santana de Souza ◽  
Dalila Lucíola Zanette ◽  
...  

Abstract Background Colorectal cancer (CRC) is one of the most common cancers worldwide; it is the fourth leading cause of death in the world and the third in Brazil. Mutations in the APC, DCC, KRAS and TP53 genes have been associated with the progression of sporadic CRC, occurring at defined pathological stages of the tumor progression and consequently modulating several genes in the corresponding signaling pathways. Therefore, the identification of gene signatures that occur at each stage during the CRC progression is critical and can present an impact on the diagnosis and prognosis of the patient. In this study, our main goal was to determine these signatures, by evaluating the gene expression of paired colorectal adenoma and adenocarcinoma samples to identify novel genetic markers in association to the adenoma-adenocarcinoma stage transition. Methods Ten paired adenoma and adenocarcinoma colorectal samples were subjected to microarray gene expression analysis. In addition, mutations in APC, KRAS and TP53 genes were investigated by DNA sequencing in paired samples of adenoma, adenocarcinoma, normal tissue, and peripheral blood from ten patients. Results Gene expression analysis revealed a signature of 689 differentially expressed genes (DEG) (fold-change> 2, p< 0.05), between the adenoma and adenocarcinoma paired samples analyzed. Gene pathway analysis using the 689 DEG identified important cancer pathways such as remodeling of the extracellular matrix and epithelial-mesenchymal transition. Among these DEG, the ETV4 stood out as one of the most expressed in the adenocarcinoma samples, further confirmed in the adenocarcinoma set of samples from the TCGA database. Subsequent in vitro siRNA assays against ETV4 resulted in the decrease of cell proliferation, colony formation and cell migration in the HT29 and SW480 colorectal cell lines. DNA sequencing analysis revealed KRAS and TP53 gene pathogenic mutations, exclusively in the adenocarcinomas samples. Conclusion Our study identified a set of genes with high potential to be used as biomarkers in CRC, with a special emphasis on the ETV4 gene, which demonstrated involvement in proliferation and migration.


2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wanting Song ◽  
Yi Bai ◽  
Jialin Zhu ◽  
Fanxin Zeng ◽  
Chunmeng Yang ◽  
...  

Abstract Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis.


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