Identification of EMT-Related Gene Signatures to Predict the Prognosis of Patients With Endometrial Cancer

Cai, Luya; Hu, Chuan; Yu, Shanshan; Liu, Lixiao; Zhao, Jinduo; Zhao, Ye; Lin, Fan; Du, Xuedan; Yu, Qiongjie; Xiao, Qin

doi:10.3389/fgene.2020.582274

Cited by 24 publications

(21 citation statements)

References 41 publications

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“…Similar to lung carcinoma, 177 other specific sets of genes that define EMT and cellular states have been reported for other cancer disorders such as neck squamous cell carcinoma, 178 endometrial cancer, 179 ovarian cancer, 180 and bladder cancer. 181 All these studies and others led to the creation of the first gene resource database for exploring EMT-related human genes in cancer in 2015.…”

Section: Novel Tools To Characterize Emtmentioning

confidence: 67%

Bioengineering strategies to control epithelial-to-mesenchymal transition for studies of cardiac development and disease

et al. 2021

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Epithelial-to-mesenchymal transition (EMT) is a process that occurs in a wide range of tissues and environments, in response to numerous factors and conditions, and plays a critical role in development, disease, and regeneration. The process involves epithelia transitioning into a mobile state and becoming mesenchymal cells. The investigation of EMT processes has been important for understanding developmental biology and disease progression, enabling the advancement of treatment approaches for a variety of disorders such as cancer and myocardial infarction. More recently, tissue engineering efforts have also recognized the importance of controlling the EMT process. In this review, we provide an overview of the EMT process and the signaling pathways and factors that control it, followed by a discussion of bioengineering strategies to control EMT. Important biological, biomaterial, biochemical, and physical factors and properties that have been utilized to control EMT are described, as well as the studies that have investigated the modulation of EMT in tissue engineering and regenerative approaches in vivo , with a specific focus on the heart. Novel tools that can be used to characterize and assess EMT are discussed and finally, we close with a perspective on new bioengineering methods that have the potential to transform our ability to control EMT, ultimately leading to new therapies.

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Section: Novel Tools To Characterize Emtmentioning

confidence: 67%

Bioengineering strategies to control epithelial-to-mesenchymal transition for studies of cardiac development and disease

et al. 2021

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“…The results of the calibration map and DCA curve show that our model has a good ability to predict UCEC patient prognosis. Compared with the other prognostic models of UCEC [ 39 , 40 ], our model contains fewer genes and is more convenient to use in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Identification of the prognostic value of a 2-gene signature of the WNT gene family in UCEC using bioinformatics and real-world data

Zheng

Zhang

et al. 2021

Cancer Cell Int

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Background The WNT gene family plays an important role in the occurrence and development of malignant tumors, but its involvement has not been systematically analyzed in uterine corpus endometrial carcinoma (UCEC). This study aimed to evaluate the prognostic value of the WNT gene family in UCEC. Methods Pan-cancer transcriptome data of the UCSC Xena database and Genotype-Tissue Expression (GTEx) normal tissue data were downloaded to analyze the expression and prognosis of 19 WNT family genes in UCEC. A cohort from The Cancer Genome Atlas-Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) was used to analyze the expression of the WNT gene family in different immune subtypes and clinical subgroups. The STRING database was used to analyze the interaction of the WNT gene family and its biological function. Univariate Cox regression analysis and Lasso cox analysis were used to identify the genes associated with significant prognosis and to construct multi signature prognosis model. An immunohistochemical assay was used to verify the predictive ability of the model. Risk score and the related clinical features were used to construct a nomogram. Results The expression levels of WNT2, WNT3, WNT3A, WNT5A, WNT7A, and WNT10A were significantly different among different immune subtypes and correlated with TP53 mutation. According to the WNT family genes related to the prognosis of UCEC, UCEC was classified into two subtypes (C1, C2). The prognosis of subtype C1 was significantly better than that of subtype C2. A 2-gene signature (WNT2 and WNT10A) was constructed and the two significantly prognostic groups can be divided based on median Risk score. These results were verified using real-world data, and the nomogram constructed using clinical features and Risk score had good prognostic ability. Conclusions The 2-gene signature including WNT2 and WNT10A can be used to predict the prognosis of patients with UCEC, which is important for clinical decision-making and individualized therapy for patients with UCEC.

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“…A total of 1,184 EMT-related genes were obtained from a previous article (Cai et al, 2020). In the TCGA training cohort, EMT-related genes that were significantly associated with DFS were screened using univariate cox regression analysis (P < 0.01).…”

Section: Construction Of the Emt-related Gene Signature For Dfs Predictionmentioning

confidence: 99%

An EMT-Related Gene Signature for Predicting Response to Adjuvant Chemotherapy in Pancreatic Ductal Adenocarcinoma

Feng

Lou

et al. 2021

Front. Cell Dev. Biol.

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BackgroundFor pancreatic ductal adenocarcinoma (PDAC) patients, chemotherapy failure is the major reason for postoperative recurrence and poor outcomes. Establishment of novel biomarkers and models for predicting chemotherapeutic efficacy may provide survival benefits by tailoring treatments.MethodsUnivariate cox regression analysis was employed to identify EMT-related genes with prognostic potential for DFS. These genes were subsequently submitted to LASSO regression analysis and multivariate cox regression analysis to identify an optimal gene signature in TCGA training cohort. The predictive accuracy was assessed by Kaplan–Meier (K-M), receiver operating characteristic (ROC) and calibration curves and was validated in PACA-CA cohort and our local cohort. Pathway enrichment and function annotation analyses were conducted to illuminate the biological implication of this risk signature.ResultsLASSO and multivariate Cox regression analyses selected an 8-gene signature comprised DLX2, FGF9, IL6R, ITGB6, MYC, LGR5, S100A2, and TNFSF12. The signature had the capability to classify PDAC patients with different DFS, both in the training and validation cohorts. It provided improved DFS prediction compared with clinical indicators. This signature was associated with several cancer-related pathways. In addition, the signature could also predict the response to immune-checkpoint inhibitors (ICIs)-based immunotherapy.ConclusionWe established a novel EMT-related gene signature that was capable of predicting therapeutic response to adjuvant chemotherapy and immunotherapy. This signature might facilitate individualized treatment and appropriate management of PDAC patients.

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Identification of EMT-Related Gene Signatures to Predict the Prognosis of Patients With Endometrial Cancer

Cited by 24 publications

References 41 publications

Bioengineering strategies to control epithelial-to-mesenchymal transition for studies of cardiac development and disease

Bioengineering strategies to control epithelial-to-mesenchymal transition for studies of cardiac development and disease

Identification of the prognostic value of a 2-gene signature of the WNT gene family in UCEC using bioinformatics and real-world data

An EMT-Related Gene Signature for Predicting Response to Adjuvant Chemotherapy in Pancreatic Ductal Adenocarcinoma

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