scholarly journals Discovery and Validation of an Epithelial-Mesenchymal Transition-Based Signature in Gastric Cancer by Genomics and Prognosis Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-35
Author(s):  
Huiyong Xu ◽  
Huilai Wan ◽  
Maoshu Zhu ◽  
Lianghua Feng ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
T Cell ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Expression Profiles ◽  
Cell Cancer ◽  
External Validation ◽  
Receptor Interaction ◽  
Related Gene ◽  
Mesenchymal Transition

Objective. Epithelial-mesenchymal transition (EMT) exerts a key function in cancer initiation and progression. Herein, we aimed to develop an EMT-based prognostic signature in gastric cancer. Methods. The gene expression profiles of gastric cancer were obtained from TCGA dataset as a training set and GSE66229 and GSE84437 datasets as validation sets. By LASSO regression and Cox regression analyses, key prognostic EMT-related genes were screened for developing a risk score (RS) model. Potential small molecular compounds were predicted by the CMap database based on the RS model. GSEA was employed to explore signaling pathways associated with the RS. ESTIMATE and seven algorithms (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC) were applied to assess the RS and immune microenvironment. Results. This study developed an EMT-related gene signature comprised of SERPINE1, PCOLCE2, MATN3, and DKK1. High-RS patients displayed poorer survival outcomes than those with low RS. ROC curves demonstrated the robustness of the model in predicting the prognosis. After external validation, the RS model was an independent risk factor for gastric cancer. Several compounds were predicted for gastric cancer treatment based on the RS model. ECM receptor interaction, focal adhesion, pathway in cancer, TGF-beta, and WNT pathways were distinctly activated in high-RS samples. Also, high RS was significantly associated with increased stromal and immune scores and increased infiltration of CD4+ T cell, CD8+ T cell, cancer-associated fibroblast, and macrophage in gastric cancer tissues. Conclusion. Our findings suggested that the EMT-related gene model may robustly predict gastric cancer prognosis, which could improve the efficacy of personalized therapy.

scholarly journals A novel prognostic model based on epithelial-mesenchymal transition-related genes predicts patient survival in gastric cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wanting Song ◽  
Yi Bai ◽  
Jialin Zhu ◽  
Fanxin Zeng ◽  
Chunmeng Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Critical Function ◽  
Mesenchymal Transition ◽  
Patient Prognosis

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.

scholarly journals An epithelial-mesenchymal transition-related 5-gene signature predicting the prognosis of hepatocellular carcinoma patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Gongmin Zhu ◽  
Hongwei Xia ◽  
Qiulin Tang ◽  
Feng Bi
Keyword(s):  
Tumor Metastasis ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis ◽  
Qrt Pcr

Abstract Background Tumor metastasis is one of the leading reasons of the dismal prognosis of hepatocellular carcinoma (HCC). Epithelial-mesenchymal transition (EMT) is closely associated with tumor metastasis including HCC. The purpose of this study is to construct and validate an EMT-related gene signature for predicting the prognosis of HCC patients. Methods Gene expression data of HCC patients was downloaded from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was performed to found the EMT-related gene sets which were obviously distinct between normal samples and paired HCC samples. Cox regression analysis was used to develop an EMT-related prognostic signature, and the performance of the signature was evaluated by Kaplan–Meier curves and time-dependent receiver operating characteristic (ROC) curves. A nomogram incorporating the independent predictors was established. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of the hub genes in HCC cell lines, and the role of PDCD6 in the metastasis of HCC was determined by functional experiments. Results An EMT-related 5-gene signature (PDCD6, TCOF1, TRIM28, EZH2 and FAM83D) was constructed using univariate and multivariate Cox regression analysis. Based on the signature, the HCC patients were classified into high- and low-risk groups, and patients in high-risk group had a poor prognosis. Time-dependent ROC and Cox regression analyses suggested that the signature could predict HCC prognosis exactly and independently. The predictive capacity of the signature was also validated in two external cohorts. GSEA results showed that many cancer-related signaling pathways such as PI3K/Akt/mTOR pathway and TGF-β/SMAD pathway were enriched in high-risk group. The result of qRT-PCR revealed that PDCD6, TCOF1 and FAM83D were highly expressed in HCC cancer cells. Among them, PDCD6 were found to promote cell migration and invasion. Conclusion The EMT-related 5-gene signature can serve as a promising prognostic biomarker for HCC patients and may provide a novel mechanism of HCC metastasis.

scholarly journals Angiogenesis-Related Gene Expression Signatures Predicting Prognosis in Gastric Cancer Patients

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3685
Author(s):  
Haoyu Ren ◽  
Jiang Zhu ◽  
Haochen Yu ◽  
Alexandr Bazhin ◽  
Christoph Westphalen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Clinical Stage ◽  
External Validation ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Related Gene ◽  
Angiogenesis Related Gene

Increasing evidence indicates that angiogenesis is crucial in the development and progression of gastric cancer (GC). This study aimed to develop a prognostic relevant angiogenesis-related gene (ARG) signature and a nomogram. The expression profile of the 36 ARGs and clinical information of 372 GC patients were extracted from The Cancer Genome Atlas (TCGA). Consensus clustering was applied to divide patients into clusters 1 and 2. Least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to identify the survival related ARGs and establish prognostic gene signatures, respectively. The Asian Cancer Research Group (ACRG) (n = 300) was used for external validation. Risk score of ARG signatures was calculated, and a prognostic nomogram was developed. Gene set enrichment analysis of the ARG model risk score was performed. Cluster 2 patients had more advanced clinical stage and shorter survival rates. ARG signatures carried prognostic relevance in both cohorts. Moreover, ARG-risk score was proved as an independent prognostic factor. The predictive value of the nomogram incorporating the risk score and clinicopathological features was superior to tumor, lymph node, metastasis (TNM) staging. The high-risk score group was associated with several cancer and metastasis-related pathways. The present study suggests that ARG-based nomogram could serve as effective prognostic biomarkers and allow a more precise risk stratification.

scholarly journals Genomics and prognosis analysis of epithelial-mesenchymal transition in colorectal cancer patients

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zizhen Zhang ◽  
Sheng Zheng ◽  
Yifeng Lin ◽  
Jiawei Sun ◽  
Ning Ding ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Related Gene ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis

Abstract Background The epithelial-mesenchymal transition (EMT) plays a pivotal role in various physiological processes, such as embryonic development, tissue morphogenesis, and wound healing. EMT also plays an important role in cancer invasion, metastasis, and chemoresistance. Additionally, EMT is partially responsible for chemoresistance in colorectal cancer (CRC). The aim of this research is to develop an EMT-based prognostic signature in CRC. Methods RNA-seq and microarray data, together with clinical information, were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. A total of 244 differentially expressed EMT-related genes (ERGs) were obtained by comparing the expression between normal and tumor tissues. An EMT-related signature of 11 genes was identified as crucially related to the overall survival (OS) of patients through univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO), and Cox regression analysis. Finally, we established a clinical nomogram to predict the survival possibility of CRC patients by integrating clinical characteristics and the EMT-related gene signature. Results Two hundred and forty-four differentially expressed ERGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that EMT-related signaling pathway genes were highly related to CRC. Kaplan-Meier analysis revealed that the 11-EMT signature could significantly distinguish high- and low-risk patients in both TCGA and GEO CRC cohorts. In addition, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusion We developed a novel EMT-related gene signature for the prognosis prediction of CRC patients, which could improve the individualized outcome prediction in CRC.

Weighted correlation network analysis identifies FN1, COL1A1 and SERPINE1 associated with the progression and prognosis of gastric cancer

2021 ◽  
pp. 1-17
Author(s):  
Qiaoyun Zhao ◽  
Jun Xie ◽  
Jinliang Xie ◽  
Rulin Zhao ◽  
Conghua Song ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Poor Prognosis ◽  
Epithelial Mesenchymal Transition ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Correlation Network ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Mesenchymal Transition ◽  
Weighted Correlation

BACKGROUND: Gastric cancer (GC) is one of the most deadliest tumours worldwide, and its prognosis remains poor. OBJECTIVE: This study aims to identify and validate hub genes associated with the progression and prognosis of GC by constructing a weighted correlation network. METHODS: The gene co-expression network was constructed by the WGCNA package based on GC samples and clinical data from the TCGA database. The module of interest that was highly related to clinical traits, including stage, grade and overall survival (OS), was identified. GO and KEGG pathway enrichment analyses were performed using the clusterprofiler package in R. Cytoscape software was used to identify the 10 hub genes. Differential expression and survival analyses were performed on GEPIA web resources and verified by four GEO datasets and our clinical gastric specimens. The receiver operating characteristic (ROC) curves of hub genes were plotted using the pROC package in R. The potential pathogenic mechanisms of hub genes were analysed using gene set enrichment analysis (GSEA) software. RESULTS: A total of ten modules were detected, and the magenta module was identified as highly related to OS, stage and grade. Enrichment analysis of magenta module indicated that ECM-receptor interaction, focal adhesion, PI3K-Akt pathway, proteoglycans in cancer were significantly enriched. The PPI network identified ten hub genes, namely COL1A1, COL1A2, FN1, POSTN, THBS2, COL11A1, SPP1, MMP13, COMP, and SERPINE1. Three hub genes (FN1, COL1A1 and SERPINE1) were finally identified to be associated with carcinogenicity and poor prognosis of GC, and all were independent risk factors for GC. The area under the curve (AUC) values of FN1, COL1A1 and SERPINE1 for the prediction of GC were 0.702, 0.917 and 0.812, respectively. GSEA showed that three hub genes share 15 common upregulated biological pathways, including hypoxia, epithelial mesenchymal transition, angiogenesis, and apoptosis. CONCLUSION: We identified FN1, COL1A1 and SERPINE1 as being associated with the progression and poor prognosis of GC.

scholarly journals Development and External Validation of a Novel Immune Checkpoint–Related Gene Signature for Prediction of Overall Survival in Hepatocellular Carcinoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Enfa Zhao ◽  
Shimin Chen ◽  
Ying Dang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Immune Checkpoint ◽  
Immune Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
External Validation ◽  
Gene Signature ◽  
Cancer Genome ◽  
Related Gene

Objective: The purpose of this study was to develop and validate a novel immune checkpoint–related gene signature for prediction of overall survival (OS) in hepatocellular carcinoma (HCC).Methods: mRNA expression profiles and clinical follow-up information were obtained in the International Cancer Genome Consortium database. An external dataset from The Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma database was used to validate the results. The univariate and multivariate Cox regression analyses were performed based on the differentially expressed genes. We generated a four-mRNA signature to predict patient survival. Furthermore, the reliability and validity were validated in TCGA cohort. An integrated bioinformatics approach was performed to evaluate its diagnostic and prognostic value.Results: A four-gene (epidermal growth factor, mutated in colorectal cancer, mitogen-activated protein kinase kinase 2, and NRAS proto-oncogene, GTPase) signature was built to classify patients into two risk groups using a risk score with different OS in two cohorts (all P < 0.0001). Multivariate regression analysis demonstrated the signature was an independent predictor of HCC. Furthermore, the signature presented an excellent diagnostic power in differentiating HCC and adjacent tissues. Immune cell infiltration analysis revealed that the signature was associated with a number of immune cell subtypes.Conclusion: We identified a four–immune checkpoint–related gene signature as a robust biomarker with great potential for clinical application in risk stratification and OS prediction in HCC patients and could be a potential indicator of immunotherapy in HCC. The diagnostic signature had been validated to accurately distinguish HCC from adjacent tissues.

scholarly journals Identification of an EMT-Related Gene Signature for Predicting Overall Survival in Gastric Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Weiyu Dai ◽  
Yizhi Xiao ◽  
Weimei Tang ◽  
Jiaying Li ◽  
Linjie Hong ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cox Regression ◽  
External Validation ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Time Dependent ◽  
Pathway Enrichment Analysis ◽  
Related Gene ◽  
Pathway Enrichment ◽  
Qrt Pcr

BackgroundIt has been widely reported that epithelial-mesenchymal transition (EMT) is associated with malignant progression in gastric cancer (GC). Integration of the molecules related to EMT for predicting overall survival (OS) is meaningful for understanding the role of EMT in GC. Here, we aimed to establish an EMT-related gene signature in GC.MethodsTranscriptional profiles and clinical data of GC were downloaded from The Cancer Genome Atlas (TCGA). We constructed EMT-related gene signature for predicting OS by using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier analysis were performed to assess its predictive value. A nomogram combining the prognostic signature with clinical characteristics for OS prediction was established. And its predictive power was estimated by concordance index (C-index), time-dependent ROC curve, calibration curve and decision curve analysis (DCA). GSE62254 dataset from Gene Expression Omnibus (GEO) was used for external validation. Quantitative real-time PCR (qRT-PCR) was used to detected the mRNA expression of the five EMT-related genes in human normal gastric mucosal and GC cell lines. To further understand the potential mechanisms of the signature, Gene Set Enrichment Analysis (GSEA), pathway enrichment analysis, predictions of transcription factors (TFs)/miRNAs were performed.ResultsA novel EMT-related gene signature (including ITGAV, DAB2, SERPINE1, MATN3, PLOD2) was constructed for OS prediction of GC. With external validation, ROC curves indicated the signature’s good performance. Patients stratified into high- and low-risk groups based on the signature yielded significantly different prognosis. Univariate and multivariate Cox regression suggested that the signature was an independent prognostic variable. Nomogram for prognostication including the signature presented better predictive accuracy and clinical usefulness than the similar model without risk score to some extent with external validation. The qRT-PCR assays suggested that high expression of the five EMT-related genes could be found in human GC cell lines compared with normal gastric mucosal cell line. GSEA and pathway enrichment analysis revealed that focal adhesion and ECM-receptor interaction might be the two important pathways to the signature.ConclusionOur EMT-related gene signature may have practical application as an independent prognostic factor in GC.

scholarly journals Identification of a Novel Epithelial–Mesenchymal Transition Gene Signature Predicting Survival in Patients With HNSCC

2021 ◽  
Vol 27 ◽  
Author(s):  
Wei Xin ◽  
Chaoran Zhao ◽  
Longyang Jiang ◽  
Dongmei Pei ◽  
Lin Zhao ◽  
...  
Keyword(s):  
High Risk ◽  
Epithelial Mesenchymal Transition ◽  
Expression Profiles ◽  
Critical Role ◽  
Cell Cancer ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Mesenchymal Transition ◽  
Potential Biomarker

Head and neck squamous cell cancer (HNSCC) is one of the most common types of cancer worldwide. There have been many reports suggesting that biomarkers explored via database mining plays a critical role in predicting HNSCC prognosis. However, a single biomarker for prognostic analysis is not adequate. Additionally, there is growing evidence indicating that gene signature could be a better choice for HNSCC prognosis. We performed a comprehensive analysis of mRNA expression profiles using clinical information of HNSCC patients from The Cancer Genome Atlas (TCGA). Gene Set Enrichment Analysis (GSEA) was performed, and we found that a set of genes involved in epithelial mesenchymal transition (EMT) contributed to HNSCC. Cox proportional regression model was used to identify a four-gene (WIPF1, PPIB, BASP1, PLOD2) signature that were significantly associated with overall survival (OS), and all the four genes were significantly upregulated in tumor tissues. We successfully classified the patients with HNSCC into high-risk and low-risk groups, where in high-risk indicated poorer patient prognosis, indicating that this gene signature might be a novel potential biomarker for the prognosis of HNSCC. The prognostic ability of the gene signature was further validated in an independent cohort from the Gene Expression Omnibus (GEO) database. In conclusion, we identified a four-EMT-based gene signature which provides the potentiality to serve as novel independent biomarkers for predicting survival in HNSCC patients, as well as a new possibility for individualized treatment of HNSCC.

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