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 Development and Validation of an Apoptosis-Related Genes Signature in Patients with Gastric Cancer

2021 ◽  
Author(s):  
Jianqiao Yang ◽  
Liang Shang ◽  
Leping Li ◽  
Zixiao Wang ◽  
Kangdi Dong ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cox Regression ◽  
External Validation ◽  
Treatment Strategies ◽  
Enrichment Analysis ◽  
Malignant Tumour ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Abstract Background: Gastric cancer (GC) is a common malignant tumour of the digestive tract. the prognosis of GC patients is still not optimistic. Apoptosis-related genes (ARGs) plays an important role in the development, invasion, metastasis and drug resistance of GC. Therefore, assessing the interaction between ARGs and the prognosis of GC patients may help identify specific biomarkers.Methods: Differentially expressed genes (DEGs) were identified by integrating gene expression profiling analyses from The Cancer Genome Atlas (TCGA) GC cohort and Gene Set Enrichment Analysis (GSEA) Database. Then, a risk score model was built based on Kaplan-Meier (K-M), least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. Another cohort (GSE84426) was used for external validation. By combining risk scores with clinical variables, a nomogram was constructed to predict the prognosis of GC patients. Results: We screened 39 DEGS and established a three-gene signature(CAV1、F2、LUM) based on 161 ARGs. In addition, three-gene signature was identified as an independent factor in predicting the prognosis of GC patients and validated in an external independent cohort. Finally, we developed a nomogram that can be applied to clinical practice.Conclusions: Our study established a three-gene signature of GC based on ARGs that has reference significance for in-depth research on the apoptosis mechanism of GC and the exploration of new clinical treatment strategies.

scholarly journals A methylation‐based mRNA signature predicts survival in patients with gastric cancer

2020 ◽  
Author(s):  
Yang Li ◽  
Rongrong Sun ◽  
Youwei Zhang ◽  
Yuan Yuan ◽  
Yufeng Miao
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Causative Role ◽  
Training Cohort ◽  
Cox Regression Analysis ◽  
Pathway Enrichment

Abstract Background: Evidence suggests that altered DNA methylation plays a causative role in the occurrence, progression and prognosis of gastric cancer (GC). Thus, methylated-differentially expressed genes (MDEGs) could potentially serve as biomarkers and therapeutic targets in GC.Methods: Four genomics profiling datasets were used to identify MDEGs. Gene Ontology enrichment and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis were used to explore the biological roles of MDEGs in GC. Univariate Cox and LASSO analysis were used to identify survival-related MDEGs and to construct a MDEGs-based signature. The prognostic performance was evaluated in two independent cohorts.Results: We identified a total of 255 MDEGs, including 192 hypermethylation-low expression and 63 Hypomethylation-high expression genes. The univariate Cox regression analysis showed that 83 MDEGs were associated with overall survival. Further we constructed an eight-MDEGs signature that was independent predictive of prognosis in the training cohort. By applying the eight-MDEGs signature, patients in the training cohort could be categorized into high-risk or low-risk subgroup with significantly different overall survival (HR = 2.62, 95%CI= 1.71-4.02, P < 0.0001). The prognostic value of the eight-MDEGs signature was confirmed in another independent GEO cohort (HR=1.35, 95% CI= 1.03-1.78, P= 0.0302) and TCGA-GC cohort (HR=1.85, 95% CI= 1.16-2.94, P= 0.0084). Multivariate cox regression analysis proved the eight-MDEGs signature was an independent prognostic factor for GC.Conclusion: We have thus established an innovative eight-MDEGs signature that is predictive of overall survival and could be a potentially useful guide for personalized treatment of GC patients.

scholarly journals Establishment and Validation of a Novel Metabolism-related Gene Signature for Predicting Overall Survival of Patients with Breast Cancer

2021 ◽  
Author(s):  
Jian Li ◽  
Yang Liu ◽  
Fei Liu ◽  
Qiang Tian ◽  
Baojiang Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Current Status ◽  
Lasso Regression ◽  
Related Gene

Abstract It is well known that Breast cancer is a heterogeneous disease.Although the current recurrence and mortality rate have been greatly improved, many people still suffer relapse and metastasis.Metabolic reprograming is currently considered to be a new hallmark of cancer.Therefore,in this study, we comprehensively analyzed the prognostic effect of metabolic-related gene signatures in breast cancer and its relationship with the immune microenvironment.We constructed a novel metabolic-related gene signature containing 6 genes to distinguish between high and low risk groups by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression, and validated its robustness and accuracy through multiple databases.The metabolic gene signature may be an independent risk factor for BC both in the training and the testing set,the nomogram has a moderately accurate performance,and the C index was 0.757 and 0.728 respectively.The signature can reveal metabolic characteristics based on gene set enrichment analysis and at the same time monitor the status of TME.This gene signature can be used as a promising independent prognostic marker for BC patients, and can indicate the current status of TME, providing more clues for exploring new diagnostic and treatment strategies.

scholarly journals A Novel DNA Replication-Related Signature Predicting Recurrence After R0 Resection of Pancreatic Ductal Adenocarcinoma: Prognostic Value and Clinical Implications

2021 ◽  
Vol 9 ◽  
Author(s):  
Zengyu Feng ◽  
Kexian Li ◽  
Jianyao Lou ◽  
Mindi Ma ◽  
Yulian Wu ◽  
...  
Keyword(s):  
Dna Replication ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Model ◽  
Cox Regression ◽  
External Validation ◽  
Gene Signature ◽  
Ductal Adenocarcinoma ◽  
R0 Resection ◽  
Pathway Enrichment Analysis ◽  
Cox Regression Analysis

The aim of any surgical resection for pancreatic ductal adenocarcinoma (PDAC) is to achieve tumor-free margins (R0). R0 margins give rise to better outcomes than do positive margins (R1). Nevertheless, postoperative morbidity after R0 resection remains high and prognostic gene signature predicting recurrence risk of patients in this subgroup is blank. Our study aimed to develop a DNA replication-related gene signature to stratify the R0-treated PDAC patients with various recurrence risks. We conducted Cox regression analysis and the LASSO algorithm on 273 DNA replication-related genes and eventually constructed a 7-gene signature. The predictive capability and clinical feasibility of this risk model were assessed in both training and external validation sets. Pathway enrichment analysis showed that the signature was closely related to cell cycle, DNA replication, and DNA repair. These findings may shed light on the identification of novel biomarkers and therapeutic targets for PDAC.

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 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.

scholarly journals Comparative proteomics analysis provides new insights into the haustorium development of Taxillus chinensis (DC.) Danser

2021 ◽  
Author(s):  
Limei Pan ◽  
Lingyun Wan ◽  
Lisha Song ◽  
Lili He ◽  
Ni Jiang ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Proteomics Analysis ◽  
Pathway Enrichment ◽  
Qrt Pcr ◽  
Protein Variation ◽  
Phenylpropanoid Biosynthesis ◽  
Itraq Proteomics ◽  
Differentially Abundant Proteins

Abstract Background Loranthus (Taxillus chinensis) is an important medicinal and parasitic plant that attacks other plants for living. To reveal the mechanisms of haustorium development, we employed an iTRAQ proteomics-based approach to identify differentially abundant proteins (DAPs) of fresh seeds (CK), baby (FB), and adult haustoria (FD). Results A total of 563 and 785 DAPs were successfully quantified in the early/later developmental stage, respectively. Pathway enrichment analysis indicated that the DAPs mainly associated with metabolic pathways, ribosome, phenylpropanoid biosynthesis and photosynthesis. In the meantime, DAPs associated with phytohormone signaling pathway changed markedly. Furthermore, we evaluated the contents change of phytohormone during the haustoria development. These results indicated that phytohormone is very important for haustorium development. qRT-PCR validation showed that the mRNA expression levels were consistent with the protein variation, suggesting that our result were reliable. Conclusions To the best of our knowledge, this is the first haustoria proteomes of loranthus, and our findings will improve our understanding of the molecular mechanism of haustoria development.

scholarly journals Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis

2020 ◽  
Vol 7 ◽  
Author(s):  
Mingde Cao ◽  
Junhui Zhang ◽  
Hualiang Xu ◽  
Zhujian Lin ◽  
Hong Chang ◽  
...  
Keyword(s):  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
External Validation ◽  
Treatment Strategies ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Immune Related Genes

Osteosarcoma (OS) is a malignant disease that develops rapidly and is associated with poor prognosis. Immunotherapy may provide new insights into clinical treatment strategies for OS. The purpose of this study was to identify immune-related genes that could predict OS prognosis. The gene expression profiles and clinical data of 84 OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. According to non-negative matrix factorization, two molecular subtypes of immune-related genes, C1 and C2, were acquired, and 597 differentially expressed genes between C1 and C2 were identified. Univariate Cox analysis was performed to get 14 genes associated with survival, and 4 genes (GJA5, APBB1IP, NPC2, and FKBP11) obtained through least absolute shrinkage and selection operator (LASSO)-Cox regression were used to construct a 4-gene signature as a prognostic risk model. The results showed that high FKBP11 expression was correlated with high risk (a risk factor), and that high GJA5, APBB1IP, or NPC2 expression was associated with low risk (protective factors). The testing cohort and entire TARGET cohort were used for internal verification, and the independent GSE21257 cohort was used for external validation. The study suggested that the model we constructed was reliable and performed well in predicting OS risk. The functional enrichment of the signature was studied through gene set enrichment analysis, and it was found that the risk score was related to the immune pathway. In summary, our comprehensive study found that the 4-gene signature could be used to predict OS prognosis, and new biomarkers of great significance for understanding the therapeutic targets of OS were identified.

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