scholarly journals Identification of key genes and pathways for esophageal squamous cell carcinoma by bioinformatics analysis

2018 ◽  
Author(s):  
Xiaohua Chen ◽  
Sina Cai ◽  
Baoxia Li ◽  
Xiaona Zhang ◽  
Wenhui Li ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Bioinformatics Analysis ◽  
Key Genes

scholarly journals Identification of Key Genes and Pathways Associated With Paclitaxel Resistance in Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhimin Shen ◽  
Mingduan Chen ◽  
Fei Luo ◽  
Hui Xu ◽  
Peipei Zhang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Differential Expression Analysis ◽  
Biological Significance ◽  
The Cancer Genome Atlas ◽  
Paclitaxel Resistance ◽  
Bioinformatics Analyses ◽  
Key Genes

Esophageal squamous cell carcinoma (ESCC) ranks as the fourth leading cause of cancer-related death in China. Although paclitaxel has been shown to be effective in treating ESCC, the prolonged use of this chemical will lead to paclitaxel resistance. In order to uncover genes and pathways driving paclitaxel resistance in the progression of ESCC, bioinformatics analyses were performed based on The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database including GSE86099 and GSE161533. Differential expression analysis was performed in TCGA data and two GEO datasets to obtain differentially expressed genes (DEGs). Based on GSE161533, weighted gene co-expression network analysis (WGCNA) was conducted to identify the key modules associated with ESCC tumor status. The DEGs common to the two GEO datasets and the genes in the key modules were intersected to obtain the paclitaxel resistance-specific or non-paclitaxel resistance-specific genes, which were subjected to subsequent least absolute shrinkage and selection operator (LASSO) feature selection, whereby paclitaxel resistance-specific or non-paclitaxel resistance-specific key genes were selected. Ten machine learning models were used to validate the biological significance of these key genes; the potential therapeutic drugs for paclitaxel resistance-specific genes were also predicted. As a result, we identified 24 paclitaxel resistance-specific genes and 18 non-paclitaxel resistance-specific genes. The ESCC machine classifiers based on the key genes achieved a relatively high AUC value in the cross-validation and in an independent test set, GSE164158. A total of 207 drugs (such as bevacizumab) were predicted to be alternative therapeutics for ESCC patients with paclitaxel resistance. These results might shed light on the in-depth research of paclitaxel resistance in the context of ESCC progression.

Integrated Bioinformatics Analysis of Differentially-Expressed Genes and Immune Cell Infiltration Characteristics in Esophageal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Zitong Feng ◽  
Jingge Qu ◽  
Xiao Liu ◽  
Jinghui Liang ◽  
Yongmeng Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Squamous Cell ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration

Abstract Esophageal squamous cell carcinoma (ESCC) is a life-threatening thoracic tumor with a poor prognosis. Identifying the best-targeted therapy, appropriate biomarkers and individual treatment for patients with ESCC remains a significant challenge. The present study aimed to elucidate key candidate genes and immune cell infiltration characteristics in ESCC by integrated bioinformatics analysis. We downloaded nine gene expression datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between ESCC tissues and normal tissues in each dataset were identified by the “limma” R package, and a total of 152 robust DEGs were identified by robust rank aggregation (RRA) algorithm. Functional enrichment analyses of the robust DEGs showed that these genes were significantly associated with extracellular matrix related process. Immune cell infiltration analysis was also conducted by CIBERSORT algorithm. We found that M0 and M1 macrophages were increased dramatically in ESCC while M2 macrophages decreased. Nine hub genes were picked out from a protein-protein interaction (PPI) network used by the CytoHubba plugin in Cytoscape. According to the receiver operating characteristic (ROC) curves and Kaplan-Meier survival analysis, the genes PLAU, SPP1 and VCAN had high diagnostic and prognostic values for ESCC patients. Based on univariate and multivariate regression analyses, seven genes (IL18, PLAU, ANO1, SLCO1B3, CST1, NELL2 and MAGEA11) from the robust DEGs were used to construct a good prognostic model. A nomogram that incorporates seven genes signature was established to develop a quantitative method for ESCC prognosis. Our results might provide aid for exploring potential therapeutic targets and prognosis evaluation in ESCC.

scholarly journals Expression and Regulatory Network Analysis of MiR-139-3p, a New Potential Serum Biomarker for Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis

2020 ◽  
Vol 19 ◽  
pp. 153303382092096
Author(s):  
Yonghong Wang ◽  
Qimei Fang ◽  
Liru Tian ◽  
Zhongzhen Yuan ◽  
Lizhen Tian ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Esophageal Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Bioinformatics Analysis ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Cancer Genome Atlas

Background: In recent studies, microRNAs have been demonstrated as stable detectable biomarkers in blood for cancer. In addition, computer-aided biomarker discovery has now become an attractive paradigm for precision diagnosis. Methods: In this study, we identified and evaluated miR-139-3p as a biomarker for screening of esophageal squamous cell carcinoma using the Cancer Genome Atlas and Gene Expression Omnibus database analyses. We identified possible miR-139-3p target genes through the predicted database and esophageal squamous cell carcinoma upregulated genes from the Cancer Genome Atlas and Gene. Bioinformatics analysis was performed to determine key miR-139-3p targets and pathways associated with esophageal carcinoma. Finally, the expression and expected significance of hub genes were evaluated via the Genotype-Tissue Expression project. Results: MiR-139-3p was significantly downregulated in patients with esophageal squamous cell carcinoma/esophageal carcinoma. In GSE 122497, the area under the curve-receiver operating characteristic value, sensitivity, and specificity for serum miR-139-3p were 0.754, 67.49%, and 80.00%, respectively. The pattern specification process, skeletal system development, and regionalization process were the most enriched interactions in esophageal carcinoma. In addition, Epstein-Barr virus infection, human T-cell leukemia virus 1 infection, and human cytomegalovirus infection were identified as crucial pathways. Six hub genes (CD1A, FCGR2A, ANPEP, CD1B, membrane metalloendopeptidase, and TWIST1) were found, and FCGR2A and membrane metalloendopeptidase were further confirmed by genotype-tissue expression. High expression of membrane metalloendopeptidase correlated with a better overall survival but not with disease-free survival of patients with esophageal carcinoma. Conclusions: MiR-139-3p was identified as a candidate biomarker for predicting esophageal squamous cell carcinoma based on network analysis. MiR-139-3p acted as a tumor suppressor by targeting membrane metalloendopeptidase in esophageal carcinoma, and low expression of membrane metalloendopeptidase may indicate a better prognosis of patients with esophageal carcinoma.

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