scholarly journals Identification of key genes and pathways associated with esophageal squamous cell carcinoma development based on weighted gene correlation network analysis

2020 ◽  
Vol 11 (6) ◽  
pp. 1393-1402
Author(s):  
Mingrui Shao ◽  
Wenya Li ◽  
Shiyang Wang ◽  
Zhenghua Liu
Keyword(s):  
Squamous Cell Carcinoma ◽  
Network Analysis ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Correlation Network ◽  
Gene Correlation ◽  
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.

Genome-wide screening and co-expression network analysis identify recurrence-specific biomarkers of esophageal squamous cell carcinoma

Tumor Biology ◽  
2014 ◽  
Vol 35 (11) ◽  
pp. 10959-10968 ◽  
Author(s):  
Zong-wu Lin ◽  
Jie Gu ◽  
Rong-hua Liu ◽  
Xiao-ming Liu ◽  
Feng-kai Xu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Network Analysis ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Genome Wide
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