Identification of Key Genes and Pathways Associated With Paclitaxel Resistance in Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis - Shen, Zhimin; Chen, Mingduan; Luo, Fei; Xu, Hui; Zhang, Peipei; Lin, Jihong; Kang, Mingqiang (Frontiers in Genetics, 2021)
Esophageal squamous cell carcinoma (ESCC) ranks 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 the paclitaxel resistance in the progression of ESCC, bioinformatics analyses were performed based on TCGA database and GEO database including GSE86099, and GSE161533. The differential expression analysis was respectively performed in TCGA data and two GEO datasets to obtain DEGs. Based on GSE161533, WGCNA was conducted to identify key modules associated with ESCC tumor status. The DEGs common to the two GEO datasets and the genes in key modules were intersected to obtain the paclitaxel resistance-specific or non-paclitaxel resistance-specific genes which were subjected to subsequent 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, respectively. The ESCC machine classifiers based on the key genes achieved relatively high AUC value in 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 a light on in-depth research of paclitaxel resistance in the context of ESCC progression.