Identification of key genes and pathways involved in T-DM1-resistance in OE-19 esophageal cancer cells through bioinformatics analysis

Abstract

Introduction: Esophageal Cancer (EC) ranks among the most common malignancies worldwide. Most EC patients acquire drug resistance to chemotherapy either intrinsically or acquired after T-DM1 treatment, which shows that increasing or decreasing the expression of particular genes might influence chemotherapeutic sensitivity or resistance. Therefore, gaining a deeper understanding of the altered expression of genes involved in EC drug resistance and developing new therapeutic methods are essential targets for continued advancement in EC therapy.

Methods: The present study aimed to find critical regulatory genes/pathways in the progression of T-DM1 resistance in OE-19 EC cells. Expression datasets were extracted from GEO omnibus. Gene interactions were analyzed, and the protein-protein interaction network was drawn. Then, enrichment analysis of the hub genes and network cluster analysis of the hub genes was performed. Finally, the genes were screened in the DrugBank database as therapeutic targets and molecular docking analysis was done on the selected targets.

Results: In the current study, nine hub genes were identified in TDM-1-resistant EC cells (CTGF, CDH17, THBS1, CXCL8, NRP1, ITGB5, EDN1, FAT1, and PTGS2). The KEGG analysis highlighted the IL-17 signaling pathway and ECM-receptor interaction pathway as the most critical pathways; cluster analysis also showed the significance of these pathways. Therefore, the genes involved in these two pathways, including CXCL8, FSCN1, PTGS2, SERPINE2, LEF1, THBS1, CCN2, TAGLN, CDH11, and ITGA6, were searched in DrugBank as therapeutic targets. The DrugBank analysis suggests a potential role for Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) in reducing T-DM1 drug resistance in EC. The docking results revealed that NSAIDs, including Diclofenac, Mefenamic acid, Celecoxib, Naproxen, and Etoricoxib, significantly suppress resistant cancer cells.

Conclusion: This comprehensive bioinformatics analysis deeply explains the molecular mechanisms governing TDM-1 resistance in EC. The identified hub genes and their associated pathways offer potential targets for therapeutic interventions. Moreover, the possible role of NSAIDs in mitigating T-DM1 resistance presents an intriguing avenue for further investigation. This research contributes significantly to the field and establishes a basis for further research to enhance treatment efficacy for EC patients.

Keywords: Bioinformatics; DrugBank; ECM-Receptor interaction; Esophageal cancer; Hub genes; IL-17 signaling pathway; NSAIDs; T-DM1 resistance.

Fig. 1

Bioinformatics workflow for data set filtering and transcriptomic analysis. This workflow outlines the steps from data acquisition and preprocessing to identifying potential therapeutic targets through molecular docking analysis.

Fig. 2

Overview of DEGs between T-DM1-sensitive and T-DM1-resistant esophageal cell lines. (a) Volcano plot showing DEGs identified with significant expression changes (red: up-regulated; blue: down-regulated; black: no difference). (b) Venn diagram illustrating the initial set of 2733 DEGs identified using an adjusted p-value <0.05. (c) UMAP plot depicting the separation of T-DM1-sensitive and T-DM1-resistant cell lines. (d) Boxplot displaying normalized expression values across the samples (Y-axis: normalized expression levels).

Fig. 3

Network of up-regulated genes in T-DM1 resistant EC cell lines, including their known interacting partners, visualized using Cytoscape software.

Fig. 4

PPI network of hub genes among the up-regulated genes in T-DM1 resistant EC cell lines, constructed using the CytoHubba plugin. The network highlights the central role of these hub genes within the broader interaction network.

Fig. 5

Gene Ontology (GO) enrichment analysis for Biological Process (BP), Cellular Component (CC), and Molecular Function categories (MF). The bar chart represents the number of observed genes associated with each GO term. The x-axis shows the GO terms, grouped into BP, CC, and MF). The y-axis indicates the count of observed genes. The blue bars correspond to BP (dark blue) and MF (light blue) categories, while the gray bars represent CC categories. This figure provides a comprehensive overview of the enriched GO terms for the differentially expressed genes in the study.

Fig. 6

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the determined subnetwork of hub genes in T-DM1 resistant EC cell line using STRING version 10.

Fig. 7

Clusters of hub genes and their associated networks in T-DM1 resistant EC cell lines. (a) Cluster 1, (b) Cluster 2, (c) Cluster 3, (d) Cluster 4. Each cluster represents a group of interconnected genes identified through network analysis. The analysis includes the number of nodes, edges, and various network properties, such as the clustering coefficient and network density.