HNCDrugResDb: a platform for deciphering drug resistance in head and neck cancers

Abstract

Drug resistance poses a significant obstacle to the success of anti-cancer therapy in head and neck cancers (HNCs). We aim to develop a platform for visualizing and analyzing molecular expression alterations associated with HNC drug resistance. Through data mining, we convened differentially expressed molecules and context-specific signaling events involved in drug resistance. The driver genes, interaction networks and transcriptional regulations were explored using bioinformatics approaches. A total of 2364 differentially expressed molecules were identified in 78 distinct drug-resistant cells against 14 anti-cancer drugs, comprising 1131 mRNAs, 746 proteins, 62 lncRNAs, 257 miRNAs, 1 circRNA, and 166 post-translational modifications. Among these, 255 molecules were considerably, the signature driver genes of HNC drug resistance. Further, we also developed a landscape of signaling pathways and their cross-talk with diverse signaling modules involved in drug resistance. Additionally, a publicly-accessible database named "HNCDrugResDb" was designed with browse, query, and pathway explorer options to fetch and enrich molecular alterations and signaling pathways altered in drug resistance. HNCDrugResDb is also enabled with a Drug Resistance Analysis tool as an initial platform to infer the likelihood of resistance based on the expression pattern of driver genes. HNCDrugResDb is anticipated to have substantial implications for future advancements in drug discovery and optimization of personalized medicine approaches.

Keywords: Chemotherapy; Drug resistance; Head and neck cancer; Signaling pathway; Transcriptional reprogramming.

Fig. 1

Workflow for data curation, analysis and development of the database. (a) Articles related to HNC drug resistance were fetched using the search term and the articles satisfying the inclusion criteria were subjected to data curation. (b) The differentially expressed genes involved in drug resistance was analyzed and visualized using Bioinformatics tools. (c) Development of a database for the visualization and enrichment of drug resistance-associated molecules.

Fig. 2

Venn diagram depicting the differentially expressed proteins/genes common among drug-resistant cell lines of head and neck cancer. (a–d) Overexpressed proteins/genes identified from 14 drugs were intersected in different combinations. (e,f) Downregulated proteins/genes identified from 8 drugs were intersected in different combinations. (g) The differentially expressed driver genes common across multiple drugs.

Fig. 3

Interaction network map of selected ncRNAs. (a) Represents an interaction map of differentially expressed lncRNAs involved in drug resistance and their experimentally known miRNA targets (red and green indicate overexpressed and downregulated lncRNAs, and blue indicates miRNA targets). (b) Represents an interaction map of differentially expressed miRNAs involved in drug resistance and their experimentally known gene targets. The overexpressed and downregulated miRNAs and target genes are represented blue and green, respectively.

Fig. 4

ChEA3 enrichment analysis of transcription factors. The interactive cluster-gram depicts the overlapping of the top query targeted genes from this study, among the top 30 transcription factors from library results.

Fig. 5

Target gene prediction for transcription factors involved in drug resistance (a) Genes regulated by > 12 transcription factors associated with drug resistance. (b) Heat map represents the target genes which are involved in resistance to various anti-cancer drugs. x-axis represents the target genes and y-axis represents the transcription factors. Target genes identified to be involved in resistance to 2 or more drugs are highlighted with different colours.

Fig. 6

Snapshot of interfaces in HNCDrugResDb. (a) Interface of Browse page with options for retrieving using drug name, cancer type, and molecule type. (b) Query interface showing the results for expression of BAX proteins in different context of HNC drug resistance. (c) The Pathway Explorer interface displaying signaling pathways associated with resistance to various drugs. Additionally, it provides options to both view and download images of these signaling pathways, explore the molecules involved in each pathway, and access associated reference materials. (d) The Drug Resistance Analysis module features an interface where data can be input on the left-side, and the results depicting the expression of driver genes associated with drug resistance are displayed on the right-side.