Hierarchical Network Analysis for Discovering Potential Therapeutic Inhibitors in Head and Neck Cancer
Year: 2026
Type: article
Abstract: Background: Head and neck squamous cell carcinoma (HNSCC) is characterized by pronounced molecular heterogeneity, immune microenvironment dysregulation, and limited therapeutic options in advanced human papillomavirus (HPV)-negative diseases. System-level approaches may facilitate the identification of core pathogenic mechanisms and clinically applicable therapeutic strategies. Aim: This study aimed to elucidate system-level mechanisms underlying HNSCC and to identify potential drug repurposing candidates using an integrative systems biology and pharmacology approach. Methods: Transcriptomic data from the Gene Expression Omnibus dataset GSE148944 (61 HNSCC tumors and 6 normal tissues) were analyzed. Differentially expressed genes (DEGs) were identified using |fold change| ≥ 2 and an adjusted P value ≤ 0.05. Hierarchical systems biology networks (HiSBiNs) quantify perturbations across genes, pathways, subsystems, and system levels using the Kyoto Encyclopedia of Genes and Genomes and meta-z scores. Drug–gene networks integrated DEGs with DrugBank/BindingDB data. Pharmacophore-guided virtual screening of FDA-approved drugs was conducted using Homopharma/SiMMap and iGEMDOCK. Results: We found 271 upregulated and 36 downregulated DEGs, including SPP1 , ARG1 , MS4A1 , CXCL13 , and SPINK5 , linked to immune regulation and tumor microenvironment. HiSBiN highlighted the immune system (meta-z = highest), and the signaling pathways were the most perturbed. The top candidates were rituximab, tocilizumab, and dasatinib. Conclusion: This integrative hierarchical systems biology and pharmacology framework elucidated the system-level mechanisms underlying HNSCC and identified hypothesis-generating drug repurposing candidates that warrant further experimental validation, providing a computational foundation, rather than definitive recommendations, for precision therapeutic development, especially in betel nut–prevalent regions, such as Taiwan, where HPV-negative HNSCC predominates.
Source: Journal of Medical Sciences
Authors: Wen-Sen Lai, Chao-Yin Kuo, Sheng-Yao Cheng, Shih-Wei Hsu, Chien-Yi Yang, Jinn-Moon Yang
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7166815009