Network pharmacology and bioinformatics analysis on the underlying mechanisms of baicalein against oral squamous cell carcinoma.

UNLABELLED: Background and aims This study performed a network pharmacology analysis to explore the potential anti-cancer activity of baicalein against oral squamous cell carcinoma (OSCC).

METHODS: The differentially expressed genes in OSCC were identified by the Linear Models for Microarray Data method, and the key module related to OSCC was acquired by the Weighted Gene Co-expression Network Analysis from GSE30784. Baicalein targets were obtained from GeneCards, SwissTargetPrediction, and TCMSP (traditional Chinese medicine systems pharmacology database and analysis platform) databases. Apart from GSE30784, OSCC-related genes were acquired from the Comparative Toxicogenomics Database and DisGeNET platform. The baicalein targets and OSCC-related genes were overlapped to acquire the drug-disease interaction genes. We input the candidate drug-disease interaction genes into the STRING database and created the protein-protein interaction network. The hub genes in the network were identified by CytoHubba. The expression levels of hub genes between cancer tissues and normal tissues were evaluated based on The Cancer Genome Atlas. Moreover, we performed the cancer immune infiltration analysis and evaluated the association between the expression of HIF1A and the abundance of infiltrating immune cells. The molecular docking between HIFA and baicalein was also performed and visualized. Additionally, the enrichment analysis was performed based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database.

RESULTS: After overlapping the candidate baicalein targets with OSCC-related genes, 34 candidate drug-disease interaction genes were obtained. CytoHubba identified the top 10 genes with high centrality measures from the network, including AKT1, CD44, EGFR, HIF1A, IGF1, MMP2, MYC, PTGS2, STAT3, and TP53. The enriched biological process terms included regulation of apoptotic signaling pathway, regulation of cysteine-type endopeptidase activity, and cellular response to chemical stress. The top 5 enriched pathways included Kaposi sarcoma-associated herpesvirus infection, hepatitis C, p53 signaling pathway, Epstein-Barr virus infection, proteoglycans in cancer. The expression of HIF1A was positively associated with the infiltration levels of CD4+ T cells (cor=0.131, P=0.004) and dendritic cells (cor=0.098, P=0.03), whereas negatively associated with B cells (cor=-0.098, P=0.03).

CONCLUSION: Our results suggested that baicalein might be a candidate drug against OSCC. More studies are necessary to validate its anti-cancer effect and explore the underlying mechanisms.