Abstract
INTRODUCTION: This study aims to identify the therapeutic targets and regulatory mechanisms of the antitumor drug gallic acid (GA) in cervical cancer (CC). METHODS: HeLa cells were treated with GA and subjected to RNA-sequencing using the DNBSEQ platform. By combining the results of the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) analysis and RNA-seq, the differentially expressed genes (DEGs), including those upregulated and downregulated genes in CC compared with the normal cervix in the GEO and TCGA database, while expressed reversed after treatment with GA, were identified. Subsequently, the function enrichment analysis and protein-protein interaction of the DEGs were conducted. The candidate genes were identified using the Cytoscape software Gentiscape2.2 and MCODE plug-ins. Furthermore, the upstream microRNA (miRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA) of the candidate genes were predicted using the online tools of MirDIP, TarBase, and ENCORI. Finally, the regulatory network was constructed using Cytoscape software. RESULTS: CC cells are significantly inhibited by GA. Combining the GEO and TCGA databases and RNA-seq analyses, 127 DEGs were obtained and subjected to functional enrichment analysis. This analysis revealed that 221 biological processes, 82 cellular components, 63 molecular functions, and 36 KEGG pathways were employed to identify three therapeutic candidate genes, including CDC20, DLGAP5, and KIF20A. The upstream 13 miRNAs, 4 lncRNA, and 42 circRNAs were detected and used to construct a lncRNA/circRNA-miRNA-mRNA-pathway regulatory network. CONCLUSION: This study identified candidate genes and the regulatory networks underlying the therapeutic effects of GA on CC using GA data mining methods, thus establishing a theoretical basis for targeted therapy of CC.