Objective: This study aimed to identify significantly differentially expressed genes (DEGs) related to cervical cancer by exploring extensive gene expression datasets to unveil new therapeutic targets.
Methods: Gene expression profiles were extracted from the Gene Expression Omnibus, The Cancer Genome Atlas, and the Genotype-Tissue Expression platforms. A differential expression analysis identified DEGs in cervical cancer cases. Weighted gene co-expression network analysis (WGCNA) was implemented to locate genes closely linked to the clinical traits of diseases. Machine learning algorithms, including LASSO regression and the random forest algorithm, were applied to pinpoint key genes.
Results: The investigation successfully isolated DEGs pertinent to cervical cancer. Interleukin-24 was recognized as a pivotal gene via WGCNA and machine learning techniques. Experimental validations demonstrated that human interleukin (hIL)-24 inhibited proliferation, migration, and invasion, while promoting apoptosis, in SiHa and HeLa cervical cancer cells, affirming its role as a therapeutic target.
Conclusion: The multi-database analysis strategy employed herein emphasized hIL-24 as a principal gene in cervical cancer pathogenesis. The findings suggest hIL-24 as a promising candidate for targeted therapy, offering a potential avenue for innovative treatment modalities. This study enhances the understanding of molecular mechanisms of cervical cancer and aids in the pursuit of novel oncological therapies.
Keywords: Cervical cancer; differentially expressed gene; gene expression data; human interleukin-24; machine learning; weighted gene co-expression network analysis.