Year: 2024
Type: conference-paper
Abstract: Metastasis is one of the main causes of death related to liver cancer. Current therapies seldom cure liver cancer metastasis, which indicates an immediate necessity to discover more effective action targets for the prophylaxis and remedy of liver cancer metastasis. The objective of our study is to screen for central genes and molecular mechanisms associated with liver cancer metastasis by bioinformatics methods, and to provide more effective objects for the clinical therapy of liver cancer. The high-throughput gene expression profile data GSE236039 and GSE203329 were retrieved from the Gene Expression Omnibus database. Both datasets contained HCC samples with different metastatic capacities. The GEO2R was employed to analyze the differentially expressed genes (DEGs) in the dataset. GO and KEGG enrichment analysis of DEGs was performed to identify genes related to liver cancer metastasis. In order to obtain key hub genes, a protein interaction network of DEGs was constructed using STRING. Then, GEPIA interaction analysis was used to the clinical prognosis data of liver cancer patients from The Cancer Genome Atlas database to verify the correlation between hub genes and patient prognosis. By integrating the GSE236039 and GSE203329 datasets, 69 co-expressed genes were screened as regulators of liver cancer metastasis. These genes mainly play important regulatory roles in efferocytosis, axon guidance, endocytosis and other pathways. Further screening resulted in 30 hub genes being identified, among which CDH26, SIRPA, CD47, NCK1, PIK3C2B, RHOF, and FSCN2 significantly associated with patient survival and prognosis of patients. These findings imply that these genes might be crucial regulators playing roles in both metastasis and prognosis of liver cancer.
Authors: Yue Zhang, Rouxian Li, Bingyu Zhang
Institution: Chongqing University of Posts and Telecommunications
Language: English
Subfield: Hepatology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4408738178