Genomics. 2023 Oct 20:110733. doi: 10.1016/j.ygeno.2023.110733. Online ahead of print.
ABSTRACT
BACKGROUND: Big data mining and experiments are widely used to mine new prognostic markers.
METHODS: Candidate genes were identified from CROEMINE and FerrDb. Kaplan-Meier survival and Cox regression analysis were applied to assess the association of genes with Overall survival time (OS) and Disease-free survival time (DFS) in two HCC cohorts. Real-time quantitative polymerase chain reaction (RT-qPCR) and Immunohistochemistry were performed in HCC samples.
RESULTS: 21 and 15 genes that can predict OS and DFS, which had not been reported before, were identified from 719 genes, respectively. Survival analysis showed elevated mRNA expression of GLMP, SLC38A6, and WDR76 were associated with poor prognosis, and three genes combination signature was an independent prognostic factor in HCC. RT-qPCR and Immunohistochemistry confirmed the results.
CONCLUSIONS: We established a novel computational process, which identified the expression levels of GLMP, SLC38A6, and WDR76 as potential ferroptosis-related biomarkers indicating the prognosis of HCC.
PMID:37866659 | DOI:10.1016/j.ygeno.2023.110733
