Abstract
Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. Existing screening and early diagnosis methods are not highly sensitive for HCC, and patients are likely to develop the disease to the middle and advanced stages before being diagnosed. Therefore, finding new and efficient diagnosis and treatment methods has become an urgent problem. We aimed at finding and verifying new liver cancer markers by combining informatics analysis with experimental exploration to provide new ideas and methods for the diagnosis and treatment of clinical liver cancer. We used two different bioinformatic pipelines to analyze sequencing data of clinical liver cancer samples and identify differentially expressed genes and key variants after combining them with The Cancer Genome Atlas sequencing data. Then, we explored the functions and mechanisms of the key variants to identify potential liver cancer markers. Through bioinformatic analysis of sequencing data, 139 differentially expressed genes were found, including 53 upregulated genes and 86 downregulated genes. Through enrichment and alternative splicing event analysis of sequencing data, we found nine key variants with exon skipping events. Metallothionein 1E (MT1E)-203 was found to be a key variant that influenced cell proliferation through the p53 cell cycle pathway through cell viability and proliferation assays, and MT1E-203 lost the ability to bind two zinc ions due to exon skipping according to the structure prediction of MT1E-203. MT1E-203 is a potential biomarker for HCC and may play an important role in the diagnosis and treatment of HCC.