Aim: Known for its high recurrence rates and potential to metastasize and recur after liver transplantation or hepatectomy, HCC requires effective management, comprehensive surveillance and specialized therapies. Bioinformatics and machine learning play a key role in analyzing large datasets to reveal genetic and molecular insights into HCC metastasis and help identify potential biomarkers and therapeutic targets. This study aims to identify biomarkers associated with HCC recurrence by analyzing gene expression in primary and recurrent tumor tissues. Material and methods: The dataset included in the study comprises gene expression data from both recurrent and primary HCC tissue. The gene expression analysis of this data set was conducted using the capabilities provided by the limma package. The distribution of each tissue in the dataset is shown by the distribution graph and the expression density graph. The UMAP graph represents the association of tissue types. The genes exhibiting different regulation are represented in the volcano plot. Results: The UMAP analysis revealed a perfect separation of the tissues in the dataset into two distinct groups: recurrent tumor tissues and primer tissues. The analysis showed that many genes differed in both groups under log2FC>1 p<0.05 and log2FC<-1 and p<0.05 conditions. The resultsshow that there are genes that are upregulated in recurrent tissues compared to primary tissues and no downregulated genes. Conclusion: Genetic research is crucial for advancing the treatment of recurrent hepatocellular carcinoma (HCC). Identified genes may serve as biomarkers, aiding in the development of targeted drug therapies and improving patient care and healthcare efficiency. As genetic research progresses, the use of these biomarkers is expected to enhance personalized medicine. Understanding the genetic basis of recurrent HCC is essential for prevention and treatment, leading to more effective strategies and early detection for high-risk individuals. Future advancements in genetic research are anticipated to yield innovative methods for preventing and treating recurrent HCC.
