Imagiomics of HCC combined with bioinformatics to analyze potential therapeutic targets for HCC

In this study, the gene expression and clinical information of HCC patients were obtained using the UCSC Xena database and GDC-TCGA platform, and the molecular characteristics of HCC were revealed by RSEM and Tpm analysis. Meanwhile, chip data were extracted from GEO database and standardized. From the perspectives of imaging, network pharmacology and metabolomics, this study deeply explores the molecular mechanism of differential protein in liver cancer, aiming to provide a scientific basis for accurate diagnosis and personalized treatment. By implementing differential gene analysis, disease-related gene intersection analysis, drug-compound-target-pathway network analysis, and protein interaction network analysis, the up-regulated genes closely related to liver cancer were selected, and the protein interaction network map was constructed with the help of STRING database and Cytoscape software. With this approach, 4420 differential genes were successfully identified, including 2108 up-regulated genes and 2312 downregulated genes. Further protein interaction analysis revealed 54 key genes that were used to construct the HCC diagnostic model by univariate Cox analysis, Lasso regression analysis, and ROC curve assessment, from which eight core genes were selected: IGF1, MCM 6, ESR 1, F9, SPP 1, TOP2A, CCNB 1, and IGFBP3. Moreover, this study also verified the generalization ability of the model through TCGA and GEO datasets. Studies further explored the association of core genes with clinical features, revealed their key role in hepatocarcinogenesis, development and treatment, and found that high expression of core genes was significantly associated with high infiltration of specific immune cell types. Finally, the expression and role of core genes in HCC were verified by GEPIA 2, providing new molecular markers for the treatment and prognostic evaluation of HCC.