Transcriptomic Profiling of Paired Tumor and Non-Tumor Biopsies Identifies Dysregulated Genes in Hepatocellular Carcinoma

Objectives: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor prognosis and limited biomarkers for early detection or targeted therapy. This study aimed to comprehensively analyze differential gene expression in paired tumor and adjacent non-tumor liver biopsies, in order to identify transcriptional alterations with potential diagnostic, prognostic, or therapeutic relevance.
Methods: We utilized the publicly available GSE64041 dataset, comprising 60 paired biopsies (120 samples in total) from HCC patients. Data preprocessing and normalization were conducted in R using the limma package. Differentially expressed genes (DEGs) were determined with thresholds of |log2 fold change| > 1 and adjusted p-value < 0.05. Visualization tools included box plots, density plots, Uniform Manifold Approximation and Projection (UMAP), volcano plots, and mean difference (MD) plots to ensure robust evaluation of expression patterns and biological clustering.
Results: The transcriptomic analysis revealed clear separation between tumor and non-tumor tissues. A total of 20 top-ranked DEGs were identified, including markedly upregulated genes such as REG3A, SPINK1, GPC3, SLC7A11, and AKR1B10, as well as downregulated genes including CRHBP, FCN3, OIT3, STAB2, and CLEC1B. Many of these genes are known to be involved in oncogenic signaling, ferroptosis regulation, immune evasion, and tumor suppression. UMAP clustering and DEG visualization confirmed distinct transcriptional landscapes, supporting the biological divergence of HCC tissues from normal liver.
Conclusion: This study highlights a panel of significantly dysregulated genes reflecting both oncogenic activation and tumor suppressor loss in HCC. The findings provide valuable insights into the molecular mechanisms of hepatocarcinogenesis and suggest potential biomarkers for diagnosis, prognosis, and therapeutic targeting. These results may contribute to improving early detection, guiding risk stratification, and supporting the development of precision medicine approaches in hepatocellular carcinoma.

Keywords: Biomarkers, Differentially expressed genes, Hepatocellular carcinoma, Paired biopsies, Transcriptomic profiling