Bioinformatics Approaches in Hepatocellular Carcinoma Studies

Advances in high-throughput sequencing and computational biology have revolutionised the study of hepatocellular carcinoma (HCC), enabling comprehensive analysis of tumour genomes, transcriptomes, epigenomes and interactomes. Integration of multi-omics datasets—from bulk and single-cell RNA sequencing to DNA methylation and proteomic profiles—has identified distinct molecular subtypes, prognostic gene signatures and therapeutic vulnerabilities. Network-based methods, including protein–protein interaction mapping and pathway enrichment analyses, have illuminated key signalling cascades such as cell-cycle regulation, PI3K–AKT and Notch. Machine learning and deep learning frameworks now underpin robust prognostic models and biomarker discovery, while network pharmacology approaches guide repurposing of compounds. Together, these bioinformatics strategies deliver mechanistic insight into HCC heterogeneity, support personalised risk stratification and inform the development of targeted therapies.

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Bioinformatics Approaches in Hepatocellular Carcinoma Studies publication trend

The graph below shows the total number of articles in bioinformatics approaches in hepatocellular carcinoma studies across all publications each year (not limited to Nature Index journals).

Technical terms

Transcriptomic profiling: Quantitative measurement of RNA transcripts across all genes to compare expression patterns between tumour and normal tissues.

KEGG pathway enrichment analysis: Statistical method to identify biological pathways over-represented among a list of genes, providing functional context.

MicroRNA (miRNA): Small non-coding RNA molecules that bind target mRNAs to inhibit their translation or promote degradation.

Single-cell RNA sequencing (scRNA-seq): Technique to profile gene expression in individual cells, revealing cellular diversity within a tumour.

LASSO Cox regression: Regularised approach combining feature selection with survival analysis to derive prognostic gene signatures.

Deep learning model: Computational framework using multilayer neural networks to learn complex patterns for tasks such as outcome prediction.

References

1. EZH2-H3K27me3-mediated silencing of mir-139-5p inhibits cellular senescence in hepatocellular carcinoma by activating TOP2A. Journal of Experimental & Clinical Cancer Research (2023).
2. Prediction of liver cancer prognosis based on immune cell marker genes. Frontiers in Immunology (2023).
3. Eleven metabolism‑related genes composed of Stard5 predict prognosis and contribute to EMT phenotype in HCC. Cancer Cell International (2023).