Hepatocellular carcinoma (HCC) remains highly fatal due to late diagnosis and scarce early biomarkers. Multi\u2011omics and liquid biopsy advances improve early detection, prognostication, and monitoring. Gene signatures derived from genomic, transcriptomic, and immune profiling predict prognosis and response to immune checkpoint inhibitors. Circulating tumor DNA enables non\u2011invasive real\u2011time tracking. However, clinical validation, cross\u2011platform reproducibility, and ethical issues (data privacy, consent, standardization) hinder routine use. Integrating multi\u2011omics with liquid biopsy offers a path to personalized therapy, but success requires multidisciplinary collaboration to overcome these barriers.<\/p>\n
Introduction<\/p>\n
HCC is the third leading cause of cancer death. Risk factors include hepatitis B\/C, alcohol, aflatoxin, obesity, and metabolic dysfunction. Curative options are limited to early stages; advanced cases rely on multi\u2011kinase inhibitors (toxicity, resistance) or immunotherapy<\/a> (variable response). Tumor heterogeneity-genomic, epigenetic, and microenvironmental-drives therapy resistance and recurrence. This review synthesises gene\u2011signature and multi\u2011omics strategies to improve prognosis and immunotherapy selection, emphasising validation and clinical feasibility.<\/p>\n Molecular basis of HCC<\/p>\n HCC exhibits marked genetic heterogeneity. Frequent alterations include TP53 (~50%), TERT promoter (~60%), and Wnt\/\u03b2\u2011catenin (~40%) mutations, influencing aggressiveness and immune evasion. Epigenetic changes (methylation, non\u2011coding RNAs) and proteomic\/metabolomic alterations contribute to tumor progression and provide potential biomarkers.<\/p>\n Prognostic gene signatures<\/p>\n Many multigene expression panels (e.g., 8\u2011gene, 9\u2011gene, 4\u2011gene) have been validated across cohorts, often outperforming conventional staging (BCLC, TNM). They stratify patients by survival and recurrence risk, guiding transplant prioritisation, adjuvant therapy, and surveillance. Public databases (TCGA, GEO) enable cross\u2011platform validation, though formalin\u2011fixed samples pose RNA quality challenges.<\/p>\n Tumor microenvironment and immune classification<\/p>\n The TME critically influences outcomes. Transcriptomic signatures quantify immune infiltration: high CD8A\/GZMB indicates cytotoxic T\u2011cell activity and better prognosis; M2 macrophage markers correlate with immunosuppression. HCC can be classified as immune\u2011”hot” (inflamed, high IFN\u2011\u03b3 signature, responsive to ICIs) or “cold” (immune\u2011excluded, Wnt\u2011active, resistant). Stromal and cytokine signatures (e.g., VEGFA, IL6) further inform tumor\u2011stroma crosstalk and metastasis<\/a>.<\/p>\n Gene signatures for immunotherapy guidance<\/p>\n ICI response is unpredictable; gene expression signatures improve patient selection. IFN\u2011\u03b3\u2011related transcripts (CXCL9\/10, IDO1, STAT1) correlate with anti\u2011PD\u20111\/PD\u2011L1 benefit. Clinical trial analyses (CheckMate 040, atezolizumab\/bevacizumab) show that multi\u2011gene immune scores outperform single biomarkers like PD\u2011L1 IHC. For cold tumors, priming strategies-oncolytic viruses, radiotherapy-can convert them to hot and enhance ICI efficacy<\/a>.<\/p>\n Multi\u2011omics integration and non\u2011coding RNAs<\/p>\n Integrating genomics, transcriptomics, epigenomics, proteomics, and metabolomics yields more robust and stable signatures than single\u2011layer approaches. Non\u2011coding RNAs (circPRDM4, HOTAIR, MALAT1) regulate PD\u2011L1 and immune evasion, contributing to immunotherapy resistance and offering potential therapeutic targets.<\/p>\n Source:<\/p>\n Journal reference:<\/p>\n