Somatic Mutation–Informed Transcriptional Architecture of Hepatocellular Carcinoma: A Multiomics Study Centered on TP53

Abstract

Hepatocellular carcinoma (HCC) is characterized by extensive somatic mutation heterogeneity, yet the cellular contexts in which recurrent driver mutation–associated transcriptional programs operate remain incompletely defined. In this study, we applied a mutation-informed multiomics framework centered on TP53 to connect somatic mutation status, bulk transcriptional programs, and hepatocyte-like cell states at single-cell resolution. Somatic mutation profiles and transcriptomic data from the TCGA-LIHC cohort were used to construct directional mutation-associated transcriptional signatures, with CTNNB1 retained as a reference driver signature and AXIN1 and ARID1A retained for descriptive comparison. These bulk-derived transcriptional programs were then projected into single-cell RNA sequencing data to evaluate inferred TP53-associated transcriptional activity across hepatocyte-like populations. TP53 mutation-associated transcriptional programs were enriched in proliferation-related pathways, DNA repair processes, and stress–response programs and were associated with adverse clinical outcomes. At the single-cell level, inferred TP53-associated transcriptional activity showed heterogeneous distribution across hepatocyte-like populations and was preferentially enriched in putative malignant hepatocyte-like states. Pseudobulk and functional program analyses further supported the association between TP53-high signature states, cell-cycle activity, DNA repair, stress programs, and altered tumor-cell communication patterns. Integrated prioritization identified CDCA8, MCM10, and TRIP13 as representative genes associated with TP53-related transcriptional programs; these genes should be interpreted as components of a TP53-associated proliferative transcriptional state rather than as experimentally validated drivers. These findings provide a mutation-informed view of transcriptional architecture in HCC and illustrate how bulk mutation-associated signatures can be integrated with single-cell data to improve biological interpretation of somatic variation.