Driver gene-based prognostic model benchmarked against machine learning survival models is associated with a CAF–TGFβ–EMT-like phenotype in hepatocellular carcinoma
Menée à l'aide d'une analyse génomique et transcriptomique d'échantillons de carcinomes hépatocellulaires, cette étude évalue l'intérêt d'un système de score basé sur l'expression de 13 "gènes conducteurs" pour prédire la survie des patients, puis démontre une corrélation entre un score élevé et un microenvironnement tumoral agressif se caractérisant par la présence marquée de fibroblastes CAF mais aussi par l'activation de la transition épithélio-mésenchymateuse et de la voie de signalisation du TGF-bêta
Hepatocellular carcinoma (HCC) is molecularly heterogeneous, limiting conventional prognostic tools. Whole-genome resequencing of six HCC cases was used only to filter candidates. Somatically mutated genes were intersected with HCC-annotated entries from five predefined driver resources, yielding 154 genes. DGPM-HCC, a 13-gene model summarized by TotalScore, was developed in 170 bulk transcriptome samples. In 20 repetitions of event-stratified fivefold cross-validation, DGPM-HCC achieved a mean overall-survival C-index of 0.761; versus continuous Cox13, the median paired ΔC-index was 0.125 (bootstrap 95% confidence interval, 0.111–0.142; P = 9.57 × 10−5). The combined nomogram achieved a training-cohort 3-year disease-free-survival AUC of 0.924. External discrimination in TCGA-LIHC was modest (C-index, 0.533; 1-, 3-, and 5-year AUCs, 0.503, 0.592, and 0.590), whereas GSE14520 lacked three model genes. Exploratory single-cell RNA sequencing of one tumor–adjacent pair, public single-cell integration, and multiplex immunohistochemistry on 16 specimens associated high TotalScore with a CAF–TGFβ–EMT-like state. Drug-sensitivity findings were computational and exploratory.
npj Precision Oncology , article en libre accès, 2026