The Prognostic and Biological Value of PGF-Based H&E Pathomics in Hepatocellular Carcinoma

Placental growth factor (PGF) is associated with the progression of hepatocellular carcinoma (HCC), but current research on this relationship remains limited. This study aims to establish a pathomics model for predicting PGF expression levels in H&E-stained HCC sections, and to explore its prognostic relevance and underlying molecular mechanisms.

Retrospective analysis utilised H&E images and clinical data from TCGA and an external cohort. Prognostic significance of PGF was assessed via survival analysis. Image segmentation employed the OTSU algorithm, followed by PyRadiomics-based feature extraction. Key features were selected using mRMR and RFE algorithms, with a gradient boosting machine (GBM) model constructed for PGF prediction. Model performance was validated through ROC and Precision-Recall (PR) curves, calibration analysis along with Brier score, and decision curve analysis. Prognostic stratification, Cox regression, and subgroup analyses were conducted for high/low pathomics score (PS: a continuous score derived from a machine learning model based on H&E image features to predict PGF expression) groups. Bioinformatics approaches identified differentially expressed genes (DEGs) and immune infiltration patterns.

PGF expression was identified as an independent prognostic factor for poor survival in HCC (HR = 1.922, 95% CI: 1.217-3.036, p = 0.005). A pathomics model integrating seven PGF-associated features demonstrated strong predictive accuracy, achieving an AUC of 0.811 (95% CI: 0.749-0.873) in the training set, 0.747 (95% CI: 0.639-0.855) in the internal validation set, and 0.740 (95% CI: 0.632-0.849) in the external test set. Patients classified into the high-pathomics score (PS) subgroup had significantly poorer survival (HR = 1.667, 95% CI: 1.024-2.713, p = 0.040). Functional analysis of DEGs in high-PS tumours revealed enrichment in ribosome- and coagulation-related pathways, upregulation of the inflammatory gene HBEGF, and increased infiltration of γδT cells. Moreover, TP53 mutations were frequently observed in this subgroup, with a mutation rate exceeding 20%.

PGF may serve as an independent prognostic biomarker in HCC. The developed pathomics model enables non-invasive PGF expression prediction through H&E image analysis. Mechanistically, PGF-associated molecular alterations involve inflammatory signalling, immune microenvironment remodelling, and frequent TP53 mutations, providing insights into HCC pathogenesis.