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

[PURPOSE] 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.

[METHODS] 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.

[RESULTS] 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%.

[CONCLUSION] 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.