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Abstract Hepatocellular carcinoma (HCC) is a highly aggressive malignancy with increasing global prevalence and is one of the leading causes of cancer-related mortality in the human population. Developing robust clinical prediction models and prognostic stratification strategies is crucial for developing individualized treatment plans. A range of novel forms of programmed cell death (PCD) plays a role in the pathological progression and advancement of HCC, and in-depth study of PCD is expected to further improve the prognosis of HCC patients. Sixteen patterns (apoptosis, autophagy, anoikis, lysosome-dependent cell death, immunogenic cell death, necroptosis, ferroptosis, netosis, pyroptosis, disulfidptosis, entotic cell death, cuproptosis, parthanatos, netotic cell death, alkaliptosis, and oxeiptosis) related to PCD were collected from the literatures and used for subsequent analysis. Supervised (Elastic net, Random Forest, XgBoost, and Boruta) and unsupervised (Nonnegative Matrix Factorization, NMF) clustering algorithms were applied to develop and validate a novel classifier for the individualized management of HCC patients at the transcriptomic, proteomic and single-cell levels. Multiple machine learning algorithms developed a programmed cell death index (PCDI) comprising five robust signatures (FTL, G6PD, SLC2A1, HTRA2, and DLAT) in four independent HCC cohorts, and a higher PCDI was predictive of higher pathological grades and worse prognoses. Furthermore, a higher PCDI was found to be correlated with the presence of a repressive tumor immune microenvironment (TME), as determined through an integrated examination of bulk and single-cell transcriptome data. In addition, patients with TP53 mutation had higher PCDI in comparison with TP53 WT patients. Three HCC subtypes were identified through unsupervised clustering (NMF), exhibiting distinct prognoses and significant biological processes, among the three subtypes, PCDcluster 3 was of particular interest as it contained a large proportion of patients with high risk and low metabolic activity. Construction and evaluation of the Nomogram model was drawn based on the multivariate logistic regression analysis, and highlighted the robustness of the Nomogram model in other independent HCC cohorts. Finally, to explore the prognostic value, we also validated the frequent upregulation of DLAT in a real-world cohort of human HCC specimens by qPCR, western blot, and immunohistochemical staining (IHC). Together, our work herein comprehensively emphasized PCD-related patterns and key regulators, such as DLAT, contributed to the evolution and prognosis of tumor foci in HCC patients, and strengthened our understanding of PCD characteristics and promoted more effective risk stratification strategies. |
