Although some pyroptosis-related (PR) prognostic models for cancers have been reported, pyroptosis-based features have not been fully discovered at the single-cell level in hepatocellular carcinoma (HCC). In this study, by deeply integrating single-cell and bulk transcriptome data, we systematically investigated significance of the shared pyroptotic signature at both single-cell and bulk levels in HCC prognosis. Based on the pyroptotic signature, a robust PR risk system was constructed to quantify the prognostic risk of individual patient. To further verify capacity of the pyroptotic signature on predicting patients’ prognosis, an attention mechanism-based deep neural network classification model was constructed. The mechanisms of prognostic difference in the patients with distinct PR risk were dissected on tumor stemness, cancer pathways, transcriptional regulation, immune infiltration and cell communications. A nomogram model combining PR risk with clinicopathologic data was constructed to evaluate the prognosis of individual patients in clinic. The PR risk could also evaluate therapeutic response to neoadjuvant therapies in HCC patients. In conclusion, the constructed PR risk system enables a comprehensive assessment of tumor microenvironment characteristics, accurate prognosis prediction and rational therapeutic options in HCC.

Primary liver cancer ranks as the third most lethal cancer globally, with hepatocellular carcinoma (HCC) being the most prevalent pathologic type. The liver plays a crucial role in maintaining normal coagulation function by synthesising, regulating and clearing coagulation factors and other bioactive substances involved in coagulation. Although several previous studies have proposed coagulation‐associated prognostic models in HCC, the mechanisms at the single‐cell level are not fully elucidated. In this study, the coagulation subtypes and their heterogeneity of HCC malignant cells were identified based on the coagulation‐related genes collected from KEGG and GO databases. Through machine learning algorithms, we defined a coagulation gene signature at the single‐cell level, based on which a coagulation‐associated risk score (CARS) model was constructed in the TCGA‐LIHC cohort. Integrating clinicopathological information and the CARS, a nomogram model was further developed for individualised prognostic assessment. Additionally, the mechanisms of prognostic differences among patients with divergent coagulation‐associated risks were dissected through tumour signalling pathways, cellular communication and pseudotime trajectory analysis, while exploring the potential application of this risk assessment system in HCC treatment. In conclusion, the established CARS system accurately predicts prognosis, providing an important theoretical basis for precision treatment of HCC.