Integrated Transcriptomic Analysis Leveraging Single-Cell and Bulk RNA Sequencing Data to Uncover Pyroptosis-Related Prognostic Signatures in HCC.

Background: The influence of pyroptosis on tumors is complex and diverse. However, its specific impact on hepatocellular carcinoma (HCC) is still not well understood. Therefore, the objective of this study was to develop a prognostic signature for HCC based on pyroptosis-related genes. Methods: The single-cell RNA sequencing (scRNA-seq) data, mRNA expression files and corresponding clinical information of HCC were obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. Python was used to process scRNA-seq data and calculated the enrichment score of pyroptosis-related genes (PRGs). Weight Co-Expression Network Analysis was used to identify pyroptosis-related hub genes. By overlapping the PRGs from scRNA-seq analysis and bulk RNA-seq analysis, respectively. Then, Univariate cox and LASSO regression were used to construct the pyroptosis prognostic model. Multivariate cox was used to identify independent factors for HCC and then developed a nomogram. The biological functions, survival analysis, immune characteristics, therapy response, and m6A modification status were analyzed. Results: Based on the scRNA-seq analysis and bulk RNA-seq analysis, hub PRGs were identified in HCC. Of those genes, five PRGs (ADGRE2, FCER1G, SLC9A9, CYBB, SLAMF6) were selected as a prognostic signature. The risk score established from the prognostic signature was an independent prognostic factor for HCC. The high-risk score group is associated with a poor prognosis, characterized by immunosuppressive features. Conclusion: This study uniquely integrates single-cell and bulk transcriptomic data to systematically identify pyroptosis-related prognostic biomarkers, pinpointing their cellular origin within the tumor microenvironment.