Developing an advanced diagnostic model for hepatocellular carcinoma through multi-omics integration leveraging diverse cell-death patterns - Wang, Chengbang; Yang, Guanglin; Feng, Guanzheng; Deng, Chengen; Zhang, Qingyun; Chen, Shaohua (Frontiers in Immunology, 2024)
Hepatocellular carcinoma (HCC), representing more than 80% of primary liver cancer cases, lacks satisfactory etiology and diagnostic methods. This study aimed to elucidate the role of programmed cell death-associated genes (CDRGs) in HCC by constructing a diagnostic model using single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data. We conducted a comprehensive analysis by intersecting CDRGs with differentially expressed genes (DEGs) identified in HCC cohorts. Leveraging blood-derived exosomal RNA-seq, cancer tissue RNA-seq, and HCC single-cell RNA-seq data, we screened DEGs using clinical indicators and machine learning techniques. This approach resulted in a seven-gene diagnostic model based on exosomal mRNA expression, demonstrating robust performance in both training (AUC = 1) and testing (AUC = 0.847) datasets. Furthermore, integrating scRNA-seq and spatial transcriptomics (stRNA-seq) data revealed that high expression of TRIB3 and NQO1 among the seven key genes not only correlates with poorer tumor prognosis but also suggests a more favorable response to immune checkpoint blockade (ICB) therapy in patients with high tumor expression of these genes. This indicates their potential as promising diagnostic markers and therapeutic targets for HCC.