A Novel Artificial Neural Network Prognostic Model Based on a Cancer-Associated Fibroblast Activation Score System in Hepatocellular Carcinoma - Luo, Yiqiao; Tan, Huaicheng; Yu, Ting; Tian, Jiangfang; Shi, Huashan (Frontiers in Immunology, 2022)
Introduction: Hepatocellular carcinoma (HCC) ranks fourth as the most common cause of cancer-related death. It is vital to identify the mechanism of progression and predict the prognosis for HCC patients. Previous studies have found that cancer-associated fibroblasts (CAFs) promote tumor proliferation and immune exclusion. However, the information about CAF-related genes is still elusive. Methods: The data were obtained from TCGA, ICGC and GEO databases. Based on single-cell transcriptome and ligand-receptor interaction analysis, CAF-related genes were selected. By performing Cox regression and random forest, we filtered 12 CAF-related prognostic genes for the construction of the ANN model based on the CAF activation score (CAS). Then, functional, immune, mutational and clinical analyses were performed. Results: We constructed a novel ANN prognostic model based on 12 CAF-related prognostic genes. Cancer-related pathways were enriched, and higher activated cell-crosstalk was identified in high CAS samples. High immune activity was observed in high CAS samples. We detected three differentially mutated genes (NBEA, RYR2 and FRAS1) between high and low CAS samples. In clinical analyses, we constructed a nomogram to predict the prognosis of HCC patients. 5-Fluorouracil had higher sensitivity in high CAS samples than in low CAS samples. Moreover, some small-molecule drugs and the immune response were predicted. Conclusion: We constructed a novel ANN model based on CAF-related genes. We revealed information about the ANN model through functional, mutational, immune and clinical analyses.