A Novel Artificial Neural Network Prognostic Model Based on a Cancer-Associated Fibroblast Activation Score System in Hepatocellular Carcinoma

Abstract

IntroductionHepatocellular 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 patients with HCC. 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.MethodsThe data were obtained from The Cancer Genome Atlas, International Cancer Genome Consortium, and Gene Expression Omnibus databases. On the basis of 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.ResultsWe 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 patients with HCC. 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.ConclusionWe constructed a novel ANN model based on CAF-related genes. We revealed information about the ANN model through functional, mutational, immune, and clinical analyses.

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“…On the other hand, it represents a primary example of how biochemical markers that are known to be modified by tumor response can be exploited to foresee treatment efficacy and modify the therapeutical attitude. Luo et al(20), instead, exploited the association of genomics/transcriptomics and AI to predict HCC sensitivity to chemotherapeutic drugs.They used machine learning and ANN to identify genes mutations responsible for cancerassociated fibroblast activation. Based on the presence of mutations, the authors defined low and high fibroblasts activation classes and, on this variable, they analyzed HCC sensitivity to chemotherapy.…”