AUTHOR=Huang Jun , Zhao Chunbei , Zhang Xinhe , Zhao Qiaohui , Zhang Yanting , Chen Liping , Dai Guifu 

TITLE=Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence-based precision medicine

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2022.1079566

DOI=10.3389/fphar.2022.1079566

ISSN=1663-9812

ABSTRACT=Background: Although immune microenvironment-related chemokines, extracellular matrix (ECM), and intrahepatic immune cells are reported to be highly involved in hepatitis B virus (HBV)-related diseases, their roles in diagnosis, prognosis, and drug sensitivity evaluation remain unclear. Here, we aimed to study their clinical use to provide a basis for precision medicine in hepatocellular carcinoma (HCC) via the amalgamation of artificial intelligence. 
Methods: High-throughput liver transcriptomes from Gene Expression Omnibus (GEO), NODE (https://www.bio sino.org/node)，the Cancer Genome Atlas (TCGA), and our in-house HCC patients were collected in this study. Core immunosignals that participated in the entire diseases course of hepatitis B were explored using the “GSVA” R package. Using ROC curve analysis, the impact of core immunosignals and amino acid utilization related gene on HCC patient’s clinical outcome were calculated. The utility of core immunosignals as a classifier for HCC tumor tissue was evaluated using explainable machine-learning methods. A novel deep residual neural network model based on immunosignals was constructed for the long-term overall survival (LS) analysis. In vivo drug sensitivity was calculated by the “oncoPredict” R package.
Results: We identified nine genes comprising chemokines and ECM related to HBV-induced inflammation and fibrosis as CLST signals. Moreover, CLST was co-enriched with activated CD4+T cells bearing harmful factors(aCD4) during all stages of HBV pathogenesis, which was also verified by our HCC data. Unexpectedly, we found that HBV-HCC patients in the CLSThighaCD4high subgroup had the shortest overall survival (OS) and were characterized by a risk gene signature associated with amino acids utilization. Importantly, characteristic genes specific to CLST/aCD4 showed promising clinical relevance in identifying patients with early-stage HCC via explainable machine learning. In addition, the 5-years LS of HCC patients can be effectively classified by CLST/aCD4 based GeneSet-ResNet model. Subgroups defined by CLST and aCD4 were significantly involved in the sensitivity of HBV-HCC patients to chemotherapy treatments. 
Conclusions: CLST and aCD4 are HBV pathogenesis-relevant immunosignals that are highly involved in HBV-induced inflammation, fibrosis, and HCC. GSVA derived immunogenomic signatures enabled efficient diagnostic and prognostic model construction. The clinical application of CLST and aCD4 as indicators would be beneficial for the precision management of HCC.