AUTHOR=Chen Min , Wu Guang-Bo , Xie Zhi-Wen , Shi Dan-Li , Luo Meng 

TITLE=A novel diagnostic four-gene signature for hepatocellular carcinoma based on artificial neural network: Development, validation, and drug screening

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.942166

DOI=10.3389/fgene.2022.942166

ISSN=1664-8021

ABSTRACT=Background: Hepatocellular carcinoma (HCC) was one of the most common cancers with high mortality in the world. HCC screening and diagnostic models were becoming effective strategies to reduce mortality and improve overall survival (OS) of patients. Here, we expected to establish an effective novel diagnostic model based on new genes and explore potential drugs for HCC therapy.
Methods: The gene expression data of HCC and normal samples (GSE14811, GSE60502, GSE84402, GSE101685, GSE102079, GSE113996 and GSE45436) were downloaded from Gene Expression Omnibus (GEO) dataset. The bioinformatics analysis was performed to distinguish two differentially expressed genes (DEGs), diagnostic candidate genes and functional enrichment pathway. QRT-PCR was used to validate the expression of diagnostic candidate genes. Diagnostic model based on candidate genes was established by artificial neural network (ANN). Drug sensitive analysis was used to dig out potential drug for HCC. CCK-8 assay was used to detect the viability of HepG2 under various presentative chemotherapy drugs.
Results: There were 82 DEGs in cancer tissues compared to normal tissue. Protein–protein interaction (PPI), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and infiltrating immune cells analysis were administered and analyzed. Diagnostic related genes of MT1M, SPINK1, AKR1B10 and SLCO1B3 were selected from DEGs and used to construct diagnostic model. Receiver operating characteristic (ROC) curves were 0.910 and 0.953 in training and testing cohort, respectively. Potential drugs including Vemurafenib, LOXO-101, Dabrafenib, Selumetinib, Arry-162 and NMS-E628 were found as well. Vemurafenib, Dabrafenib and Selumetinib were observed to significantly affect HepG2 cell viability.
Conclusion: The diagnostic model based on the four diagnostic related genes by ANN could provide predictive significance for diagnosis of HCC patients, which would be worthy of clinical application. And potential chemotherapy drugs might be effective for HCC therapy.
Keywords: hepatocellular carcinoma; diagnostic model; artificial neural network; MT1M; SPINK1; AKR1B10; SLCO1B3