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

Abstract

Background: Hepatocellular carcinoma (HCC) is one of the most common cancers with high mortality in the world. HCC screening and diagnostic models are becoming effective strategies to reduce mortality and improve the 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 the Gene Expression Omnibus (GEO) dataset. Bioinformatics analysis was performed to distinguish two differentially expressed genes (DEGs), diagnostic candidate genes, and functional enrichment pathways. QRT-PCR was used to validate the expression of diagnostic candidate genes. A diagnostic model based on candidate genes was established by an artificial neural network (ANN). Drug sensitivity analysis was used to explore potential drugs 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 cell analysis were administered and analyzed. Diagnostic-related genes of MT1M, SPINK1, AKR1B10, and SLCO1B3 were selected from DEGs and used to construct a diagnostic model. The receiver operating characteristic (ROC) curves were 0.910 and 0.953 in the training and testing cohorts, 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 the ANN could provide predictive significance for diagnosis of HCC patients, which would be worthy of clinical application. Also, potential chemotherapy drugs might be effective for HCC therapy.

Comprehensive Profiling and Therapeutic Insights into Differentially Expressed Genes in Hepatocellular Carcinoma

“…For the genes that are up-regulated in HCC, the p53 signaling pathway and distinct events associated with cell division were enriched. Our results corroborate previous studies that evaluated distinct GSEs [10,55,56].…”

An artificial neural network model to diagnose non-obstructive azoospermia based on RNA-binding protein-related genes

“…For example, Kherraf et al employed whole-exome sequencing to construct a 7-gene panel to improve the classification of NOA, helping the patients receive a clearer diagnosis [ 36 ]. Given the satisfying performance of machine learning algorithms, especially ANN, in various diseases [ 37 , 38 ], we then used LR, RF, and ANN to construct the diagnosis models based on the mRNA expression levels of DDX20 and NCBP2 and validated and compared their predictive ability in the training cohort, 3 external public validation datasets, and the local cohort. Similar to those previous works, the ANN model was observed to exhibit the highest predictive ability on average.…”

Identification of immune cell-related prognostic genes characterized by a distinct microenvironment in hepatocellular carcinoma

“…Likewise, increasing the number and activity of mature dendritic cells could improve the prognosis of HCC patients[ 7 ]. To date, the specific status of different cell subtypes and programmed cell death have been explored in the tumor microenvironment (TME) of HCC[ 8 , 9 ]. However, few researchers have focused on exploring the specific association or mechanism between immune infiltration and liver cancer through the genes of all immune cells.…”