Construction of a combined random forest and artificial neural network diagnosis model to screening potential biomarker for hepatoblastoma.

Purpose: The purpose of our study is to identify potential biomarkers of hepatoblastoma (HB) and further explore the pathogenesis of it.

Methods: Differentially expressed genes (DEGs) were incorporated into the combined random forest and artificial neural network diagnosis model to screen candidate genes for HB. Gene set enrichment analysis (GSEA) was used to analyze the ARHGEF2. Student's t test was performed to evaluate the difference of tumor-infiltrating immune cells (TIICs) between normal and HB samples. Spearson correlation analysis was used to calculate the correlation between ARHGEF2 and TIICs.

Results: ARHGEF2, TCF3, TMED3, STMN1 and RAVER2 were screened by the new model. The GSEA of ARHGEF2 included cell cycle pathway and antigen processing presenting pathway. There were significant differences in the composition of partial TIICs between HB and normal samples (p < 0.05). ARHGEF2 was significantly correlated with memory B cells (Cor = 0.509, p < 0.05).

Conclusion: These 5 candidate genes contribute to the molecular diagnosis and targeted therapy of HB. And we found "ARHGEF2-RhoA-Cyclin D1/CDK4/CDK6-EF2" is a key mechanism regulating cell cycle pathway in HB. This will be helpful in the treatment of HB. The occurrence of HB is related to abnormal TIICs. We speculated that memory B cells play an important role in HB.