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. 2022 Jul 31:2022:2417134.
doi: 10.1155/2022/2417134. eCollection 2022.

Exploration of Potential Biomarkers and Immune Landscape for Hepatoblastoma: Evidence from Machine Learning Algorithm

Peng Zhou  1 , Shanshan Gao  2 , Bin Hu  1
Affiliations

Exploration of Potential Biomarkers and Immune Landscape for Hepatoblastoma: Evidence from Machine Learning Algorithm

Peng Zhou et al. Evid Based Complement Alternat Med. .

Retraction in

Abstract

This study aimed to investigate the immune landscape in hepatoblastoma (HB) based on deconvolution methods and identify a biomarkers panel for diagnosis based on a machine learning algorithm. Firstly, we identified 277 differentially expressed genes (DEGs) and differentiated and functionally identified the modules in DEGs. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and GO (gene ontology) were used to annotate these DEGs, and the results suggested that the occurrence of HB was related to DNA adducts, bile secretion, and metabolism of xenobiotics by cytochrome P450. We selected the top 10 genes for our final diagnostic panel based on the random forest tree method. Interestingly, TNFRSF19 and TOP2A were significantly down-regulated in normal samples, while other genes (TRIB1, MAT1A, SAA2-SAA4, NAT2, HABP2, CYP2CB, APOF, and CFHR3) were significantly down-regulated in HB samples. Finally, we constructed a neural network model based on the above hub genes for diagnosis. After cross-validation, the area under the ROC curve was close to 1 (AUC = 0.972), and the AUC of the validation set was 0.870. In addition, the results of single-sample gene-set enrichment analysis (ssGSEA) and deconvolution methods revealed a more active immune responses in the HB tissue. In conclusion, we have developed a robust biomarkers panel for HB patients.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Differential expression analysis. (a) The heatmap of differential expression genes in screening dataset. (b) The volcano plot of differential expression genes in the screening dataset.
Figure 2
Figure 2
Hub modules from metascape online tools.
Figure 3
Figure 3
Enrichment analysis. (a) The terms of KEGG enrichment analysis. (b) The terms of GO enrichment analysis.
Figure 4
Figure 4
Differential expression analysis. (a) The process of constructing random forest trees. (b) Ranking of gene importance. (c) The heatmap of the top 10 genes in the screening dataset.
Figure 5
Figure 5
Construction and validation of the artificial neural network model. (a) Construction of the artificial neural network model. (b) ROC curve in screening dataset. (c) ROC curve in the validation dataset.
Figure 6
Figure 6
The landscape of infiltrating immune cells is based on 4 deconvolution methods. (a) Heatmap of immune cell content in different tissues (EPIC algorithm). (b) Heatmap of immune cell content in different tissues (MCPcounter algorithm). (c) Heatmap of immune cell content in different tissues (TIMER algorithm). (d) Heatmap of immune cell content in different tissues (xCELL algorithm).
Figure 7
Figure 7
The landscape of TME-associated pathways.
Figure 8
Figure 8
qRT-PCR validation.
See this image and copyright information in PMC

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