Single-Cell RNA-Seq Analysis Links DNMT3B and PFKFB4 Transcriptional Profiles with Metastatic Traits in Hepatoblastoma

Hepatoblastoma is the most common primary liver cancer in children. Poor outcomes are primarily associated with patients who have distant metastases. Using the Mammalian Metabolic Enzyme Database, we investigated the overexpression of metabolic enzymes in hepatoblastoma tumors compared to noncancerous liver tissue in the GSE131329 transcriptome dataset. For the overexpressed enzymes, we applied ElasticNet machine learning to assess their predictive value for metastasis. A metabolic expression score was then computed from the significant enzymes and integrated into a clinical-biological logistic regression model. Forty-one overexpressed enzymes distinguished hepatoblastoma tumors from noncancerous liver tissues. Eighteen of these enzymes predicted metastasis status with an AUC of 0.90, demonstrating 85.7% sensitivity and 92.3% specificity. ElasticNet machine learning identified <i>DNMT3B</i> and <i>PFKFB4</i> as key predictors of metastasis. Univariate analyses confirmed the significance of these enzymes, with respective <i>p</i>-values of 0.0058 and 0.0091. A metabolic score based on <i>DNMT3B</i> and <i>PFKFB4</i> expression discriminated metastasis status and high-risk CHIC scores (<i>p</i>-value = 0.005). The metabolic score was more sensitive than the C1/C2 classifier in predicting metastasis (accuracy: 0.72 vs. 0.55). In a regression model integrating the metabolic score with epidemiological parameters (gender, age at diagnosis, histological type, and clinical PRETEXT stage), the metabolic score was confirmed as an independent adverse predictor of metastasis (<i>p</i>-value = 0.003, odds ratio: 2.12). This study identified the dual overexpression of <i>PFKFB4</i> and <i>DNMT3B</i> in hepatoblastoma patients at risk of metastasis (high-risk CHIC classification). The combined tumor expression of <i>DNMT3B</i> and <i>PFKFB4</i> was used to compute a metabolic score, which was validated as an independent predictor of metastatic status in hepatoblastoma.

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Journal
Biomolecules
Published
2024-10-31
DOI
https://doi.org/10.3390/biom14111394
Citations
5
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
1.43
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article

Single-Cell RNA-Seq Analysis Links DNMT3B and PFKFB4 Transcriptional Profiles with Metastatic Traits in Hepatoblastoma

Jorge Mata-Garrido, Raquel Francés, Pascal Pineau, Agnès Marchio et al.
5 citations
Biomolecules
Cancer Genomics and Diagnostics
1.43
article

Single-Cell RNA-Seq Analysis Links DNMT3B and PFKFB4 Transcriptional Profiles with Metastatic Traits in Hepatoblastoma

Jorge Mata-Garrido, Raquel Francés, Pascal Pineau, Agnès Marchio, Claudia Monge, Christophe Desterke
article en
5 citations

Abstract

Hepatoblastoma is the most common primary liver cancer in children. Poor outcomes are primarily associated with patients who have distant metastases. Using the Mammalian Metabolic Enzyme Database, we investigated the overexpression of metabolic enzymes in hepatoblastoma tumors compared to noncancerous liver tissue in the GSE131329 transcriptome dataset. For the overexpressed enzymes, we applied ElasticNet machine learning to assess their predictive value for metastasis. A metabolic expression score was then computed from the significant enzymes and integrated into a clinical-biological logistic regression model. Forty-one overexpressed enzymes distinguished hepatoblastoma tumors from noncancerous liver tissues. Eighteen of these enzymes predicted metastasis status with an AUC of 0.90, demonstrating 85.7% sensitivity and 92.3% specificity. ElasticNet machine learning identified <i>DNMT3B</i> and <i>PFKFB4</i> as key predictors of metastasis. Univariate analyses confirmed the significance of these enzymes, with respective <i>p</i>-values of 0.0058 and 0.0091. A metabolic score based on <i>DNMT3B</i> and <i>PFKFB4</i> expression discriminated metastasis status and high-risk CHIC scores (<i>p</i>-value = 0.005). The metabolic score was more sensitive than the C1/C2 classifier in predicting metastasis (accuracy: 0.72 vs. 0.55). In a regression model integrating the metabolic score with epidemiological parameters (gender, age at diagnosis, histological type, and clinical PRETEXT stage), the metabolic score was confirmed as an independent adverse predictor of metastasis (<i>p</i>-value = 0.003, odds ratio: 2.12). This study identified the dual overexpression of <i>PFKFB4</i> and <i>DNMT3B</i> in hepatoblastoma patients at risk of metastasis (high-risk CHIC classification). The combined tumor expression of <i>DNMT3B</i> and <i>PFKFB4</i> was used to compute a metabolic score, which was validated as an independent predictor of metastatic status in hepatoblastoma.

BiomoleculesVol. 14(11)
Université Paris-Sud (FR), Universidad de Cantabria (ES), International Potato Center (PE), Université Paris-Saclay (FR), Instituto de Investigación Marqués de Valdecilla (ES)
Openalex Percentile: Top 17%
Cancer Genomics and Diagnostics
1.43
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