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

被引:1
|
作者
Desterke, Christophe [1 ]
Frances, Raquel [2 ]
Monge, Claudia [3 ]
Marchio, Agnes [3 ]
Pineau, Pascal [3 ]
Mata-Garrido, Jorge [3 ]
机构
[1] Univ Paris Saclay, Univ Paris Sud, Fac Med Kremlin Bicetre, F-94270 Le Kremlin Bicetre, France
[2] PSL Res Univ, Brain Plast Unit, CNRS, Energy & Memory,ESPCI Paris, F-75006 Paris, France
[3] Univ Paris Cite, Inst Pasteur, Unite Org Nucl & Oncogenese, INSERM,U993, F-75015 Paris, France
关键词
hepatoblastoma; metastasis; CHIC risk; metabolism; epigenetics; DNA methylation; glycolysis; transcriptome; HUMAN HEPATOCELLULAR-CARCINOMA; DNA METHYLATION; PROGNOSTIC-FACTORS; TUMOR; 6-PHOSPHOFRUCTO-2-KINASE/FRUCTOSE-2,6-BISPHOSPHATASE; DATABASE; BREAST; GEO;
D O I
10.3390/biom14111394
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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 DNMT3B and PFKFB4 as key predictors of metastasis. Univariate analyses confirmed the significance of these enzymes, with respective p-values of 0.0058 and 0.0091. A metabolic score based on DNMT3B and PFKFB4 expression discriminated metastasis status and high-risk CHIC scores (p-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 (p-value = 0.003, odds ratio: 2.12). This study identified the dual overexpression of PFKFB4 and DNMT3B in hepatoblastoma patients at risk of metastasis (high-risk CHIC classification). The combined tumor expression of DNMT3B and PFKFB4 was used to compute a metabolic score, which was validated as an independent predictor of metastatic status in hepatoblastoma.
引用
收藏
页数:15
相关论文
共 37 条
  • [31] Atf4 regulates angiogenic differences between alveolar bone and long bone macrophages by regulating M1 polarization, based on single-cell RNA sequencing, RNA-seq and ATAC-seq analysis
    Gu, Lanxin
    Wang, Zhongyuan
    Gu, Hong
    Wang, Hua
    Liu, Luwei
    Zhang, Wei-Bing
    JOURNAL OF TRANSLATIONAL MEDICINE, 2023, 21 (01)
  • [32] Full-length single-cell RNA-seq applied to a viral human cancer: applications to HPV expression and splicing analysis in HeLa S3 cells
    Wu, Liang
    Zhang, Xiaolong
    Zhao, Zhikun
    Wang, Ling
    Li, Bo
    Li, Guibo
    Dean, Michael
    Yu, Qichao
    Wang, Yanhui
    Lin, Xinxin
    Rao, Weijian
    Mei, Zhanlong
    Li, Yang
    Jiang, Runze
    Yang, Huan
    Li, Fuqiang
    Xie, Guoyun
    Xu, Liqin
    Wu, Kui
    Zhang, Jie
    Chen, Jianghao
    Wang, Ting
    Kristiansen, Karsten
    Zhang, Xiuqing
    Li, Yingrui
    Yang, Huanming
    Wang, Jian
    Hou, Yong
    Xu, Xun
    GIGASCIENCE, 2015, 4
  • [33] Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis
    Liu, Xinbing
    Gao, Wei
    Liu, Wei
    DISEASE MARKERS, 2021, 2021
  • [34] Clustering Mycobacterium tuberculosis-specific CD154+CD4+T cells for distinguishing tuberculosis disease from infection based on single-cell RNA-seq analysis
    Wang, Xiaochen
    Jiang, Kaishan
    Xing, Wenjin
    Xin, Qiudan
    Hu, Qiongjie
    Wu, Shiji
    Sun, Ziyong
    Hou, Hongyan
    Ren, Yi
    Wang, Feng
    JOURNAL OF INFECTION, 2025, 90 (04)
  • [35] FB5P-seq: FACS-Based 5-Prime End Single-Cell RNA-seq for Integrative Analysis of Transcriptome and Antigen Receptor Repertoire in B and T Cells (vol 11, 216, 2020)
    Attaf, Noudjoud
    Cervera-Marzal, Inaki
    Dong, Chuang
    Gil, Laurine
    Renand, Amedee
    Spinelli, Lionel
    Milpied, Pierre
    FRONTIERS IN IMMUNOLOGY, 2020, 11
  • [36] FB5P-seq: FACS-Based 5-Prime End Single-Cell RNA-seq for Integrative Analysis of Transcriptome and Antigen Receptor Repertoire in B and T Cells (vol 11, 216, 2020)
    Attaf, Noudjoud
    Cervera-Marzal, Inaki
    Dong, Chuang
    Gil, Laurine
    Renand, Amedee
    Spinelli, Lionel
    Milpied, Pierre
    FRONTIERS IN IMMUNOLOGY, 2020, 11
  • [37] M2 macrophage marker CHI3L2 could serve as a potential prognostic and immunological biomarker in glioma by integrated single-cell and bulk RNA-Seq analysis
    Qian, Wenbo
    Wang, Qi
    Zhang, Chi
    Zhu, Junle
    Zhang, Qing
    Luo, Chun
    JOURNAL OF GENE MEDICINE, 2023, 25 (09):