ArticleFrontiers in cell and developmental biology2022
Identification of Energy Metabolism-Related Gene Signatures From scRNA-Seq Data to Predict the Prognosis of Liver Cancer Patients.
Article in Frontiers in cell and developmental biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 24 citations in OpenAlex.
- HPRT1 drives mitochondrial metabolic reprogramming and immunosuppressive microenvironment in oral squamous cell carcinoma.Apoptosis : an international journal on programmed cell death · 2026Article
- Single cell and single nucleus RNA sequencing in liver tissues: applications and prospects in model and non-model organisms.Frontiers in genetics · 2026Review
- Multi-omics dissection of CPNE1 reveals its prognostic value and immune-regulatory function in liver cancer.Discover oncology · 2025Article
- Role of ENO1 and its targeted therapy in tumors.Journal of translational medicine · 2024Review
- Comparative Analysis of Different Proteins and Metabolites in the Liver and Ovary of Local Breeds of Chicken and Commercial Chickens in the Later Laying Period.International journal of molecular sciences · 2023Article
- To metabolomics and beyond: a technological portfolio to investigate cancer metabolism.Signal transduction and targeted therapy · 2023Review
- Comprehensive Analysis of Cellular Senescence-Related Genes in Prognosis, Molecular Characterization and Immunotherapy of Hepatocellular Carcinoma.Biological procedures online · 2022Article
- Pathological images for personal medicine in Hepatocellular carcinoma: Cross-talk of gene sequencing and pathological images.Oncology research · 2022Article
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7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The increasingly common usage of single-cell sequencing in cancer research enables analysis of tumor development mechanisms from a wider range of perspectives. Metabolic disorders are closely associated with liver cancer development. In recent years, liver cancer has been evaluated from different perspectives and classified into different subtypes to improve targeted treatment strategies. Here, we performed an analysis of liver cancer from the perspective of energy metabolism based on single-cell sequencing data. Single-cell and bulk sequencing data of liver cancer patients were obtained from GEO and TCGA/ICGC databases, respectively. Using the Seurat R package and protocols such as consensus clustering analysis, genes associated with energy metabolism in liver cancer were identified and validated. An energy metabolism-related score (EM score) was established based on five identified genes. Finally, the sensitivity of patients in different scoring groups to different chemotherapeutic agents and immune checkpoint inhibitors was analyzed. Tumor cells from liver cancer patients were found to divide into nine clusters, with cluster 4 having the highest energy metabolism score. Based on the marker genes of this cluster and TCGA database data, the five most stable key genes (ADH4, AKR1B10, CEBPZOS, ENO1, and FOXN2) were identified as energy metabolism-related genes in liver cancer. In addition, drug sensitivity analysis showed that patients in the low EM score group were more sensitive to immune checkpoint inhibitors and chemotherapeutic agents AICAR, metformin, and methotrexate.
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