Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma
Abstract Hepatocellular carcinoma (HCC) is associated with high mortality rate. This study investigated the status of lipid metabolism-related genes in HCC. Bulk transcriptomic and single-cell sequencing data for HCC were retrieved from public databases. The single-cell sequencing data was subjected...
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Springer
2024-10-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-024-01487-y |
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| author | Feiyu Qi Guiming Zha Yanfang Zhang Sihua Liu Yuhang Yang Wanliang Sun Dongdong Wang Zhong Liu Zheng Lu Dengyong Zhang |
| author_facet | Feiyu Qi Guiming Zha Yanfang Zhang Sihua Liu Yuhang Yang Wanliang Sun Dongdong Wang Zhong Liu Zheng Lu Dengyong Zhang |
| author_sort | Feiyu Qi |
| collection | DOAJ |
| description | Abstract Hepatocellular carcinoma (HCC) is associated with high mortality rate. This study investigated the status of lipid metabolism-related genes in HCC. Bulk transcriptomic and single-cell sequencing data for HCC were retrieved from public databases. The single-cell sequencing data was subjected to dimensionality reduction, which facilitated the annotation of distinct cell subpopulations and marker gene expression analysis within each subpopulation. Genes associated with lipid metabolism in liver cells were identified, and a machine-learning model was developed using the bulk transcriptomic data randomly partitioned into training and validation sets. The efficacy of the model was validated using these two sets. A multifactorial Cox analysis on the model genes combined with clinical features, led to the identification of age, HMGCS2, HNRNPU, and RAN as independent prognostic factors, which were included in the nomogram model construction and validation. A weighted gene co-expression analysis of all genes of the bulk transcriptome samples revealed the correlation between gene modules and risk score. Genes with cor > 0.4 in the highest-expressing module were selected for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis. Immune-related analysis was conducted based on seven algorithms for immune cell infiltration prediction. For the genes in the nomogram model, the expression in clinical pathological factors was also analyzed. The drug sensitivity analysis offered a reference for the selection of targeting drugs. This investigation provides novel insights and a theoretical basis for the prognosis, treatment, and pharmaceutical advancements for patients diagnosed with HCC. |
| format | Article |
| id | doaj-art-9fb7da694040405fb9f0843dbec1f3db |
| institution | OA Journals |
| issn | 2730-6011 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-9fb7da694040405fb9f0843dbec1f3db2025-08-20T02:11:26ZengSpringerDiscover Oncology2730-60112024-10-0115111910.1007/s12672-024-01487-yIntegrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinomaFeiyu Qi0Guiming Zha1Yanfang Zhang2Sihua Liu3Yuhang Yang4Wanliang Sun5Dongdong Wang6Zhong Liu7Zheng Lu8Dengyong Zhang9Department of General Surgery, The First Affiliated Hospital of Bengbu Medical CollegeDepartment of Chest Surgery, The First Affiliated Hospital of Bengbu Medical CollegeDepartment of General Surgery, The First Affiliated Hospital of Bengbu Medical CollegeDepartment of General Surgery, The First Affiliated Hospital of Bengbu Medical CollegeDepartment of General Surgery, The First Affiliated Hospital of Bengbu Medical CollegeDepartment of General Surgery, The First Affiliated Hospital of Bengbu Medical CollegeDepartment of General Surgery, The First Affiliated Hospital of Bengbu Medical CollegeDepartment of General Surgery, The First Affiliated Hospital of Bengbu Medical CollegeDepartment of General Surgery, The First Affiliated Hospital of Bengbu Medical CollegeDepartment of General Surgery, The First Affiliated Hospital of Bengbu Medical CollegeAbstract Hepatocellular carcinoma (HCC) is associated with high mortality rate. This study investigated the status of lipid metabolism-related genes in HCC. Bulk transcriptomic and single-cell sequencing data for HCC were retrieved from public databases. The single-cell sequencing data was subjected to dimensionality reduction, which facilitated the annotation of distinct cell subpopulations and marker gene expression analysis within each subpopulation. Genes associated with lipid metabolism in liver cells were identified, and a machine-learning model was developed using the bulk transcriptomic data randomly partitioned into training and validation sets. The efficacy of the model was validated using these two sets. A multifactorial Cox analysis on the model genes combined with clinical features, led to the identification of age, HMGCS2, HNRNPU, and RAN as independent prognostic factors, which were included in the nomogram model construction and validation. A weighted gene co-expression analysis of all genes of the bulk transcriptome samples revealed the correlation between gene modules and risk score. Genes with cor > 0.4 in the highest-expressing module were selected for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis. Immune-related analysis was conducted based on seven algorithms for immune cell infiltration prediction. For the genes in the nomogram model, the expression in clinical pathological factors was also analyzed. The drug sensitivity analysis offered a reference for the selection of targeting drugs. This investigation provides novel insights and a theoretical basis for the prognosis, treatment, and pharmaceutical advancements for patients diagnosed with HCC.https://doi.org/10.1007/s12672-024-01487-yHepatocellular carcinomaHCCTranscriptome analysisSingle-cell sequencingPrognostic factors |
| spellingShingle | Feiyu Qi Guiming Zha Yanfang Zhang Sihua Liu Yuhang Yang Wanliang Sun Dongdong Wang Zhong Liu Zheng Lu Dengyong Zhang Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma Discover Oncology Hepatocellular carcinoma HCC Transcriptome analysis Single-cell sequencing Prognostic factors |
| title | Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma |
| title_full | Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma |
| title_fullStr | Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma |
| title_full_unstemmed | Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma |
| title_short | Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma |
| title_sort | integrative analysis of bulk and single cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma |
| topic | Hepatocellular carcinoma HCC Transcriptome analysis Single-cell sequencing Prognostic factors |
| url | https://doi.org/10.1007/s12672-024-01487-y |
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