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Single-Cell Sequencing of Hepatocellular Carcinoma Reveals Cell Interactions and Cell Heterogeneity in the Microenvironment
BACKGROUND: Hepatocellular carcinoma (HCC) is the main histological subtype of liver cancer, which has the characteristics of poor prognosis and high fatality rate. Single-cell sequencing can provide quantitative and unbiased characterization of cell heterogeneity by analyzing the molecular profile...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8711111/ https://www.ncbi.nlm.nih.gov/pubmed/34992435 http://dx.doi.org/10.2147/IJGM.S338090 |
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author | Li, Xinyao Wang, Lei Wang, Liusong Feng, Zanjie Peng, Cijun |
author_facet | Li, Xinyao Wang, Lei Wang, Liusong Feng, Zanjie Peng, Cijun |
author_sort | Li, Xinyao |
collection | PubMed |
description | BACKGROUND: Hepatocellular carcinoma (HCC) is the main histological subtype of liver cancer, which has the characteristics of poor prognosis and high fatality rate. Single-cell sequencing can provide quantitative and unbiased characterization of cell heterogeneity by analyzing the molecular profile of the whole genome of thousands of single cells. Thus, the purpose of this study was to identify novel prognostic markers for HCC based on single-cell sequencing data. METHODS: Single-cell sequencing of 21 HCC samples and 256 normal liver tissue samples in the GSE124395 dataset was collected from the Gene Expression Omnibus (GEO) database. The quality-controlled cells were grouped by unsupervised cluster analysis and identified the marker genes of each cell cluster. Hereafter, these cell clusters were annotated by singleR and CellMarker according to the expression patterns of the marker genes. Pseudotime analysis was performed to construct the trajectory of cell evolution and to define hub genes in the evolution process. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the potential regulatory mechanism of hub genes in HCC. Next, the differential expression of hub genes and the correlation of the expression of these genes with patients’ survival and diagnosis were investigated in The Cancer Genome Atlas (TCGA) database. RESULTS: A total of 9 clusters corresponding to 9 cell types, including NKT cells, hepatocytes, endothelial cells, Kupffer cells, EPCAM(+) cells, cancer cells, plasma cells (B cells), immature B cells, and myofibroblasts were identified. We screened 63 key genes related to cell differentiation through trajectory analysis, which were enriched in the process of coagulation. Ultimately, we identified 10 survival-related hub genes in the TCGA database, namely ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN. CONCLUSION: In conclusion, ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN may serve as hub genes in the diagnosis and prognosis for HCC. |
format | Online Article Text |
id | pubmed-8711111 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-87111112022-01-05 Single-Cell Sequencing of Hepatocellular Carcinoma Reveals Cell Interactions and Cell Heterogeneity in the Microenvironment Li, Xinyao Wang, Lei Wang, Liusong Feng, Zanjie Peng, Cijun Int J Gen Med Original Research BACKGROUND: Hepatocellular carcinoma (HCC) is the main histological subtype of liver cancer, which has the characteristics of poor prognosis and high fatality rate. Single-cell sequencing can provide quantitative and unbiased characterization of cell heterogeneity by analyzing the molecular profile of the whole genome of thousands of single cells. Thus, the purpose of this study was to identify novel prognostic markers for HCC based on single-cell sequencing data. METHODS: Single-cell sequencing of 21 HCC samples and 256 normal liver tissue samples in the GSE124395 dataset was collected from the Gene Expression Omnibus (GEO) database. The quality-controlled cells were grouped by unsupervised cluster analysis and identified the marker genes of each cell cluster. Hereafter, these cell clusters were annotated by singleR and CellMarker according to the expression patterns of the marker genes. Pseudotime analysis was performed to construct the trajectory of cell evolution and to define hub genes in the evolution process. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the potential regulatory mechanism of hub genes in HCC. Next, the differential expression of hub genes and the correlation of the expression of these genes with patients’ survival and diagnosis were investigated in The Cancer Genome Atlas (TCGA) database. RESULTS: A total of 9 clusters corresponding to 9 cell types, including NKT cells, hepatocytes, endothelial cells, Kupffer cells, EPCAM(+) cells, cancer cells, plasma cells (B cells), immature B cells, and myofibroblasts were identified. We screened 63 key genes related to cell differentiation through trajectory analysis, which were enriched in the process of coagulation. Ultimately, we identified 10 survival-related hub genes in the TCGA database, namely ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN. CONCLUSION: In conclusion, ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN may serve as hub genes in the diagnosis and prognosis for HCC. Dove 2021-12-22 /pmc/articles/PMC8711111/ /pubmed/34992435 http://dx.doi.org/10.2147/IJGM.S338090 Text en © 2021 Li et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Li, Xinyao Wang, Lei Wang, Liusong Feng, Zanjie Peng, Cijun Single-Cell Sequencing of Hepatocellular Carcinoma Reveals Cell Interactions and Cell Heterogeneity in the Microenvironment |
title | Single-Cell Sequencing of Hepatocellular Carcinoma Reveals Cell Interactions and Cell Heterogeneity in the Microenvironment |
title_full | Single-Cell Sequencing of Hepatocellular Carcinoma Reveals Cell Interactions and Cell Heterogeneity in the Microenvironment |
title_fullStr | Single-Cell Sequencing of Hepatocellular Carcinoma Reveals Cell Interactions and Cell Heterogeneity in the Microenvironment |
title_full_unstemmed | Single-Cell Sequencing of Hepatocellular Carcinoma Reveals Cell Interactions and Cell Heterogeneity in the Microenvironment |
title_short | Single-Cell Sequencing of Hepatocellular Carcinoma Reveals Cell Interactions and Cell Heterogeneity in the Microenvironment |
title_sort | single-cell sequencing of hepatocellular carcinoma reveals cell interactions and cell heterogeneity in the microenvironment |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8711111/ https://www.ncbi.nlm.nih.gov/pubmed/34992435 http://dx.doi.org/10.2147/IJGM.S338090 |
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