4.4 Article

Intra-tumor heterogeneity and prognostic risk signature for hepatocellular carcinoma based on single-cell analysis

期刊

EXPERIMENTAL BIOLOGY AND MEDICINE
卷 247, 期 19, 页码 1741-1751

出版社

SAGE PUBLICATIONS LTD
DOI: 10.1177/15353702221110659

关键词

Hepatocellular carcinoma; single-cell RNA sequencing; intra-tumor heterogeneity; prognosis

资金

  1. Clinical research project of Air Force Medical Center [2021LC013]
  2. Youth Science and Technology Support Project of Air Force Medical Center [2020KTC14]

向作者/读者索取更多资源

This study used single-cell RNA sequencing to investigate the intra-tumor heterogeneity of hepatocellular carcinoma (HCC). The researchers identified different cell subpopulations and developed a nine-gene prognostic model to assess the mortality risk of HCC patients. These findings enhance our understanding of HCC heterogeneity and could inspire future research.
Intra-tumor heterogeneity poses a serious challenge in the treatment of cancer, including hepatocellular carcinoma (HCC). Recent developments in single-cell RNA sequencing (scRNA-seq) make it possible to examine the heterogeneity of tumor cells. The Gene Expression Omnibus (GEO) database was retrieved to obtain scRNA-seq data of 13 HCC and 8 para cancer samples, and the cells were clustered using FindNeighbors and FindClusters functions. Cell subsets were defined using the Enricher function of the clusterProfiler package. Monocle was used to predict cell developmental trajectory. The LIMMA package included in the R program was utilized to detect differentially expressed genes (DEGs) between HCC and paracancerous tissues. Univariate Cox analysis and Least Absolute and Selection Operator (Lasso) Cox regression analysis were conducted to establish a risk assessment model. Thirteen cell subpopulations were identified from the sequencing data of 64,634 single cells. Four cell subgroups (dendritic cells, hepatocytes, liver bud hepatic cells, and liver progenitor cells) in tumor tissues were highly enriched. Between HCC and para cancer tissues, 3024 DEGs were identified, and 641 were specific markers of four cell subgroups. To develop a prognostic risk model, 9 genes among the 641 genes were selected. In the training and validation sets, the model demonstrated a higher 5-year AUC and independently served as a prognostic marker as confirmed by multivariate and univariate Cox analyses. This study revealed the characteristics of different cell subpopulations of immune cells and tumor cells from the HCC microenvironment. We established a novel nine-gene prognostic model to determine the death risk of HCC patients. The discoveries in this research improved the current knowledge of HCC heterogeneity and may inspire future research.

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