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Comprehensive Analysis of ScRNA-Seq and Bulk RNA-Seq Data Reveals Dynamic Changes in Tumor-associated Neutrophils in the Tumor Microenvironment of Hepatocellular Carcinoma and Leads to the Establishment of a Neutrophil-related Prognostic Model

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Abstract

Background: Analysis of hepatocellular carcinoma (HCC) single-cell sequencing data was conducted to explore the role of tumor-associated neutrophils in the tumor microenvironment.

Methods: Analysis of single-cell sequencing data from 12 HCC tumor cores and five HCC paracancerous tissues identified cellular subpopulations and cellular marker genes. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to establish and validate prognostic models. xCELL, TIMER, QUANTISEQ, CIBERSORT, and CIBERSORT-abs analyses were performed to explore immune cell infiltration. Finally, the pattern of tumor-associated neutrophil roles in tumor microenvironmental components was explored.

Results: A total of 271 marker genes for tumor-associated neutrophils were identified based on single-cell sequencing data. Prognostic models incorporating eight genes were established based on TCGA data. Immune cell infiltration differed between the high- and low-risk groups. The low-risk group benefited more from immunotherapy. Single-cell analysis indicated that tumor-associated neutrophils were able to influence macrophage, NK cell, and T-cell functions through the IL16, IFN-II, and SPP1 signaling pathways.

Conclusion: Tumor-associated neutrophils regulate immune functions by influencing macrophages and NK cells. Models incorporating tumor-associated neutrophil-related genes can be used to predict patient prognosis and immunotherapy responses.

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References
1.
Bray F, Ferlay J, Soerjomataram I, Siegel R, Torre L, Jemal A . Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018; 68(6):394-424. DOI: 10.3322/caac.21492. View

2.
Huang D, Singal A, Kono Y, Tan D, El-Serag H, Loomba R . Changing global epidemiology of liver cancer from 2010 to 2019: NASH is the fastest growing cause of liver cancer. Cell Metab. 2022; 34(7):969-977.e2. PMC: 9762323. DOI: 10.1016/j.cmet.2022.05.003. View

3.
Liu Z, Jiang Y, Yuan H, Fang Q, Cai N, Suo C . The trends in incidence of primary liver cancer caused by specific etiologies: Results from the Global Burden of Disease Study 2016 and implications for liver cancer prevention. J Hepatol. 2018; 70(4):674-683. DOI: 10.1016/j.jhep.2018.12.001. View

4.
Liu C, Chen K, Chen P . Treatment of Liver Cancer. Cold Spring Harb Perspect Med. 2015; 5(9):a021535. PMC: 4561392. DOI: 10.1101/cshperspect.a021535. View

5.
Grady W, Yu M, Markowitz S . Epigenetic Alterations in the Gastrointestinal Tract: Current and Emerging Use for Biomarkers of Cancer. Gastroenterology. 2020; 160(3):690-709. PMC: 7878343. DOI: 10.1053/j.gastro.2020.09.058. View