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13 December 2021 : Database Analysis  

Integrated Analysis of Angiogenesis-Mediated Tumor Immune Microenvironment Pattern in Hepatocellular Carcinoma (HCC) and a Novel Prognostic Model Construction to Predict Patient Outcome

Chengqian Lv1BCEF, Qianqian Huang1CF, Xu Zhang1CF, Huimin Cai1CF, Xuechun Ji1CF, Jing Shao1CF, Bingrong Liu ORCID logo1AC*

DOI: 10.12659/MSM.934937

Med Sci Monit 2021; 27:e934937

Abstract

BACKGROUND: Hepatocellular carcinoma (HCC) is a malignant tumor which is famous for its high heterogeneity and complex pathogenesis. Angiogenesis is an important driver of tumor progression and immune-suppressive microenvironment formation.

MATERIAL AND METHODS: A training set was acquired from the TCGA-LIHC cohort. An angiogenesis-active subtype was identified by consensus clustering analysis. The tumor subtype’s immune microenvironment pattern was analyzed using quanTIseq. DEGs-mediated biology function was analyzed by enrichment analysis based on GO and KEGG. A prognostic model was constructed using LASSO Cox regression analysis and validated by 2 external datasets derived from GEO and ICGC. Quantitative real-time PCR assay was conducted to analyze CDCA8’s expression status in the HCC line and normal liver cell line.

RESULTS: In HCC, patients with the angiogenesis-active subtype had a poor prognosis. Angiogenesis can shape the tumor microenvironment into high-M2 microphage infiltration and activity pattern. Here, we identified an angiogenesis-active HCC subtype and constructed an angiogenesis feature-based prognostic model to predict patient outcome. The external validation sets were enrolled to verify the accuracy of this model.

CONCLUSIONS: Our research demonstrated angiogenesis can confer the tumor immune-suppressive characteristic. We provide a robust method to evaluate the HCC’s angiogenesis potential and help identify the angiogenesis-active subtype. Validation in the external validation cohort further confirmed the accuracy of our prognostic model.

Keywords: Carcinoma, Hepatocellular, Immunity, Prognosis

Comments

Figures

Figure 1. Identification of active angiogenesis subtype and inactive angiogenesis subtype. (A) Cumulative distribution function (CDF) curve with K value 2 to 6. (B) CDF Delta area curve for K=2 to 6. (C) VEGF expression of G1, G2, G3, G4. (D) Kaplan-Meier survival curves for G1 and G4. (R software Version 4.0.3, http://www.r-project.org).Figure 2. Immune status in active and inactive angiogenesis subtypes. (A) Immune infiltration status in active angiogenesis and inactive subtypes. (B) M2 microphage related genes expression status in active and inactive angiogenesis subtypes.* P<0.05; ** P<0.01; *** P<0.001. (R software Version 4.0.3, http://www.r-project.org).Figure 3. Acquisition of DEGs between active angiogenesis subtype and inactive subtype and related functional enrichment analysis. (A) Volcano plots of DEGS between active angiogenesis subtype and inactive subtype. (B) Heatmap of DEGS between active angiogenesis subtype and inactive subtype. (C) KEGG enrichment of upregulated genes in active angiogenesis subtype. (D) GO enrichment of upregulated genes in active angiogenesis subtype. (E) KEGG enrichment of downregulated genes in active angiogenesis subtype. (F) GO enrichment of downregulated genes in active angiogenesis subtype. (R software Version 4.0.3, http://www.r-project.org).Figure 4. Construction of the angiogenesis-based gene risk model. (A) LASSO coefficient. (B) The partial likelihood deviance plot. (C) Risk score distribution. (D) Overall survival time of HCC patients. (E) Heatmap of 13 prognostic-related genes. (F) Kaplan-Meier survival analysis in the high- and low-risk groups. (G) ROC curve for the 1-, 3-, 5-year survival rates of HCC patients. (R software Version 4.0.3, http://www.r-project.org).Figure 5. Validation of the prognostic model in the external validation set GSE14520 and ICGC-JP. (A) Kaplan-Meier plots in high- and low-risk groups in GSE14520. (B) ROC curves for 1-, 3-, 5-year OS prediction in GSE14520. (C) Kaplan-Meier plots in high- and low-risk groups in ICGC-JP. (D) ROC curves for 1-,3-,5-year OS prediction in ICGC-JP. (R software Version 4.0.3, http://www.r-project.org).Figure 6. Correlation between the risk score (including prognostic model component genes) and immune features. (R software Version 4.0.3, http://www.r-project.org).Figure 7. CDCA8 has the higher expression level in HCC cell line. (A) CDCA8 has the highest absolute value of coefficient in all prognostic model component genes. (B) CDCA8 expression was examined by quantitative real-time PCR assay in HCC cell line and normal liver cell line. (GraphPad Prism 8 Version 8.3.0, GraphPad).Figure 8. Flow chart of the study. (Adobe Illustrator Version 2020, Adobe).

Editorial

01 January 2022 : Editorial  

Editorial: Current Status of Oral Antiviral Drug Treatments for SARS-CoV-2 Infection in Non-Hospitalized Patients

Dinah V. Parums
Science Editor, Medical Science Monitor, International Scientific Information, Inc., Melville, NY, USA

DOI: 10.12659/MSM.935952

Med Sci Monit 2022; 28:e935952

SARS-CoV-2/COVID-19

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31 December 2021 : Clinical Research  

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Med Sci Monit 2021; 27:e935075

In Press

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Med Sci Monit In Press; DOI: 10.12659/MSM.936131  

27 Jan 2022 : Clinical Research  

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Med Sci Monit In Press; DOI: 10.12659/MSM.935002  

25 Jan 2022 : Editorial  

Editorial: The 2022 World Health Organization (WHO) Priority Recommendations and Response to the Omicron Va...

Med Sci Monit In Press; DOI: 10.12659/MSM.936199  

25 Jan 2022 : Clinical Research  

Experience of the Polish Medical Air Rescue Service During the First Year of the COVID-19 Pandemic and Meas...

Med Sci Monit In Press; DOI: 10.12659/MSM.935474  

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750