Research Paper Volume 13, Issue 13 pp 17707—17733
Screening and identification of angiogenesis-related genes as potential novel prognostic biomarkers of hepatocellular carcinoma through bioinformatics analysis
- 1 Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai 201508, China
- 2 Department of General Surgery, Jinshan Hospital, Fudan University, Shanghai 201508, China
- 3 Department of Surgery, Shanghai Medical College, Fudan University, Shanghai 200032, China
- 4 Department of Paediatrics, the Second Hospital of Jilin University, Changchun 130041, Jilin, China
Received: December 3, 2020 Accepted: June 23, 2021 Published: July 12, 2021https://doi.org/10.18632/aging.203260
How to Cite
Copyright: © 2021 Zhen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality, which makes the prognostic prediction challenging. Angiogenesis appears to be of critical importance in the progression and metastasis of HCC. Some of the angiogenesis-related genes promote this process, while other anti-angiogenesis genes suppress tumor growth and metastasis. Therefore, the comprehensive prognostic value of multiple angiogenesis-related genes in HCC needs to be further clarified. In this study, the mRNA expression profile of HCC patients and the corresponding clinical data were acquired from multiple public databases. Univariate Cox regression analysis was utilized to screen out differentially expressed angiogenesis-related genes with prognostic value. A multigene signature was established with the least absolute shrinkage and selection operator Cox regression in the Cancer Genome Atlas cohort, and validated through an independent cohort. The results suggested that a total of 16 differentially expressed genes (DEGs) were associated with overall survival (OS) and a 7-gene signature was constructed. The risk score of each patient was calculated using this signature, the median value of which was used to divide these patients into a high-risk group and a low-risk group. Compared with the low-risk group, the patients in the high-risk group had a poor prognosis. The risk score was an independent predictor for OS through multivariate Cox regression analysis. Then, unsupervised learning was used to verify the validity of this 7-gene signature. A nomogram by further integrating clinical information and the prognostic signature was utilized to predict prognostic risk and individual OS. Functional enrichment analyses demonstrated that these DEGs were enriched in the pathways of cell proliferation and mitosis, and the immune cell infiltration was significantly different between the two risk groups. In summary, a novel angiogenesis-related genes signature could be used to predict the prognosis of HCC and for targeted therapy.
AUC: area under the curve; DEGs: differentially expressed genes; DFS: disease-free survival; DSS: disease-specific survival; FDR: false discovery rate; GO: Gene Ontology; HCC: hepatocellular carcinoma; HPA: Human Protein Atlas; ICGC: International Cancer Genome Consortium; IPS: immunophenoscore; KEGG: Kyoto Encyclopedia of Genes and Genomes; LASSO: least absolute shrinkage and selection operator; OS: overall survival; PCA: Principal component analysis; PFS: progression-free survival; ROC: receiver operating characteristic; ssGSEA: single sample Gene Set Enrichment Analysis; TCIA: The Cancer Imaging Archive; TCGA: the Cancer Genome Atlas; TME: tumor microenvironment; t-SNE: t-distributed stochastic neighbor embedding.