A Hypoxia Signature for Predicting Prognosis and Tumor Immune Microenvironment in Adrenocortical Carcinoma.
Xi ChenLijun YanYu LuFeng JiangNi ZengShufang YangXianghua MaPublished in: Journal of oncology (2021)
Adrenocortical carcinoma (ACC) is a rare malignancy with dismal prognosis. Hypoxia is one of characteristics of cancer leading to tumor progression. For ACC, however, no reliable prognostic signature on the basis of hypoxia genes has been built. Our study aimed to develop a hypoxia-associated gene signature in ACC. Data of ACC patients were obtained from TCGA and GEO databases. The genes included in hypoxia risk signature were identified using the Cox regression analysis as well as LASSO regression analysis. GSEA was applied to discover the enriched gene sets. To detect a possible connection between the gene signature and immune cells, the CIBERSORT technique was applied. In ACC, the hypoxia signature including three genes (CCNA2, COL5A1, and EFNA3) was built to predict prognosis and reflect the immune microenvironment. Patients with high-risk scores tended to have a poor prognosis. According to the multivariate regression analysis, the hypoxia signature could be served as an independent indicator in ACC patients. GSEA demonstrated that gene sets linked to cancer proliferation and cell cycle were differentially enriched in high-risk classes. Additionally, we found that PDL1 and CTLA4 expression were significantly lower in the high-risk group than in the low-risk group, and resting NK cells displayed a significant increase in the high-risk group. In summary, the hypoxia risk signature created in our study might predict prognosis and evaluate the tumor immune microenvironment for ACC.
Keyphrases
- poor prognosis
- genome wide
- endothelial cells
- genome wide identification
- cell cycle
- end stage renal disease
- stem cells
- long non coding rna
- ejection fraction
- newly diagnosed
- chronic kidney disease
- copy number
- genome wide analysis
- peritoneal dialysis
- cell proliferation
- dna methylation
- heart rate
- machine learning
- bioinformatics analysis
- gene expression
- deep learning
- heart rate variability
- data analysis
- artificial intelligence