Screening the Cancer Genome Atlas Database for Genes of Prognostic Value in Acute Myeloid Leukemia

Jie Ni; Yang Wu; Feng Qi; Xiao Li; Shaorong Yu; Siwen Liu; Jifeng Feng; Yuxiao Zheng

doi:10.3389/fonc.2019.01509

Screening the Cancer Genome Atlas Database for Genes of Prognostic Value in Acute Myeloid Leukemia

Front Oncol. 2020 Jan 21:9:1509. doi: 10.3389/fonc.2019.01509. eCollection 2019.

Authors

Jie Ni¹, Yang Wu², Feng Qi³, Xiao Li⁴, Shaorong Yu¹, Siwen Liu², Jifeng Feng¹, Yuxiao Zheng⁴

Affiliations

¹ Department of Medical Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.
² Research Center for Clinical Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.
³ Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
⁴ Department of Urology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.

Abstract

Object: To identify genes of prognostic value which associated with tumor microenvironment (TME) in acute myeloid leukemia (AML). Methods and Materials: Level 3 AML patients gene transcriptome profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Clinical characteristics and survival data were extracted from the Genomic Data Commons (GDC) tool. Then, limma package was utilized for normalization processing. ESTIMATE algorithm was used for calculating immune, stromal and ESTIMATE scores. We examined the distribution of these scores in Cancer and Acute Leukemia Group B (CALGB) cytogenetics risk category. Kaplan-Meier (K-M) curves were used to evaluate the relationship between immune scores, stromal scores, ESTIMATE scores and overall survival. We performed clustering analysis and screened differential expressed genes (DEGs) by using heatmaps, volcano plots and Venn plots. After pathway enrichment analysis and gene set enrichment analysis (GESA), protein-protein interaction (PPI) network was constructed and hub genes were screened. We explore the prognostic value of hub genes by calculating risk scores (RS) and processing survival analysis. Finally, we verified the expression level, association of overall survival and gene interactions of hub genes in the Vizome database. Results: We enrolled 173 AML samples from TCGA database in our study. Higher immune score was associated with higher risk rating in CALGB cytogenetics risk category (P = 0.0396) and worse overall survival outcomes (P = 0.0224). In Venn plots, 827 intersect genes were screened with differential analysis. Functional enrichment clustering analysis revealed a significant association between intersect genes and the immune response. After PPI network, 18 TME-related hub genes were identified. RS was calculated and the survival analysis results revealed that high RS was related with poor overall survival (P < 0.0001). Besides, the survival receiver operating characteristic curve (ROC) showed superior predictive accuracy (area under the curve = 0.725). Finally, the heatmap from Vizome database demonstrated that 18 hub genes showed high expression in patient samples. Conclusion: We identified 18 TME-related genes which significantly associated with overall survival in AML patients from TCGA database.

Keywords: acute myeloid leukemia (AML); biomarkers; immune infiltrates; immune/stromal scores; tumor microenvironment (TME).