| Autor: |
Ziming Jiang, Junyu Long, Kaige Deng, Yongchang Zheng, Miao Chen |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Frontiers in Molecular Biosciences, Vol 9 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2296-889X |
| DOI: |
10.3389/fmolb.2022.877117 |
| Popis: |
Background: Enhancer RNAs (eRNAs) play an essential role in tumorigenesis as non-coding RNAs transcribed from enhancer regions. However, the landscape of eRNAs in acute myeloid leukemia (AML) and the potential roles of eRNAs in the tumor microenvironment (TME) remain unclear.Method: Gene expression data collected from The Cancer Genome Atlas (TCGA) project were combined with Histone ChIP-seq so as to reveal the comprehensive landscape of eRNAs. Single-sample gene set enrichment analysis algorithm (ssGSEA) and ESTIMATE were employed to enumerate immune cell infiltration and tumor purity.Results: Most prognostic eRNAs were enriched in immune-related pathways. Two distinct immune microenvironment patterns, the immune-active subtype and the immune-resistant subtype, were identified in AML. We further developed an eRNA-derived score (E-score) that could quantify immune microenvironment patterns and predict the response to immune checkpoint inhibitor (ICI) treatment. Finally, we established a prognostic nomogram combining E-score and other clinical features, which showed great discriminative power in both the training set [Harrell’s concordance index (C index): 0.714 (0.651–0.777), p < 0.0001] and validation set [C index: 0.684 (0.614–0.755), p < 0.0001]. Calibration of the nomogram was also validated independently.Conclusion: In this study, we systematically understood the roles of eRNAs in regulating TME diversity and complexity. Moreover, our E-score model provided the first predictive model for ICI treatment in AML. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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