{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:03:37Z","timestamp":1753891417632,"version":"3.41.2"},"reference-count":66,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T00:00:00Z","timestamp":1651449600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Mol. Biosci."],"abstract":"<jats:p><jats:bold>Background:<\/jats:bold> 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.<\/jats:p><jats:p><jats:bold>Method:<\/jats:bold> 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.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> 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\u2019s concordance index (C index): 0.714 (0.651\u20130.777), <jats:italic>p<\/jats:italic> &amp;lt; 0.0001] and validation set [C index: 0.684 (0.614\u20130.755), <jats:italic>p<\/jats:italic> &amp;lt; 0.0001]. Calibration of the nomogram was also validated independently.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> 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.<\/jats:p>","DOI":"10.3389\/fmolb.2022.877117","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T04:50:54Z","timestamp":1651467054000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["eRNAs Identify Immune Microenvironment Patterns and Provide a Novel Prognostic Tool in Acute Myeloid Leukemia"],"prefix":"10.3389","volume":"9","author":[{"given":"Ziming","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyu","family":"Long","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaige","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongchang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2022,5,2]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/nbt.2153","article-title":"BLUEPRINT to Decode the Epigenetic Signature Written in Blood","volume":"30","author":"Adams","year":"2012","journal-title":"Nat. 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