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Research Paper|Volume 16, Issue 6|pp 5163—5183

Novel histone acetylation-related lncRNA signature for predicting prognosis and tumor microenvironment in esophageal carcinoma

Batter Han1, Ying Ma2, Pengjie Yang1, Fangchao Zhao3, Haiyong Zhu3, Shujun Li3, Rong Yu1, Subudao Bao4
  • 1Department of Thoracic Surgery, Peking University Cancer Hospital Inner Mongolia Hospital, Hohhot 010010, China
  • 2Department of Thoracic Surgery, Mongolia Medical University Affiliated Hospital, Hohhot 010050, China
  • 3Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
  • 4Mongolian Medicine College, Inner Mongolia Medical University, Hohhot 010110, China
* Equal contribution
Received: June 30, 2023Accepted: January 2, 2024Published: March 13, 2024
https://doi.org/10.18632/aging.205636

Copyright: © 2024 Han et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Histone acetylation is one of the most common epigenetic modifications and plays a crucial role in tumorigenesis. However, the prognostic significance of histone acetylation-related lncRNAs (HARlncRNAs) in esophageal carcinoma (ESCA) is not well understood. A total of 653 differentially expressed lncRNAs (DElncRNAs) were identified between 162 ESCA tissues and 11 normal tissues in the TCGA database, and 7 of them were correlated with acetylation regulators. We employed univariate Cox regression analysis, combining it with clinical prognosis information, to select 3 prognostic-related HARlncRNAs for further analysis. Subsequently, we used LASSO regression analysis to construct a risk signature for ESCA and identified C21orf62-AS1 and SSTR5.AS1 as potential biomarkers for the prognosis of ESCA patients. Based on the risk score calculated using the risk signature, we categorized patients into high- and low-risk groups. We identified the risk score as an independent risk factor and validated it in the training, test, and GSE53624 datasets. Additionally, patients categorized by their risk scores exhibited distinct immune statuses, tumor mutation burdens, responses to immunotherapy, and drug sensitivities.