尽管治疗方案取得了进步，食管鳞状细胞癌（ESCC）是一种普遍且具有侵袭性的癌症，其预后仍然很差。为了解决食管鳞癌 circRNA 表达谱得出的综合预后信息方面的差距，我们的研究旨在建立 circRNA 表达与食管鳞癌预后之间的联系。为了实现这一目标，我们首先开发了一种名为 T 细胞相关风险评分 (TRRS) 的优化预后模型，它将 T 细胞相关特征与机器学习算法相结合。与此同时，我们重新分析了现有的 RNA-seq 数据集，以重新定义 circRNA 和 mRNA 的表达谱。利用 TRRS 作为基础“桥梁”，我们鉴定了与 TRRS 相关的 circRNA，从而开发了一种新型的基于 circRNA 对的预后模型 TCRS，该模型独立于特定表达水平。进一步的研究发现了两个具有潜在功能意义的circRNA：circNLK(5,6,7).1和circRC3H1(2).1。这些发现强调了这些风险评分作为预测 ESCC 患者总体生存率和确定潜在治疗靶点的工具的实用性。版权所有 © 2024 Elsevier B.V. 保留所有权利。

The prognosis for esophageal squamous cell carcinoma (ESCC), a prevalent and aggressive form of cancer, remains poor despite advancements in treatment options. Addressing the gap in comprehensive prognostic information derived from circRNA expression profiles for ESCC, our study aimed to establish a linkage between circRNA expressions and ESCC prognosis. To achieve this, we first developed an optimized prognostic model named T cell-related risk score (TRRS), which integrates T cell-associated features with machine learning algorithms. In parallel, we re-analyzed existing RNA-seq datasets to redefine the expression profiles of circRNAs and mRNAs. Utilizing the TRRS as a foundational "bridge," we identified circRNAs correlated with TRRS, leading to the development of a novel circRNA pair-based prognostic model, the TCRS, which is independent of specific expression levels. Further investigations uncovered two circRNAs, circNLK(5,6,7).1 and circRC3H1(2).1, with potential functional significance. These findings underscore the utility of these risk scores as tools for predicting overall survival and identifying potential therapeutic targets for ESCC patients.Copyright © 2024 Elsevier B.V. All rights reserved.