College of Bioengineering, Chongqing University, Chongqing, China.
Radiation and Cancer Biology Laboratory, Radiation Oncology Center, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Institute and Chongqing Cancer Hospital, Chongqing University Cancer Hospital and Chongqing Cancer, Chongqing 400030, China.
Comput Intell Neurosci. 2022 May 9;2022:5443709. doi: 10.1155/2022/5443709. eCollection 2022.
Radiotherapy (RT) is one of the major cancer treatments. However, the responses to RT vary among individual patients, partly due to the differences of the status of gene expression and mutation in tumors of patients. Identification of patients who will benefit from RT will improve the efficacy of RT. However, only a few clinical biomarkers were currently used to predict RT response. Our aim is to obtain gene signatures that can be used to predict RT response by analyzing the transcriptome differences between RT responder and nonresponder groups.
We obtained transcriptome data of 1664 patients treated with RT from the TCGA database across 15 cancer types. First, the genes with a significant difference between RT responder (R group) and nonresponder groups (PD group) were identified, and the top 100 genes were used to build the gene signatures. Then, we developed the predictive model based on binary logistic regression to predict patient response to RT.
We identified a series of differentially expressed genes between the two groups, which are involved in cell proliferation, migration, invasion, EMT, and DNA damage repair pathway. Among them, MDC1, UCP2, and RBM45 have been demonstrated to be involved in DNA damage repair and radiosensitivity. Our analysis revealed that the predictive model was highly specific for distinguishing the and PD patients in different cancer types with an area under the curve (AUC) ranging from 0.772 to 0.972. It also provided a more accurate prediction than that from a single-gene signature for the overall survival (OS) of patients.
The predictive model has a potential clinical application as a biomarker to help physicians create optimal treatment plans. Furthermore, some of the genes identified here may be directly involved in radioresistance, providing clues for further studies on the mechanism of radioresistance.
放射治疗（RT）是癌症治疗的主要手段之一。然而，由于患者肿瘤中基因表达和突变状态的差异，不同患者对 RT 的反应存在差异。确定哪些患者将从 RT 中获益将提高 RT 的疗效。然而，目前仅使用少数临床生物标志物来预测 RT 反应。我们的目的是通过分析 RT 应答组和非应答组之间的转录组差异，获得可用于预测 RT 反应的基因特征。
我们从 TCGA 数据库中获取了 15 种癌症类型的 1664 例接受 RT 治疗患者的转录组数据。首先，鉴定出 RT 应答者（R 组）和非应答者（PD 组）之间存在显著差异的基因，并使用前 100 个基因构建基因特征。然后，我们基于二项逻辑回归建立预测模型，以预测患者对 RT 的反应。
我们鉴定出两组之间存在一系列差异表达基因，这些基因参与细胞增殖、迁移、侵袭、EMT 和 DNA 损伤修复途径。其中，MDC1、UCP2 和 RBM45 已被证明参与 DNA 损伤修复和放射敏感性。我们的分析表明，该预测模型在不同癌症类型中具有高度特异性，用于区分 和 PD 患者的曲线下面积（AUC）范围为 0.772 至 0.972。它还为患者的总生存（OS）提供了比单个基因特征更准确的预测。
该预测模型具有作为生物标志物的潜在临床应用价值，可帮助医生制定最佳治疗计划。此外，这里鉴定的一些基因可能直接参与放射抵抗，为进一步研究放射抵抗机制提供线索。
