State Key Laboratory of Bioactive Substances and Function of Natural Medicine, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Clinical Laboratory, The Affiliated Hospital of Qingdao University, Qingdao, China.
Front Immunol. 2023 Feb 22;14:1054700. doi: 10.3389/fimmu.2023.1054700. eCollection 2023.
Currently, a very small number of patients with colorectal cancer (CRC) respond to immune checkpoint inhibitor (ICI) treatment. Therefore, there is an urgent need to investigate effective biomarkers to determine the responsiveness to ICI treatment. Recently, aberrant 5-methylcytosine (mC) RNA modification has emerged as a key player in the pathogenesis of cancer. Thus, we aimed to explore the predictive signature based on mC regulator-related genes for characterizing the immune landscapes and predicting the prognosis and response to therapies.
The Cancer Genome Atlas (TCGA) cohort was used as the training set, while GEO data sets, real-time quantitative PCR (RT-qPCR) analysis from paired frozen tissues, and immunohistochemistry (IHC) data from tissue microarray (TMA) were used for validation. We constructed a novel signature based on three mC regulator-related genes in patients with rectal adenocarcinoma (READ) using a least absolute shrinkage and selection operator (LASSO)-Cox regression and unsupervised consensus clustering analyses. Additionally, we correlated the three-gene signature risk model with the tumor immune microenvironment, immunotherapy efficiency, and potential applicable drugs.
The mC methylation-based signature was an independent prognostic factor, where low-risk patients showed a stronger immunoreactivity phenotype and a superior response to ICI therapy. Conversely, the high-risk patients had enriched pathways of cancer hallmarks and presented immune-suppressive state, which demonstrated that they are more insensitive to immunotherapy. Additionally, the signature markedly correlated with drug susceptibility.
We developed a reliable mC regulator-based risk model to predict the prognosis, clarify the molecular and tumor microenvironment status, and identify patients who would benefit from immunotherapy or chemotherapy. Our study could provide vital guidance to improve prognostic stratification and optimize personalized therapeutic strategies for patients with rectal cancer.
目前,只有极少数结直肠癌(CRC)患者对免疫检查点抑制剂(ICI)治疗有反应。因此,迫切需要研究有效的生物标志物来确定对 ICI 治疗的反应性。最近,异常的 5-甲基胞嘧啶(mC)RNA 修饰已成为癌症发病机制中的关键因素。因此,我们旨在探索基于 mC 调节因子相关基因的预测特征,以描绘免疫景观并预测预后和对治疗的反应。
使用癌症基因组图谱(TCGA)队列作为训练集,同时使用 GEO 数据集、来自配对冷冻组织的实时定量 PCR(RT-qPCR)分析以及组织微阵列(TMA)的免疫组化(IHC)数据进行验证。我们使用最小绝对收缩和选择算子(LASSO)-Cox 回归和无监督共识聚类分析,基于直肠腺癌(READ)中的三个 mC 调节因子相关基因构建了一个新的特征。此外,我们将三基因特征风险模型与肿瘤免疫微环境、免疫治疗效率和潜在适用药物相关联。
mC 甲基化特征是一个独立的预后因素,低风险患者表现出更强的免疫反应表型,对 ICI 治疗的反应更好。相反,高风险患者具有丰富的癌症标志途径,并呈现出免疫抑制状态,表明他们对免疫治疗更不敏感。此外,该特征与药物敏感性显著相关。
我们开发了一种可靠的基于 mC 调节剂的风险模型,以预测预后、阐明分子和肿瘤微环境状态,并确定受益于免疫治疗或化疗的患者。我们的研究可以为改善预后分层和优化直肠癌患者的个性化治疗策略提供重要指导。