Chen Jie, Ji Chao, Liu Silin, Wang Jin, Wang Che, Pan Jue, Qiao Jinyu, Liang Yu, Cai Mengjiao, Ma Jinlu
Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.
Cancer Pathog Ther. 2023 Dec 12;2(4):299-313. doi: 10.1016/j.cpt.2023.12.002. eCollection 2024 Oct.
Colon cancer is a malignant tumor with high malignancy and a low survival rate whose heterogeneity limits systemic immunotherapy. Transforming growth factor-β (TGF-β) signaling pathway-related genes are associated with multiple tumors, but their role in prognosis prediction and tumor microenvironment (TME) regulation in colon cancer is poorly understood. Using bioinformatics, this study aimed to construct a risk prediction signature for colon cancer, which may provide a means for developing new effective treatment strategies.
Using consensus clustering, patients in The Cancer Genome Atlas (TCGA) with colon adenocarcinoma were classified into several subtypes based on the expression of TGF-β signaling pathway-related genes, and differences in survival, molecular, and immunological TME characteristics and drug sensitivity were examined in each subtype. Ten genes that make up a TGF-β-related predictive signature were found by least absolute shrinkage and selector operation (LASSO) regression using colon cancer data from the TCGA database and confirmed using a Gene Expression Omnibus (GEO) dataset. A nomogram incorporating risk scores and clinicopathologic factors was developed to stratify the prognosis of patients with colon cancer for accurate clinical diagnosis and therapy.
Two TGF-β subtypes were identified, with the TGF-β-high subtype being associated with a poorer prognosis and superior sensitivity to immunotherapy. Mutation analyses showed a high incidence of gene mutations in the TGF-β-high subtype. After completing signature construction, patients with colon cancer were categorized into high- and low-risk subgroups based on the median risk score of the TGF-β-related predictive signature. The risk score exhibited superior predictive performance relative to age, gender, and stage, as evidenced by its AUC of 0.686. Patients in the high-risk subgroup had higher levels of immunosuppressive cell infiltration and immune checkpoints in the TME, suggesting that these patients had better responses to immunotherapy.
Patients with colon cancer were divided into two subtypes with different survival and immune characteristics using consensus clustering analysis based on TGF-β signaling pathway-related genes. The constructed risk prediction signature may show promise as a biomarker for evaluating the prognosis of colon cancer, with potential utility for screening individuals for immunotherapy.
结肠癌是一种恶性程度高、生存率低的恶性肿瘤，其异质性限制了全身免疫治疗。转化生长因子-β（TGF-β）信号通路相关基因与多种肿瘤相关，但其在结肠癌预后预测和肿瘤微环境（TME）调节中的作用尚不清楚。本研究旨在利用生物信息学构建结肠癌风险预测特征，为开发新的有效治疗策略提供方法。
采用一致性聚类，根据TGF-β信号通路相关基因的表达，将癌症基因组图谱（TCGA）中的结肠腺癌患者分为几个亚型，并检测各亚型在生存、分子和免疫TME特征以及药物敏感性方面的差异。利用TCGA数据库中的结肠癌数据，通过最小绝对收缩和选择算子（LASSO）回归发现了构成TGF-β相关预测特征的10个基因，并使用基因表达综合数据库（GEO）数据集进行了验证。开发了一个包含风险评分和临床病理因素的列线图，以对结肠癌患者的预后进行分层，从而实现准确的临床诊断和治疗。
鉴定出两种TGF-β亚型，其中TGF-β高表达亚型与较差的预后和对免疫治疗的较高敏感性相关。突变分析显示TGF-β高表达亚型中基因突变的发生率较高。在完成特征构建后，根据TGF-β相关预测特征的中位风险评分，将结肠癌患者分为高风险和低风险亚组。风险评分相对于年龄、性别和分期表现出更好的预测性能，其曲线下面积（AUC）为0.686证明了这一点。高风险亚组患者的TME中免疫抑制细胞浸润和免疫检查点水平较高，这表明这些患者对免疫治疗有更好的反应。
基于TGF-β信号通路相关基因的一致性聚类分析，将结肠癌患者分为具有不同生存和免疫特征的两个亚型。构建的风险预测特征可能有望作为评估结肠癌预后的生物标志物，具有筛选免疫治疗个体的潜在用途。
