Su Zhaoran, El Hage Maria, Linnebacher Michael
Department of Gastrointestinal Surgery, People's Hospital of Tongling City, China.
College of Mathematics and Computer Science, Tongling University, Tongling 244000, China.
Heliyon. 2024 Aug 20;10(17):e36550. doi: 10.1016/j.heliyon.2024.e36550. eCollection 2024 Sep 15.
Colorectal cancer (CRC) is a prevalent malignancy and a leading cause of cancer-related mortality. Extensive research into the aetiology of CRC has revealed that somatic mutations in certain genes play a crucial role in CRC development.AIM: In this study, we utilized data from public databases to investigate prevalent mutation patterns in CRC and developed a prognostic predictive model for CRC patients based on mutant genetic characteristics and other relevant clinical features.
We initially gathered mutation information from CRC patients by analysing data from 15 datasets to identify genes with a mutation frequency of ≥10 %. Next, log-rank analyses were used to determine the relationship between prognosis and the mutational status of the most commonly mutated genes; the SIGnaling database was utilized to generate a protein‒protein interaction network. We consolidated and classified the gene mutation patterns of CRC patients in the database based on frequently mutated genes related to prognosis. A predictive nomogram was constructed, including age, sex, TNM stage, and mutation partner, based on available clinical, mutational, and prognostic information for CRC patients at our institution. Finally, the reliability of the model was verified using time-dependent ROC curve analysis.
The top 7 genes somatically mutated ≥10 % in 4477 samples from 4255 patients were (67 %), (66 %), (43 %), (18 %), (14 %), (14 %), and (10 %). Log-rank analysis demonstrated that the mutation status of 5 genes, namely, , , , , and , correlated significantly with prognosis. Protein‒protein interaction analysis confirmed functional interactions between these 5 genes, implicating them in tumorigenesis. We exhaustively enumerated the mutation patterns involving these five genes in 4255 patients, resulting in identification of 32 mutational patterns. After consolidation and classification, these patterns were divided into 3 grades based on patient prognosis. Next, a predictive nomogram based on the clinical, mutational, and prognostic information of 107 CRC patients treated at University Medical Center Rostock was constructed. The area under the curve (AUC) values for the model for predicting 1-, 3-, and 5-year overall survival were 0.779, 0.721, and 0.815, respectively.
Common mutational patterns based on frequently mutated genes are associated with prognosis in CRC patients. Our study provides a valuable and concise prognostic predictor for determining outcomes in patients with CRC.
结直肠癌（CRC）是一种常见的恶性肿瘤，也是癌症相关死亡的主要原因。对CRC病因的广泛研究表明，某些基因的体细胞突变在CRC发展中起关键作用。
在本研究中，我们利用公共数据库的数据调查CRC中普遍的突变模式，并基于突变基因特征和其他相关临床特征为CRC患者开发一种预后预测模型。
我们首先通过分析15个数据集的数据收集CRC患者的突变信息，以识别突变频率≥10%的基因。接下来，采用对数秩分析确定预后与最常见突变基因的突变状态之间的关系；利用SIGnaling数据库生成蛋白质-蛋白质相互作用网络。我们根据与预后相关的频繁突变基因，对数据库中CRC患者的基因突变模式进行整合和分类。基于我们机构CRC患者的可用临床、突变和预后信息，构建了一个预测列线图，包括年龄、性别、TNM分期和突变伙伴。最后，使用时间依赖性ROC曲线分析验证模型的可靠性。
在来自4255例患者的4477个样本中，体细胞突变≥10%的前7个基因分别是（67%）、（66%）、（43%）、（18%）、（14%）、（14%）和（10%）。对数秩分析表明，5个基因，即、、、和的突变状态与预后显著相关。蛋白质-蛋白质相互作用分析证实了这5个基因之间的功能相互作用，表明它们参与肿瘤发生。我们详尽列举了4255例患者中涉及这五个基因的突变模式，从而识别出32种突变模式。经过整合和分类后，这些模式根据患者预后分为3个等级。接下来，基于罗斯托克大学医学中心治疗的107例CRC患者的临床、突变和预后信息构建了一个预测列线图。预测1年、3年和5年总生存的模型的曲线下面积（AUC）值分别为0.779、0.721和0.815。
基于频繁突变基因的常见突变模式与CRC患者的预后相关。我们的研究为确定CRC患者的预后提供了一种有价值且简洁的预测指标。
