利用综合生物信息学分析鉴定结肠癌的预后基因特征。
Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis.
机构信息
Department of Anorectal Surgery, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310006, Zhejiang Province, China.
Department of General Practice, The First Affiliated Hospital of Zhejiang Chinese Medical University, #54 Youdian Road, Shangcheng District, Hangzhou, 310006, Zhejiang Province, China.
出版信息
World J Surg Oncol. 2021 Jan 13;19(1):13. doi: 10.1186/s12957-020-02116-y.
BACKGROUND
Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer.
METHODS
The gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram.
RESULTS
Five colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses identified there were 14 prognosis-associated DEGs, of which 12 DEGs were included in the optimized prognostic gene signature. This prognostic model presented a high forecast ability for the prognosis of colon cancer both in the TCGA training dataset and the validation datasets (GSE38832 and GSE17538; AUC > 0.8). In addition, patients' age, T classification, recurrence status, and prognostic risk score were associated with the prognosis of TCGA patients with colon cancer. The nomogram was constructed using the above factors, and the predictive 3- and 5-year survival probabilities had high compliance with the actual survival proportions.
CONCLUSIONS
The 12-gene signature prognostic model had a high predictive ability for the prognosis of colon cancer.
背景
结肠癌是全球导致癌症相关死亡的主要原因之一，结肠癌的预后仍有待提高。本研究旨在构建预测结肠癌预后的模型。
方法
从 TCGA、GSE44861 和 GSE44076 数据集获取结肠癌的基因表达谱数据。使用 WGCNA 模块基因和常见差异表达基因（DEG）筛选出与预后相关的 DEG，用于构建预后模型。在 TCGA 训练和微阵列验证集（GSE38832 和 GSE17538）中评估和验证预后模型的性能。最后，将模型和与预后相关的临床因素用于构建列线图。
结果
鉴定出 5 个与结肠癌相关的 WGCNA 模块（包含 1160 个基因）和肿瘤与正常组织之间的 1153 个 DEG，其中包含 556 个重叠 DEG。逐步 Cox 回归分析确定了 14 个与预后相关的 DEG，其中 12 个 DEG 包含在优化的预后基因特征中。该预后模型在 TCGA 训练数据集和验证数据集（GSE38832 和 GSE17538；AUC＞0.8）中对结肠癌的预后具有较高的预测能力。此外，患者年龄、T 分类、复发状态和预后风险评分与 TCGA 结肠癌患者的预后相关。使用上述因素构建了列线图，预测的 3 年和 5 年生存率与实际生存率具有较高的一致性。
结论
该 12 基因特征预后模型对结肠癌的预后具有较高的预测能力。
Zotero 插件