PDF(Pubmed)
Abstract：
Background: Individualized recurrence risk prediction in patients with stage II/III colorectal cancer (CRC) is crucial for making postoperative treatment decisions. However, there is still a lack of effective approaches for identifying patients with stage II and III CRC at a high risk of recurrence. In this study, we aimed to establish a credible gene model for improving the risk assessment of patients with stage II/III CRC. Methods: Recurrence-free survival (RFS)-related genes were screened using Univariate Cox regression analysis in GSE17538, GSE39582, and GSE161158 cohorts. Common prognostic genes were identified by Venn diagram and subsequently subjected to least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis for signature construction. Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy and superiority of our risk model. Single-sample gene set enrichment analysis (ssGSEA) was employed to investigate the relationship between the infiltrative abundances of immune cells and risk scores. Genes significantly associated with the risk scores were identified to explore the biological implications of the 9-gene signature. Results: Survival analysis identified 347 RFS-related genes. Using these genes, a 9-gene signature was constructed, which was composed of MRPL41, FGD3, RBM38, SPINK1, DKK1, GAL3ST4, INHBB, CTB-113P19.1, and FAM214B. K-M curves verified the survival differences between the low- and high-risk groups classified by the 9-gene signature. The area under the curve (AUC) values of this signature were close to or no less than the previously reported prognostic signatures and clinical factors, suggesting that this model could provide improved RFS prediction. The ssGSEA algorithm estimated that eight immune cells, including regulatory T cells, were aberrantly infiltrated in the high-risk group. Furthermore, the signature was associated with multiple oncogenic pathways, including cell adhesion and angiogenesis. Conclusion: A novel RFS prediction model for patients with stage II/III CRC was constructed using multicohort validation. The proposed signature may help clinicians better manage patients with stage II/III CRC.
摘要：
背景：II/III期结直肠癌（CRC）患者的个体化复发风险预测对于制定术后治疗决策至关重要。然而,目前仍缺乏确定II期和III期CRC患者复发风险高的有效方法.在这项研究中,我们旨在建立一个可靠的基因模型,以改善II/III期CRC患者的风险评估.方法：在GSE17538，GSE39582和GSE161158队列中使用单变量Cox回归分析筛选无复发生存（RFS）相关基因。通过维恩图鉴定常见的预后基因，随后进行最小绝对收缩和选择算子（LASSO）回归分析和多变量Cox回归分析，以进行签名构建。Kaplan-Meier(K-M),校准,和受试者工作特征（ROC）曲线用于评估我们的风险模型的预测准确性和优越性。采用单样本基因集富集分析（ssGSEA）研究免疫细胞浸润丰度与风险评分之间的关系。鉴定了与风险评分显着相关的基因，以探索9基因签名的生物学意义。结果:生存分析鉴定出347个RFS相关基因。利用这些基因，构建了9个基因签名，由MRPL41、FGD3、RBM38、SPINK1、DKK1、GAL3ST4、INHBB、CTB-113P19.1和FAM214B。K-M曲线验证了通过9-基因标记分类的低风险组和高风险组之间的生存差异。该特征的曲线下面积（AUC）值接近或不低于先前报道的预后特征和临床因素，表明该模型可以提供改进的RFS预测。ssGSEA算法估计8个免疫细胞,包括调节性T细胞,在高危人群中异常浸润。此外，签名与多个致癌途径有关，包括细胞粘附和血管生成。结论：使用多队列验证构建了II/III期CRC患者的新型RFS预测模型。建议的签名可能有助于临床医生更好地管理II/III期CRC患者。
