当前位置: X-MOL 学术Cancer Med. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Predicting the effect of 5-fluorouracil-based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles.
Cancer Medicine ( IF 4 ) Pub Date : 2020-03-09 , DOI: 10.1002/cam4.2952
Quan Chen 1 , Peng Gao 1 , Yongxi Song 1 , Xuanzhang Huang 1 , Qiong Xiao 1 , Xiaowan Chen 1 , Xinger Lv 1 , Zhenning Wang 1
Affiliation  

It is critical to identify patients with stage II and III colorectal cancer (CRC) who will benefit from adjuvant chemotherapy (ACT) after curative surgery, while the only use of clinical factors is insufficient to predict this beneficial effect. In this study, we performed genetic algorithm (GA) to select ACT candidate genes, and built a predictive model of support vector machine (SVM) using gene expression profiles from the Gene Expression Omnibus database. The model contained four ACT candidate genes (EDEM1, MVD, SEMA5B, and WWP2) and TNM stage (stage II or III). After using Subpopulation Treatment Effect Pattern Plot to determine the optimal cutoff value of predictive scores, the validated patients from The Cancer Genome Atlas database can be divided into the predictive ACT-benefit/-futile groups. Patients in the predictive ACT-benefit group with 5-fluorouracil (5-Fu)-based ACT had significantly longer relapse-free survival (RFS) compared to those without ACT (P = .015); However, the difference in RFS in the predictive ACT-futile group was insignificant (P = .596). The multivariable analysis found that the predictive groups were significantly associated with the effect of ACT (Pinteraction = .011). Consequently, we developed a predictive model based on the SVM and GA algorithm which was further validated to define patients who benefit from ACT on recurrence.

中文翻译:

预测基于5-氟尿嘧啶的辅助化疗对结直肠癌复发的影响:使用基因表达谱的模型。

关键是要确定在治愈性手术后将受益于辅助化疗(ACT)的II期和III期大肠癌(CRC)患者,而仅使用临床因素不足以预测这种有益效果。在这项研究中,我们执行了遗传算法(GA)以选择ACT候选基因,并使用Gene Expression Omnibus数据库中的基因表达谱建立了支持向量机(SVM)的预测模型。该模型包含四个ACT候选基因(EDEM1,MVD,SEMA5B和WWP2)和TNM阶段(第二阶段或第三阶段)。在使用亚人群治疗效果模式图确定预测得分的最佳临界值之后,来自癌症基因组图谱数据库的经过验证的患者可以分为预测性ACT获益/无效人群。与不含ACT的患者相比,基于5-氟尿嘧啶(5-Fu)的ACT预测性ACT组的患者的无复发生存期(RFS)明显更长(P = .015);但是,预测性ACT无效组的RFS差异不明显(P = .596)。多变量分析发现,预测组与ACT的效果显着相关(交互作用= .011)。因此,我们基于SVM和GA算法开发了一种预测模型,该模型经过进一步验证以定义从ACT复发中受益的患者。多变量分析发现,预测组与ACT的效果显着相关(交互作用= .011)。因此,我们基于SVM和GA算法开发了一种预测模型,该模型经过进一步验证以定义从ACT复发中受益的患者。多变量分析发现,预测组与ACT的效果显着相关(交互作用= .011)。因此,我们基于SVM和GA算法开发了一种预测模型,该模型经过进一步验证以定义从ACT复发中受益的患者。
更新日期:2020-03-09
down
wechat
bug