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FOLFOX treatment response prediction in metastatic or recurrent colorectal cancer patients via machine learning algorithms

机译:通过机器学习算法转移或复发结直肠癌患者的Folfox治疗响应预测

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Early identification of metastatic or recurrent colorectal cancer (CRC) patients who will be sensitive to FOLFOX (5‐FU, leucovorin and oxaliplatin) therapy is very important. We performed microarray meta‐analysis to identify differentially expressed genes (DEGs) between FOLFOX responders and nonresponders in metastatic or recurrent CRC patients, and found that the expression levels of WASHC4, HELZ, ERN1, RPS6KB1, and APPBP2 were downregulated, while the expression levels of IRF7, EML3, LYPLA2, DRAP1, RNH1, PKP3, TSPAN17, LSS, MLKL, PPP1R7, GCDH, C19ORF24, and CCDC124 were upregulated in FOLFOX responders compared with nonresponders. Subsequent functional annotation showed that DEGs were significantly enriched in autophagy, ErbB signaling pathway, mitophagy, endocytosis, FoxO signaling pathway, apoptosis, and antifolate resistance pathways. Based on those candidate genes, several machine learning algorithms were applied to the training set, then performances of models were assessed via the cross validation method. Candidate models with the best tuning parameters were applied to the test set and the final model showed satisfactory performance. In addition, we also reported that MLKL and CCDC124 gene expression were independent prognostic factors for metastatic CRC patients undergoing FOLFOX therapy.
机译:早期鉴定转移或复发结直肠癌(CRC)对Folfox(5-FU,Leucovorin和Oxaliplatin)治疗敏感的患者非常重要。我们进行了微阵列荟萃分析以鉴定Folfox响应者和转移或复发性CRC患者中的非反应者之间的差异表达基因(DEGS),发现WASHC4,HELZ,ERN1,RPS6KB1和APPBP2的表达水平进行了下调,而表达水平与无反应者相比,IRF7,EML3,LyPLA2,DRAP1,RNH1,PKP3,TSPAN17,LSS,MLKL,PPP1R7,GCH,C19ORF24和CCDC124上调。随后的官能注释显示,在自噬,ERBB信号通路,MITOCHAY,内吞作用,FOXO信号通路,细胞凋亡和防雾性途径中显着富集。基于这些候选基因,将几种机器学习算法应用于训练集,然后通过交叉验证方法评估模型的性能。使用最佳调谐参数的候选模型应用于测试集,最终模型显示出令人满意的性能。此外,我们还报道了MLK1和CCDC124基因表达是接受Folfox疗法的转移CRC患者的独立预后因素。

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