FOLFOX treatment response prediction in metastatic or recurrent colorectal cancer patients via machine learning algorithms. (1st January 2020)

• Record Type: Journal Article
• Authors: Lu, Wei; Fu, Dongliang; Kong, Xiangxing; Huang, Zhiheng; Hwang, Maxwell; Zhu, Yingshuang; Chen, Liubo; Jiang, Kai; Li, Xinlin; Wu, Yihua; Li, Jun; Yuan, Ying; Ding, Kefeng
• Is Part Of: Cancer medicine. Volume 9:Number 4(2020)
• Page Start: 1419
• Page End: 1429
• Publication Date: 2020-01-01
• Subjects: colorectal cancer -- FOLFOX -- machine learning algorithm -- microarray meta‐analysis; 616.994005
• DOI: 10.1002/cam4.2786
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract: 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. Abstract : We performed the microarray meta‐analysis to identify common differentially expressed genes between FOLFOX responders and non‐responders in metastatic or recurrent colorectal cancer patients, and built prediction models with machine learning algorithms.