Predicting the efficacy of neoadjuvant chemoradiotherapy for locally advanced rectal cancer based on lipid metabolism-related genes
10.3760/cma.j.cn113030-20230517-00114
- VernacularTitle:基于脂代谢相关基因预测局部晚期直肠癌新辅助放化疗疗效
- Author:
Qiliang PENG
1
;
Yaqun ZHU
;
Ye TIAN
Author Information
1. 苏州大学附属第二医院放疗科，苏州大学放射肿瘤学研究所，苏州 215004
- Keywords:
Rectal neoplasms, locally advanced;
Chemoradiotherapy, neoadjuvant;
Lipid metabolism;
Efficacy prediction;
Biomarker
- From:
Chinese Journal of Radiation Oncology
2024;33(2):123-129
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of lipid metabolism-related genes (LMRG) for predicting the efficacy of neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC).Methods:GSE46862, a genome-wide expression data of LARC treated with neoadjuvant radiotherapy, was obtained from the Gene Expression Database, and differential expression analysis was performed to obtain differentially expressed genes. The LMRG were collected from the MSigDB database and intersected with differentially expressed genes to obtain differentially expressed LMRG. Candidate LMRG were identified based on three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine - recursive feature elimination (SVM-RFE), and random forest (RF). Functional enrichment analysis was performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to obtain potential function and involved pathways. The accuracy of the candidate LMRG in predicting the efficacy of neoadjuvant chemoradiotherapy for LARC was assessed using receiver operating characteristic (ROC) curve analysis.Results:A total of eight candidate LMRG ( ALOX5AP, FADS2, GALC, PLA2G12A, AGPAT1, AACS, DGKG, ACSBG2) were screened which were mainly involved in biological processes related to lipid metabolism and were involved in the regulation of several important lipid metabolism-related signaling pathways. In addition, these eight candidate LMRG possessed high area under the ROC curve (AUC) for predicting the efficacy of neoadjuvant chemoradiotherapy for LARC. Conclusion:The eight LMRG identified based on three machine learning algorithms had high accuracy in predicting the efficacy of neoadjuvant chemoradiotherapy for LARC, providing clues to identify molecular markers and potential therapeutic targets for preoperative neoadjuvant radiotherapy evaluation of LARC.
