Zhou Qing-Nan, Lei Rong-E, Liang Yun-Xiao, Li Si-Qi, Guo Xian-Wen, Hu Bang-Li
Department of Gastroenterology, The People's Hospital of Guangxi Zhuang Autonomous Region & Research center of Gastroenterology, Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Nanning, 530021, Guangxi, China.
Department of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China.
Cancer Cell Int. 2023 May 27;23(1):103. doi: 10.1186/s12935-023-02945-3.
Oxaliplatin-based chemotherapy is the first-line treatment for colorectal cancer (CRC). Long noncoding RNAs (lncRNAs) have been implicated in chemotherapy sensitivity. This study aimed to identify lncRNAs related to oxaliplatin sensitivity and predict the prognosis of CRC patients underwent oxaliplatin-based chemotherapy.
Data from the Genomics of Drug Sensitivity in Cancer (GDSC) was used to screen for lncRNAs related to oxaliplatin sensitivity. Four machine learning algorithms (LASSO, Decision tree, Random-forest, and support vector machine) were applied to identify the key lncRNAs. A predictive model for oxaliplatin sensitivity and a prognostic model based on key lncRNAs were established. The published datasets, and cell experiments were used to verify the predictive value.
A total of 805 tumor cell lines from GDSC were divided into oxaliplatin sensitive (top 1/3) and resistant (bottom 1/3) groups based on their IC50 values, and 113 lncRNAs, which were differentially expressed between the two groups, were selected and incorporated into four machine learning algorithms, and seven key lncRNAs were identified. The predictive model exhibited good predictions for oxaliplatin sensitivity. The prognostic model exhibited high performance in patients with CRC who underwent oxaliplatin-based chemotherapies. Four lncRNAs, including C20orf197, UCA1, MIR17HG, and MIR22HG, displayed consistent responses to oxaliplatin treatment in the validation analysis.
Certain lncRNAs were associated with oxaliplatin sensitivity and predicted the response to oxaliplatin treatment. The prognostic models established based on the key lncRNAs could predict the prognosis of patients given oxaliplatin-based chemotherapy.
基于奥沙利铂的化疗是结直肠癌（CRC）的一线治疗方法。长链非编码RNA（lncRNAs）与化疗敏感性有关。本研究旨在鉴定与奥沙利铂敏感性相关的lncRNAs，并预测接受基于奥沙利铂化疗的CRC患者的预后。
使用来自癌症药物敏感性基因组学（GDSC）的数据筛选与奥沙利铂敏感性相关的lncRNAs。应用四种机器学习算法（LASSO、决策树、随机森林和支持向量机）来鉴定关键lncRNAs。建立了奥沙利铂敏感性预测模型和基于关键lncRNAs的预后模型。使用已发表的数据集和细胞实验来验证预测价值。
根据IC50值，将来自GDSC的总共805个肿瘤细胞系分为奥沙利铂敏感（前1/3）和耐药（后1/3）组，选择两组之间差异表达的113个lncRNAs并纳入四种机器学习算法，鉴定出七个关键lncRNAs。该预测模型对奥沙利铂敏感性表现出良好的预测能力。该预后模型在接受基于奥沙利铂化疗的CRC患者中表现出高性能。在验证分析中，包括C20orf197、UCA1、MIR17HG和MIR22HG在内的四个lncRNAs对奥沙利铂治疗表现出一致的反应。
某些lncRNAs与奥沙利铂敏感性相关，并可预测对奥沙利铂治疗的反应。基于关键lncRNAs建立的预后模型可以预测接受基于奥沙利铂化疗患者的预后。
