Oxaliplatin related lncRNAs prognostic models predict the prognosis of patients given oxaliplatin-based chemotherapy

Cancer Cell International

Research

Rong-e Lei¹  Qing-nan Zhou²  Xian-wen Guo²  Yun-xiao Liang²  Bang-li Hu³  Si-qi Li³ 

[1] Department of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, 530021, Nanning, Guangxi, China;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, 530021, Nanning, Guangxi, China;Department of Research, Guangxi Medical University Cancer Hospital, No. 71 Hedi Road, 530021, Nanning, Guangxi, China;

关键词: Cancer;    Oxaliplatin sensitivity;    lncRNAs;    Machine learning algorithm;   

DOI  :  10.1186/s12935-023-02945-3

 received in 2023-03-29, accepted in 2023-05-11,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundOxaliplatin-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.MethodsData 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.ResultsA 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.ConclusionCertain 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.

【 授权许可】

CC BY   
© The Author(s) 2023

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