A refined therapeutic plan based on the machine-learning prognostic model of liver hepatocellular carcinoma

作者全名："Sun, Xiangcheng; Guo, Peng; Wang, Ning; Shi, Yun; Li, Yan"

作者地址："[Sun, Xiangcheng; Wang, Ning; Shi, Yun; Li, Yan] Sichuan Univ, West China Biopharm Res Inst, West China Hosp, Chengdu 610041, Sichuan, Peoples R China; [Guo, Peng] Chongqing Med Univ, Dept Hepatobiliary & Pancreat Surg, Affiliated Hosp 3, Chongqing 401120, Peoples R China"

通信作者："Li, Y (通讯作者)，Sichuan Univ, West China Biopharm Res Inst, West China Hosp, Chengdu 610041, Sichuan, Peoples R China."

来源：COMPUTERS IN BIOLOGY AND MEDICINE

ESI学科分类：COMPUTER SCIENCE

WOS号：WOS:001152847400001

JCR分区：Q1

影响因子：7.7

年份：2024

卷号：169

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Liver hepatocellular carcinoma; Prognostic risk assessment; Machine learning model; Targeted drug selection; Precision therapy

摘要："To deeply explore new strategy of the individual therapy for the patients with liver hepatocellular carcinoma (LIHC), we observed gene expression profile in patients with LIHC and made a comprehensive analysis of the inflammation-related phenotypes, we detected a set of characteristic genes associated with the biological activities of tumor cells, among which 3 genes and 2 lncRNAs are tagged on the LIHC prognosis. Then we constructed a novel prognostic model by machine learning, called Inf-PR model, and evaluated the drug sensitivity and immune targets by a series of bioinformatics tools. Ten-fold cross-validation testified that the model achieved excellent performance on prediction and classification of prognostic risks, which was not only able to get more reliable prognosis information than the age, gender, grade and stage, but also exceeded those previously reported similar models. Accordingly, drug sensitivity was detected in different prognostic risk groups, the result displayed that 10 FDA-approved small molecular drugs including lovastatin and sorafenib had higher sensitivities and perturbativities in the high-risk group, and other 15 drugs including doxorubicin and lenvatinib had better sensitivities and perturbativities in the low-risk group. Moreover, it suggested the patients with high risk would better combine with immunotherapy than those with low risk. Taken together, this study presents a new individual precision strategy about drug and target selection to treat LIHC based on this evaluation model, which is a powerful supplement for current anti-tumor therapy."

基金机构：Natural Science Foundation of Chongqing [cstc2020jcyj-msxmX0565]

基金资助正文：The work was supported by Natural Science Foundation of Chongqing (No. cstc2020jcyj-msxmX0565) .