Integrated machine learning constructed a circadian-rhythm-related model to assess clinical outcomes and therapeutic advantages in hepatocellular carcinoma.

IF 1.5 4区 医学 Q4 ONCOLOGY
Translational cancer research Pub Date : 2025-03-30 Epub Date: 2025-03-18 DOI:10.21037/tcr-24-1155
Ziyuan Xu, Wei Huang, Xi Zou, Shenlin Liu
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引用次数: 0

Abstract

Background: Circadian rhythm (CR) coordinates a variety of internal biological processes with the external daily cycles of light and dark. However, the implications of CR-related regulator in hepatocellular carcinoma (HCC) are quite obscure. Here, we aimed to identify pivotal CR-related markers in HCC for predicting survival and treatment outcomes.

Methods: The prognostic value of CR regulators in HCC was analyzed. Multi-step machine learning feature selection approaches were employed to establish a model. Thereafter, we evaluated its capacity of clinical prediction and treatment guidance.

Results: First, we depicted the prognostic stratification value of CR regulators in HCC. Two CR-related phenotypes were identified, revealing a distinct clinical outcome, biological pathways and drug sensitivity. Subsequently, via four topological approaches and differentially expressed genes (DEGs) from real-world cohorts, we screened out CRY2 as the pivotal CR regulator with significant prognostic value in HCC. We performed the relevant basic assay validation for CRY2. Overexpression of CRY2 inhibited the proliferation and migration abilities of Huh7 and Hep3B cells. Moreover, three machine learning algorithms [random forest (RF), extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO)] were implemented to construct a risk-scoring model named CR predictor, which exhibited clinical benefits and therapeutic advantages for HCC. An online nomogram based on CR predictor was developed for predicting individualized survival (https://lihc.shinyapps.io/CR_predictor/). Finally, Mendelian randomization (MR) was performed. Among model genes in CR predictor, PPARGC1A revealed a significant causal effect on HCC.

Conclusions: We proposed a CR-related risk classifier in HCC, to predict patients' overall survival (OS) and therapeutic response. Targeting CR could be a promising treatment modality against HCC.

综合机器学习构建了一个与昼夜节律相关的模型来评估肝细胞癌的临床结果和治疗优势。
背景:昼夜节律(CR)协调各种内部生物过程与外部昼夜周期的光和暗。然而,cr相关调节因子在肝细胞癌(HCC)中的意义尚不清楚。在这里,我们的目的是确定HCC中预测生存和治疗结果的关键cr相关标志物。方法:分析CR调节剂在HCC中的预后价值。采用多步机器学习特征选择方法建立模型。评估其临床预测和治疗指导能力。结果:首先,我们描述了CR调节剂在HCC中的预后分层价值。鉴定出两种cr相关表型,揭示了不同的临床结果,生物学途径和药物敏感性。随后,通过四种拓扑方法和来自现实世界队列的差异表达基因(DEGs),我们筛选出CRY2作为HCC中具有重要预后价值的关键CR调节因子。我们对CRY2进行了相关的基础实验验证。过表达CRY2抑制了Huh7和Hep3B细胞的增殖和迁移能力。此外,采用三种机器学习算法[随机森林(RF),极端梯度增强(XGBoost)和最小绝对收缩和选择算子(LASSO)]构建了一个名为CR predictor的风险评分模型,该模型显示了HCC的临床益处和治疗优势。开发了基于CR预测器的在线nomogram用于预测个体化生存(https://lihc.shinyapps.io/CR_predictor/)。最后,进行孟德尔随机化(MR)。在CR预测因子的模型基因中,PPARGC1A对HCC有显著的因果影响。结论:我们提出了HCC中cr相关的风险分类,以预测患者的总生存期(OS)和治疗反应。靶向CR可能是一种很有希望的治疗HCC的方式。
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来源期刊
CiteScore
2.10
自引率
0.00%
发文量
252
期刊介绍: Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
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