预测肝细胞癌TACE应答的转录组学生物标志物与肿瘤微环境和放射组学特征相关。

IF 4.2 3区 医学 Q2 ONCOLOGY
Journal of Hepatocellular Carcinoma Pub Date : 2024-11-25 eCollection Date: 2024-01-01 DOI:10.2147/JHC.S480540
Chendong Wang, Bin Leng, Ran You, Zeyu Yu, Ya Lu, Lingfeng Diao, Hao Jiang, Yuan Cheng, Guowen Yin, Qingyu Xu
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引用次数: 0

摘要

目的:肝细胞癌(HCC)患者对经动脉化疗栓塞(TACE)的反应不同。本研究旨在确定一种预测HCC患者TACE反应的生物标志物,并探讨其与肿瘤微环境和TACE前放射组学特征的相关性。患者和方法:GSE104580数据来自基因表达Omnibus (GEO)数据库。差异表达基因分析和机器学习算法用于识别构建TACE故障特征(TFS)的基因。然后计算癌症基因组图谱(TCGA)队列中HCC患者的TFS评分。从The Cancer Imaging Archive (TCIA)获取图像,进行肿瘤标记和放射组学特征提取后,生成Rad-score模型。对TFS评分与rad评分进行相关性分析。采用CIBERSORT、ssGSEA和TME分析,探讨不同风险组间免疫景观的差异。比较各组免疫治疗效果。结果:ADH1C、CXCL11、EMCN、SPARCL1和LIN28B入选TFS,预测TACE疗效满意。高TFS评分组患者的总生存期(OS)低于低TFS评分组。利用6个放射组学特征构建Rad-score模型,Rad-score与hub基因表达和TFS评分显著相关。高tfs组还具有免疫抑制肿瘤微环境的特征,并且对PD-1和CTLA-4检查点抑制剂的免疫治疗表现出不利的反应。结论:本研究建立了一种预测TACE疗效的转录组学生物标志物,该标志物与HCC患者的前处理影像学、肿瘤免疫微环境特征、免疫治疗和靶向治疗的疗效等放射组学特征相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Transcriptomic Biomarker for Predicting the Response to TACE Correlates with the Tumor Microenvironment and Radiomics Features in Hepatocellular Carcinoma.

Purpose: The response to transarterial chemoembolization (TACE) varies among individuals with hepatocellular carcinoma (HCC). This study aimed to identify a biomarker for predicting TACE response in HCC patients and to investigate its correlations with the tumor microenvironment and pre-TACE radiomics features.

Patients and methods: GSE104580 data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis and machine learning algorithms were used to identify genes for constructing the TACE failure signature (TFS). TFS scores were then calculated for HCC patients in The Cancer Genome Atlas (TCGA) cohort. After obtaining images from The Cancer Imaging Archive (TCIA), tumor labeling and radiomics feature extraction, the Rad-score model was generated. Correlation analysis was performed between the TFS score and the Rad-score. CIBERSORT, ssGSEA and TME analysis were performed to explore differences in the immune landscape among distinct risk groups. The immunotherapy response was compared between different groups.

Results: ADH1C, CXCL11, EMCN, SPARCL1 and LIN28B were selected and incorporated into the TFS, which demonstrated satisfactory performance in predicting TACE response. Patients in the high TFS score group had poorer overall survival (OS) than those in the low TFS score group. The Rad-score model was constructed using six radiomics features, and the Rad-score was significantly correlated with hub gene expression and the TFS score. The high-TFS group was also characterized by an immunosuppressive tumor microenvironment and exhibited unfavorable responses to immunotherapy with PD-1 and CTLA-4 checkpoint inhibitors.

Conclusion: This study established a transcriptomic biomarker for predicting the efficacy of TACE that correlates with radiomics features on pretreatment imaging, tumor immune microenvironment characteristics, and the efficacy of immunotherapy and targeted therapy in HCC patients.

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来源期刊
CiteScore
0.50
自引率
2.40%
发文量
108
审稿时长
16 weeks
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