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一种用于预测经动脉化疗栓塞术(TACE)反应的转录组生物标志物与肝细胞癌的肿瘤微环境和放射组学特征相关。

A Transcriptomic Biomarker for Predicting the Response to TACE Correlates with the Tumor Microenvironment and Radiomics Features in Hepatocellular Carcinoma.

作者信息

Wang Chendong, Leng Bin, You Ran, Yu Zeyu, Lu Ya, Diao Lingfeng, Jiang Hao, Cheng Yuan, Yin Guowen, Xu Qingyu

机构信息

Department of Interventional Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, People's Republic of China.

Department of Medical Oncology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People's Republic of China.

出版信息

J Hepatocell Carcinoma. 2024 Nov 25;11:2321-2337. doi: 10.2147/JHC.S480540. eCollection 2024.

Abstract

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.

摘要

目的

肝细胞癌(HCC)患者对经动脉化疗栓塞术(TACE)的反应存在个体差异。本研究旨在确定一种预测HCC患者TACE反应的生物标志物,并研究其与肿瘤微环境及TACE术前放射组学特征的相关性。

患者与方法

从基因表达综合数据库(GEO)获取GSE104580数据。采用差异表达基因分析和机器学习算法来识别用于构建TACE失败特征(TFS)的基因。然后为癌症基因组图谱(TCGA)队列中的HCC患者计算TFS评分。从癌症影像存档(TCIA)获取图像后,进行肿瘤标记和放射组学特征提取,生成Rad评分模型。对TFS评分与Rad评分进行相关性分析。采用CIBERSORT、单样本基因集富集分析(ssGSEA)和肿瘤微环境(TME)分析来探讨不同风险组之间免疫格局的差异。比较不同组之间的免疫治疗反应。

结果

选择乙醇脱氢酶1C(ADH1C)、CXC趋化因子配体11(CXCL11)、内皮细胞黏附分子(EMCN)、富含半胱氨酸的酸性分泌蛋白1(SPARCL1)和LIN28B并纳入TFS,其在预测TACE反应方面表现出令人满意的性能。TFS评分高的组患者总生存期(OS)比TFS评分低的组患者差。利用六个放射组学特征构建了Rad评分模型,且Rad评分与核心基因表达及TFS评分显著相关。高TFS组的特征还包括免疫抑制性肿瘤微环境,并且对程序性死亡受体1(PD-1)和细胞毒性T淋巴细胞相关蛋白4(CTLA-4)检查点抑制剂的免疫治疗反应不佳。

结论

本研究建立了一种转录组生物标志物,用于预测TACE的疗效,该生物标志物与治疗前影像学的放射组学特征、肿瘤免疫微环境特征以及HCC患者的免疫治疗和靶向治疗疗效相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/988f/11606151/f9355e235a6a/JHC-11-2321-g0001.jpg

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