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CCL26作为肝细胞癌的预后生物标志物:整合生物信息学分析、临床验证和影像组学评分

CCL26 as a prognostic biomarker in hepatocellular carcinoma: integrating bioinformatics analysis, clinical validation, and radiomics score.

作者信息

Yan Junjun, Liao Qiangming, Xie Yong, Chen Sihai

机构信息

Department of Gastroenterology, Jiangxi Provincial Key Laboratory of Digestive Diseases, Jiangxi Clinical Research Center for Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330000, China.

Department of Gastroenterology, Jiujiang City Key Laboratory of Cell Therapy, The First Hospital of Jiujiang City, Jiujiang, 332000, China.

出版信息

Discov Oncol. 2025 Apr 9;16(1):502. doi: 10.1007/s12672-025-02280-1.

DOI:10.1007/s12672-025-02280-1
PMID:40205283
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11981991/
Abstract

BACKGROUND

CCL26 has been identified as a potential prognostic biomarker in hepatocellular carcinoma (HCC). This study aimed to assess the prognostic significance of CCL26 and develop a radiomics score (Rad-score) for predicting outcomes in HCC patients.

METHODS

Data from 316 HCC patients, including genomic information, computed tomography (CT) images, and clinicopathological data, were analyzed. The prognostic value of CCL26 was evaluated in 295 TCGA patients using Kaplan-Meier and Cox regression analyses, and validated in 21 patients from Jiujiang No. 1 People's Hospital. Gene set variation and immune cell infiltration analyses were conducted to elucidate the biological functions of CCL26. Radiomic models for predicting CCL26 expression were constructed using CT images and genomic data from 34 TCGA patients. Radiomic features were extracted from tumor regions and screened using maximum relevance minimum redundancy (mRMR) and recursive feature elimination (RFE). Two Rad-scores were generated via logistic regression and validated using internal fivefold cross-validation. A prognostic nomogram incorporating the optimal Rad-score, gender, and hepatic inflammation was developed using Cox proportional hazards regression.

RESULTS

Elevated CCL26 levels correlated with poor prognosis, as confirmed by immunohistochemistry. The optimal Rad-score, combined with gender and hepatic inflammation, accurately predicted overall survival (OS), with areas under the receiver operating characteristic curve (AUCs) of 0.819, 0.902, and 0.982 for 24-, 36-, and 48 month survival, respectively. Calibration curves and decision curve analysis (DCA) demonstrated the accuracy and clinical utility of the model.

CONCLUSIONS

CCL26 serves as a significant prognostic biomarker in HCC. The developed Rad-score provides an effective, non-invasive tool for predicting patient outcomes and enhancing clinical decision-making. This study not only highlights the prognostic role of CCL26 but also offers a novel approach for evaluating HCC patient prognosis through radiomics.

摘要

背景

CCL26已被确定为肝细胞癌(HCC)潜在的预后生物标志物。本研究旨在评估CCL26的预后意义,并开发一种放射组学评分(Rad评分)以预测HCC患者的预后。

方法

分析了316例HCC患者的数据,包括基因组信息、计算机断层扫描(CT)图像和临床病理数据。使用Kaplan-Meier法和Cox回归分析评估了295例TCGA患者中CCL26的预后价值,并在九江市第一人民医院的21例患者中进行了验证。进行基因集变异和免疫细胞浸润分析以阐明CCL26的生物学功能。使用来自34例TCGA患者的CT图像和基因组数据构建预测CCL26表达的放射组学模型。从肿瘤区域提取放射组学特征,并使用最大相关最小冗余(mRMR)和递归特征消除(RFE)进行筛选。通过逻辑回归生成两个Rad评分,并使用内部五折交叉验证进行验证。使用Cox比例风险回归开发了一个包含最佳Rad评分、性别和肝脏炎症的预后列线图。

结果

免疫组织化学证实CCL26水平升高与预后不良相关。最佳Rad评分结合性别和肝脏炎症可准确预测总生存期(OS),24个月、36个月和48个月生存期的受试者工作特征曲线下面积(AUC)分别为0.819、0.902和0.982。校准曲线和决策曲线分析(DCA)证明了该模型的准确性和临床实用性。

结论

CCL26是HCC中一种重要的预后生物标志物。开发的Rad评分为预测患者预后和加强临床决策提供了一种有效的非侵入性工具。本研究不仅突出了CCL26的预后作用,还提供了一种通过放射组学评估HCC患者预后的新方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55de/11981991/10b727d6fe40/12672_2025_2280_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55de/11981991/28e77c9cfb4a/12672_2025_2280_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55de/11981991/6bcd731903fc/12672_2025_2280_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55de/11981991/f0c3a109339a/12672_2025_2280_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55de/11981991/10b727d6fe40/12672_2025_2280_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55de/11981991/28e77c9cfb4a/12672_2025_2280_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55de/11981991/6bcd731903fc/12672_2025_2280_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55de/11981991/f0c3a109339a/12672_2025_2280_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55de/11981991/10b727d6fe40/12672_2025_2280_Fig4_HTML.jpg

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