Identification of Microvascular Invasion-Related Biomarkers for Personalized Treatment of Hepatocellular Carcinoma.

IF 3.5 4区医学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current medicinal chemistry Pub Date : 2025-10-01 DOI:10.2174/0109298673421660250922113033

Wei Xiang, Xue Liu, Tingting Bao, Fei Yang, Jintao Huang, Jian Shen, Xiaoli Zhu

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引用次数: 0

Abstract

Introduction: Hepatocellular Carcinoma (HCC) exhibits high recurrence rates, particularly when accompanied by Microvascular Invasion (MVI). We identified MVI-related biomarkers and established a prognostic model for personalized HCC treatment.

Methods: Data were downloaded from The Cancer Genome Atlas (TCGA) and HCCDB databases. Key radiomics features were identified using the support vector machine-recursive feature elimination (SVM-RFE) algorithm, and differential expression analysis was performed with DESeq2. This was followed by functional enrichment analysis using the clusterProfiler package. Through univariate and Lasso regression analyses, we constructed a robust RiskScore model to effectively stratify HCC patients into distinct risk groups based on the median RiskScore value. The model prediction performance was evaluated using ROC curves and Kaplan-Meier (KM) analysis. We used the CIBERSORT algorithm to characterize immune cell infiltration patterns and conducted GSEA to identify differentially activated pathways between the risk groups.

Results: Radiomic analysis revealed four significant features strongly associated with MVI, enabling the construction of a nomogram model with robust classification performance (AUC = 0.742). Subsequent analysis identified 241 overlapping MVI-related Differentially Expressed Genes (DEGs) enriched in critical tumor proliferation and invasion pathways. A 10-gene RiskScore model was developed, demonstrating excellent prognostic discrimination in training and validation cohorts. CIBERSORT analysis revealed significant correlations between specific immune cell infiltration and the 10 genes. GSEA analysis showed significant enrichment of cell cycle regulation pathways in the high-risk group, suggesting their important role in MVI.

Discussion: The RiskScore was established using MVI-related features for prognosis assessment in HCC.

Conclusion: Our findings provided novel biomarkers and a theoretical basis for the early diagnosis and personalized treatment of HCC.

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肝细胞癌个体化治疗中微血管侵袭相关生物标志物的鉴定

肝细胞癌（HCC）表现出高复发率，特别是当伴有微血管侵犯（MVI）时。我们确定了mvi相关的生物标志物，并建立了个体化HCC治疗的预后模型。方法：从癌症基因组图谱（TCGA）和HCCDB数据库中下载数据。使用支持向量机递归特征消除（SVM-RFE）算法识别关键放射组学特征，并使用DESeq2进行差异表达分析。接下来是使用clusterProfiler包进行功能丰富分析。通过单变量和Lasso回归分析，我们构建了一个稳健的RiskScore模型，根据中位RiskScore值有效地将HCC患者划分为不同的风险组。采用ROC曲线和Kaplan-Meier （KM）分析评价模型的预测性能。我们使用CIBERSORT算法来表征免疫细胞浸润模式，并进行GSEA来识别风险组之间的差异激活途径。结果：放射组学分析揭示了与MVI密切相关的四个显著特征，能够构建具有稳健分类性能的nomogram模型（AUC = 0.742）。随后的分析确定了241个重叠的mvi相关差异表达基因（DEGs），这些基因在关键的肿瘤增殖和侵袭途径中富集。开发了一个10基因风险评分模型，在训练和验证队列中显示出良好的预后区分。CIBERSORT分析显示特异性免疫细胞浸润与10个基因之间存在显著相关性。GSEA分析显示高危组细胞周期调控通路显著富集，提示其在MVI中起重要作用。讨论：RiskScore是利用mvi相关特征来评估HCC的预后。结论：我们的发现为HCC的早期诊断和个性化治疗提供了新的生物标志物和理论基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Current medicinal chemistry 医学-生化与分子生物学

CiteScore

8.60

自引率

2.40%

发文量

468

审稿时长

3 months

期刊介绍： Aims & Scope Current Medicinal Chemistry covers all the latest and outstanding developments in medicinal chemistry and rational drug design. Each issue contains a series of timely in-depth reviews and guest edited thematic issues written by leaders in the field covering a range of the current topics in medicinal chemistry. The journal also publishes reviews on recent patents. Current Medicinal Chemistry is an essential journal for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important developments.