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基于 TCGA 数据库基因组学的肝癌伴微血管侵犯的预测和预后模型。

A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics.

机构信息

Faculty of Hepato-Biliary-Pancreatic Surgery, Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853, China.

Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, 225 Changhai Road, Shanghai, 200433, China.

出版信息

BMC Cancer. 2021 Dec 16;21(1):1337. doi: 10.1186/s12885-021-09047-1.

DOI:10.1186/s12885-021-09047-1
PMID:34911488
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8675478/
Abstract

BACKGROUND

Microvascular invasion (MVI) adversely affects postoperative long-term survival outcomes in patients with hepatocellular carcinoma (HCC). There is no study addressing genetic changes in HCC patients with MVI. We first screened differentially expressed genes (DEGs) in patients with and without MVI based on TCGA data, established a prediction model and explored the prognostic value of DEGs for HCC patients with MVI.

METHODS

In this paper, gene expression and clinical data of liver cancer patients were downloaded from the TCGA database. The DEG analysis was conducted using DESeq2. Using the least absolute shrinkage and selection operator, MVI-status-related genes were identified. A Kaplan-Meier survival analysis was performed using these genes. Finally, we validated two genes, HOXD9 and HOXD10, using two sets of HCC tissue microarrays from 260 patients.

RESULTS

Twenty-three MVI-status-related key genes were identified. Based on the key genes, we built a classification model using random forest and time-dependent receiver operating characteristic (ROC), which reached 0.814. Then, we performed a survival analysis and found ten genes had a significant difference in survival time. Simultaneously, using two sets of 260 patients' HCC tissue microarrays, we validated two key genes, HOXD9 and HOXD10. Our study indicated that HOXD9 and HOXD10 were overexpressed in HCC patients with MVI compared with patients without MVI, and patients with MVI with HOXD9 and 10 overexpression had a poorer prognosis than patients with MVI with low expression of HOXD9 and 10.

CONCLUSION

We established an accurate TCGA database-based genomics prediction model for preoperative MVI risk and studied the prognostic value of DEGs for HCC patients with MVI. These DEGs that are related to MVI warrant further study regarding the occurrence and development of MVI.

摘要

背景

微血管侵犯(MVI)会对肝细胞癌(HCC)患者的术后长期生存结果产生不利影响。目前尚无研究针对 MVI 患者的基因变化进行探讨。我们首先基于 TCGA 数据筛选出 MVI 患者和无 MVI 患者的差异表达基因(DEG),建立预测模型并探讨 DEG 对 MVI 肝癌患者的预后价值。

方法

本文从 TCGA 数据库下载肝癌患者的基因表达和临床数据。使用 DESeq2 进行 DEG 分析。使用最小绝对收缩和选择算子(LASSO)识别与 MVI 状态相关的基因。使用这些基因进行 Kaplan-Meier 生存分析。最后,我们使用来自 260 名患者的两个 HCC 组织微阵列验证了两个基因,HOXD9 和 HOXD10。

结果

鉴定出 23 个与 MVI 状态相关的关键基因。基于这些关键基因,我们使用随机森林和时间依赖性接收器操作特征(ROC)构建分类模型,达到 0.814。然后,我们进行生存分析,发现十个基因在生存时间上有显著差异。同时,使用来自 260 名患者的两个 HCC 组织微阵列,我们验证了两个关键基因,HOXD9 和 HOXD10。我们的研究表明,与无 MVI 的 HCC 患者相比,MVI 患者中 HOXD9 和 HOXD10 表达上调,HOXD9 和 10 高表达的 MVI 患者的预后比 HOXD9 和 10 低表达的 MVI 患者差。

结论

我们建立了一个基于 TCGA 数据库的准确基因组学预测模型,用于术前 MVI 风险,并研究了 DEG 对 MVI 肝癌患者的预后价值。这些与 MVI 相关的 DEG 值得进一步研究其在 MVI 发生和发展中的作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/d6870d9a2df5/12885_2021_9047_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/daa54e658b79/12885_2021_9047_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/bb92a9a00607/12885_2021_9047_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/3cc2ad0037d6/12885_2021_9047_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/5e347d50d1b5/12885_2021_9047_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/d6870d9a2df5/12885_2021_9047_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/daa54e658b79/12885_2021_9047_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/d22f7ecc257a/12885_2021_9047_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/e0aebcecb130/12885_2021_9047_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/bb92a9a00607/12885_2021_9047_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/3cc2ad0037d6/12885_2021_9047_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/5e347d50d1b5/12885_2021_9047_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90ba/8675478/d6870d9a2df5/12885_2021_9047_Fig7_HTML.jpg

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