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机器学习和多组学分析的融合为筛选与衰老相关的基因，并预测肝细胞癌的预后和免疫治疗疗效提供了一种强大的方法。

The integration of machine learning and multi-omics analysis provides a powerful approach to screen aging-related genes and predict prognosis and immunotherapy efficacy in hepatocellular carcinoma.

机构信息

Department of Pharmacy, Huzhou Maternity and Child Health Care Hospital, Huzhou, China.

Department of Stomatology, First Affiliated Hospital of Huzhou University, Huzhou, China.

出版信息

Aging (Albany NY). 2023 Jul 28;15(14):6848-6864. doi: 10.18632/aging.204876.

DOI:10.18632/aging.204876
PMID:37517087
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10415564/
Abstract

BACKGROUND

Hepatocellular carcinoma (HCC) is a highly malignant tumor with high incidence and mortality rates. Aging-related genes are closely related to the occurrence and development of cancer. Therefore, it is of great significance to evaluate the prognosis of HCC patients by constructing a model based on aging-related genes.

METHOD

Non-negative matrix factorization (NMF) clustering analysis was used to cluster the samples. The correlation between the risk score and immune cells, immune checkpoints, and Mismatch Repair (MMR) was evaluated through Spearman correlation test. Real Time Quantitative PCR (RT-qPCR) and immunohistochemistry were used to validate the expression levels of key genes in tissue and cells for the constructed model.

RESULT

By performing NMF clustering, we were able to effectively group the liver cancer samples into two distinct clusters. Considering the potential correlation between aging-related genes and the prognosis of liver cancer patients, we used aging-related genes to construct a prognostic model. Spearman correlation analysis showed that the model risk score was closely related to MMR and immune checkpoint expression. Drug sensitivity analysis also provided guidance for the clinical use of chemotherapy drugs. RT-qPCR showed that TFDP1, NDRG1, and FXR1 were expressed at higher levels in different liver cancer cell lines compared to normal liver cells.

CONCLUSION

In summary, we have developed an aging-related model to predict the prognosis of hepatocellular carcinoma and guide clinical drug treatment for different patients.

摘要

背景

肝细胞癌（HCC）是一种高度恶性肿瘤，具有较高的发病率和死亡率。与衰老相关的基因与癌症的发生和发展密切相关。因此，基于衰老相关基因构建模型来评估 HCC 患者的预后具有重要意义。

方法

采用非负矩阵分解（NMF）聚类分析对样本进行聚类。通过 Spearman 相关检验评估风险评分与免疫细胞、免疫检查点和错配修复（MMR）的相关性。采用实时定量 PCR（RT-qPCR）和免疫组织化学法验证组织和细胞中关键基因构建模型的表达水平。

结果

通过进行 NMF 聚类，我们能够有效地将肝癌样本分为两个不同的聚类。考虑到与衰老相关的基因与肝癌患者预后之间的潜在相关性，我们使用与衰老相关的基因构建了一个预后模型。Spearman 相关分析表明，模型风险评分与 MMR 和免疫检查点表达密切相关。药物敏感性分析也为化疗药物的临床应用提供了指导。RT-qPCR 显示，在不同的肝癌细胞系中，TFDP1、NDRG1 和 FXR1 的表达水平均高于正常肝细胞。

结论

综上所述，我们开发了一种与衰老相关的模型来预测肝细胞癌的预后，并指导不同患者的临床药物治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e30/10415564/378bf77aceef/aging-15-204876-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e30/10415564/378bf77aceef/aging-15-204876-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e30/10415564/b155c063ac92/aging-15-204876-g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e30/10415564/cef874c2dbb5/aging-15-204876-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e30/10415564/378bf77aceef/aging-15-204876-g009.jpg

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