基于机器学习的 4 个长链非编码 RNA 标志物联合 AFP 和 TNM 分期预测肝细胞癌早期复发。
Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma.
机构信息
Shanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China.
Collaborative Innovation Center for Biomedicines, Shanghai University of Medicine and Health Sciences, Shanghai, China.
出版信息
BMC Genomics. 2023 Feb 27;24(1):89. doi: 10.1186/s12864-023-09194-8.
BACKGROUND
Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence.
METHODS
Data of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted.
RESULTS
A 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC.
CONCLUSION
Combined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.
背景
近 70%的肝细胞癌（HCC）复发发生在术后 2 年内的早期复发。长链非编码 RNA（lncRNA）广泛参与 HCC 的进展，并可作为 HCC 预后的生物标志物。本研究旨在构建基于 lncRNA 的signature 预测 HCC 早期复发。
方法
从癌症基因组图谱肝脏肝癌（TCGA-LIHC）数据库中获取 RNA 表达及相关临床信息数据。通过三种 DEG 方法和两种生存分析方法确定与复发相关的差异表达 lncRNA（DELncs）。通过三种机器学习方法和多变量 Cox 分析选择参与 signature 的 DELncs。此外，还在来自外部来源的 HCC 患者队列中验证了 signature。为了深入了解该 signature 的生物学功能，进行了基因集富集分析、免疫浸润分析以及免疫和药物治疗预测分析。
结果
构建了一个由 AC108463.1、AF131217.1、CMB9-22P13.1、TMCC1-AS1 组成的 4-lncRNA signature。高危组患者的早期复发率明显高于低危组患者。signature 与 AFP 和 TNM 的联合进一步提高了 HCC 早期复发的预测性能。与 HCC 发病机制相关的几个分子途径和基因集在高危组中得到富集。富含低危 HCC 患者的抗肿瘤免疫细胞，如活化 B 细胞、1 型 T 辅助细胞、自然杀伤细胞和有效记忆 CD8 T 细胞。低危和高危 HCC 患者对各种抗肿瘤药物的敏感性不同。最后，在 HCC 患者的外部队列中验证了该 signature 的预测性能。
结论
该 4-lncRNA signature 结合 TNM 和 AFP，对 HCC 早期复发具有优异的预测能力。
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