基于生物信息学分析筛选肝癌核心基因并构建诊断预后模型

陈伊伊; 荣媛

doi:10.14188/j.1671-8852.2024.0045

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基于生物信息学分析筛选肝癌核心基因并构建诊断预后模型

临床医学研究 | 更新时间：2026-11-05

- 基于生物信息学分析筛选肝癌核心基因并构建诊断预后模型
- Unveiling hepatocellular carcinoma through bioinformatics: Screening core genes and constructing diagnosticand prognostic models
- 武汉大学学报（医学版） 2025年46卷第5期页码：644-654
- 作者机构：
  
  武汉大学中南医院检验科湖北武汉 430071
- 作者简介：
  
  陈伊伊，女，1996—，医学硕士生，主要从事肝癌生物标志物研究,E‑mail：2021283030211@whu.edu.cn
  荣媛，女，1980—，医学博士，副教授，硕士生导师，主要从事肿瘤免疫学研究,E‑mail：whushine@163.com
- 基金信息：
- DOI：10.14188/j.1671-8852.2024.0045
  中图分类号： R735.7
- 收稿：2024-01-18，
  
  纸质出版：2025-05-25
- 稿件说明：
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陈伊伊,荣媛.基于生物信息学分析筛选肝癌核心基因并构建诊断预后模型[J].武汉大学学报（医学版）,2025,46(05):644-654.

CHEN Yiyi,RONG Yuan.Unveiling hepatocellular carcinoma through bioinformatics: Screening core genes and constructing diagnosticand prognostic models[J].Medical Journal of Wuhan University,2025,46(05):644-654.
陈伊伊,荣媛.基于生物信息学分析筛选肝癌核心基因并构建诊断预后模型[J].武汉大学学报（医学版）,2025,46(05):644-654. DOI： 10.14188/j.1671-8852.2024.0045.

CHEN Yiyi,RONG Yuan.Unveiling hepatocellular carcinoma through bioinformatics: Screening core genes and constructing diagnosticand prognostic models[J].Medical Journal of Wuhan University,2025,46(05):644-654. DOI： 10.14188/j.1671-8852.2024.0045.

摘要

目的

通过生物信息学多维分析，筛选肝细胞癌（HCC）特异性生物标志物，探索其诊断及预后预测价值。

方法

从GEO数据库中下载HCC数据集GSE136247和TCGA数据库中肝癌相关基因表达数据。利用R包edgeR分别对两个队列数据进行差异表达基因（DEGs）的分析并取交集。随后基于GO、KEGG、Reactive和WikiPathway数据库对DEGs进行富集和功能注释。利用STRING数据库构建基于DEGs的蛋白质相互作用（PPI）网络，并通过Cytoscape软件获取PPI网络中的关键基因。利用GEPIA数据库、HPA数据库及GEO额外数据队列验证关键基因表达，使用逐步回归法筛选关键基因，构建肝癌预后Cox模型，并通过二元逻辑回归构建诊断模型。通过R包rms绘制Nomogram图，R包IOBR的Timer算法探索核心基因与免疫浸润的关联；R包clusterProfiler进行核心基因的GSEA分析；Spearman相关性分析核心基因与免疫检查点基因的关联；R包oncoPredict探索核心基因的药物相关性。

结果

在肝细胞癌和癌旁组织中共识别出432个DEGs和15个关键基因，通过一系列生物信息学分析方法筛选出了两个高风险分子

CCT3

和

KPNA2

，并构建双分子联合Cox模型。双分子联合Cox模型分数和肿瘤分期是独立的临床风险评估指标，该风险模型用于预测患者1、3、5、7年预后具有价值。同时，Nomogram图对患者预后预测与实际吻合良好。此外，基于双分子逻辑回归诊断模型可有效区分癌与非癌（

AUC

0.95）。免疫相关性分析表明，

CCT3

和

KPNA2

表达与免疫细胞浸润水平有显著相关性。GSEA分析显示，

CCT3

和

KPNA2

富集通路主要在E2F‑TARGETS和G2M‑CHECKPOINT；免疫检查点相关性分析显示，

CCT3

和

KPNA2

都与CD276相关性最高；药物相关性分析发现

CCT3

和

KPNA2

都对西地尼布敏感。

结论

该研究成功筛选出HCC特异性生物标志物

CCT3

和

KPNA2

，基于双分子的逻辑回归诊断模型、Cox风险模型和Nomogram图，有效用于HCC诊断及预后预测，

CCT3

和

KPNA2

可作为潜在的治疗靶点。

Abstract

Objective

To screen the specific biomarkers of hepatocellular carcinoma (HCC) through multi‑dimensional analysis of bioinformatics and explore their diagnostic and prognostic value.

Methods

The HCC dataset GSE136247 from the GEO database and the gene expression data related to liver cancer from the TCGA database were downloaded. The R package edgeR was used to analyze the differential expression genes (DEGs) of the data from the two cohorts separately and the intersection was taken. DEGs were subsequently enriched and functionally annotated based on GO

KEGG

Reactive

and WikiPathway databases. The protein⁃protein interaction (PPI) network based on DEGs was constructed by the STRING database

and the key genes in the PPI network were obtained by Cytoscape software. GEPIA database

HPA database

and the GEO additional data cohort were used to verify the expression of key genes

and the stepwise regression method was used to screen the key genes to construct the Cox model of liver cancer prognosis

and binary logistic regression was used to construct the diagnostic model. The Nomogram was drawn by the R package rms

and the Timer algorithm of the R package IOBR was used to explore the association between core genes and immune infiltration. The R package clusterProfiler was used for the GSEA analysis of core genes. Spearman correlation analysis was used to analyze the association between core genes and immune checkpoint genes. The R package oncoPredict explores the drug associations of core genes.

Results

A total of 432 DEGs and 15 key genes were identified in HCC and adjacent tissues. Two high‑risk molecules

CCT3

and

KPNA2

were screened out by a series of bioinformatics analysis methods

and a bi‑molecular Cox model was constructed. The bimolecular Cox model score and tumor stage are independent clinical risk assessment indicators

and the risk model has value in predicting the 1‑

3‑

5‑

and 7‑year prognosis of patients. At the same time

the prognosis predicted by the Nomogram was in good agreement with the actual prognosis. In addition

the bimolecular logistic regression model could effectively distinguish cancer from non‑cancer (

AUC

0.95). Immune correlation analysis showed that the expression of

CCT3

and

KPNA2

was significantly correlated with the level of immune cell infiltration. GSEA analysis showed that

CCT3

KPNA2

were mainly enriched in E2F‑TARGETS and G2M‑checkpoints. Immune checkpoint correlation analysis showed that

CCT3

and

KPNA2

were the most correlated with CD276.

Conclusion

CCT3

and

KPNA2

are HCC‑specific biomarkers successfully screened in this study. The bi‑molecular logistic regression diagnostic model

Cox risk model

and Nomogram can be effectively used for HCC diagnosis and prognosis prediction.

关键词

Keywords

references

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