Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma

LI Chaoqun; ZHENG Hongliang; HUANG Ping; YANG Huan; BAI Jianying

doi:10.16016/j.2097-0927.202502080

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Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma

Clinical Medicine | 更新时间：2025-07-14

- Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma
- Journal of Army Medical University Vol. 47, Issue 13, Pages: 1536-1547(2025)
- 作者机构：
  
  陆军军医大学（第三军医大学）第二附属医院消化内科，重庆
- 作者简介：
  
  BAI Jianying， E-mail： baijianying_xq@tmmu.edu.cn
- 基金信息：
  
  the Innovative Leading Talents of Chongqing Talent Program(2022CQ010)
- DOI：10.16016/j.2097-0927.202502080
  CLC： R394.3;R730.7;R735.1
- Received：26 February 2025，
  
  Revised：19 May 2025，
  
  Published：15 July 2025
- 稿件说明：
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李超群，郑泓量，黄平，等.基于机器学习的中心体扩增相关基因预测食管鳞状细胞癌的预后［J］.陆军军医大学学报，2025，47（13）：1536-1547.DOI：10.16016/j.2097-0927.202502080.

LI C Q，ZHENG H L，HUANG P，et al.Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma［J］.J Army Med Univ，2025，47（13）：1536-1547.DOI：10.16016/j.2097-0927.202502080.
李超群，郑泓量，黄平，等.基于机器学习的中心体扩增相关基因预测食管鳞状细胞癌的预后［J］.陆军军医大学学报，2025，47（13）：1536-1547.DOI：10.16016/j.2097-0927.202502080. DOI：

LI C Q，ZHENG H L，HUANG P，et al.Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma［J］.J Army Med Univ，2025，47（13）：1536-1547.DOI：10.16016/j.2097-0927.202502080. DOI：

摘要

目的

通过机器学习构建中心体扩增相关基因（centrosome amplification-related genes， CARGs）的预后模型，研究CARGs对食管鳞状细胞癌（esophageal squamous cell carcinoma， ESCC）预后的影响。

方法

从基因本体论（gene ontology， GO）和京都基因与基因组百科全书（Kyoto encyclopedia of genes and genomes， KEGG）数据库中下载CARGs。下载癌症基因组图谱（the Cancer Genome Atlas， TCGA）和基因表达综合数据库（Gene Expression Omnibus， GEO）中的ESCC转录组测序（RNA-sequencing， RNA-seq）数据，作为训练集和验证集。基于单样本基因集富集分析（single-sample gene set enrichment analysis， ssGSEA）和加权基因共表达网络（weighted gene co-expression network analysis， WGCNA）筛选CARGs。使用集成了12种机器学习算法构建CARGs的预后模型，采用单因素和多因素Cox回归分析判断模型是否可以作为独立预后因素。从陆军军医大学第二附属医院消化内科收集15对ESCC和癌旁组织样本，采用RT-qPCR和免疫组化验证基因在组织样本中的表达。

结果

构建了包含9个CARGs的预后模型，其中基因

DENR

、

TRIP13

、

BRCA2

、

TTF2

、

TCFL5

和

NUP188

经实验验证其mRNA表达水平高于正常组织（

0.05），DENR、TRIP13、TTF2和TCFL5的蛋白表达水平高于正常组织。

结论

DENR

、

TRIP13

、

TTF2

和TCFL5在ESCC中表达水平升高，与临床不良预后密切相关，提示这些基因在ESCC发生发展中可能发挥作用

。

Abstract

Objective‍　‍To construct a prognostic model of centrosome amplification-related genes (CARGs) by machine learni

ng and evaluate its prediction performance for the prognosis of esophageal squamous cell carcinoma (ESCC)

Methods‍　‍CARGs were obtained from Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) datasets. The RNA-sequencing (RNA-seq) transcriptome datasets of ESCC were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO)

and assigned into training and validation sets

respectively. Subsequently

single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were employed to screen CARGs. A prognostic model of CARGs was constructed by incorporating 12 machine learning algorithms

and univariate and multivariate Cox regression analyses were applied to evaluate whether the 12 models as an independent prognostic factor or not. Eventually

15 paired ESCC and adjacent non-tumor tissue samples were collected from the Department of Gastroenterology of the Second Affiliated Hospital of Army Medical University

and real-time quantitative PCR (RT-qPCR) and immunohistochemistry staining were performed to detect the expression of these genes ESCC samples. Results‍　‍Our 9-CARGs prediction model for ESCC prognosis was constructed. RT-qPCR confirmed that the mRNA expression levels of

DENR

TRIP13

BRCA2

TTF2

TCFL5

and

NUP188

were significantly higher in ESCC tissues than normal tissues (

0.05)

and the protein levels of DENR

TRIP13

TTF2 and TCFL5 were also elevated when compared to normal tissues. Conclusion‍　‍

DENR

TRIP13

TTF2

and

TCFL5

are highly expressed and closely associated with poor prognosis of ESCC

suggesting their potential roles in the pathogenesis and progression of this malignancy.

关键词

Keywords

references

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