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Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma
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Chaoqun LI, Hongliang ZHENG, Ping HUANG, Huan YANG, Jianying BAI
Journal of Army Medical University | 2025, 47(13) : 1536 - 1547
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Journal of Army Medical University | 2025, 47(13): 1536-1547
• Clinical Medicine •
Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma
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Chaoqun LI, Hongliang ZHENG, Ping HUANG, Huan YANG, Jianying BAI
Affiliations
  • 1Department of Gastroenterology,Second Affiliated Hospital,Army Medical University (Third Military Medical University),Chongqing,China
Published: 2025-07-15 doi: 10.16016/j.2097-0927.202502080
Outline
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Objective‍

To construct a prognostic model of centrosome amplification-related genes (CARGs) by machine learning 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 (P<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.

machine learning  /  esophageal squamous cell carcinoma  /  centrosome amplification-related genes  /  prognosis
李超群, 郑泓量, 黄平, 杨欢, 柏健鹰. 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. 陆军军医大学学报, 2025 , 47 (13) : 1536 -1547 . DOI: 10.16016/j.2097-0927.202502080
Chaoqun LI, Hongliang ZHENG, Ping HUANG, Huan YANG, Jianying BAI. Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma[J]. Journal of Army Medical University, 2025 , 47 (13) : 1536 -1547 . DOI: 10.16016/j.2097-0927.202502080
Year 2025 volume 47 Issue 13
16
5
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Article Info
doi: 10.16016/j.2097-0927.202502080
  • Receive Date:2025-02-26
  • Online Date:2026-10-28
  • Published:2025-07-15
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History
  • Received:2025-02-26
  • Revised:2025-05-19
Affiliations
    1Department of Gastroenterology,Second Affiliated Hospital,Army Medical University (Third Military Medical University),Chongqing,China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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