Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma
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 Ge...
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Editorial Office of Journal of Army Medical University
2025-07-01
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| Series: | 陆军军医大学学报 |
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| Online Access: | https://aammt.tmmu.edu.cn/html/202502080.html |
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| author | LI Chaoqun ZHENG Hongliang HUANG Ping |
| author_facet | LI Chaoqun ZHENG Hongliang HUANG Ping |
| author_sort | LI Chaoqun |
| collection | DOAJ |
| description | 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.
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| format | Article |
| id | doaj-art-b63549c4e035407cb6e686568688aa98 |
| institution | Kabale University |
| issn | 2097-0927 |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | Editorial Office of Journal of Army Medical University |
| record_format | Article |
| series | 陆军军医大学学报 |
| spelling | doaj-art-b63549c4e035407cb6e686568688aa982025-08-20T03:49:59ZzhoEditorial Office of Journal of Army Medical University陆军军医大学学报2097-09272025-07-0147131536154710.16016/j.2097-0927.202502080Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinomaLI Chaoqun0ZHENG Hongliang1HUANG Ping2Department of Gastroenterology， Second Affiliated Hospital， Army Medical University （Third Military Medical University）， Chongqing， ChinaDepartment of Gastroenterology， Second Affiliated Hospital， Army Medical University （Third Military Medical University）， Chongqing， ChinaDepartment of Gastroenterology， Second Affiliated Hospital， Army Medical University （Third Military Medical University）， Chongqing， ChinaObjective 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. https://aammt.tmmu.edu.cn/html/202502080.htmlmachine learningesophageal squamous cell carcinomacentrosome amplification-related genesprognosis |
| spellingShingle | LI Chaoqun ZHENG Hongliang HUANG Ping Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma 陆军军医大学学报 machine learning esophageal squamous cell carcinoma centrosome amplification-related genes prognosis |
| title | Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma |
| title_full | Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma |
| title_fullStr | Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma |
| title_full_unstemmed | Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma |
| title_short | Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma |
| title_sort | construction of machine learning based prognostic model of centrosome amplification related genes for esophageal squamous cell carcinoma |
| topic | machine learning esophageal squamous cell carcinoma centrosome amplification-related genes prognosis |
| url | https://aammt.tmmu.edu.cn/html/202502080.html |
| work_keys_str_mv | AT lichaoqun constructionofmachinelearningbasedprognosticmodelofcentrosomeamplificationrelatedgenesforesophagealsquamouscellcarcinoma AT zhenghongliang constructionofmachinelearningbasedprognosticmodelofcentrosomeamplificationrelatedgenesforesophagealsquamouscellcarcinoma AT huangping constructionofmachinelearningbasedprognosticmodelofcentrosomeamplificationrelatedgenesforesophagealsquamouscellcarcinoma |