AccScience Publishing / EJMO / Online First / DOI: 10.36922/EJMO025060024
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ORIGINAL RESEARCH ARTICLE

Predictive value of an eight-mRNA signature in colon adenocarcinoma prognosis

Yuying Yang1† ,  Cuiying Wang2† ,  Hongqian Wei1 ,  Bing Zhou1 ,  Songtao Hou1 ,  Xiaochen Pang1 ,  Wenhai Dong1 ,  Zhongqiu Chai1*
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1 Department of Anal Medicine, Binhai New Area Hospital of Traditional Chinese Medicine, Fourth Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
2 Department of Geriatrics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
†These authors contributed equally to this work.
EJMO, 025060024 https://doi.org/10.36922/EJMO025060024
Received: 5 February 2025 | Revised: 1 April 2025 | Accepted: 8 April 2025 | Published online: 9 May 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Colorectal cancer is a prevalent malignancy, with colon adenocarcinoma as the most common type. Early diagnosis biomarkers and effective risk stratification are crucial for optimal treatment. In this study, gene expression data from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) were analyzed to identify relevant genes for colon adenocarcinoma. These datasets were standardized and subjected to weighted gene co-expression network analysis and differentially expressed gene analysis. Univariate Cox regression and least absolute shrinkage and selection operator Cox regression analyses were performed to generate a risk profile and identify prognosis-related genes. Receiver operating characteristic (ROC) analysis, Kaplan–Meier (KM) curve, and Cox analyses validated the risk signature. Immune cell infiltration patterns and immunological activities in high- and low-risk groups were assessed using single-sample gene set enrichment analysis (ssGSEA). GSEA was used to investigate the signaling pathways associated with low-risk and high-risk groups, whereas ssGSEA was used to analyze those associated with high-risk groups. A line graph was created to predict the overall survival (OS) of patients. Quantitative real-time polymerase chain reaction confirmed differential gene expression between normal and cancerous colon tissues. The eight genes identified – ACOX1, ATP8B1, CHGA, NAT2, PKIB, SLC39A8, TINAG, and VEGFA – correlated with tumor immunity and clinical outcomes. This eight-gene risk profile can accurately stratify risk and predict OS based on KM curves, ROC analysis, and regression models. GSEA analysis revealed calcium ion metabolism as the top pathway in the GEO dataset. These findings provide a foundation for prognostic evaluation and may guide therapeutic decision-making in colon adenocarcinoma.

Keywords
Colon adenocarcinoma
Weighted gene co-expression network analysis
Gene set enrichment analysis
Prognosis
Funding
This study was supported by the Tianjin Municipal Health Commission Chinese Medicine and Western Medicine Research Project (2023193), the Tianjin Municipal Health Commission Chinese Medicine and Western Medicine Research Project (2023222), and the Tianjin Municipal Education Commission Subjects (2022KJ187).
Conflict of interest
The authors declare no competing interest.
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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