A novel machine learning model based on ubiquitin-related gene pairs and clinical features to predict prognosis and treatment effect in colon adenocarcinoma

European Journal of Medical Research

Research

Le Liu¹  Liping Liang²  Ye Chen³  Shijie Mai⁴ 

[1] Department of Gastroenterology, Integrated Clinical Microecology Center, Shenzhen Hospital, Southern Medical University, 1333 New Lake Road, 518100, Shenzhen, China;Department of Gastroenterology, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China;Department of Gastroenterology, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China;Department of Gastroenterology, Integrated Clinical Microecology Center, Shenzhen Hospital, Southern Medical University, 1333 New Lake Road, 518100, Shenzhen, China;Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China;

关键词: Colon adenocarcinoma;    Prognostic signature;    Tumor immune microenvironment;   

DOI  :  10.1186/s40001-023-00993-z

 received in 2022-08-02, accepted in 2023-01-04,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundUbiquitin and ubiquitin-like (UB/UBL) conjugations are essential post-translational modifications that contribute to cancer onset and advancement. In colon adenocarcinoma (COAD), nonetheless, the biological role, as well as the clinical value of ubiquitin-related genes (URGs), is unclear. The current study sought to design and verify a ubiquitin-related gene pairs (URGPs)-related prognostic signature for predicting COAD prognoses.MethodsUsing univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, URGP's predictive signature was discovered. Signatures differentiated high-risk and low-risk patients. ROC and Kaplan–Meier assessed URGPs' signature. Gene set enrichment analysis (GSEA) examined biological nomogram enrichment. Chemotherapyand tumor immune microenvironment were also studied.ResultsThe predictive signature used six URGPs. High-risk patients had a worse prognosis than low-risk patients, according to Kaplan–Meier. After adjusting for other clinical characteristics, the URGPs signature could reliably predict COAD patients. In the low-risk group, we found higher amounts of invading CD4 memory-activated T cells, follicular helper T cells, macrophages, and resting dendritic cells. Moreover, low-risk group had higher immune checkpoint-related gene expression and chemosensitivity.ConclusionOur research developed a nomogram and a URGPs prognostic signature to predict COAD prognosis, which may aid in patient risk stratification and offer an effective evaluation method of individualized treatment in clinical settings.

【 授权许可】

CC BY   
© The Author(s) 2023

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