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

BACKGROUND: Ubiquitin 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. METHODS: Using 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. Chemotherapy and tumor immune microenvironment were also studied. RESULTS: The 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. CONCLUSION: Our 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40001-023-00993-z.