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

Eur J Med Res. 2023 Jan 21;28(1):41. doi: 10.1186/s40001-023-00993-z.

Abstract

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.

Keywords: Colon adenocarcinoma; Prognostic signature; Tumor immune microenvironment.

MeSH terms

  • Adenocarcinoma* / drug therapy
  • Adenocarcinoma* / genetics
  • Colonic Neoplasms* / drug therapy
  • Colonic Neoplasms* / genetics
  • Humans
  • Machine Learning
  • Prognosis
  • Tumor Microenvironment
  • Ubiquitin

Substances

  • Ubiquitin