Frontiers in Genetics (Feb 2022)
Immune ULBP1 is Elevated in Colon Adenocarcinoma and Predicts Prognosis
Abstract
Background: Colon adenocarcinoma (COAD) is still the main cause of cancer deaths worldwide. Although immunotherapy has made progress in recent years, there is still a need to improve diagnosis, prognosis, and treatment tools. UL-16 binding protein 1 (ULBP1) is a ligand that activates the receptor natural killer cell group 2 receptor D (NKG2D) and plays an important immunomodulatory role. We aimed to investigate the clinical significance of ULBP1 in COAD.Methods: We obtained the relevant data from The Cancer Genome Atlas (TCGA). A total of 438 patients with COAD were included in this study, with a mean age of 67.1 ± 13.03 years old, of which 234 (53.42%) were male. The diagnostic value of COAD tumor tissues and adjacent tissues was analyzed by ROC curve. Univariate and multivariate survival analysis investigated the prognostic value of ULBP1 gene, and Gene Set Enrichment Analysis (GSEA) curve was performed to analyze the biological process and enriched enrichment pathway of ULBP1 in COAD. Combination survival analysis investigated the combined prognostic effect of prognostic genes.Results:ULBP1 gene had a high diagnostic value in COAD [AUC (TCGA) = 0.959; AUC (Guangxi) = 0.898]. Up-regulated ULBP1 gene of patients with COAD predicted a worse prognosis compared to those patients with down-regulated ULBP1 gene (Adjusted HR = 1.544, 95% CI = 1.020–2.337, p = 0.040). The GSEA showed that ULBP1 was involved in the apoptotic pathway and biological process of T cell mediated cytotoxicity, regulation of natural killer cell activation, and T cell mediated immunity of COAD. The combination survival analysis showed that the combination of high expression of ULBP1, AARS1, and DDIT3 would increase the 2.2-fold death risk of COAD when compared with those of low expression genes.Conclusion: The immune-related ULBP1 gene had diagnostic and prognostic value in COAD. The combination of ULBP1, AARS1, and DDIT3 genes could improve the prognostic prediction performance in COAD.
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