Background: Colon cancer (CC) is one of the most common cancers with high morbidity globally. Ubiquitination is involved in the characterization of multiple biological processes, and some ubiquitinated enzymes are associated with the prognosis of CC. However, the prognostic model associated with ubiquitination-related genes (URGs) for CC is unavailable.
Methods: Gene expression data, somatic mutations, transcriptome profiles, microsatellite instability status (MSI) status, and clinical information for CC were obtained from The Cancer Genome Atlas (TCGA) dataset. Seven URGs were used for establishing a prognostic prediction model, which was constructed and validated in GSE17538. Besides, genomic variance analysis (GSVA) was used to explore further the differences in biological pathway activation status between the high-risk and low-risk groups. Finally, the single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithm analysis were used to characterize the cellular infiltration in the microenvironment.
Results: A seven-URG prognostic signature was established, based on which patients in the training and test groups could be divided into high-risk and low-risk groups. The results demonstrated that the model has a solid ability to predict the prognosis of CC patients.
Conclusions: We established a prognostic prediction model for CC based on ubiquitination. Then we analyzed the genetic characteristics associated with ubiquitination and the tumor microenvironment (TME) cell infiltration in CC. These results are worthy of exploring new clinical treatment strategies for CC.
Keywords: Colon cancer (CC); prognostic; tumor microenvironment (TME); ubiquitination.
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