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pmid:39463392

Development and verification of a novel risk model related to ubiquitination linked with prognosis and therapeutic response in clear cell renal cell carcinoma.

Yingzhi Chen · Zhixuan Wu · Kenan Cen · Yangyang Guo · Junhui Jiang

Scientific reports · 2024 doi:10.1038/s41598-024-75948-3

Paper

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Experiment

complete

Data

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Development and verification of a novel risk model related to ubiquitination linked with prognosis and therapeutic response in clear cell renal cell carcinoma.
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10.1038/s41598-024-75948-3
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Yingzhi Chen
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Zhixuan Wu
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Kenan Cen
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Yangyang Guo
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Junhui Jiang
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2024
abstract
Increasing evidence highlights the important role of ubiquitination in cancer. The objective of our study is to establish a reliable marker for predicting clinical outcomes and treatment responses in patients with clear cell renal cell carcinoma (ccRCC) using genes related to ubiquitination (URGs). The URGs subtypes were identified using consensus clustering based on TCGA-KIRC, and a signature containing the prognostic differentially expressed genes of the subtypes was determined using LASSO and Cox regression analysis. To demonstrate the strength of the signature, verification analyses were performed on both E-MTAB-1980 and TCGA-KIRC test datasets. We developed a nomogram to enhance the effectiveness of our predictive tool. Risk genes expression was determined through RT-qPCR. Six genes were combined to create the URGs signature, which had a highly correlated with patient prognosis in patients with ccRCC. A nomogram was developed based on the URGs signature and clinicopathological characteristics. We found that the predictive power was substantially greater than the other individual predictors. Moreover, the study on the immune microenvironment revealed significant variations in the levels of immune cells and the expression of immune checkpoint genes among the groups categorized as high-risk and low-risk. Furthermore, it was found that immunotherapy yielded better outcomes in cohorts with low risk. The URGs signature might serve as a novel and powerful prognosis biomarker and offer a momentous reference for individualized treatment for patients in ccRCC.
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Prognosis-associated DEGs was identified through univariate Cox regression analysis following the criteria of p<0.05. Next, a combination of least absolute shrinkage and selection operator (LASSO) analysis19 following the criteria of log(lambda)= -2.3 and multivariate Cox regression were employed to identify the genes incorporated in the prognostic signature.
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In the TCGA-test cohort (Fig. 3G), the AUC values for the 1-, 3-, and 5-year periods were 0.777, 0.733, and 0.770, respectively.
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