AUTHOR=Tang Yunliang , Guo Yinhong 

TITLE=A Ubiquitin-Proteasome Gene Signature for Predicting Prognosis in Patients With Lung Adenocarcinoma

JOURNAL=Frontiers in Genetics

VOLUME=13

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.893511

DOI=10.3389/fgene.2022.893511

ISSN=1664-8021

ABSTRACT=<p><bold>Background:</bold> Dysregulation of the ubiquitin-proteasome system (UPS) can lead to instability in the cell cycle and may act as a crucial factor in both tumorigenesis and tumor progression. However, there is no established prognostic signature based on UPS genes (UPSGs) for lung adenocarcinoma (LUAD) despite their value in other cancers.</p><p><bold>Methods:</bold> We retrospectively evaluated a total of 703 LUAD patients through multivariate Cox and Lasso regression analyses from two datasets, the Cancer Genome Atlas (<italic>n</italic> = 477) and GSE31210 (<italic>n</italic> = 226). An independent dataset (GSE50081) containing 128 LUAD samples were used for validation.</p><p><bold>Results:</bold> An eight-UPSG signature, including <italic>ARIH2</italic>, <italic>FBXO9</italic>, <italic>KRT8</italic>, <italic>MYLIP</italic>, <italic>PSMD2</italic>, <italic>RNF180</italic>, <italic>TRIM28</italic>, and <italic>UBE2V2</italic>, was established. Kaplan-Meier survival analysis and time-receiver operating characteristic curves for the training and validation datasets revealed that this risk signature presented with good performance in predicting overall and relapsed-free survival. Based on the signature and its associated clinical features, a nomogram and corresponding web-based calculator for predicting survival were established. Calibration plot and decision curve analyses showed that this model was clinically useful for both the training and validation datasets. Finally, a web-based calculator (<ext-link ext-link-type="uri" xlink:href="https://ostool.shinyapps.io/lungcancer" xmlns:xlink="http://www.w3.org/1999/xlink">https://ostool.shinyapps.io/lungcancer</ext-link>) was built to facilitate convenient clinical application of the signature.</p><p><bold>Conclusion:</bold> An UPSG based model was developed and validated in this study, which may be useful as a novel prognostic predictor for LUAD.</p>