A circadian rhythm-related gene signature for predicting relapse risk and immunotherapeutic effect in prostate adenocarcinoma.

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Therefore, it is required to develop an effective signature for predicting the relapse risk of PRAD. To identify a circadian rhythm- (CR-) related predictive signature, we analyzed RNA-seq data of patients with prostate adenocarcinoma (PRAD) from the TCGA and GEO cohort. Seven circadian rhythm- (CR-) related genes (FBXL22, MTA1, TP53, RORC, DRD4, PPARGC1A, ZFHX3) were eventually identified to develop a CR-related signature. AUCs for 3-year overall survival were 0.852, 0.856 and 0.944 in the training set, validation set and an external independent test set (GSE70768), respectively. Kaplan-Meier curve analysis showed that the high-risk group has a reduced relapse-free survival (RFS) than the low-risk group in the training set, validation set, and test set, respectively (P < 0.05). We constructed a prognostic nomogram combining the CR-related signature with T staging to precisely estimate relapse risk of PRAD patients. Finally, we observed that the CR-related gene signature was associated with tumor mutation burden, multiple immune checkpoint molecules and microsatellite instability, and thus could predict response to immune checkpoint inhibitors in PRAD. Conclusively, we developed a circadian rhythm-related gene signature for predicting RFS and immunotherapy efficacy in prostate adenocarcinoma.", "claims": [{"id": "claim-method", "statement": "The CR-associated genes were screened for eligible genes for establishing the predictive signature using univariate Cox regression firstly, and then further analyzed using least absolute shrinkage and selection operator (LASSO) regression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36103249", "locator": "sections[5] \"Construction of the gene signature\" paragraph 2", "quote": "The CR-associated genes were screened for eligible genes for establishing the predictive signature using univariate Cox regression firstly, and then further analyzed using least absolute shrinkage and selection operator (LASSO) regression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated and described in this article are available from the corresponding web servers, and are freely available to any scientist wishing to use them for noncommercial purposes, without breaching participant confidentiality. 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