Integrative transcriptional characterization of cell cycle checkpoint genes promotes clinical management and precision medicine in bladder carcinoma - Shi, Wei-Wei; Guan, Jing-Zhi; Long, Ya-Ping; Song, Qi; Xiong, Qi; Qin, Bo-Yu; Ma, Zhi-Qiang; Hu, Yi; Yang, Bo (Frontiers in Oncology, 2022)
Background: The aberrant regulation of cell cycle is significantly correlated with cancer carcinogenesis and progression, in which cell cycle checkpoints control phase transitions, cell cycle entry, progression and exit. However, the integrative role of cell cycle checkpoint related genes (CRGs) in bladder carcinoma (BC) remains unknown. Methods: The transcriptomic data and clinical features of BC patients were downloaded from The Cancer Genome Atlas (TCGA), used to identify those CRGs correlated with overall survival (OS) by univariate Cox regression analysis. Then, the multivariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses further developed a prognostic CRGs signature, which was validated in three external datasets retrieved from Gene Expression Omnibus (GEO). The receiver operating characteristic curve (ROC) analysis was conducted for evaluating performance of prognosis prediction. Ultimately, genomic profiles and tumor microenvironment (TME), and the Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/) were investigated to guide precision treatment for different BC patients. Results: The novel constructed 23-CRGs signature, of which 19 CRGs were novel identified genes that related to BC progression, could classify BC patients into high-risk and low-risk groups. In additional three validation datasets (GSE13507, GSE31684 and GSE32548), higher CRGs scores all indicated inferior survivals. Besides, CRGs signature as an independent prognostic factor had robust risk stratification for BC patients with different clinical features. Then, a CRGs signature-based nomogram with better performance (C-index=0.76) in prognostic prediction was established. Functional enrichment analysis revealed collagen-containing extracellular matrix (ECM), ECM-related and MAPK signaling pathways were significantly associated with the signature. Further analysis showed low-risk patients were characterized by particularly distinctive prevalence of FGFR3 and POLE alterations, also enrichment of immune infiltrated cells (including CD8+ T cells, CD4+ naïve T cells, follicular helper T cells, Tregs, and myeloid dendritic cells). Additionally, CRGs signature score plus FGFR3 status could successfully distinguish BC patients who have higher possibility of response to immunotherapy or chemotherapy drugs. Conclusions: The CRGs signature is a potent prognostic model for BC patients, and in combination with FGFR3 alterations, it had more practical capacity in prediction of chemotherapy and immunotherapy response, helping guide clinical decision-making.