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pmid:32596394
Identification of an Individualized Prognostic Signature Based on the RWSR Model in Early-Stage Bladder Carcinoma.
Liyang Liu · Xiaodan Zhong · Haining Cui · Hao Zhang · Linyu Wang · Yuanning Liu
BioMed research international · 2020 doi:10.1155/2020/9186546
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Complete file
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BLCA has a high recurrence rate, with over 50% of patients which has at least one recurrence within five years. Due to the complexity of the molecular mechanisms and heterogeneous cancer feature, BLCA clinicians find it hard to make an efficient management decision as they lack reliable assessment of mortality risk. Meanwhile, there is currently no screening suitable prognostic signature or method recommended for early detection, which is significantly important to early-stage detection and prognosis. In this study, a novel model, named the risk-weighted sparse regression (RWSR) model, is constructed to identify a robust signature for patients of early-stage BLCA. The 17-gene signature is generated and then validated as an independent prognostic factor in BLCA cohorts from GSE13507 and TCGA_BLCA datasets. Meanwhile, a risk score model is developed and validated among the 17-gene signature. The risk score is also considered an independent factor for prognosis prediction, which is confirmed through prognosis analysis. The Kaplan-Meier with the log-rank test is used to assess survival difference. Furthermore, the predictive capacity of the signature is proved through stratification analysis. Finally, an effective patient classification is completed by a combination of the 17-gene signature and stage information, which is for better survival prediction and treatment decisions. Besides, 11 genes in the signature, such as coiled-coil domain containing 73 (CCDC73) and protein kinase, DNA-activated, and catalytic subunit (PRKDC), are proved to be prognosis marker genes or strongly associated with prognosis and progress of other types of cancer in published literature already. As a result, this paper would more accurately predict a patient's prognosis and improve surveillance in the clinical setting, which may provide a quantitative and reliable decision-making basis for the treatment plan.", "claims": [{"id": "claim-method", "statement": "To predict the differences between the two risk BLCA patient groups based on survival time, we use the Kaplan-Meier method and calculate the log-rank value to identify the statistical significance between groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32596394", "locator": "sections[7] \"2.5. 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