A Qualitative Transcriptional Signature for Predicting Recurrence Risk of Stage I-III Bladder Cancer Patients After Surgical Resection.

Background: Previously reported transcriptional signatures for predicting the prognosis of stage I-III bladder cancer (BLCA) patients after surgical resection are commonly based on risk scores summarized from quantitative measurements of gene expression levels, which are highly sensitive to the measurement variation and sample quality and thus hardly applicable under clinical settings. It is necessary to develop a signature which can robustly predict recurrence risk of BLCA patients after surgical resection. Methods: The signature is developed based on the within-sample relative expression orderings (REOs) of genes, which are qualitative transcriptional characteristics of the samples. Results: A signature consisting of 12 gene pairs (12-GPS) was identified in training data with 158 samples. In the first validation dataset with 114 samples, the low-risk group of 54 patients had a significantly better overall survival than the high-risk group of 60 patients (HR = 3.59, 95% CI: 1.34~9.62, p = 6.61 × 10-03). The signature was also validated in the second validation dataset with 57 samples (HR = 2.75 × 1008, 95% CI: 0~Inf, p = 0.05). Comparison analysis showed that the transcriptional differences between the low- and high-risk groups were highly reproducible and significantly concordant with DNA methylation differences between the two groups. Conclusions: The 12-GPS signature can robustly predict the recurrence risk of stage I-III BLCA patients after surgical resection. It can also aid the identification of reproducible transcriptional and epigenomic features characterizing BLCA metastasis.

Frontiers in oncology. 2019 Jul 10*** epublish ***

Yawei Li, Huarong Zhang, You Guo, Hao Cai, Xiangyu Li, Jun He, Hung-Ming Lai, Qingzhou Guan, Xianlong Wang, Zheng Guo

Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China., Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China.