Abstract
Reliable biomarkers are needed to recognize urologic cancer patients at high risk for recurrence. In this study, we built a novel immune-related gene pairs signature to simultaneously predict recurrence for three urologic cancers. We gathered 14 publicly available gene expression profiles including bladder, prostate and kidney cancer. A total of 2,700 samples were classified into the training set (n = 1,622) and validation set (n = 1,078). The 25 immune-related gene pairs signature consisting of 41 unique genes was developed by the least absolute shrinkage and selection operator regression analysis and Cox regression model. The signature stratified patients into high- and low-risk groups with significantly different relapse-free survival in the meta-training set and its subpopulations, and was an independent prognostic factor of urologic cancers. This signature showed a robust ability in the meta-validation and multiple independent validation cohorts. Immune and inflammatory response, chemotaxis and cytokine activity were enriched with genes relevant to the signature. A significantly higher infiltration level of M1 macrophages was found in the high-risk group versus the low-risk group. In conclusion, our signature is a promising prognostic biomarker for predicting relapse-free survival in patients with urologic cancer.
Highlights
Bladder cancer, prostate cancer and kidney cancer are the main tumors in the urinary system, and nearly 2.4 million new cases are diagnosed each year [1]
When patients were stratified by different tumor stages, genders and age groups, low and high immune-related gene pairs index (IRGPI) groups remained significantly different for relapse-free survival (RFS), and a higher IRGPI score was associated with significantly worse prognosis (Figure 2)
We developed a signature based on 25 immune-related gene pairs (IRGPs) to simultaneously predict the prognosis of urinary cancer, including bladder, prostate, and kidney cancer
Summary
Prostate cancer and kidney cancer are the main tumors in the urinary system, and nearly 2.4 million new cases are diagnosed each year [1]. A reliable prognostic biomarker which could identify patients with a higher risk for relapse and select patients who have response to therapies would be valuable for management of urologic cancers. Gene-expression signatures have been identified for survival stratification of bladder cancer [8, 9], prostate cancer [10, 11] and kidney cancer [12, 13]. A chance to develop more reliable prognostic biomarkers has been brought by sufficient large-scale public gene expression datasets [15, 16]. New methods based on the relative ranking of gene expression levels have been used to eliminate the requirement for data preprocessing, and have attained robust results in many applications [19,20,21]
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