Dataset Information

Urine protein biomarkers of bladder cancer arising from 16-plex antibody-based screens.

ABSTRACT:

Purpose

The purpose of this study is to identify novel urine protein biomarkers of bladder cancer using a Luminex based screening platform.

Materials and methods

The current study examines urine samples from 66 subjects, comprised of 31 Urology clinic controls and 35 bladder cancer patients, using a Luminex based screening platform. ELISA validation was carried out for the top 4 prospective urine biomarkers using an independent cohort of 20 Urology clinic controls and 60 bladder cancer (BC) subjects.

Results

Of the 16 proteins screened by Luminex, 10 showed significant elevation in BC compared to the controls. Eight of these urine proteins were able to differentiate BC from control urine with ROC AUC values exceeding 0.70 at p < 0.0001, with specificity values exceeding 0.9. Upon ELISA validation, urine IL-1α, IL-1ra, and IL-8 were able to distinguish control urine from urine drawn from various bladder cancer stages, with IL-8 being the best discriminator. Compared to members of the IL-1 cytokine family, urine IL-8 was also best at discriminating T1 and/or T2-T4 from Ta BC (ROC AUC ≥ 0.83), as well as high grade from low grade BC (ROC AUC ≥ 0.82).

Conclusions

These findings suggest that urine IL-1α, IL-1ra and IL-8 are useful indicators of bladder cancer. Urine IL-8 not only distinguishes bladder cancer from controls, it also discriminates high grade from low grade disease, and the successive clinical stages of bladder cancer. While supportive of previous reports, these findings warrant further analysis in prospective cohorts.

SUBMITTER: Vanarsa K

PROVIDER: S-EPMC8057279 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background:Current standard methods used to detect and monitor bladder cancer (BC) are invasive or have low sensitivity. This study aimed to develop a urine methylation biomarker classifier for BC monitoring and validate this classifier in patients in follow-up for bladder cancer (PFBC). Methods:Voided urine samples (N?=?725) from BC patients, controls, and PFBC were prospectively collected in four centers. Finally, 626 urine samples were available for analysis. DNA was extracted from the urinary cells and bisulfite modificated, and methylation status was analyzed using pyrosequencing. Cytology was available from a subset of patients (N?=?399). In the discovery phase, seven selected genes from the literature (CDH13, CFTR, NID2, SALL3, TMEFF2, TWIST1, and VIM2) were studied in 111 BC and 57 control samples. This training set was used to develop a gene classifier by logistic regression and was validated in 458 PFBC samples (173 with recurrence). Results:A three-gene methylation classifier containing CFTR, SALL3, and TWIST1 was developed in the training set (AUC 0.874). The classifier achieved an AUC of 0.741 in the validation series. Cytology results were available for 308 samples from the validation set. Cytology achieved AUC 0.696 whereas the classifier in this subset of patients reached an AUC 0.768. Combining the methylation classifier with cytology results achieved an AUC 0.86 in the validation set, with a sensitivity of 96%, a specificity of 40%, and a positive and negative predictive value of 56 and 92%, respectively. Conclusions:The combination of the three-gene methylation classifier and cytology results has high sensitivity and high negative predictive value in a real clinical scenario (PFBC). The proposed classifier is a useful test for predicting BC recurrence and decrease the number of cystoscopies in the follow-up of BC patients. If only patients with a positive combined classifier result would be cystoscopied, 36% of all cystoscopies can be prevented.