Urine cell-based DNA methylation classifier for monitoring bladder cancer

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

The lipid droplet coat protein perilipin 5 also localizes to muscle mitochondria

Perilipin 5 (PLIN5/OXPAT) is a lipid droplet (LD) coat protein mainly present in tissues with a high fat-oxidative capacity, suggesting a role for PLIN5 in facilitating fatty acid oxidation. Here, we investigated the role of PLIN5 in fat oxidation in skeletal muscle. In human skeletal muscle, we observed that PLIN5 (but not PLIN2) protein content correlated tightly with OXPHOS content and in rat muscle PLIN5 content correlated with mitochondrial respiration rates on a lipid-derived substrate. This prompted us to examine PLIN5 protein expression in skeletal muscle mitochondria by means of immunogold electron microscopy and Western blots in isolated mitochondria. These data show that PLIN5, in contrast to PLIN2, not only localizes to LD but also to mitochondria, possibly facilitating fatty acid oxidation. Unilateral overexpression of PLIN5 in rat anterior tibialis muscle augmented myocellular fat storage without increasing mitochondrial density as indicated by the lack of change in protein content of five components of the OXPHOS system. Mitochondria isolated from PLIN5 overexpressing muscles did not possess increased fatty acid respiration. Interestingly though, 14C-palmitate oxidation assays in muscle homogenates from PLIN5 overexpressing muscles revealed a 44.8% (P = 0.05) increase in complete fatty acid oxidation. Thus, in mitochondrial isolations devoid of LD, PLIN5 does not augment fat oxidation, while in homogenates containing PLIN5-coated LD, fat oxidation is higher upon PLIN5 overexpression. The presence of PLIN5 in mitochondria helps to understand why PLIN5, in contrast to PLIN2, is of specific importance in fat oxidative tissues. Our data suggests involvement of PLIN5 in directing fatty acids from the LD to mitochondrial fatty acid oxidation

Ability of a urine gene expression classifier to reduce the number of follow-up cystoscopies in bladder cancer patients

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A five-gene expression signature to predict progression in T1G3 bladder cancer

Contains fulltext : 165718.pdf (publisher's version ) (Closed access)OBJECTIVE: The aim of this study was to analyze tumour gene expression profiles of progressive and non-progressive T1G3 bladder cancer (BC) patients to develop a gene expression signature to predict tumour progression. METHODS: Retrospective, multicenter study of 96 T1G3 BC patients without carcinoma in situ (CIS) who underwent a transurethral resection. Formalin-fixed paraffin-embedded tissue samples were collected. Global gene expression patterns were analyzed in 21 selected samples from progressive and non-progressive T1G3 BC patients using Illumina microarrays. Expression levels of 94 genes selected based on microarray data and based on literature were studied by quantitative polymerase chain reaction (qPCR) in an independent series of 75 progressive and non-progressive T1G3 BC patients. Univariate logistic regression was used to identify individual predictors. A variable selection method was used to develop a multiplex biomarker model. Discrimination of the model was measured by area under the receiver-operating characteristic curve. Interaction networks between the genes of the model were built by GeneMANIA Cytoscape plugin. RESULTS: A total of 1294 genes were found differentially expressed between progressive and non-progressive patients. Differential expression of 15 genes was validated by qPCR in an additional set of samples. A five-gene expression signature (ANXA10, DAB2, HYAL2, SPOCD1, and MAP4K1) discriminated progressive from non-progressive T1G3 BC patients with a sensitivity of 79% and a specificity of 86% (AUC = 0.83). Direct interactions between the five genes of the model were not found. CONCLUSIONS: Progressive and non-progressive T1G3 bladder tumours have shown different gene expression patterns. To identify T1G3 BC patients with a high risk of progression, a five-gene expression signature has been developed