VIRMA-dependent N6-methyladenosine modifications regulate the expression of long non-coding RNAs CCAT1 and CCAT2 in prostate cancer

RNA methylation at position N6 in adenosine (m6A) and its associated methyltransferase complex (MTC) are involved in tumorigenesis. We aimed to explore m6A biological function for long non-coding RNAs (lncRNAs) in prostate cancer (PCa) and its clinical significance. m6A and MTC levels in PCa cells were characterized by ELISA and western blot. Putative m6A-regulated lncRNAs were identified and validated by lncRNA profiler qPCR array and bioinformatics analysis, followed by m6A/RNA co-immunoprecipitation. Impact of m6A depletion on RNA stability was assessed by Actinomycin D assay. The association of m6A-levels with PCa prognosis was examined in clinical samples. Higher m6A-levels and VIRMA overexpression were detected in metastatic castration-resistant PCa (mCRPC) cells (p < 0.05). VIRMA knockdown in PC-3 cells significantly decreased m6A-levels (p = 0.0317), attenuated malignant phenotype and suppressed the expression of oncogenic lncRNAs CCAT1 and CCAT2 (p < 0.00001). VIRMA depletion and m6A reduction decreased the stability and abundance of CCAT1/2 transcripts. Higher expression of VIRMA, CCAT1, and CCAT2 as a group variable was an independent predictor of poor prognosis (HR = 9.083, CI95% 1.911–43.183, p = 0.006). VIRMA is a critical factor sustaining m6A-levels in PCa cells. VIRMA downregulation attenuates the aggressive phenotype of PCa by overall reduction of m6A-levels decreasing stability and abundance of oncogenic lncRNAs

XIST-promoter demethylation as tissue biomarker for testicular germ cell tumors and spermatogenesis quality

Background: The event of X chromosome inactivation induced by XIST, which is physiologically observed in females, is retained in testicular germ cell tumors (TGCTs), as a result of a supernumerary X chromosome constitution. X chromosome inactivation also occurs in male germline, specifically during spermatogenesis. We aimed to analyze the promoter methylation status of XIST in a series of TGCT tissues, representative cell lines, and testicular parenchyma. Methods: Two independent cohorts were included, comprising a total of 413 TGCT samples, four (T)GCT cell lines, and 86 testicular parenchyma samples. The relative amount of methylated and demethylated XIST promoter fragments was assessed by quantitative methylation-specific PCR (qMSP) and more sensitive high-resolution melting (HRM) methylation analyses. Results: Seminomas showed a lower amount of methylated XIST fragments as compared to non-seminomas or normal testis (p < 0.0001), allowing for a good discrimination among these groups (area under the curve 0.83 and 0.81, respectively). Seminomas showed a significantly higher content of demethylated XIST as compared to non-seminomas. The percentage of demethylated XIST fragment in cell lines reflected their chromosomal constitution (number of extra X chromosomes). A novel and strong positive correlation between the Johnsen’s score and XIST demethylation was identified (r = 0.75, p < 0.0001). Conclusions: The X chromosome inactivation event and demethylated XIST promoter are promising biomarkers for TGCTs and for assessing spermatogenesis quality

Identification of a biomarker panel for improvement of prostate cancer diagnosis by volatile metabolic profiling of urine

Background: The lack of sensitive and specific biomarkers for the early detection of prostate cancer (PCa) is a major hurdle to improve patient management. Methods: A metabolomics approach based on GC-MS was used to investigate the performance of volatile organic compounds (VOCs) in general and, more specifically, volatile carbonyl compounds (VCCs) present in urine as potential markers for PCa detection. Results: Results showed that PCa patients (n = 40) can be differentiated from cancer-free subjects (n = 42) based on their urinary volatile profile in both VOCs and VCCs models, unveiling significant differences in the levels of several metabolites. The models constructed were further validated using an external validation set (n = 18 PCa and n = 18 controls) to evaluate sensitivity, specificity and accuracy of the urinary volatile profile to discriminate PCa from controls. The VOCs model disclosed 78% sensitivity, 94% specificity and 86% accuracy, whereas the VCCs model achieved the same sensitivity, a specificity of 100% and an accuracy of 89%. Our findings unveil a panel of 6 volatile compounds significantly altered in PCa patients' urine samples that was able to identify PCa, with a sensitivity of 89%, specificity of 83%, and accuracy of 86%. Conclusions: It is disclosed a biomarker panel with potential to be used as a non-invasive diagnostic tool for PCa.info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio