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Abstract Background From the first steps of prostate cancer (PCa) initiation, tumours are in contact with the most-proximal adipose tissue called periprostatic adipose tissue (PPAT). Extracellular vesicles are important carriers of non-coding RNA such as miRNAs that are crucial for cellular communication. The secretion of extracellular vesicles by PPAT may play a key role in the interactions between adipocytes and tumour. Analysing the PPAT exovesicles (EVs) derived-miRNA content can be of great relevance for understanding tumour progression and aggressiveness. Methods A total of 24 samples of human PPAT and 17 samples of perivesical adipose tissue (PVAT) were used. EVs were characterized by western blot and transmission electron microscopy (TEM), and uptake by PCa cells was verified by confocal microscopy. PPAT and PVAT explants were cultured overnight, EVs were isolated, and miRNA content expression profile was analysed. Pathway and functional enrichment analyses were performed seeking potential miRNA targets. In vitro functional studies were evaluated using PCa cells lines, miRNA inhibitors and target gene silencers. Results Western blot and TEM revealed the characteristics of EVs derived from PPAT (PPAT-EVs) samples. The EVs were up taken and found in the cytoplasm of PCa cells. Nine miRNAs were differentially expressed between PPAT and PVAT samples. The RORA gene (RAR Related Orphan Receptor A) was identified as a common target of 9 miRNA-regulated pathways. In vitro functional analysis revealed that the RORA gene was regulated by PPAT-EVs-derived miRNAs and was found to be implicated in cell proliferation and inflammation. Conclusion Tumour periprostatic adipose tissue is linked to PCa tumour aggressiveness and could be envisaged for new therapeutic strategies.

Abstract Background Cancer-secreted exovesicles are important for cell-to-cell communication by altering cancer-related signalling pathways. Exovesicles-derived miRNAs (exomiRNAs)-target genes can be useful for diagnostic and prognostic purposes. Methods ExomiRNA from prostate cancer (PCa) cells (PC-3 and LNCaP) were quantified by qRT-PCR and compared to the healthy cell line RWPE-1 by using miRNome PCR 752 miRNAs Panel. MiRNet database was used to predict exomiRNA-target genes. ExomiRNA-target genes pathway functional enrichment was performed by using Reactome database and Enrichr platform. Protein–protein interaction analysis was carried out by using the STRING database. RNA target-gene sequencing data from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) database was screened out in 465 PCa patients for candidate gene expression in prostate tumour (PT) tissue and non-pathologic prostate (N-PP) tissue. Signature gene candidates were statistically analysed for diagnosis and prognosis usefulness. Results A total of 36 exomiRNAs were found downregulated when comparing PCa cells vs a healthy cell line; and when comparing PC-3 vs LNCaP, 14 miRNAs were found downregulated and 52 upregulated. Reactome pathway database revealed altered pathways and genes related to miRNA biosynthesis, miRNA-mediated gene silencing (TNRC6B and AGO1), and cell proliferation (CDK6), among others. Results showed that TNRC6B gene expression was up-regulated in PT tissue compared to N-PP (n = 52 paired samples) and could be useful for diagnostic purposes. Likewise, gene expression levels of CDK6, TNRC6B, and AGO1 were down-regulated in high-risk PT (n = 293) compared to low-risk PCa tissue counterparts (n = 172). When gene expression levels of CDK6, TNRC6B, and AGO1 were tested as a prognostic panel, the results showed that these improve the prognostic power of classical biomarkers. Conclusion ExomiRNAs-targets genes, TNRC6B, CDK6, and AGO1, showed a deregulated expression profile in PCa tissue and could be useful for PCa diagnosis and prognosis.

ObjectiveThere is an urgent need for novel biomarkers to improve the early diagnosis of rheumatoid arthritis (ERA). Current serum biomarkers used in the management of ERA, including rheumatoid factor and anti-cyclic citrullinated peptide (ACPA), show limited specificity and sensitivity. Here, we used metabolomics to uncover new serum biomarkers of ERA.MethodsWe applied an untargeted metabolomics approach including gas chromatography time-of-flight mass spectrometry in serum samples from an ERA cohort (n=32) and healthy controls (n=19). Metabolite set enrichment analysis was performed to explore potentially important biological pathways. Partial least squares discriminant analysis and variable importance in projection analysis were performed to construct an ERA biomarker panel.ResultsSignificant differences in the content of 11/81 serum metabolites were identified in patients with ERA. Receiver operating characteristic (ROC) analysis showed that a panel of only three metabolites (glyceric acid, lactic acid, and 3-hydroxisovaleric acid) could correctly classify 96.7% of patients with ERA, with an area under the ROC curve of 0.963 and with 94.4% specificity and 93.5% sensitivity, outperforming ACPA-based diagnosis by 2.9% and, thus, improving the preclinical detection of ERA. Aminoacyl-tRNA biosynthesis and serine, glycine, and phenylalanine metabolism were the most significant dysregulated pathways in patients with ERA.ConclusionA metabolomics serum-based biomarker panel composed of glyceric acid, lactic acid, and 3-hydroxisovaleric acid offers potential for the early clinical diagnosis of RA.

Abstract Background Periprostatic adipose tissue (PPAT) plays a role in prostate cancer (PCa) progression. PPAT lipidomic composition study may allow us to understand the tumor metabolic microenvironment and provide new stratification factors. Methods We used ultra-high-performance liquid chromatography-mass spectrometry-based non-targeted lipidomics to profile lipids in the PPAT of 40 patients with PCa (n = 20 with low-risk and n = 20 high-risk). Partial least squares-discriminant analysis (PLS-DA) and variable importance in projection (VIP) analysis were used to identify the most relevant features of PPAT between low- and high-risk PCa, and metabolite set enrichment analysis was used to detect disrupted metabolic pathways. Metabolic crosstalk between PPAT and PCa cell lines (PC-3 and LNCaP) was studied using ex vivo experiments. Lipid uptake and lipid accumulation were measured. Lipid metabolic-related genes (SREBP1, FASN, ACACA, LIPE, PPARG, CD36, PNPLA2, FABP4, CPT1A, FATP5, ADIPOQ), inflammatory markers (IL-6, IL-1B, TNFα), and tumor-related markers (ESRRA, MMP-9, TWIST1) were measured by RT-qPCR. Results Significant differences in the content of 67 lipid species were identified in PPAT samples between high- and low-risk PCa. PLS-DA and VIP analyses revealed a discriminating lipidomic panel between low- and high-risk PCa, suggesting the occurrence of disordered lipid metabolism in patients related to PCa aggressiveness. Functional analysis revealed that alterations in fatty acid biosynthesis, linoleic acid metabolism, and β-oxidation of very long-chain fatty acids had the greatest impact in the PPAT lipidome. Gene analyses of PPAT samples demonstrated that the expression of genes associated with de novo fatty acid synthesis such as FASN and ACACA were significantly lower in PPAT from high-risk PCa than in low-risk counterparts. This was accompanied by the overexpression of inflammatory markers (IL-6, IL-1B, and TNFα). Co-culture of PPAT explants with PCa cell lines revealed a reduced gene expression of lipid metabolic-related genes (CD36, FASN, PPARG, and CPT1A), contrary to that observed in co-cultured PCa cell lines. This was followed by an increase in lipid uptake and lipid accumulation in PCa cells. Tumor-related genes were increased in co-cultured PCa cell lines. Conclusions Disturbances in PPAT lipid metabolism of patients with high-risk PCa are associated with tumor cell metabolic changes.

BackgroundThe etiology of rheumatoid arthritis (RA) remains poorly understood. Early and accurate diagnosis still difficult to achieve. Inflammatory related molecules released into the circulation such cytokines and exosome-derived microRNAs (exomiRNAs) could be good candidates for early diagnosis of autoimmune diseases. We sought to discover a serum biomarker panel for the early detection of RA based on exomiRNAs and inflammatory markers.MethodsA 179 miRNAs-microarray panel was analyzed in a pilot study (4 early RA and 4 controls). Validation of deregulated exomiRNAs was performed in a larger cohort (24 patients with early RA and 24 controls). miRNet software was used to predict exomiRNA gene-targets interactions. Potentially altered pathways were analyzed by Reactome pathway database search. STRING database was used to predict protein-protein interaction networks. Enzyme-linked immunosorbent assay was used to measure serum levels of sTWEAK and sCD163. Signature biomarker candidates were statistical analyzed.ResultsWe detected 11 differentially expressed exomiRNAs in early RA pilot study. Validation analysis revealed that 6/11 exomiRNAs showed strong agreement with the pilot microarray data (exomiR-144-3p, -25-3p, -15a-5p, -451a, -107 and -185-5p). sTWEAK and sCD163 biomarkers were significantly elevated in the serum of patients with early RA. Receiver operating characteristic (ROC) analysis showed that the best panel to diagnose early RA contained exomiR-451a, exomiR-25-3p and sTWEAK, and could correctly classify 95.6% of patients, with an area under the ROC curve of 0.983 and with 100% specificity and 85.7% sensitivity. The YWHAB gene was identified as a common target of the putative miRNA-regulated pathways.ConclusionA novel serum biomarker panel composed of exomiR-451a, exomiR-25-3p and serum levels of sTWEAK may have use in the early clinical diagnosis of RA. A new predicted exomiRNA-target gene YHWAB has been identified and may have a relevant role in the development of RA.

Abstract Background Conventional clinical biomarkers cannot accurately differentiate indolent from aggressive prostate cancer (PCa). We investigated the usefulness of a biomarker panel measured exclusively in biofluids for assessment of PCa aggressiveness. Methods We collected biofluid samples (plasma/serum/semen/post-prostatic massage urine) from 98 patients that had undergone radical prostatectomy. Clinical biochemistry was performed and several cytokines/chemokines including soluble(s) TWEAK, sFn14, sCD163, sCXCL5 and sCCL7 were quantified by ELISA in selected biofluids. Also, the expression of KLK2, KLK3, Fn14, CD163, CXCR2 and CCR3 was quantified by real-time PCR in semen cell sediment. Univariate, logistic regression, and receiver operating characteristic (ROC) analyses were used to assess the predictive ability of the selected biomarker panel in conjunction with clinical and metabolic variables for the evaluation of PCa aggressiveness. Results Total serum levels of prostate-specific antigen (PSA), semen levels of sTWEAK, fasting glycemia and mRNA levels of Fn14, KLK2, CXCR2 and CCR3 in semen cell sediment constituted a panel of markers that was significantly different between patients with less aggressive tumors [International Society of Urological Pathology (ISUP) grade I and II] and those with more aggressive tumors (ISUP grade III, IV and V). ROC curve analysis showed that this panel could be used to correctly classify tumor aggressiveness in 90.9% of patients. Area under the curve (AUC) analysis revealed that this combination was more accurate [AUC = 0.913 95% confidence interval (CI) 0.782–1] than a classical non-invasive selected clinical panel comprising age, tumor clinical stage (T-classification) and total serum PSA (AUC = 0.721 95% CI 0.613–0.830). Conclusions TWEAK/Fn14 axis in combination with a selected non-invasive biomarker panel, including conventional clinical biochemistry, can improve the predictive power of serum PSA levels and could be used to classify PCa aggressiveness.