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ObjectivesEarly diagnosis of rheumatoid arthritis (RA) is an unmet medical need in the field of rheumatology. Previously, we performed high-density transcriptomic studies on synovial biopsies from patients with arthritis, and found that synovial gene expression profiles were significantly different according to the underlying disorder. Here, we wanted to further explore the consistency of the gene expression signals in synovial biopsies of patients with arthritis, using low-density platforms.MethodsLow-density assays (cDNA microarray and microfluidics qPCR) were designed, based on the results of the high-density microarray data. Knee synovial biopsies were obtained from patients with RA, spondyloarthropathies (SA) or osteoarthritis (OA) (n = 39), and also from patients with initial undifferentiated arthritis (UA) (n = 49).ResultsAccording to high-density microarray data, several molecular pathways are differentially expressed in patients with RA, SA and OA: T and B cell activation, chromatin remodelling, RAS GTPase activation and extracellular matrix regulation. Strikingly, disease activity (DAS28-CRP) has a significant influence on gene expression patterns in RA samples. Using the low-density assays, samples from patients with OA are easily discriminated from RA and SA samples. However, overlapping molecular patterns are found, in particular between RA and SA biopsies. Therefore, prediction of the clinical diagnosis based on gene expression data results in a diagnostic accuracy of 56.8%, which is increased up to 98.6% by the addition of specific clinical symptoms in the prediction algorithm. Similar observations are made in initial UA samples, in which overlapping molecular patterns also impact the accuracy of the diagnostic algorithm. When clinical symptoms are added, the diagnostic accuracy is strongly improved.ConclusionsGene expression signatures are overall different in patients with OA, RA and SA, but overlapping molecular signatures are found in patients with these conditions. Therefore, an accurate diagnosis in patients with UA requires a combination of gene expression and clinical data.

Human prostate-specific membrane antigen (PSMA), a 100-kDa integral transmembrane glycoprotein, is considered to be a highly specific marker of the prostate gland, and has successfully been used as a marker of circulating prostatic epithelial cells. Extended PSMA homology has been demonstrated with a cDNA found in rat cerebral and renal tissues. In this study, we aimed to evaluate the expression of PSMA mRNA in a variety of human renal cancer tissues (n ‫؍‬ 20) and cell lines (n ‫؍‬ 12). Using reverse transcriptase-polymerase chain reaction, DNA sequencing, blottings, and specific anti-PSMA labelling with CYT 351 antibody, we identified PSMA mRNA and protein in normal and in neoplastic renal tissue. The sequence of the polymerase-chain-reaction products is identical to that of PSMA cDNA derived from prostate tissue. Immunological staining with the CYT 351 reveals that PSMA is expressed mainly in tubular cells. Since PSMA does not appear to be restricted to prostatic tissue, this novel biomarker may prove useful in the staging of renal cancer and in the search for the hematogenous spread of renal cells. Int.

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