Rabbit Anti-Human TGFB1 Polyclonal Antibody

Although similar to 40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis >= 1-year post-RP, (ii) biochemical recurrence >= 1-year post-RP, and (iii) no progression after >= 10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance.

The mechanisms underlying aortic dissection remain to be fully elucidated. We aimed to identify key molecules driving dissection through gene expression profiling achieved by microarray analysis and subsequent in vitro experiments using human aortic endothelial cells (HAECs) and aortic vascular smooth muscle cells (AoSMCs). Total RNA, including microRNA (miRNA), was isolated from the intima-media layer of dissected ascending aorta obtained intraoperatively from acute type A aortic dissection (ATAAD) patients without familial thoracic aortic disease (n = 8) and that of non-dissected ascending aorta obtained from transplant donors (n = 9). Gene expression profiling was performed with mRNA and miRNA microarrays, and results were confirmed by quantitative polymerase chain reaction (qPCR). Target genes and miRNA were identified by gene ontology analysis and a literature search. To reproduce the in silico results, HAECs and AoSMCs were stimulated in vitro by upstream cytokines, and expression of target genes was assessed by qPCR. Microarray analysis revealed 1536 genes (3.6%, 1536/42 545 probes) and 41 miRNAs (3.0%, 41/1368 probes) that were differentially expressed in the ATAAD group (versus donor group). The top 15 related pathways included regulation of inflammatory response, growth factor activity and extracellular matrix. Gene ontology analysis identified JAK2 (regulation of inflammatory response), PDGFA, TGFB1, VEGFA (growth factor activity) and TIMP3, TIMP4, SERPINE1 (extracellular matrix) as the target genes and miR-21-5p, a TIMP3 repressor, as target miRNA that interacts with the target genes. Validation qPCR confirmed the altered expression of all 7 target genes and miR-21-5p in dissected aorta specimens (all genes, P < 0.05). Ingenuity pathway analysis showed TNF-alpha and TGF-beta to be upstream cytokines for the target genes. In vitro experiments showed these cytokines inhibit TIMP3 expression (P < 0.05) and enhance VEGFA expression (P < 0.01) in AoSMCs but not HAECs. miR-21-5p expression increases in AoSMCs under TNF-alpha and TGF-beta stimulation (fold change: 1.36; P = 0.011). Results of our novel approach, integrating in vitro assessment into gene expression profiling, implicated chronic inflammation characterized by MMP-TIMP dysregulation, increased VEGFA expression, and TGF-beta signalling in the development of dissection. Further investigation may reveal novel diagnostic biomarkers and uncover the mechanism(s) underlying ATAAD.