Urokinase type Plasminogen Activator Human ELISA Kit

We previously identified 529 proteins that had been reported by multiple different studies to change their expression level with age in human plasma. In the present study, we measured the q-value and age coefficient of these proteins in a plasma proteomic dataset derived from 4263 individuals. A bioinformatics enrichment analysis of proteins that significantly trend toward increased expression with age strongly implicated diverse inflammatory processes. A literature search revealed that at least 64 of these 529 proteins are capable of regulating life span in an animal model. Nine of these proteins (AKT2, GDF11, GDF15, GHR, NAMPT, PAPPA, PLAU, PTEN, and SHC1) significantly extend life span when manipulated in mice or fish. By performing machine-learning modeling in a plasma proteomic dataset derived from 3301 individuals, we discover an ultra-predictive aging clock comprised of 491 protein entries. The Pearson correlation for this clock was 0.98 in the learning set and 0.96 in the test set while the median absolute error was 1.84 years in the learning set and 2.44 years in the test set. Using this clock, we demonstrate that aerobic-exercised trained individuals have a younger predicted age than physically sedentary subjects. By testing clocks associated with 1565 different Reactome pathways, we also show that proteins associated with signal transduction or the immune system are especially capable of predicting human age. We additionally generate a multitude of age predictors that reflect different aspects of aging. For example, a clock comprised of proteins that regulate life span in animal models accurately predicts age.

Cytotoxic chemotherapy improves survival for some, but not all, cancer patients. Non-responders may experience unnecessary toxicity and cancer progression, thus creating an urgent need for biomarkers that can predict the response to chemotherapy. So far, the search for such biomarkers has primarily been focused on the cancer cells and less on their surrounding stroma. This stroma is known to act as a key regulator of tumour progression and, in addition, has been associated with drug delivery and drug efficacy. Fibroblasts represent the major cell type in cancer-associated stroma and they secrete extracellular matrix proteins as well as growth factors. This Medline-based literature review summarises the results from studies on epithelial cancers and aimed at investigating relationships between the quantity and quality of the intra-tumoral stroma, the cancer-associated fibroblasts, the proteins they produce and the concomitant response to chemotherapy. Biomarkers were selected for review that are known to affect cancer-related characteristics and patient prognosis. The current literature supports the hypothesis that biomarkers derived from the tumour stroma may be useful to predict response to chemotherapy. This notion appears to be related to the overall quantity and cellularity of the intra-tumoural stroma and the predominant constituents of the extracellular matrix. Increasing evidence is emerging showing that tumour-stroma interactions may not only affect tumour progression and patient prognosis, but also the response to chemotherapy. The tumour stroma-derived biomarkers that appear to be most appropriate to determine the patient's response to chemotherapy vary by tumour origin and the availability of pre-treatment tissue. For patients scheduled for adjuvant chemotherapy, the most promising biomarker appears to be the PLAU: SERPINE complex, whereas for patients scheduled for neo-adjuvant chemotherapy the tumour stroma quantity appears to be most relevant.