Mary Sproull, Peter Mathen, Charlotte Anne Miller, Megan Mackey, Teresa Cooley, Deedee Smart, Uma Shankavaram, Kevin Camphausen
{"title":"A Serum Proteomic Signature Predicting Survival in Patients with Glioblastoma.","authors":"Mary Sproull, Peter Mathen, Charlotte Anne Miller, Megan Mackey, Teresa Cooley, Deedee Smart, Uma Shankavaram, Kevin Camphausen","doi":"10.16966/2576-5833.117","DOIUrl":"10.16966/2576-5833.117","url":null,"abstract":"<p><strong>Purpose: </strong>Glioblastoma (GBM) is the most common form of brain tumor and has a uniformly poor prognosis. Development of prognostic biomarkers in easily accessible serum samples have the potential to improve the outcomes of patients with GBM through personalized therapy planning.</p><p><strong>Material/methods: </strong>In this study pre-treatment serum samples from 30 patients newly diagnosed with GBM were evaluated using a 40-protein multiplex ELISA platform. Analysis of potentially relevant gene targets using The Cancer Genome Atlas database was done using the Glioblastoma Bio Discovery Portal (GBM-BioDP). A ten-biomarker subgroup of clinically relevant molecules was selected using a functional grouping analysis of the 40 plex genes with two genes selected from each group on the basis of degree of variance, lack of co-linearity with other biomarkers and clinical interest. A Multivariate Cox proportional hazard approach was used to analyze the relationship between overall survival (OS), gene expression, and resection status as covariates.</p><p><strong>Results: </strong>Thirty of 40 of the MSD molecules mapped to known genes within TCGA and separated the patient cohort into two main clusters centered predominantly around a grouping of classical and proneural versus the mesenchymal subtype as classified by Verhaak. Using the values for the 30 proteins in a prognostic index (PI) demonstrated that patients in the entire cohort with a PI below the median lived longer than those patients with a PI above the median (HR 1.8, p=0.001) even when stratified by both age and MGMT status. This finding was also consistent within each Verhaak subclass and highly significant (range p=0.0001-0.011). Additionally, a subset of ten proteins including, CRP, SAA, VCAM1, VEGF, MDC, TNFA, IL7, IL8, IL10, IL16 were found to have prognostic value within the TCGA database and a positive correlation with overall survival in GBM patients who had received gross tumor resection followed by conventional radiation therapy and temozolomide treatment concurrent with the addition of valproic acid.</p><p><strong>Conclusion: </strong>These findings demonstrate that proteomic approaches to the development of prognostic assays for treatment of GBM may hold potential clinical value.</p>","PeriodicalId":92393,"journal":{"name":"Journal of biochemistry and analytical studies","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057620/pdf/nihms-1556504.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38897046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Factors that Affect the Osteoclastogenesis of RAW264.7 Cells.","authors":"John Nguyen, Anja Nohe","doi":"10.16966/2576-5833.109","DOIUrl":"https://doi.org/10.16966/2576-5833.109","url":null,"abstract":"<p><p>Osteoclasts and their activity are key regulators of bone formation. However, studying osteoclasts is difficult. Primary osteoclast cultures are difficult to maintain and isolate. Also, the amount of cells that are isolated and their properties depend on the origin and differentiation protocols. These protocols are usually developed in a distinct lab and multiple protocols exist. A cell line to study osteoclasts and a thorough study of osteoclast differentiation and culturing is currently lacking. The RAW264.7 cell line is most commonly used to study osteoclast differentiation and its signaling pathways. RAW264.7 cells are not a homogenous cell line. They don't often exclusively differentiate into osteoclast but also into other multinucleated cells as well including macrophage polykaryons. A challenge of culturing RAW264.7 cells are culture conditions. Different conditions can affect survival, proliferation, and differentiation of RAW264.7 cells. Currently published protocols of culturing RAW264.7 cells often assume multinucleated cells that have three or more nuclei with distinguished osteoclast characteristics (such as TRAP+) as osteoclasts. However, osteoclasts and macrophage polykaryons are almost indistinguishable under a light microscope (TRAP+ with three or more nuclei). The goal of this paper is to examine the effect of culture conditions on the osteoclastogenesis ability of RAW264.7 cells. The focus will be on establishing the crucial parameters for culture density, time of stimulation, RANKL, and L-Gln concentrations. Although we are unable to establish the condition that offers a homogenous population of osteoclasts; nevertheless, we are able to identify the optimal conditions at which osteoclasts are found to be more than macrophage polykaryons. Finally, this article also demonstrates that osteoclasts and macrophage polykaryons can be distinguished by immunofluorescence staining for cathepsin K.</p>","PeriodicalId":92393,"journal":{"name":"Journal of biochemistry and analytical studies","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.16966/2576-5833.109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36411056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}