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10.15252/emmm.201404874 http://scihub22266oqcxt.onion/10.15252/emmm.201404874 C4568949!4568949!26253080     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       EMBO+Mol+Med 2015 ; 7 (9): 1153-65 Nephropedia Template TP {{tp|p=26253080|t=2015. Non-invasive prognostic protein biomarker signatures associated with colorectal cancer |pdf=|usr=}}{{26253080}} gab.com Text Non-invasive prognostic protein biomarker signatures associated with colorectal cancer JO: EMBO Mol Med http://www.kidney.de/pget.php?&tw=1&pmid=26253080 #FOAvip The current management of colorectal cancer (CRC) would greatly benefit from non-invasive prognostic biomarkers indicative of clinicopathological tumor characteristics. Here, we employed targeted proteomic profiling of 80 glycoprotein biomarker candidates across plasma samples of a well-annotated patient cohort with comprehensive CRC characteristics. Clinical data included 8-year overall survival, tumor staging, histological grading, regional localization, and molecular tumor characteristics. The acquired quantitative proteomic dataset was subjected to the development of biomarker signatures predicting prognostic clinical endpoints. Protein candidates were selected into the signatures based on significance testing and a stepwise protein selection, each within 10-fold cross-validation. A six-protein biomarker signature of patient outcome could predict survival beyond clinical stage and was able to stratify patients into groups of better and worse prognosis. We further evaluated the performance of the signature on the mRNA level and assessed its prognostic value in the context of previously published transcriptional signatures. Additional signatures predicting regional tumor localization and disease dissemination were also identified. The integration of rich clinical data, quantitative proteomic technologies, and tailored computational modeling facilitated the characterization of these signatures in patient circulation. These findings highlight the value of a simultaneous assessment of important prognostic disease characteristics within a single measurement. Twit Text FOAVip Non-invasive prognostic protein biomarker signatures associated with colorectal cancer ????EMBO Mol Med http://www.kidney.de/pget.php?&tw=1&pmid=26253080 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26253080 #FOAvip English Wikipedia <ref>{{Cite pmid|26253080}}</ref> Non-invasive prognostic protein biomarker signatures associated with colorectal cancer #MMPMID26253080 Surinova S; Radová L; Choi M; Srovnal J; Brenner H; Vitek O; Hajdúch M; Aebersold R EMBO Mol Med 2015[Sep]; 7 (9): 1153-65 PMID26253080show ga The current management of colorectal cancer (CRC) would greatly benefit from non-invasive prognostic biomarkers indicative of clinicopathological tumor characteristics. Here, we employed targeted proteomic profiling of 80 glycoprotein biomarker candidates across plasma samples of a well-annotated patient cohort with comprehensive CRC characteristics. Clinical data included 8-year overall survival, tumor staging, histological grading, regional localization, and molecular tumor characteristics. The acquired quantitative proteomic dataset was subjected to the development of biomarker signatures predicting prognostic clinical endpoints. Protein candidates were selected into the signatures based on significance testing and a stepwise protein selection, each within 10-fold cross-validation. A six-protein biomarker signature of patient outcome could predict survival beyond clinical stage and was able to stratify patients into groups of better and worse prognosis. We further evaluated the performance of the signature on the mRNA level and assessed its prognostic value in the context of previously published transcriptional signatures. Additional signatures predicting regional tumor localization and disease dissemination were also identified. The integration of rich clinical data, quantitative proteomic technologies, and tailored computational modeling facilitated the characterization of these signatures in patient circulation. These findings highlight the value of a simultaneous assessment of important prognostic disease characteristics within a single measurement. äNon-invasive prognostic protein biomarker signatures associated with c(epmc).pdf DeepDyve Pubget Overpricing