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10.1002/cncr.28963 http://scihub22266oqcxt.onion/10.1002/cncr.28963 C4441619!4441619!25100294     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 300.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Cancer 2014 ; 120 (24): 3902-13 Nephropedia Template TP {{tp|p=25100294|t=2014. Evaluation of a four-protein serum biomarker panel ? biglycan, annexin-A6, myeloperoxidase and protein S100-A9 (B-AMPŠ) ? for detection of esophageal adenocarcinoma |pdf=|usr=}}{{25100294}} gab.com Text Evaluation of a four-protein serum biomarker panel biglycan, annexin-A6, myeloperoxidase and protein S100-A9 (B-AMPŠ) for detection of esophageal adenocarcinoma JO: Cancer http://www.kidney.de/pget.php?&tw=1&pmid=25100294 #FOAvip Objective: Esophageal adenocarcinoma (EAC) is associated with a dismal prognosis. The identification of cancer biomarkers advances the possibility for early detection and better monitoring of tumor progression and/or response to therapy. The current study presents results of the development of a serum based four-protein (biglycan, myeloperoxidase, annexin-A6, and protein S100-A9) biomarker-panel for EAC. Design: A vertically integrated proteomics-based biomarker discovery approach was used to identify candidate serum biomarkers for detection of EAC. Liquid chromatography-mass spectrometry (LC-MS/MS) analysis was performed on FFPE tissue samples that were collected from across the Barrett's esophagus (BE)-EAC disease spectrum. The MS-based spectral count data was used to guide the selection of candidate serum biomarkers. The serum ELISA data was validated in an independent cohort and used to develop a multi-parametric risk assessment model to predict the presence of disease. Results: With a minimum threshold of 10 spectral counts, 351 proteins were identified as differentially abundant along the spectrum of BE, HGD and EAC (p < 0.05). Eleven proteins from this dataset were then tested using ELISAs in serum samples of which five proteins were significantly elevated in abundance in the EAC patients compared to normal controls, which mirrored trends across the disease spectrum present in the tissue data. Using serum data a Bayesian Rule Learning predictive model with four biomarkers was developed to accurately classify disease class; the cross-validation results for the merged dataset yielded accuracy of 87% and AUROC of 93 %. Conclusion: Serum biomarkers hold significant promise for early non-invasive detection of EAC. Twit Text FOAVip Evaluation of a four-protein serum biomarker panel biglycan, annexin-A6, myeloperoxidase and protein S100-A9 (B-AMPŠ) for detection of esophageal adenocarcinoma ????Cancer http://www.kidney.de/pget.php?&tw=1&pmid=25100294 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25100294 #FOAvip English Wikipedia <ref>{{Cite pmid|25100294}}</ref> Evaluation of a four-protein serum biomarker panel ? biglycan, annexin-A6, myeloperoxidase and protein S100-A9 (B-AMPŠ) ? for detection of esophageal adenocarcinoma #MMPMID25100294 Zaidi AH; Gopalakrishnan V; Kasi PM; Zeng X; Malhotra U; Balasubramanian J; Visweswaran S; Sun M; Flint MS; Davison JM; Hood BL; Conrads TP; Bergman JJ; Bigbee WL; Jobe BA Cancer 2014[Dec]; 120 (24): 3902-13 PMID25100294show ga Objective: Esophageal adenocarcinoma (EAC) is associated with a dismal prognosis. The identification of cancer biomarkers advances the possibility for early detection and better monitoring of tumor progression and/or response to therapy. The current study presents results of the development of a serum based four-protein (biglycan, myeloperoxidase, annexin-A6, and protein S100-A9) biomarker-panel for EAC. Design: A vertically integrated proteomics-based biomarker discovery approach was used to identify candidate serum biomarkers for detection of EAC. Liquid chromatography-mass spectrometry (LC-MS/MS) analysis was performed on FFPE tissue samples that were collected from across the Barrett's esophagus (BE)-EAC disease spectrum. The MS-based spectral count data was used to guide the selection of candidate serum biomarkers. The serum ELISA data was validated in an independent cohort and used to develop a multi-parametric risk assessment model to predict the presence of disease. Results: With a minimum threshold of 10 spectral counts, 351 proteins were identified as differentially abundant along the spectrum of BE, HGD and EAC (p < 0.05). Eleven proteins from this dataset were then tested using ELISAs in serum samples of which five proteins were significantly elevated in abundance in the EAC patients compared to normal controls, which mirrored trends across the disease spectrum present in the tissue data. Using serum data a Bayesian Rule Learning predictive model with four biomarkers was developed to accurately classify disease class; the cross-validation results for the merged dataset yielded accuracy of 87% and AUROC of 93 %. Conclusion: Serum biomarkers hold significant promise for early non-invasive detection of EAC. äEvaluation of a four-protein serum biomarker panel ? biglycan, annexin(epmc).pdf DeepDyve Pubget Overpricing