Serum biomarker panels for the diagnosis of gastric adenocarcinoma
Background: Currently, serum biomarkers, which are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinoma do not exist. Therefore, this study identified a panel of serum biomarkers for the diagnosis of gastric adenocarcinoma. Methods: A 29-plex arra...
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| Published in | British journal of cancer Vol. 106; no. 4; pp. 733 - 739 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
14.02.2012
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0007-0920 1532-1827 1532-1827 |
| DOI | 10.1038/bjc.2011.592 |
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| Abstract | Background:
Currently, serum biomarkers, which are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinoma do not exist. Therefore, this study identified a panel of serum biomarkers for the diagnosis of gastric adenocarcinoma.
Methods:
A 29-plex array platform with 29 biomarkers, consisting of 11 proteins discovered through proteomics and 18 previously known to be cancer-associated, was constructed. A test/training set consisting of 120 gastric adenocarcinoma and 120 control samples were examined. After 13 proteins were selected as candidate biomarkers, multivariate classification analyses were used to identify algorithms for diagnostic biomarker combinations. These algorithms were independently validated using a set of 95 gastric adenocarcinoma and 51 control samples.
Results:
Epidermal growth factor receptor (EGFR), pro-apolipoprotein A1 (proApoA1), apolipoprotein A1, transthyretin (TTR), regulated upon activation, normally T-expressed and presumably secreted (RANTES), D-dimer, vitronectin (VN), interleukin-6, α-2 macroglobulin, C-reactive protein and plasminogen activator inhibitor-1 were selected as classifiers in the two algorithms. These algorithms differentiated between the majority of gastric adenocarcinoma and control serum samples in the training/test set with high accuracy (>88%). These algorithms also accurately classified in the validation set (>85%).
Conclusion:
Two panels of combinatorial biomarkers, including EGFR, TTR, RANTES, and VN, are developed, which are less invasive method for the diagnosis of gastric adenocarcinoma. They could supplement clinical gastroscopic evaluation of symptomatic patients to enhance diagnostic accuracy. |
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| AbstractList | Currently, serum biomarkers, which are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinoma do not exist. Therefore, this study identified a panel of serum biomarkers for the diagnosis of gastric adenocarcinoma.
A 29-plex array platform with 29 biomarkers, consisting of 11 proteins discovered through proteomics and 18 previously known to be cancer-associated, was constructed. A test/training set consisting of 120 gastric adenocarcinoma and 120 control samples were examined. After 13 proteins were selected as candidate biomarkers, multivariate classification analyses were used to identify algorithms for diagnostic biomarker combinations. These algorithms were independently validated using a set of 95 gastric adenocarcinoma and 51 control samples.
Epidermal growth factor receptor (EGFR), pro-apolipoprotein A1 (proApoA1), apolipoprotein A1, transthyretin (TTR), regulated upon activation, normally T-expressed and presumably secreted (RANTES), D-dimer, vitronectin (VN), interleukin-6, α-2 macroglobulin, C-reactive protein and plasminogen activator inhibitor-1 were selected as classifiers in the two algorithms. These algorithms differentiated between the majority of gastric adenocarcinoma and control serum samples in the training/test set with high accuracy (>88%). These algorithms also accurately classified in the validation set (>85%).
Two panels of combinatorial biomarkers, including EGFR, TTR, RANTES, and VN, are developed, which are less invasive method for the diagnosis of gastric adenocarcinoma. They could supplement clinical gastroscopic evaluation of symptomatic patients to enhance diagnostic accuracy. Background: Currently, serum biomarkers, which are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinoma do not exist. Therefore, this study identified a panel of serum biomarkers for the diagnosis of gastric adenocarcinoma. Methods: A 29-plex array platform with 29 biomarkers, consisting of 11 proteins discovered through proteomics and 18 previously known to be cancer-associated, was constructed. A test/training set consisting of 120 gastric adenocarcinoma and 120 control samples were examined. After 13 proteins were selected as candidate biomarkers, multivariate classification analyses were used to identify algorithms for diagnostic biomarker combinations. These algorithms were independently validated using a set of 95 gastric adenocarcinoma and 51 control samples. Results: Epidermal growth factor receptor (EGFR), pro-apolipoprotein A1 (proApoA1), apolipoprotein A1, transthyretin (TTR), regulated upon activation, normally T-expressed and presumably secreted (RANTES), D-dimer, vitronectin (VN), interleukin-6, α-2 macroglobulin, C-reactive protein and plasminogen activator inhibitor-1 were selected as classifiers in the two algorithms. These algorithms differentiated between the majority of gastric adenocarcinoma and control serum samples in the training/test set with high accuracy (>88%). These algorithms also accurately classified in the validation set (>85%). Conclusion: Two panels of combinatorial biomarkers, including EGFR, TTR, RANTES, and VN, are developed, which are less invasive method for the diagnosis of gastric adenocarcinoma. They could supplement clinical gastroscopic evaluation of symptomatic patients to enhance diagnostic accuracy. Background: Currently, serum biomarkers, which are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinoma do not exist. Therefore, this study identified a panel of serum biomarkers for the diagnosis of gastric adenocarcinoma. Methods: A 29-plex array platform with 29 biomarkers, consisting of 11 proteins discovered through proteomics and 18 previously known to be cancer-associated, was constructed. A test/training set consisting of 120 gastric adenocarcinoma and 120 control samples were examined. After 13 proteins were selected as candidate biomarkers, multivariate classification analyses were used to identify algorithms for diagnostic biomarker combinations. These algorithms were independently validated using a set of 95 gastric adenocarcinoma and 51 control samples. Results: Epidermal growth factor receptor (EGFR), pro-apolipoprotein A1 (proApoA1), apolipoprotein A1, transthyretin (TTR), regulated upon activation, normally T-expressed and presumably secreted (RANTES), D-dimer, vitronectin (VN), interleukin-6, alpha -2 macroglobulin, C-reactive protein and plasminogen activator inhibitor-1 were selected as classifiers in the two algorithms. These algorithms differentiated between the majority of gastric adenocarcinoma and control serum samples in the training/test set with high accuracy (>88%). These algorithms also accurately classified in the validation set (>85%). Conclusion: Two panels of combinatorial biomarkers, including EGFR, TTR, RANTES, and VN, are developed, which are less invasive method for the diagnosis of gastric adenocarcinoma. They could supplement clinical gastroscopic evaluation of symptomatic patients to enhance diagnostic accuracy. Currently, serum biomarkers, which are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinoma do not exist. Therefore, this study identified a panel of serum biomarkers for the diagnosis of gastric adenocarcinoma. A 29-plex array platform with 29 biomarkers, consisting of 11 proteins discovered through proteomics and 18 previously known to be cancer-associated, was constructed. A test/training set consisting of 120 gastric adenocarcinoma and 120 control samples were examined. After 13 proteins were selected as candidate biomarkers, multivariate classification analyses were used to identify algorithms for diagnostic biomarker combinations. These algorithms were independently validated using a set of 95 gastric adenocarcinoma and 51 control samples. Epidermal growth factor receptor (EGFR), pro-apolipoprotein A1 (proApoA1), apolipoprotein A1, transthyretin (TTR), regulated upon activation, normally T-expressed and presumably secreted (RANTES), D-dimer, vitronectin (VN), interleukin-6, α-2 macroglobulin, C-reactive protein and plasminogen activator inhibitor-1 were selected as classifiers in the two algorithms. These algorithms differentiated between the majority of gastric adenocarcinoma and control serum samples in the training/test set with high accuracy (>88%). These algorithms also accurately classified in the validation set (>85%). Two panels of combinatorial biomarkers, including EGFR, TTR, RANTES, and VN, are developed, which are less invasive method for the diagnosis of gastric adenocarcinoma. They could supplement clinical gastroscopic evaluation of symptomatic patients to enhance diagnostic accuracy. Currently, serum biomarkers, which are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinoma do not exist. Therefore, this study identified a panel of serum biomarkers for the diagnosis of gastric adenocarcinoma.BACKGROUNDCurrently, serum biomarkers, which are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinoma do not exist. Therefore, this study identified a panel of serum biomarkers for the diagnosis of gastric adenocarcinoma.A 29-plex array platform with 29 biomarkers, consisting of 11 proteins discovered through proteomics and 18 previously known to be cancer-associated, was constructed. A test/training set consisting of 120 gastric adenocarcinoma and 120 control samples were examined. After 13 proteins were selected as candidate biomarkers, multivariate classification analyses were used to identify algorithms for diagnostic biomarker combinations. These algorithms were independently validated using a set of 95 gastric adenocarcinoma and 51 control samples.METHODSA 29-plex array platform with 29 biomarkers, consisting of 11 proteins discovered through proteomics and 18 previously known to be cancer-associated, was constructed. A test/training set consisting of 120 gastric adenocarcinoma and 120 control samples were examined. After 13 proteins were selected as candidate biomarkers, multivariate classification analyses were used to identify algorithms for diagnostic biomarker combinations. These algorithms were independently validated using a set of 95 gastric adenocarcinoma and 51 control samples.Epidermal growth factor receptor (EGFR), pro-apolipoprotein A1 (proApoA1), apolipoprotein A1, transthyretin (TTR), regulated upon activation, normally T-expressed and presumably secreted (RANTES), D-dimer, vitronectin (VN), interleukin-6, α-2 macroglobulin, C-reactive protein and plasminogen activator inhibitor-1 were selected as classifiers in the two algorithms. These algorithms differentiated between the majority of gastric adenocarcinoma and control serum samples in the training/test set with high accuracy (>88%). These algorithms also accurately classified in the validation set (>85%).RESULTSEpidermal growth factor receptor (EGFR), pro-apolipoprotein A1 (proApoA1), apolipoprotein A1, transthyretin (TTR), regulated upon activation, normally T-expressed and presumably secreted (RANTES), D-dimer, vitronectin (VN), interleukin-6, α-2 macroglobulin, C-reactive protein and plasminogen activator inhibitor-1 were selected as classifiers in the two algorithms. These algorithms differentiated between the majority of gastric adenocarcinoma and control serum samples in the training/test set with high accuracy (>88%). These algorithms also accurately classified in the validation set (>85%).Two panels of combinatorial biomarkers, including EGFR, TTR, RANTES, and VN, are developed, which are less invasive method for the diagnosis of gastric adenocarcinoma. They could supplement clinical gastroscopic evaluation of symptomatic patients to enhance diagnostic accuracy.CONCLUSIONTwo panels of combinatorial biomarkers, including EGFR, TTR, RANTES, and VN, are developed, which are less invasive method for the diagnosis of gastric adenocarcinoma. They could supplement clinical gastroscopic evaluation of symptomatic patients to enhance diagnostic accuracy. |
| Author | Park, P J Kim, Y Ahn, H S Kim, C W Lee, H-J Shin, Y S Yang, H-K Kang, K N |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22240791$$D View this record in MEDLINE/PubMed |
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Currently, serum biomarkers, which are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinoma do... Currently, serum biomarkers, which are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinoma do not exist.... Background: Currently, serum biomarkers, which are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinoma do... |
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| Title | Serum biomarker panels for the diagnosis of gastric adenocarcinoma |
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