An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer
Abstract Screening programs for colorectal cancer (CRC) often rely on detection of blood in stools, which is unspecific and leads to a large number of colonoscopies of healthy subjects. Painstaking research has led to the identification of a large number of different types of biomarkers, few of whic...
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2019
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/ab9bb67a288e45d0bb73b6b76a175059 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:ab9bb67a288e45d0bb73b6b76a175059 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:ab9bb67a288e45d0bb73b6b76a1750592021-12-02T15:08:20ZAn algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer10.1038/s41598-019-51999-92045-2322https://doaj.org/article/ab9bb67a288e45d0bb73b6b76a1750592019-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-51999-9https://doaj.org/toc/2045-2322Abstract Screening programs for colorectal cancer (CRC) often rely on detection of blood in stools, which is unspecific and leads to a large number of colonoscopies of healthy subjects. Painstaking research has led to the identification of a large number of different types of biomarkers, few of which are in general clinical use. Here, we searched for highly accurate combinations of biomarkers by meta-analyses of genome- and proteome-wide data from CRC tumors. We focused on secreted proteins identified by the Human Protein Atlas and used our recently described algorithms to find optimal combinations of proteins. We identified nine proteins, three of which had been previously identified as potential biomarkers for CRC, namely CEACAM5, LCN2 and TRIM28. The remaining proteins were PLOD1, MAD1L1, P4HA1, GNS, C12orf10 and P3H1. We analyzed these proteins in plasma from 80 patients with newly diagnosed CRC and 80 healthy controls. A combination of four of these proteins, TRIM28, PLOD1, CEACAM5 and P4HA1, separated a training set consisting of 90% patients and 90% of the controls with high accuracy, which was verified in a test set consisting of the remaining 10%. Further studies are warranted to test our algorithms and proteins for early CRC diagnosis.Danuta R. GawelEun Jung LeeXinxiu LiSandra LiljaAndreas MatussekSamuel SchäferRenate Slind OlsenMargaretha StenmarkerHuan ZhangMikael BensonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-12 (2019) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Danuta R. Gawel Eun Jung Lee Xinxiu Li Sandra Lilja Andreas Matussek Samuel Schäfer Renate Slind Olsen Margaretha Stenmarker Huan Zhang Mikael Benson An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer |
| description |
Abstract Screening programs for colorectal cancer (CRC) often rely on detection of blood in stools, which is unspecific and leads to a large number of colonoscopies of healthy subjects. Painstaking research has led to the identification of a large number of different types of biomarkers, few of which are in general clinical use. Here, we searched for highly accurate combinations of biomarkers by meta-analyses of genome- and proteome-wide data from CRC tumors. We focused on secreted proteins identified by the Human Protein Atlas and used our recently described algorithms to find optimal combinations of proteins. We identified nine proteins, three of which had been previously identified as potential biomarkers for CRC, namely CEACAM5, LCN2 and TRIM28. The remaining proteins were PLOD1, MAD1L1, P4HA1, GNS, C12orf10 and P3H1. We analyzed these proteins in plasma from 80 patients with newly diagnosed CRC and 80 healthy controls. A combination of four of these proteins, TRIM28, PLOD1, CEACAM5 and P4HA1, separated a training set consisting of 90% patients and 90% of the controls with high accuracy, which was verified in a test set consisting of the remaining 10%. Further studies are warranted to test our algorithms and proteins for early CRC diagnosis. |
| format |
article |
| author |
Danuta R. Gawel Eun Jung Lee Xinxiu Li Sandra Lilja Andreas Matussek Samuel Schäfer Renate Slind Olsen Margaretha Stenmarker Huan Zhang Mikael Benson |
| author_facet |
Danuta R. Gawel Eun Jung Lee Xinxiu Li Sandra Lilja Andreas Matussek Samuel Schäfer Renate Slind Olsen Margaretha Stenmarker Huan Zhang Mikael Benson |
| author_sort |
Danuta R. Gawel |
| title |
An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer |
| title_short |
An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer |
| title_full |
An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer |
| title_fullStr |
An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer |
| title_full_unstemmed |
An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer |
| title_sort |
algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer |
| publisher |
Nature Portfolio |
| publishDate |
2019 |
| url |
https://doaj.org/article/ab9bb67a288e45d0bb73b6b76a175059 |
| work_keys_str_mv |
AT danutargawel analgorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT eunjunglee analgorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT xinxiuli analgorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT sandralilja analgorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT andreasmatussek analgorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT samuelschafer analgorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT renateslindolsen analgorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT margarethastenmarker analgorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT huanzhang analgorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT mikaelbenson analgorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT danutargawel algorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT eunjunglee algorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT xinxiuli algorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT sandralilja algorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT andreasmatussek algorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT samuelschafer algorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT renateslindolsen algorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT margarethastenmarker algorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT huanzhang algorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer AT mikaelbenson algorithmbasedmetaanalysisofgenomeandproteomewidedataidentifiesacombinationofpotentialplasmabiomarkersforcolorectalcancer |
| _version_ |
1718388205029425152 |