A prognostic model for colorectal cancer based on CEA and a 48-multiplex serum biomarker panel

Abstract Mortality in colorectal cancer (CRC) remains high, resulting in 860,000 deaths annually. Carcinoembryonic antigen is widely used in clinics for CRC patient follow-up, despite carrying a limited prognostic value. Thus, an obvious need exists for multivariate prognostic models. We analyzed 48...

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Autores principales:	Kajsa Björkman, Sirpa Jalkanen, Marko Salmi, Harri Mustonen, Tuomas Kaprio, Henna Kekki, Kim Pettersson, Camilla Böckelman, Caj Haglund
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ac21611d9097442fbd90fbe9222b2422
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spelling	oai:doaj.org-article:ac21611d9097442fbd90fbe9222b24222021-12-02T14:28:14ZA prognostic model for colorectal cancer based on CEA and a 48-multiplex serum biomarker panel10.1038/s41598-020-80785-12045-2322https://doaj.org/article/ac21611d9097442fbd90fbe9222b24222021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80785-1https://doaj.org/toc/2045-2322Abstract Mortality in colorectal cancer (CRC) remains high, resulting in 860,000 deaths annually. Carcinoembryonic antigen is widely used in clinics for CRC patient follow-up, despite carrying a limited prognostic value. Thus, an obvious need exists for multivariate prognostic models. We analyzed 48 biomarkers using a multiplex immunoassay panel in preoperative serum samples from 328 CRC patients who underwent surgery at Helsinki University Hospital between 1998 and 2003. We performed a multivariate prognostic forward-stepping background model based on basic clinicopathological data, and a multivariate machine-learned prognostic model based on clinicopathological data and biomarker variables, calculating the disease-free survival using the value of importance score. From the 48 analyzed biomarkers, only IL-8 emerged as a significant prognostic factor for CRC patients in univariate analysis (HR 4.88; 95% CI 2.00–11.92; p = 0.024) after correcting for multiple comparisons. We also developed a multivariate model based on all 48 biomarkers using a random survival forest analysis. Variable selection based on a minimal depth and the value of importance yielded two tentative candidate CRC prognostic markers: IL-2Ra and IL-8. A multivariate prognostic model using machine-learning technologies improves the prognostic assessment of survival among surgically treated CRC patients.Kajsa BjörkmanSirpa JalkanenMarko SalmiHarri MustonenTuomas KaprioHenna KekkiKim PetterssonCamilla BöckelmanCaj HaglundNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kajsa Björkman Sirpa Jalkanen Marko Salmi Harri Mustonen Tuomas Kaprio Henna Kekki Kim Pettersson Camilla Böckelman Caj Haglund A prognostic model for colorectal cancer based on CEA and a 48-multiplex serum biomarker panel
description	Abstract Mortality in colorectal cancer (CRC) remains high, resulting in 860,000 deaths annually. Carcinoembryonic antigen is widely used in clinics for CRC patient follow-up, despite carrying a limited prognostic value. Thus, an obvious need exists for multivariate prognostic models. We analyzed 48 biomarkers using a multiplex immunoassay panel in preoperative serum samples from 328 CRC patients who underwent surgery at Helsinki University Hospital between 1998 and 2003. We performed a multivariate prognostic forward-stepping background model based on basic clinicopathological data, and a multivariate machine-learned prognostic model based on clinicopathological data and biomarker variables, calculating the disease-free survival using the value of importance score. From the 48 analyzed biomarkers, only IL-8 emerged as a significant prognostic factor for CRC patients in univariate analysis (HR 4.88; 95% CI 2.00–11.92; p = 0.024) after correcting for multiple comparisons. We also developed a multivariate model based on all 48 biomarkers using a random survival forest analysis. Variable selection based on a minimal depth and the value of importance yielded two tentative candidate CRC prognostic markers: IL-2Ra and IL-8. A multivariate prognostic model using machine-learning technologies improves the prognostic assessment of survival among surgically treated CRC patients.
format	article
author	Kajsa Björkman Sirpa Jalkanen Marko Salmi Harri Mustonen Tuomas Kaprio Henna Kekki Kim Pettersson Camilla Böckelman Caj Haglund
author_facet	Kajsa Björkman Sirpa Jalkanen Marko Salmi Harri Mustonen Tuomas Kaprio Henna Kekki Kim Pettersson Camilla Böckelman Caj Haglund
author_sort	Kajsa Björkman
title	A prognostic model for colorectal cancer based on CEA and a 48-multiplex serum biomarker panel
title_short	A prognostic model for colorectal cancer based on CEA and a 48-multiplex serum biomarker panel
title_full	A prognostic model for colorectal cancer based on CEA and a 48-multiplex serum biomarker panel
title_fullStr	A prognostic model for colorectal cancer based on CEA and a 48-multiplex serum biomarker panel
title_full_unstemmed	A prognostic model for colorectal cancer based on CEA and a 48-multiplex serum biomarker panel
title_sort	prognostic model for colorectal cancer based on cea and a 48-multiplex serum biomarker panel
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ac21611d9097442fbd90fbe9222b2422
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A prognostic model for colorectal cancer based on CEA and a 48-multiplex serum biomarker panel

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