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




Björkman Kajsa, Jalkanen Sirpa, Salmi Marko, Mustonen Harri, Kaprio Tuomas, Kekki Henna, Pettersson Kim, Böckelman Camilla, Haglund Caj

PublisherNATURE RESEARCH

2021

Scientific Reports

SCIENTIFIC REPORTS

SCI REP-UK

ARTN 4287

11

1

9

2045-2322

DOIhttps://doi.org/10.1038/s41598-020-80785-1(external)

https://research.utu.fi/converis/portal/detail/Publication/53697638(external)



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.

Last updated on 2024-26-11 at 22:59