Establishment and preliminary verification of diagnostic model for colorectal cancer based on matrix-assisted laser desorp-tion ionization time-of-flight mass spectrometry technology
Objective To establish a diagnostic model for colorectal cancer(CRC)based on matrix-assisted laser desorption ioniza-tion time-of-flight mass spectrometry(MALDI-TOF MS),and to search for potential serum markers of CRC.Methods Serum samples were collected from 114 newly diagnosed CRC patients(CRC group)and 60 healthy persons who underwent physical examination(healthy group)in 960th Hospital of the PLA from December 2018 to December 2020.The patients were randomly divided into a training group of 131(87 CRC patients and 44 healthy individuals)and a validation group of 43(27 CRC patients and 16 healthy in-dividuals)according to a 3∶1 ratio for model building and initial validation,respectively.The low abundance protein in serum was ex-tracted and purified by magnetic bead-weak cation exchange(MB-WCX),and the differential protein peaks in CRC group and healthy group were screened by MALDI-TOF MS.The diagnostic model was established based on three algorithms(genetic algorithm,super-vised neural network algorithm and fast classification algorithm),and the two protein peaks with the most significant differences were selected for cluster analysis,and the data of the verification group was brought into the diagnostic model to verify its sensitivity,speci-ficity and diagnostic efficiency.Results There were significant differences in protein fingerprints between CRC group and healthy group.A total of 9 different protein peaks with statistical significance were screened(area under the curve>0.70),7 expressions were up-regulated and 2 expressions were down-regulated in CRC group.m/z 4645.32 and m/z 5906.48 showed the most significant differ-ence(P<0.01),and area under the curve values were 0.91 and 0.76,respectively.The expression of m/z 4645.32 was significantly down-regulated in CRC group,while the expression of m/z 5906.48 was up-regulated.Comparison showed that supervised neural network model had the best diagnostic efficacy,with sensitivity of 92.60%,specificity of 81.25%and accuracy of 88.37%.Conclusion The CRC diagnostic model established in this study has good diagnostic efficacy,among which the protein peaks m/z 4645.32 and m/z 5906.48 are expected to be potential serum markers of CRC.
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