Abstract
Objectives: Blood-based tests have been shown to be an effective strategy for colorectal cancer (CRC) detection in screening programs. This study was aimed to test the performance of 20 blood markers including tumor antigens, inflammatory markers, and apolipoproteins as well as their combinations.Methods: In total 203 healthy volunteers and 102 patients with CRC were enrolled into the study. Differences between healthy and cancer subjects were evaluated using Wilcoxon rank-sum test. Several multivariate classification algorithms were employed using information about different combinations of biomarkers altered in CRC patients as well as age and gender of the subjects; random sub-sampling cross-validation was done to overcome overfitting problem. Diagnostic performance of single biomarkers and multivariate classification models was evaluated by receiver operating characteristic (ROC) analysis.Results: Of 20 biomarkers, 16 were significantly different between the groups (p-value ≤ 0.001); ApoA1, ApoA2 and ApoA4 levels were decreased, whereas levels of tumor antigens (e.g. carcinoembriogenic antigen) and inflammatory markers (e.g., C-reactive protein) were increased in CRC patients vs. healthy subjects. Combinatorial markers including information about all 16 significant analytes, age and gender of patients, demonstrated better performance over single biomarkers with average accuracy on test datasets ≥95% and area under ROC curve (AUROC) ≥98%.Conclusions: Combinatorial approach was shown to be a valid strategy to improve performance of blood-based CRC diagnostics. Further evaluation of the proposed models in screening programs will be performed to gain a better understanding of their diagnostic value.
Highlights
Colorectal cancer (CRC) is the third most commonly diagnosed malignancy worldwide with the highest prevalence in developed countries [1]
Classification models were assembled based on the measurements of biomarkers, which were significantly different between healthy subjects and CRC patients (p-value 0.6)
Several classification algorithms including random forest (RF), support vector machine (SVM), linear discriminant analysis (LDA), and naïve Bayes classifier (NBC), as well as multiple logistic regression (MLR) [21] were trained using the whole dataset and their discriminative ability was assessed via receiver operating characteristics (ROC) analysis, similar to single biomarkers
Summary
Colorectal cancer (CRC) is the third most commonly diagnosed malignancy worldwide with the highest prevalence in developed countries [1]. Diagnosis of cancer represents an effective way to reduce mortality rates, since clinical symptoms are often minor and non-specific until advanced disease stages, dedicated screening programs are required [3]. [4, 5] While these methods are required to confirm diagnosis, their usage in screening programs is limited due to invasiveness, labor intensiveness, risk of complications and the need for specific equipment. DNA-based methods represent another strategy of CRC detection, but despite the diagnostic advantage over FOBT these systems cannot be used in screening programs due to their expense [9]
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