Abstract
Background
Patients with colorectal cancer usually have a poor prognosis because of the absence of suitable biomarkers for diagnosing asymptomatic patients. Here we determined the ability of MIC-1 to detect precancerous lesions and CRC in an asymptomatic cohort from CRC Screening Program.
Methods
We screened 2759 subjects with risk factors. Endoscopic and histopathological analyses revealed that 19 and 47 subjects had CRC or precancerous lesions. We randomly selected 24 subjects with normal colonoscopies as healthy controls. We used receiver operating characteristic curve analysis to evaluate the diagnostic efficacy of MIC-1 for CRC and precancerous lesions.
Results
The optimal thresholds of MIC-1 levels with precancerous lesions or CRC were 314.12 pg/mL (sensitivity, 91.50%; specificity, 54.20%) and 357.64 pg/mL (sensitivity, 82.40%; specificity, 70.80%). Moreover, MIC-1 levels distinguished precancerous lesions better than CEA, CA19-9, or CA24-2 (AUC: 0.760 vs. 0.529, 0.624, and 0.585) or CRC (AUCs: 0.821 vs. 0.743, 0.657, and 0.688) from the healthy controls. The combination of MIC-1, CEA, CA19-9, and CA24-2 showed the highest in sensitivity and specificity for CRC diagnosis (sensitivity, 94.10%; specificity, 87.50%).
Conclusions
Serum MIC-1 levels increased the sensitivity of detection of precancerous colorectal lesions and CRC and can be used to improve screening.
Acknowledgments
We thank Edanz Group (https://en-author-services.edanzgroup.com/ac) for editing a draft of this manuscript.
Ethical approval
The study was approved by the Medical Ethics Committee of The First Affiliated Hospital of USTC, Anhui Provincial Cancer Hospital. All patients provided written informed consent.
Author contributions
All authors read and approved the final manuscript.
CYD and ML designed the study. CYD, YLM, and XLZ analyzed the data and interpreted the results. CYD wrote the manuscript. YZ, ZWC, and SHC revised the manuscript from preliminary draft to submission. CYD and YZ were responsible for statistical design and analysis. ML supervised the whole study. All authors read and approved the final manuscript.
Disclosure statement
There are no conflicts of interest.
Data availability statement
All data generated or analyzed during this study are included in this published article.