Abstract
BACKGROUND: Colorectal cancer (CRC) is the second most common cause of cancer-related death (9.4% of the 9.9 million cancer deaths). However, CRC develops slowly, and early detection and intervention can effectively improve the survival rate and quality of life. Although colonoscopy can detect and diagnose CRC, it is unsuitable for CRC screening in average-risk populations. Some commercial kits based on DNA mutation or methylation are approved for screening, but the low sensitivity for advanced adenoma or early-stage CRC would limit the applications. MAIN RESULTS: Recently, researchers have focused on developing noninvasive or minimally invasive, easily accessible biomarkers with higher sensitivity and accuracy for CRC screening. Numerous reports describe advances in biomarkers, including DNA mutations and methylation, mRNA and miRNA, gut microbes, and metabolites, as well as low-throughput multiomics panels. In small cohorts, the specificity and sensitivity improved when fecal immunochemical testing combined with other biomarkers; further verification in large cohorts is expected. In addition, the continuous improvement of laboratory technology has also improved the sensitivity of detection technology, such as PCR, and the application of CRISPR/Cas technology. Besides, artificial intelligence has extensively promoted the mining of biomarkers. Machine learning was performed to construct a diagnosis model for CRC screening based on the cfDNA fragment features from whole-genome sequencing data. In another study, multiomics markers, including cfDNA, epigenetic, and protein signals, were also discovered by machine learning. Finally, advancements in sensor technology promote the applicability of volatile organic compounds in CRC early detection. CONCLUSION: Here, the authors review advances in early detection and screening of CRC based on different biomarker types. Most studies reported optimistic findings based on preliminary research, and prospective clinical studies are ongoing. These promising biomarkers are expected to more accurately identify early-stage patients with CRC and be applied in the future.