Despite improvements in diagnostic imaging-such as CT, MRI, and PET-CT-the sensitivity of EHM detection remains limited, particularly for small or occult lesions in the lung, peritoneum, or lymph nodes. As a result, patients may be inappropriately excluded from curative surgery or exposed to non-beneficial interventions. Thus, there is a pressing need for novel, non-invasive biomarkers capable of detecting EHM with higher accuracy than imaging alone.
MicroRNAs (miRNAs), especially those encapsulated within exosomes, have emerged as stable and reproducible biomarkers reflecting tumor dynamics. Recent studies have shown that circulating miRNA signatures are associated with liver metastasis, therapeutic response, and recurrence risk in CRC. However, their utility for detecting extrahepatic metastasis has not yet been validated in clinical cohorts.
In this research effort, the investigators will leverage small RNA sequencing and machine learning to develop a predictive model for the presence of hepatic and extrahepatic metastases in patients with CRLM. The research plan will consist of three phases:
1. A discovery phase, identifying candidate miRNAs associated with the presence of EHM using next-generation sequencing (NGS) or microarray-based profiling of serum exosomal miRNAs.
2. A model development phase, establishing a quantitative reverse transcription PCR (RT-qPCR) assay and training a predictive algorithm.
3. A validation phase, independently testing the predictive accuracy of the model in an external cohort.
This diagnostic framework is provisionally termed "EXELION" (Exosome-derived Extrahepatic Metastasis Detection by LIquid Biopsy in Colorectal Cancer Liver Metastases). At the end of this study, the EXELION assay is expected to serve as a non-invasive tool to assist clinical decision-making by accurately predicting the presence of extrahepatic metastasis in patients with CRLM.
