Combining Histopathologic and Gene-Expression Profiling for Risk Stratification of Nodal Metastasis in Colorectal Cancer
DOI:
https://doi.org/10.33192/smj.v78i2.279649Keywords:
Colorectal neoplasm, Lymphatic metastasis, RNA sequencing, Gene expression regulation, Logistic ModelsAbstract
Objective: To identify gene-expression features associated with lymph node metastasis (LNM) in colorectal cancer (CRC) and to develop a transcriptomic-clinical predictive model for preoperative nodal assessment.
Materials and Methods: A total of 151 CRC tissue samples (74 LNM– and 77 LNM+) were analyzed using RNA sequencing. Differentially expressed genes (DEGs) were identified with DESeq2, and functional enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model integrating gene-expression features with clinical variables was developed to predict LNM status. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.
Results: A total of 302 DEGs were identified in LNM+ CRC, including 178 upregulated and 124 downregulated genes. Upregulated genes were enriched in chemokine-mediated signaling, epithelial morphogenesis, and intermediate filament organization, whereas downregulated genes were associated with adaptive immune response and complement activation. In multivariate analysis, lymphovascular invasion (LVI) was the only clinical variable independently associated with LNM. The optimized LASSO model, combining LVI with selected transcriptomic features demonstrated excellent discriminatory performance (AUC ≈ 0.92). Key upregulated genes included CCL21, CCL26, DEFB1, LST1, KANK4, TNNC1, PFDN6, TENM1, CST6, and PADI3, while IGHV2-26 was downregulated.
Conclusion: Integration of LVI with transcriptomic signatures enables accurate prediction of lymph node metastasis in CRC and supports biopsy-based risk assessment to guide clinical decision-making.
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