ArticleFrontiers in immunology2026
Mass spectrometry-based multi-omics analysis elucidates immune microenvironmental characteristics and the risk of distant metastasis in N1c colorectal cancer.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
Background: Colorectal cancer (CRC) ranks third in global cancer incidence and second in mortality. Tumor deposit (TD), a specific regional spread form, is a crucial independent risk factor for survival and have biological differences from lymph node metastasis (LNM). However, it is underestimated in current staging systems, which results in biased treatment decisions and prognosis evaluation. Moreover, the biological features and distant metastasis patterns of N1c CRC remain largely unknown. Method: We performed Data-independent Acquisition Mass Spectrometry (DIA-MS) analysis of formalin-fixed, paraffin-embedded (FFPE) samples from 13 T1-T4N1cM1 CRC patients to reveal their molecular characteristics. 9 machine learning algorithms identified 10 TD-related metastasis genes (TDRGs), the multi-cohort validation in 1,582 CRC patients confirmed their role in prognosis. Immune landscape and immunotherapy response were assessed by the CIBERSORT, tumor mutation burden (TMB), Tumor Immune Dysfunction and Exclusion (TIDE) score, Consensus molecular subtype (CMS), immune checkpoint gene expression. scRNA-seq analysis identified Procollagen-Lysine,2-Oxoglutarate 5-Dioxygenase 1 (PLOD1) expression in CRC, Immunohistochemical staining (IHC) and Masson's trichrome staining were used to assess PLOD1 expression and collagen fiber content in CRC, its role in tumor invasion and migration was elucidated by wound healing and transwell assays. Results: N1c samples exhibit enhanced extracellular matrix (ECM) remodeling and epithelial mesenchymal transition (EMT). The TDRGs identified by machine learning robustly predicting disease-free survival (DFS) across multiple cohorts (Mean C-index = 0.72) and immune activity. A higher risk score predicted early metastasis and poorer response to immunotherapy, marked by lower level of immune infiltration, higher TIDE scores, lower TMB, and downregulated immune checkpoint genes. scRNA-seq analysis pinpoints highest PLOD1 expression in fibroblasts. Histological analysis of N1c samples demonstrated PLOD1 expression patterns and their significant correlation with stromal collagen fiber abundance (p < 0.01). Wound healing and transwell assays indicating the knockdown of PLOD1 hinders the migration and invasion of CRC DLD-1 cell. Conclusion: We assessed the protein expression profiles and pathway characteristics of N1c CRC. The model developed based on TDRGs, effectively predicted DFS and immunotherapy response, supporting precision treatment and staging system optimization. PLOD1's role in ECM remodeling and CRC cell migration and invasion suggests its potential as a prognostic biomarker and therapeutic target.
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