Prognostic Model for colon cancer(COAD) Based on Migrasome-Related LncRNAs

Yongqing  Yang; Lan  Chen; Xianling  Guo; Zhui Ke

doi:10.62177/apjcmr.v2i2.1285

Authors

Yongqing Yang Jinjiang Municipal Hospital, Shanghai Sixth People’s Hospital Fujian
Lan Chen Anhui University of Science and Technology
Xianling Guo Shanghai Tenth People’s Hospital; Tongji University Cancer Center
Zhui Ke Shanghai Tenth People’s Hospital; Tongji University Cancer Center

DOI:

https://doi.org/10.62177/apjcmr.v2i2.1285

Keywords:

Colon Cancer, Migrasome, LncRNA, Immunotherapy, Tumor Microenvironment

Abstract

Objective: Colon adenocarcinoma (COAD) is a leading cause of cancer-related mortality worldwide. In recent years, migrasomes—a newly identified class of extracellular vesicles—have attracted increasing attention for their potential role in intercellular communication. This study aimed to investigate the role of migrasome-associated long non-coding RNAs (CMiSlncRNAs) in COAD progression using bioinformatics approaches and to evaluate their potential as diagnostic biomarkers or therapeutic targets. Method: Migrasome-associated genes were identified through a comprehensive literature review and intersected with LncRNAs expressed in COAD. A total of 41 co-expressed CMiSLncRNAs were identified. Univariate Cox regression analysis revealed 13 CMiSLncRNAs with significant prognostic value. A prognostic model was constructed using LASSO regression combined with multivariate Cox proportional hazards analysis, and patients were stratified into high- and low-risk groups based on the median risk score. Kaplan–Meier analysis, principal component analysis (PCA), and functional enrichment analysis were performed to compare the two risk groups. Finally, the influence of CMiSLncRNAs on tumor immune infiltration, immune function, and drug sensitivity was investigated. Results: Thirteen CMiSLncRNAs with prognostic significance were identified. The prognostic model demonstrated strong discriminatory ability, with low-risk patients showing significantly better overall survival than high-risk patients across training, testing, and full cohorts. Multivariate Cox analysis confirmed that the risk score was an independent prognostic factor. Functional enrichment analysis indicated that CMiSLncRNAs are involved in pathways such as Hippo, mTOR, and Wnt signaling. Immune analysis revealed a more active immune microenvironment in the low-risk group, characterized by higher immune function scores and increased infiltration of activated NK cells and mast cells. Drug sensitivity analysis revealed distinct drug response profiles between the two risk groups. Conclusion: The CMiSlncRNA-based prognostic model offers novel insights for risk stratification and personalized treatment in COAD. These findings highlight the significant roles of CMiSLncRNAs in tumor progression, immune regulation, and drug sensitivity.

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