Construction and validation of a prognostic model for colorectal cancer based on migrasome-related long non-coding RNAs.

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Tác giả: Hongsheng Chen, Xu Hu, Ming Liu, Qiang Lv, Yang Wang, Yuliuming Wang, Qingzhu Yang

Ngôn ngữ: eng

Ký hiệu phân loại: 623.38 Protective construction

Thông tin xuất bản: United States : PeerJ , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 748248

BACKGROUND: Colon adenocarcinoma (COAD) is a globally prevalent and deadly malignancy of the digestive system. Recently, migrasomes have gained significant attention as important regulators of tumor cell migration and metastasis. The current research developed a highly accurate prognostic model using migrasome-related long non-coding RNAs (lncRNAs) in COAD, providing new insights for prognostic assessment and immunotherapy of COAD patients. METHODS: RNA sequencing data from COAD patients were acquired from The Cancer Genome Atlas Program (TCGA) database to construct a prognostic lncRNA model based on known migrasome-related genes (MRGs). The model's predictive accuracy was then assessed using concordance index (C-index) analysis, nomograms, principal component analysis, and receiver operating characteristic curves. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify significant differences in biological functions and signaling pathways associated with differentially expressed genes in the high-risk subgroup. A comprehensive evaluation of the model incorporated clinical-pathological features, tumor microenvironment, and chemotherapy sensitivity. The expression levels of prognostic genes in COAD patients were validated RESULTS: Migrasome-related lncRNAs were identified as robust prognostic predictors for COAD. Multivariate analysis revealed that the risk score derived from these lncRNAs is an independent prognostic factor for COAD. Patients in the low-risk group exhibited significantly longer overall survival (OS) compared to those in the high-risk group. Accordingly, the nomogram prediction model we developed, which integrates clinical features and risk scores, demonstrated excellent prognostic performance.
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