ArticleJournal of cellular and molecular medicine2025
Identification and Validation of a m6A-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer.
Article in Journal of cellular and molecular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Accumulating research indicates that N6-methyladenosine (m6A) modification plays a pivotal role in colorectal cancer (CRC). Hence, investigating the m6A-related long noncoding RNAs (lncRNAs) significantly improves therapeutic strategies and prognostic assessments. This study aimed to develop and validate a prognostic model based on m6A-related lncRNAs to improve the prediction of clinical outcomes and identify potential immunological mechanisms in CRC. We obtained high-throughput CRC data from The Cancer Genome Atlas to identify a prognostic model based on m6A-related lncRNAs. Then, the model was constructed and validated through LASSO analysis and Cox regression using R software. The clinical applicability was enhanced by developing a nomogram. We further conducted experiments to reveal the biological function of LINC00543. The prognostic model based on eight m6A-related lncRNAs exhibited impressive accuracy, achieving an area under the receiver-operating curve value of 0.753, 0.682 and 0.706 for predictions after 1, 3 and 5 years, respectively. The Kaplan-Meier analysis confirmed the consistency of the model across different pathological characteristics, with a high-risk group showing a poorer prognosis. Furthermore, the model was linked to immune function, particularly the type I interferon response, through gene set enrichment analysis and experimental validation. Our study presented a m6A-related lncRNA prognostic model for CRC with potential clinical utility. The model not only provided improved accuracy over traditional staging but also offered insights into the immunological mechanisms of CRC, facilitating personalised medicine approaches.
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