Background: Colorectal cancer (CRC) is a common form of cancer, with rectal cancer accounting for approximately one-third of all cases. Among rectal cancers, 95% are classified as rectal adenocarcinoma (READ). Emerging evidence suggests that long noncoding RNAs (lncRNAs) play a significant role in the development and progression of various cancers. In our study, we aimed to identify differentially expressed lncRNAs potentially associated with m6A and establish a risk assessment model to predict clinical outcomes for READ patients.
Methods: The READ dataset from the TCGA database was utilized in this study to synergistically and logically integrate m6A and lncRNA, while employing bioinformatics technology for the identification of suitable biomarkers. A risk prediction model comprising m6A-associated lncRNAs was constructed to investigate the prognostic, diagnostic, and biological functional relevance of these m6A-related lncRNAs.
Results: Our research builds a composed of three related to m6A lncRNA rectal gland cancer prognosis model, and the model has been proved in the multi-dimensional can serve as the potential of the prognosis of rectal gland cancer biomarkers. Our study constructed a prognostic model of rectal adenocarcinoma consisting of three related m6A lncRNAs: linc00702, ac106900.1 and al583785.1.
Conclusion: The model has been validated as a potential prognostic biomarker for rectal cancer in multiple dimensions, aiming to provide clinicians with an indicator to assess the duration of straight adenocarcinoma. This enables early detection of rectal cancer and offers a promising target for immunotherapy.
Keywords: Long noncoding RNA (lncRNAs); immune microenvironment; m6A; prognostic model; rectal adenocarcinoma.