ArticleFrontiers in cell and developmental biology2021
Construction and Analysis of a Colorectal Cancer Prognostic Model Based on N6-Methyladenosine-Related lncRNAs.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 38 citations in OpenAlex.
- A mitochondrial ferroptosis-related gene signature predicts prognosis and immune landscape in colon cancer.Frontiers in medicine · 2025Article
- mScientific reports · 2024Article
- Development of a novel colon adenocarcinoma m6A-related lncRNA pair prognostic model.Translational cancer research · 2024Article
- Ramifications of m6A Modification on ncRNAs in Cancer.Current genomics · 2024Review
- Analysis of disulfidptosis- and cuproptosis-related LncRNAs in modulating the immune microenvironment and chemosensitivity in colon adenocarcinoma.IET systems biology · 2024Article
- Interaction of the intestinal cytokines-JAKs-STAT3 and 5 axes with RNA N6-methyladenosine to promote chronic inflammation-induced colorectal cancer.Frontiers in oncology · 2024Review
- The m6A modulator-mediated cytarabine sensitivity and immune cell infiltration signature in acute myeloid leukemia.Journal of cancer research and clinical oncology · 2023Article
- PI3K/Akt signalling pathway-associated long noncoding RNA signature predicts the prognosis of laryngeal cancer patients.Scientific reports · 2023Article
- Comprehensive analysis of m6A regulators and relationship with tumor microenvironment, immunotherapy strategies in colorectal adenocarcinoma.BMC genomic data · 2023Article
- A Ferroptosis-Related lncRNAs Signature Predicts Prognosis of Colon Adenocarcinoma.Life (Basel, Switzerland) · 2023Article
- Effective oxygen metabolism-based prognostic signature for colorectal cancer.Frontiers in oncology · 2023Article
- RNA modifications: importance in immune cell biology and related diseases.Signal transduction and targeted therapy · 2022Review
- The role of N6-methyladenosine-modified non-coding RNAs in the pathological process of human cancer.Cell death discovery · 2022Review
- Comprehensive analysis of cuproptosis-related lncRNAs to predict prognosis and immune infiltration characteristics in colorectal cancer.Frontiers in genetics · 2022Article
- Roles of m5C RNA Modification Patterns in Biochemical Recurrence and Tumor Microenvironment Characterization of Prostate Adenocarcinoma.Frontiers in immunology · 2022Article
- Identification of N6-methylandenosine related lncRNA signatures for predicting the prognosis and therapy response in colorectal cancer patients.Frontiers in genetics · 2022Article
- Development and validation of a cancer-associated fibroblast-derived lncRNA signature for predicting clinical outcomes in colorectal cancer.Frontiers in immunology · 2022Article
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given the relatively poor understanding of the expression and functional effects of the N6-methyladenosine (m6A) RNA methylation on colorectal cancer (CRC), we attempted to measure its prognostic value and clinical significance. We comprehensively screened 37 m6A-related prognostic long non-coding RNAs (lncRNAs) with significant differences in expression based on 21 acknowledged regulators of m6A modification and data on 473 colorectal cancer tissues and 41 para-cancer tissues obtained from the TCGA database. Accordingly, we classified 473 CRC patients into two clusters by consensus clustering on the basis of significantly different survival outcomes. We also found a potential correlation between m6A-related prognostic lncRNAs and BRAF-KRAS expression, as well as immune cell infiltration. Then, we established a prognostic model by selecting 16 m6A-related prognostic lncRNAs via LASSO Cox analysis and grouped the CRC patients into low- and high-risk groups to calculate risk scores. Then, we performed stratified sampling to validate and confirm our model by categorising the 473 samples into a training group (
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