ArticleJournal of gastrointestinal oncology2021
A methylation-based prognostic model predicts survival in patients with colorectal cancer.
Article in Journal of gastrointestinal oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- Engineering overexpressing SYNGR1 inhibited the progression of GBM cells by suppressing the intracellular FGF1-mediated LDs accumulation and cytoskeleton remodeling.Journal of neuro-oncology · 2025Article
- Characteristics of endoplasmic reticulum stress in colorectal cancer for predicting prognosis and developing treatment options.Cancer medicine · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo construct a model that could effectively predict the prognosis of colorectal cancer (CRC) by searching for methylated-differentially expressed genes (MDEGs).
methodsWe identified MDEGs through four databases from Gene Expression Omnibus (GEO) and annotated their functions via bioinformatics analysis. Subsequently, after adjusting for gender, age, and grading, multivariate Cox hazard analysis was utilized to select MDEGs interrelated with the prognosis of CRC, and LASSO analysis was utilized to fit the prediction model in the training set. Furthermore, another independent dataset was harnessed to verify the effectiveness of the model in predicting prognosis.
resultsIn total, 252 hypomethylated and up-regulated genes and 132 hypermethylated and down-regulated genes were identified, 27 of which were correlated with the prognosis of CRC, and a 10-gene prognostic model was established after LASSO analysis. The overall survival rate could be effectively grouped into different risks by the median score of this model in the training set [risk ratio (HR) =2.27, confidence interval (95% CI), 1.69-3.13, P=8.15×10
conclusionsOur model could effectively predict the overall survival rate of patients with CRC and provides potential application guidelines for its clinically personalized treatment.
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