Establishment of a m6A‐Related Molecular Pattern in the Prognosis and Immune Infiltration of Osteosarcoma Using Machine Learning and Experiments
Na He, Xia Chen, Chunyan Zhang
TL;DR
This study creates a new model using m6A-related genes to predict osteosarcoma prognosis and immune infiltration, validated through machine learning and experiments.
Contribution
The study introduces a novel m6A-related gene model for osteosarcoma prognosis and immune infiltration prediction.
Findings
A prognostic model using 14 m6A-related genes showed strong predictive power with AUC values up to 0.9091.
The high-risk group had reduced immune infiltration and enriched malignancy-related pathways like E2F targets and MYC targets.
A clinical nomogram was developed to support personalized treatment decisions in osteosarcoma.
Abstract
To determine the prognosis of osteosarcoma, multiple predictive models have been constructed in recent years. Nevertheless, the model for N6‐methyladenosine (m6A)‐related genes, a critical subset of molecular regulators for osteosarcoma, has not been identified. Gene expression matrices and clinical data were extracted from the GEO datasets GSE21257 and GSE16091. Randomly selected 70% of samples from GSE21257 were assigned as the training dataset, while the remaining 30% of samples from GSE21257 and all samples from GSE16091 were designated as the internal test and external test datasets, respectively. The predictive model was developed using elastic net–penalized Cox regression. Receiver operating characteristic (ROC) analysis, Kaplan–Meier analysis, and Wilcoxon′s tests were conducted in the training, internal test, and external test datasets to validate its efficacy. Additionally, a…
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Taxonomy
TopicsRNA modifications and cancer · Ferroptosis and cancer prognosis · Sarcoma Diagnosis and Treatment