Published online Jan 24, 2023. doi: 10.5306/wjco.v14.i1.27
Peer-review started: October 5, 2022
First decision: October 24, 2022
Revised: November 2, 2022
Accepted: December 13, 2022
Article in press: December 13, 2022
Published online: January 24, 2023
Processing time: 97 Days and 3.4 Hours
Lung adenocarcinoma patients with localized or locally advanced disease have a high risk of death, and their 5-year overall survival rate is less than 15%.
To evaluate the prognosis of Lung adenocarcinoma (LUAD) patients and optimize treatment, effective clinical research prediction models.
To identify reliable prognostic biomarkers to predict clinical outcomes and to help clinicians to make accurate clinical treatment decisions.
The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) were used to screen for differential genes for lung adenocarcinoma. Univariate regression analysis combined with LASSO regression analysis was used to screen for prognostic-related genes. Multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model.
We establish a prognostic risk model for lung adenocarcinoma based on 5 mRNAs (TCN1, CENPF, MAOB, CRTAC1, and PLEK2). These five new genes were significantly correlated with the prognosis of LUAD patients. To improve the prognostic predictive power of the five prognostic gene markers, a predictive nomogram combining risk scores and conventional clinical prognostic parameters (including age and tumor stage) was constructed to enable clinicians to determine the prognosis of each patient.
A 5-mRNA-based model was constructed to predict the prognosis of lung adenocarcinoma, which may provide clinicians with reliable prognostic assessment tools and help clinical treatment decisions.
Our study identified a 5-gene model and constructed a nomogram which may have important implications for clinical medical decision and personalized treatment of patients with lung adenocarcinoma.