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Updated: Aug 12, 2025
MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
Published on: October 26, 2017
5-mRNA-based prognostic signature of survival in lung adenocarcinoma
Qian-Lin Xia1, Xiao-Meng He2, Yan Ma2
1Laboratory Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China.
Background:
Lung adenocarcinoma (LUAD) is the most common non-small-cell lung cancer, with a high incidence and a poor prognosis.
Aim:
To construct effective predictive models to evaluate the prognosis of LUAD patients.
Methods:
In this study, we thoroughly mined LUAD genomic data from the Gene Expression Omnibus (GEO) (GSE43458, GSE32863, and GSE27262) and the Cancer Genome Atlas (TCGA) datasets, including 698 LUAD and 172 healthy (or adjacent normal) lung tissue samples. Univariate regression and LASSO regression analyses were used to screen differentially expressed genes (DEGs) related to patient prognosis, and multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model. Receiver operating characteristic curve and Kaplan-Meier survival analyses with clinically independent prognostic parameters were performed to verify the predictive power of the model and further establish a prognostic nomogram.
Results:
A total of 380 DEGs were identified in LUAD tissues through GEO and TCGA datasets, and 5 DEGs (TCN1, CENPF, MAOB, CRTAC1 and PLEK2) were screened out by multivariate Cox regression analysis, indicating that the prognostic risk model could be used as an independent prognostic factor (Hazard ratio = 1.520, P < 0.001). Internal and external validation of the model confirmed that the prediction model had good sensitivity and specificity (Area under the curve = 0.754, 0.737). Combining genetic models and clinical prognostic factors, nomograms can also predict overall survival more effectively.
Conclusion:
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.
