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Identification of a 7-miRNA signature for predicting the prognosis of patients with lung adenocarcinoma.

Authors :
Liu R
Guo Z
Huang J
Li J
Tan Q
Luo Q
Source :
Experimental biology and medicine (Maywood, N.J.) [Exp Biol Med (Maywood)] 2022 Apr; Vol. 247 (8), pp. 641-657. Date of Electronic Publication: 2022 Jan 22.
Publication Year :
2022

Abstract

The role of microRNAs (miRNAs) in tumor diagnosis and patients' prognosis has recently gained extensive research attention. This study was designed to analyze miRNA in lung adenocarcinoma (LUAD) using bioinformatics analysis and to identify novel biomarkers to predict overall survival (OS) for LUAD patients. Differential miRNA expression analysis was performed on LUAD, and normal tissues were extracted from The Cancer Genome Atlas (TCGA). Univariate Cox risk regression and least absolute shrinkage and selection operator (LASSO) Cox analysis were used to screen prognostic miRNAs and develop a risk score model. The prognostic performance of the system was examined utilizing the Kaplan-Meier and receiver operating characteristic (ROC) curves. Independent prognostic factors of LUAD were determined by multivariate Cox regression analysis. Nomogram was constructed according to the independent prognostic factors to evaluate the patients' one-, three- and five-year OS. A 7-miRNA signature based on miR-584-5p, miR-31-3p, miR-490-3, miR-4661-5p, miR-30e-5p, miR-582-5p, and miR-148a-3p was established. To categorize patients into high- and low-risk groups, the risk score was computed. The OS of the low-risk group was significantly longer than the high-risk group, and the signature showed high sensitivity and specificity in anticipating the one-, three- and five-year OS. The system was an independent factor in predicting the OS of LUAD patients and performed better when combined with the N stage in nomogram. A 7-miRNA signature developed in this study could accurately predict LUAD survival.

Details

Language :
English
ISSN :
1535-3699
Volume :
247
Issue :
8
Database :
MEDLINE
Journal :
Experimental biology and medicine (Maywood, N.J.)
Publication Type :
Academic Journal
Accession number :
35068222
Full Text :
https://doi.org/10.1177/15353702211067450