A microRNA disease signature associated with lymph node metastasis of lung adenocarcinoma.

Autor:	Cen SY; Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China., Fu KY; School of Medicine, Zhejiang University, Hangzhou 310016, China., Shi Y; School of Medicine, Zhejiang University, Hangzhou 310016, China., Jiang HL; Department of Respiratory Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China., Shou JW; Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China., You LK; Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China., Han WD; Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China., Pan HM; Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China., Liu Z; Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China.
Jazyk:	angličtina
Zdroj:	Mathematical biosciences and engineering : MBE [Math Biosci Eng] 2020 Feb 27; Vol. 17 (3), pp. 2557-2568.
DOI:	10.3934/mbe.2020140
Abstrakt:	Background: Lymph node metastasis (LNM) of lung cancer is an important factor associated with prognosis. Dysregulated microRNAs (miRNAs) are becoming a new powerful tool to characterize tumorigenesis and metastasis. We have developed and validated a miRNA disease signature to predict LNM in lung adenocarcinoma (LUAD). Method: LUAD miRNAs and clinical data from The Cancer Genome Atlas (TCGA) were obtained and divided randomly into training (n = 259) and validation (n = 83) cohorts. A miRNA signature was built using least absolute shrinkage and selection operator (LASSO) (λ =-1.268) and logistic regression model. The performance of the miRNA signature was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). We performed decision curve analysis (DCA) to assess the clinical usefulness of the signature. We also conducted a miRNA-regulatory network analysis to look for potential genes engaged in LNM in LUAD. Result: Thirteen miRNAs were selected to build our miRNA disease signature. The model showed good calibration in the training cohort, with an AUC of 0.782 (95% CI: 0.725-0.839). In the validation cohort, AUC was 0.691 (95% CI: 0.575-0.806). DCA demonstrated that the miRNA signature was clinically useful. Conclusion: The miRNA disease signature can be used as a noninvasive method to predict LNM in patients with lung adenocarcinoma objectively and the signature achieved high accuracy for prediction.
Databáze:	MEDLINE
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