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

Shuyi Cen; Kaiyou Fu; Yue Shi; Hanliang Jiang; Jiawei Shou; Liangkun You; Weidong Han; Hongming Pan; Zhen Liu; Shuyi Cen; Kaiyou Fu; Yue Shi; Hanliang Jiang; Jiawei Shou; Liangkun You; Weidong Han; Hongming Pan; Zhen Liu

doi:10.3934/mbe.2020140

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 3: 2557-2568. doi: 10.3934/mbe.2020140

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

1.
Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China
2.
School of Medicine, Zhejiang University, Hangzhou 310016, China
3.
Department of Respiratory Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China

Received: 24 December 2019 Accepted: 19 February 2020 Published: 27 February 2020

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.
- lung adenocarcinoma,
- lymph node metastasis,
- microRNA,
- TCGA,
- disease signature
Citation: Shuyi Cen, Kaiyou Fu, Yue Shi, Hanliang Jiang, Jiawei Shou, Liangkun You, Weidong Han, Hongming Pan, Zhen Liu. A microRNA disease signature associated with lymph node metastasis of lung adenocarcinoma[J]. Mathematical Biosciences and Engineering, 2020, 17(3): 2557-2568. doi: 10.3934/mbe.2020140

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Abstract

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.

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