Dataset Information

Machine Learning Based Prediction of Brain Metastasis of Patients with IIIA-N2 Lung Adenocarcinoma by a Three-miRNA Signature.

ABSTRACT:

Objectives

MicroRNAs (miRNAs) play a key role in governing posttranscriptional regulation through binding to the mRNAs of target genes. This study is to assess miRNAs expression profiles for identifying brain metastasis-related miRNAs to develop the predictive model by microarray in tumor tissues.

Methods

For this study, we screened the significant brain metastasis-related miRNAs from 77 lung adenocarcinoma (LUAD) patients with brain metastasis (BM+) or non-brain metastasis (BM-). A predictive model was developed from the training set (n=42) using a random Forest supervised classification algorithm and a Class Centered Method, and then validated in a test set (n=35) and further analysis in GSE62182 (n=73). The independence of this signature in BM prediction was measured by multivariate logistic regression analysis.

Results

From the training set, the predictive model (including hsa-miR-210, hsa-miR-214 and hsa-miR-15a) stratified the patients into two groups with significantly different BM subtypes (90.4% of accuracy). The similar predictive power (91.4% of accuracy) was obtained in the test cohort. As an independent predictive factor, it was closely associated with BM and had high sensitivity and specificity in predicting BM in clinical practice. Moreover, functional enrichment analysis demonstrated that this signature involved in the signaling pathways positively correlated with cancer metastasis.

Conclusion

These results suggested that the three-miRNA signature could develop a new random Forest model to predict the BM of LUAD patients. These findings emphasized the importance of miRNAs in diagnosing BM, and provided evidence for selecting treatment decisions and designing clinical trials.

SUBMITTER: Zhao S

PROVIDER: S-EPMC6002353 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Publications

Machine Learning Based Prediction of Brain Metastasis of Patients with IIIA-N2 Lung Adenocarcinoma by a Three-miRNA Signature.

Zhao Shuangtao S Yu Jiangyong J Wang Luhua L

Translational oncology 20171227 1

<h4>Objectives</h4>MicroRNAs (miRNAs) play a key role in governing posttranscriptional regulation through binding to the mRNAs of target genes. This study is to assess miRNAs expression profiles for identifying brain metastasis-related miRNAs to develop the predictive model by microarray in tumor tissues.<h4>Methods</h4>For this study, we screened the significant brain metastasis-related miRNAs from 77 lung adenocarcinoma (LUAD) patients with brain metastasis (BM+) or non-brain metastasis (BM-). ...[more]

PMID: 29288987

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Project description:BackgroundLocal relapses and metastases are primary causes of death in lung cancer patients. In the present study, we aimed to develop a prognostic signature based on metastasis-associated lncRNAs in patients with lung adenocarcinoma (LUAD).MethodsFirstly, the potential metastasis-associated lncRNAs were identified by analyzing high-throughput data from The Cancer Genome Atlas (TCGA), and based on which, an lncRNA signature was constructed for prediction of relapse in LUAD patients using Cox proportional hazards regression analysis. Moreover, the prognostic performance of the lncRNA signature was evaluated using Kaplan-Meier survival analysis, time-dependent receiver operating characteristic (ROC) curve and Cox analysis, respectively. In addition, the potential metastasis-associated function of these six lncRNAs was confirmed by lncRNA over-expression or depletion and in vitro transwell assays in LUAD cells.ResultsAn lncRNA signature consisting of six most important prognostic factors (LINC01819, ZNF649-AS1, HNF4A-AS1, FAM222A-AS1, LINC02323 and LINC00672) was developed. The signature was an independent predictor for patients' relapse-free survival (RFS), which could provide higher tumor relapse prediction capability compared with the TNM staging system at three years and five years, respectively (P = 0.0209 and P = 0.0468). Furthermore, the combination of this lncRNA signature and TNM stage had better prognostic value than TNM stage alone at three and five years, respectively (P = 0.0006 and P = 0.0096). Additionally, all the lncRNAs of the signature had a regulatory role in the LUAD cell mobility.ConclusionsThis novel six-lncRNA signature had considerable prognostic value for prediction of relapse in LUAD patients.Key pointsSignificant findings of the study The unique metastasis-associated lncRNA signature was related to tumor metastasis and prognosis in LUAD patients. What this study adds This signature had considerable prognostic value for prediction of relapse in LUAD patients.

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Dataset Information

Machine Learning Based Prediction of Brain Metastasis of Patients with IIIA-N2 Lung Adenocarcinoma by a Three-miRNA Signature.

Objectives

Methods

Results

Conclusion

Publications

Machine Learning Based Prediction of Brain Metastasis of Patients with IIIA-N2 Lung Adenocarcinoma by a Three-miRNA Signature.

Similar Datasets

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