Screening of Serum miRNAs as Diagnostic Biomarkers for Lung Cancer Using the Minimal-Redundancy-Maximal-Relevance Algorithm and Random Forest Classifier Based on a Public Database.

Abstract

Background: Lung cancer is one of the deadliest cancers, early diagnosis of which can efficiently enhance patient's survival. We aimed to screening out the serum miRNAs as diagnostic biomarkers for patients with lung cancer.

Methods: A total of 416 remarkably differentially expressed miRNAs were acquired using the limma package, and next feature ranking was derived by the minimal-redundancy-maximal-relevance method. An incremental feature selection algorithm of a random forest (RF) classifier was utilized to choose the top 5 miRNA combination with the optimum predictive performance. The performance of the RF classifier of top 5 miRNAs was analyzed using the receiver operator characteristic (ROC) curve. Afterward, the classification effect of the 5-miRNA combination was validated through principal component analysis and hierarchical clustering analysis. Analysis of top 5 miRNA expressions between lung cancer patients and normal people was performed based on GSE137140 dataset, and their expression was validated by qPCR. The hierarchical clustering analysis was used to analyze the similarity of 5 miRNAs expression profiles. ROC analysis was undertaken on each miRNA.

Results: We acquired top 5 miRNAs finally, with the Matthews correlation coefficient value as 0.988 and the area under the curve (AUC) value as 0.996. The 5 feature miRNAs were capable of distinguishing most cancer patients and normal people. Furthermore, except for the lowly expressed miR-6875-5p in lung cancer tissue, the other 4 miRNAs all expressed highly in cancer patients. Performance analysis revealed that their AUC values were 0.92, 0.96, 0.94, 0.95, and 0.93, respectively.

Conclusion: By and large, the 5 feature miRNAs screened here were anticipated to be effective biomarkers for lung cancer.