Improved lung cancer classification by employing diverse molecular features of microRNAs
Lung adenocarcinoma (LUAD) is one of the most common pathological and histological subtypes of primary lung cancer, with high morbidity and mortality. MicroRNAs (miRNAs) are endogenous small non-coding RNAs that regulate the expression of genes at post-transcriptional level. It was reported that A-to-I miRNA editing was decreased in tumors, suggesting the potential value of miRNA editing in cancer classification. However, existing miRNA-based cancer classification models mainly used the frequencies of miRNAs. In order to validate the contribution of miRNA editing information in cancer classification, we extracted three types of miRNA features, including the abundances of original miRNAs, the abundances of edited miRNAs, and the editing levels of miRNA editing sites. Our results show that four classification algorithms selected, i.e., kNN, C4.5, RF and SVM, generally had better performances on all features than on the abundances of miRNAs alone. Since the number of features were large, we used three feature selection (FS) methods to further improve the classification models. One of the FS methods, the DFL algorithm, selected only three features, i.e., the frequencies of hsa-miR-135b-5p, hsa-miR-210-3p and hsa-miR-182 48u (an edited miRNA), from 316 training samples. And all of the four classification algorithms achieved 100% accuracy on these three features for 79 independent testing samples. These results indicate that the additional information of miRNA editing are useful in improving the classification of LUAD samples. And the three miRNAs selected by DFL potentially represent an effective molecular signature for LUAD diagnosis. Overall design: Small RNA-Seq for 19 lung adenocarcinoma (LUAD) and 19 adjacent normal tissues were obtained, and put into liquid nitrogen immediately after resection. The total RNAs were retrieved and the small RNA sequencing libraries were prepared and sequenced by BGI (Shenzhen, China). Next, the mutation and editing sites of miRNAs were analyzed with the MiRME algorithm for all of these 38 sRNA-seq profiles and 357 public LUAD and normal samples. Then, abundance of original and edited miRNAs, editing levels of identified miRNA editing sites were obtained for these 395 samples. Four machine learning algorithms were used to classify these samples as LUAD or normal samples. Three Feature Selection algorithms were used to select molecular features that were accurate in predicting the samples.