Improved lung cancer classification by employing diverse molecular features of microRNAs
Shiyong Guo,
Chunyi Mao,
Jun Peng,
Shaohui Xie,
Jun Yang,
Wenping Xie,
Wanran Li,
Huaide Yang,
Hao Guo,
Zexuan Zhu,
Yun Zheng
Affiliations
Shiyong Guo
State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China; College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China; College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
Chunyi Mao
State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
Jun Peng
Department of Thoracic Surgery, The First People's Hospital of Yunnan Province, i.e., The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650032, China
Shaohui Xie
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
Jun Yang
School of Criminal Investigation, Yunnan Police College, Kunming, Yunnan 650223, China
Wenping Xie
College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China; College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
Wanran Li
College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China; College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
Huaide Yang
College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China; College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China
Hao Guo
Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
Zexuan Zhu
National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, Guangdong 518060, China
Yun Zheng
College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China; College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China; Corresponding author at: College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China.
MiRNAs are edited or modified in multiple ways during their biogenesis pathways. It was reported that miRNA editing was deregulated in tumors, suggesting the potential value of miRNA editing in cancer classification. Here we extracted three types of miRNA features from 395 LUAD and control samples, including the abundances of original miRNAs, the abundances of edited miRNAs, and the editing levels of miRNA editing sites. Our results show that eight classification algorithms selected generally had better performances on combined features than on the abundances of miRNAs or editing features of miRNAs alone. One feature selection algorithm, i.e., 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. Seven classification algorithms achieved 100% accuracies on these three features for 79 independent testing samples. These results indicate that the additional information of miRNA editing is useful in improving the classification of LUAD samples.
