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Improved lung cancer classification by employing diverse molecular features of microRNAs

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机构： [1]State Key Laboratory of Primate Biomedical Research ,Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China [2]College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China [3]College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China [4]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 [5]College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China [6]School of Criminal Investigation, Yunnan Police College, Kunming, Yunnan 650223, China [7]Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China [8]National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, Guangdong 518060, China

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关键词： Lung adenocarcinoma MicroRNA (miRNA) MiRNA editing Classification Feature selection

摘要：

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.© 2024 The Authors.

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无

最新[2023]版：

大类 | 3 区综合性期刊

小类 | 3 区综合性期刊

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出版当年[2023]版：

Q1 MULTIDISCIPLINARY SCIENCES

最新[2023]版：

Q1 MULTIDISCIPLINARY SCIENCES

影响因子： 3.4 最新[2023版] 3.9 最新五年平均 3.4 出版当年[2023版] 3.9 出版当年五年平均 4 出版前一年[2022版]

第一作者：

第一作者机构： [1]State Key Laboratory of Primate Biomedical Research ,Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China [2]College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China [3]College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China

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通讯作者：

通讯机构： [2]College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China [3]College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China [*1]College of Horticulture and Landscape, Yunnan Agricultural University, Kunming, Yunnan, 650201, China

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