Machine learning identifies 10 feature miRNAs for lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is a common type of malignancy. The mechanism behind its tumor progression is not clear yet. The aim of this study is to use machine learning to identify the feature miRNAs, which can be reliably used as biomarkers for diagnosis LUSC. We downloaded microRNA expression data and clinical data from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database to identify differences in microRNA expression of primary tumor tissues and para-carcinoma tissues from LUSC. Construction of miRNA-mRNA interaction network, GO, KEGG pathway analysis and Kaplan-Meier survival analysis were used to explore the biological functions of the identified microRNAs. 21 feature miRNAs were identified between lung SCC tumor tissues and para-carcinoma tissues with the support of SVM and PCA methods. Among them, ten feature miRNAs: mir-143, mir-100, mir-101-1, mir-101-2, mir-182, mir-183, mir205, mir-21, mir-30a, mir-30-d were identified which could be used as a feature group to separate the cancer tissues from the adjacent tissues ultimately, and cross-validation of the obtained data showed that it can achieve extremely high accuracy and recall rate. Using KEGG, Reactome, GO databases, these 10 miRNAs and their target genes were found to be highly correlated with cancer. Survival analysis found that this group of miRNAs had a significant relationship with the survival rate of cancer patients, and the expression was significantly different between tumor tissues and healthy tissues. The dysregulated feature miRNAs might be involved in the pathology of LUSC and could be used as potential diagnostic biomarkers or therapeutic targets for LUSC.