Investigation of serum markers of esophageal squamous cell carcinoma based on machine learning methods.

Abstract

Esophageal squamous cell carcinoma (ESCC) is one of the malignant tumors with high mortality in humans, and there is a lack of effective and convenient early diagnosis methods. By analyzing the serum miRNA expression data in ESCC tumor samples and normal samples, on the basis of the maximal relevance and minimal redundancy (mRMR) feature selection and the incremental feature selection method, a random forest classifier constructed by five-feature miRNAs was acquired in our study. The receiver operator characteristic curve showed that the model was able to distinguish samples. Principal component analysis (PCA) and sample hierarchical cluster analysis showed that five-feature miRNAs could well distinguish ESCC patients from healthy individuals. The expression levels of miR-663a, miR-5100 and miR-221-3p all showed a higher expression level in ESCC patients than those in healthy individuals. On the contrary, miR-6763-5p and miR-7111-5p both showed lower expression levels in ESCC patients than those in healthy individuals. In addition, the collected clinical serum samples were used for qRT-PCR analysis. It was uncovered that the expression trends of the five-feature miRNAs followed a similar pattern with those in the training set. The above findings indicated that the five-feature miRNAs may be serum tumor markers of ESCC. This study offers new insights for the early diagnosis of ESCC.