HTLV-1-associated genes as potential biomarkers for endometrial cancer.

Abstract

Endometrial carcinoma (EC) is a malignant neoplasm of the endometrial epithelium, which may be diagnosed by pathological investigations. The aim of the current study was to identify new markers for the diagnosis of EC using machine learning. The association between human T cell lymphotropic virus type 1 (HTLV-1) infection and endometrial cancer risk have not been widely reported. It remains ambiguous whether HTLV-1 infection is associated with several types of cancer. The present study investigated the association between HTLV-1 infection-associated genes and EC risk. RNA sequencing uterine cancer expression data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified between normal and matched tumor samples. A total of 41 genes were selected by an overlap between HTLV-1 infection pathway-associated genes and the DEGs. Two-way hierarchical clustering analysis (HCA) and a support vector machine (SVM) classifier were constructed using the 41 genes. The accuracy of the candidate genes in risk-stratifying the samples was 100%. The accuracy of the proposed SVM model was 100%. In addition, the classification power of the SVM model was validated using a merged dataset (TCGA and the Genotype-Tissue Expression project). This predictive feature achieved reliable outcomes with risk-stratifying samples of almost 99% in two-way HCA, and an accuracy yield of 98% of the SVM classifier. In conclusion, the 41 genes identified in the current study may be implicated in the development of EC and may be of prognostic value for the disease. The results obtained the current study suggest that HTLV-1 may be potentially associated with EC and highlight potential disease mechanisms.