Abstract
Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18–25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.
Highlights
Ovarian cancer is a major clinical challenge in gynecologic oncology
The International Federation of Gynecology and Obstetrics (FIGO) reported that in the majority of those diagnosed in stage three or four ovarian cancer (2014), more than 70% will have a relapse of their disease within the first 5 years (Reid et al, 2017)
We evaluated our model prediction performances based on several measures of accuracy, including sensitivity, specificity, miRNAs as Ovarian Cancer Biomarkers
Summary
Ovarian cancer is a major clinical challenge in gynecologic oncology. The International Federation of Gynecology and Obstetrics (FIGO) reported that in the majority of those diagnosed in stage three or four ovarian cancer (2014), more than 70% will have a relapse of their disease within the first 5 years (Reid et al, 2017). One of the most common gynecologic malignancy is epithelial ovarian cancer (EOC), with each year of about 230,000 new cases and almost 140,000. In 2020, it is estimated that approximately 21,750 new cases and 13,940 deaths occurred in the United States and 29,000 deaths happened in Europe due to ovarian cancer (Iorio et al, 2007). Accurate and reliable prediction models would enable preventative interventions to reduce the morbidity and mortality associated with ovarian cancer (Harter et al, 2008)
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