Most dominant metabolomic biomarkers identification for lung cancer

Abstract

Metabolomic biomarkers play a vital role in the early identification and prediction of cancer. It is possible to save numerous lives if biomarkers are used to assist medical providers in diagnosing their patients faster. Many researchers have been trying to identify the crucial biomarkers in the early diagnosis of diseases. This paper presents several steps divided into two phases for determining the most important metabolomic biomarkers in the blood for lung cancer prediction using Plasma and Serum samples. We used the Shapiro–Wilk Test, Bartlett’s Test, Levene’s Test, Student’s t-Test, and Kruskal–Wallis Test in the first phase to determine the potential biomarkers. Recursive Feature Elimination with Random Forest was used to identify the final most dominant metabolomic biomarker at the second phase. Lastly, we ended with Ridge Classifier and XGBoost Classifier to assess the consistency of our approaches. Despite the declining number of metabolites up to a greater level, our prediction accuracy was 100% and 90.91% for Plasma and Serum samples, respectively which is higher than the state-of-the-art method. Finally, we made some analysis using the most dominant metabolites that can serve as a source of inspiration for our work.

Keywords

• Metabolomic biomarkers
• Normal distribution test
• Variance test
• Student’s T-Test
• Kruskal–Wallis Test
• Recursive Feature Elimination