Metabolomic Biomarker Identification for Lung Cancer By Combining Multiple Statistical Approaches

Metabolomic biomarkers are tools that can be used in early disease prediction and drug designing for diseases like lung cancer. Knowing the most differentially expressed metabolites creates a much higher probability of diagnosing lung cancer faster than normal, which can reduce the mortality rate. They are crucial during drug design too. Previously, various works have been done on discovering biomarkers for different diseases. However, it is still nowhere near sufficient since reducing the number of biomarkers and maintaining good classification accuracy are urgent issues in a sector where people's lives are at stake. Thus, to contribute more, in this paper, we have identified the influential metabolites in plasma and serum blood sample for lung cancer and then selected biomarkers from them. We first considered a parametric test (Student‘s t-test) and two non-parametric tests (Kruskal-Wallis and Mann-Whitney-Wilcoxon test) to identify the influential metabolites. We also differentiated the up-regulated and down-regulated metabolites using FC values and heatmap plot. We used SVM classifier to ascertain good accuracy with our set of influential metabolites and ROC Curve Analysis to rank the metabolites and choose biomarkers. Our analysis resulted in 28 influential $(\mathbf{p}-\mathbf{value}<$ 0.05) metabolites from plasma sample and 13 influential (p-value $<\pmb{ 0.05)}$ metabolites from serum sample. Finally, 10 metabolites were chosen from each of the samples as respective biomarkers. All the files and codes used in our work are available at https://github.com/Zeronfinity/LungCancerBiomarkers.