Title

Serum and Plasma Metabolomic Biomarkers for Lung Cancer

Authors

Nishith Kumar^1,2,*, Md. Shahjaman^1,3, Md. Nurul Haque Mollah¹, S. M. Shahinul Islam⁴ and Md. Aminul Hoque¹

Affiliation

¹Bioinformatics Lab, Department of Statistics, Rajshahi University, Rajshahi, Bangladesh;

²Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh;

³Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh;

⁴Institute of Biological Sciences, Rajshahi University, Rajshahi, Bangladesh.

Email

nk.bru09@gmail.com

Article Type

Hypothesis

Date

Received May 19, 2017; Accepted June 5, 2017; Published June 30, 2017

In drug invention and early disease prediction of lung cancer, metabolomic biomarker detection is very important. Mortality rate can be decreased, if cancer is predicted at the earlier stage. Recent diagnostic techniques for lung cancer are not prognosis diagnostic techniques. However, if we know the name of the metabolites, whose intensity levels are considerably changing between cancer subject and control subject, then it will be easy to early diagnosis the disease as well as to discover the drug. Therefore, in this paper we have identified the influential plasma and serum blood sample metabolites for lung cancer and also identified the biomarkers that will be helpful for early disease prediction as well as for drug invention. To identify the influential metabolites, we considered a parametric and a nonparametric test namely student's t-test as parametric and Kruskal-Wallis test as non-parametric test. We also categorized the up-regulated and down-regulated metabolites by the heatmap plot and identified the biomarkers by support vector machine (SVM) classifier and pathway analysis. From our analysis, we got 27 influential (p-value<0.05) metabolites from plasma sample and 13 influential (p-value<0.05) metabolites from serum sample. According to the importance plot through SVM classifier, pathway analysis and correlation network analysis, we declared 4 metabolites (taurine, aspertic acid, glutamine and pyruvic acid) as plasma biomarker and 3 metabolites (aspartic acid, taurine and inosine) as serum biomarker.

Keywords

Metabolomics, biomarker identification, Student t-test, Kruskal-Wallis test, support vector machine (SVM), pathway analysis.

Citation

Kumar et al. Bioinformation 13(6): 202-208 (2017)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.