Discrepancies in metabolomic biomarker identification from patient-derived lung cancer revealed by combined variation in data pre-treatment and imputation methods.

ABSTRACT:

Introduction

The identification of metabolomic biomarkers predictive of cancer patient response to therapy and of disease stage has been pursued as a "holy grail" of modern oncology, relying on the metabolic dysfunction that characterizes cancer progression. In spite of the evaluation of many candidate biomarkers, however, determination of a consistent set with practical clinical utility has proven elusive.

Objective

In this study, we systematically examine the combined role of data pre-treatment and imputation methods on the performance of multivariate data analysis methods and their identification of potential biomarkers.

Methods

Uniquely, we are able to systematically evaluate both unsupervised and supervised methods with a metabolomic data set obtained from patien

SUBMITTER: Miller HA 

PROVIDER: S-EPMC8138701 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature