Serum Metabolic disturbances associated with Lung cancer: An elaborative NMR based metabolomics study on North Indian patients

Lung cancer (LC) is one of the most common and dreadful cancers in the world. The high mortality and poor prognosis of lung cancer are mainly due to the difficulty in lung cancer diagnosis in the initial stages either because of hidden symptoms or symptoms overlapping with other interstitial lung diseases. So far, there are no reliable clinical markers that can be used to diagnose the disease at an early stage and majority of LC patients are diagnosed at advanced stages when a clinical intervention is unlikely to succeed. Therefore, there is immense clinical interest to identify non-invasive and reliable clinical markers to improve early diagnosis and non-invasive prognostic screening without involving bronchoscopy to obtain a tissue biopsy. Studies have established that tumor growth and progression is characterized by a series of perturbations in cellular metabolism. Since, metabolomics is an analytical approach to metabolism; it has opened up new perspectives to perform quantitative and comparative analysis of metabolic profiles so that to investigate the metabolic disturbances associated with pathophysiological state of malignancy. Metabolomics further allows discovering new biomarkers for the diagnosis and prognosis of the disease.

Starting our efforts in this direction, the serum metabolic profiles of 39 lung cancer (LC) patients were measured by 800 MHz NMR spectroscopy and compared with those of 47 normal control (NC) subjects employing chemometric analysis tools. Partial Least Square Discriminant Analysis (PLS-DA) model revealed a distinct separation between LC and NC groups suggesting significant metabolic disturbances associated with LC. We further made composite use of variable importance in projection (VIP) statistics and receiver operating characteristic curve (ROC) analysis to identify key metabolic entities having potential to discriminate LC patients from NC subjects. The results of this study will serve as a guiding attributes for future serum based metabolomics studies aiming to evaluate the in diagnosis, grading, staging and monitoring the treatment response.