Lung cancer biomarkers: State of the art

Abstract

Lung cancer is one of the deadliest cancers worldwide, with the highest incidence and mortality amongst all cancers. While the prognosis of lung cancer is generally grim, with 5-year survival rates of only 15%, there is hope, and evidence, that early detection of lung cancer can reduce mortality. Today, only computed tomography screening has shown to lead to early detection and reduction in mortality, but is limited by being anatomic in nature, unable to differentiate between inflammatory and neoplastic pathways, and therefore, susceptible to false positives. There is increasing interest in biomarkers for lung cancer, especially those that predict metastatic risk. Some biomarkers like DNA mutations and epigenetic changes potentially require tissue from the at-risk site; some like serum proteins and miRNAs are minimally invasive, but may not be specific to the lung. In comparison, emerging biomarkers from exhaled breath, like volatile organic compounds (VOC), and exhaled breath condensate, e.g., small molecules and nucleic acids, have the potential to combine the best of both. This mini review is intended to provide an overview of the field, briefly discussing the potential of what is known and highlighting the exciting recent developments, particularly with miRNAs and VOCs.

Keywords: Exhaled breath; lung cancer; miRNA; volatile organic compounds.

Figure 1

Expression analyses suggested NM23 H2 as a candidate biomarker metastasis suppressor gene in lung cancer. (a) Heat map representing transcript levels of 23 metastasis suppressor genes (MSG) (upper panel) in early/advanced lung cancer transcriptomes; 4 MSGs showed change in transcript level with NM23 H2 being most significant across tumors grouped stage wise (lower panel). (b) Box plot for relative expression of NM23 H2 in 93 lung cancer transcriptomes. Gene expression values were Z score normalized for comparison. Significance was calculated using student's t-test

Figure 2

Breath volatile organic compounds could be useful for non-invasive diagnosis as well as enhance system-level understating of biochemical changes of lung cancer. (a) Schematic workflow. (b) Partial Least Squares Discriminant Analysis score plot showing grouping trends (Lung cancer [LC]) and Healthy (h). (c) The network of the selected metabolites (identified in the study) and genes that might be involved in LC