Discriminating Benign from Malignant Lung Diseases Using Plasma Glycosaminoglycans and Cell-Free DNA
Journal article, 2024

We aimed to investigate the use of free glycosaminoglycan profiles (GAGomes) and cfDNA in plasma to differentiate between lung cancer and benign lung disease, in a cohort of 113 patients initially suspected of lung cancer. GAGomes were analyzed in all samples using the MIRAM® Free Glycosaminoglycan Kit with ultra-high-performance liquid chromatography and electrospray ionization triple quadrupole mass spectrometry. In a subset of samples, cfDNA concentration and NGS-data was available. We detected two GAGome features, 0S chondroitin sulfate (CS), and 4S CS, with cancer-specific changes. Based on the observed GAGome changes, we devised a model to predict lung cancer. The model, named the GAGome score, could detect lung cancer with 41.2% sensitivity (95% CI: 9.2–54.2%) at 96.4% specificity (95% CI: 95.2–100.0%, n = 113). When we combined the GAGome score with a cfDNA-based model, the sensitivity increased from 42.6% (95% CI: 31.7–60.6%, cfDNA alone) to 70.5% (95% CI: 57.4–81.5%) at 95% specificity (95% CI: 75.1–100%, n = 74). Notably, the combined GAGome and cfDNA testing improved the sensitivity, compared to cfDNA alone, especially in ASCL stage I (55.6% vs 11.1%). Our findings show that plasma GAGome profiles can enhance cfDNA testing performance, highlighting the applicability of a multiomics approach in lung cancer diagnostics.

multiomics

cfDNA

glycosaminoglycans

lung cancer

GAGome

Author

Alvida Qvick

Faculty of Medicine and Health

Sinisa Bratulic

Chalmers, Life Sciences, Systems and Synthetic Biology

Jessica Carlsson

Faculty of Medicine and Health

Bianca Stenmark

Faculty of Medicine and Health

Christina Karlsson

Örebro University

Jens B Nielsen

BioInnovation Institute

Chalmers, Life Sciences, Systems and Synthetic Biology

Francesco Gatto

Karolinska Institutet

Chalmers, Life Sciences, Systems and Synthetic Biology

Gisela Helenius

Faculty of Medicine and Health

International Journal of Molecular Sciences

16616596 (ISSN) 14220067 (eISSN)

Vol. 25 18 9777

Subject Categories

Cancer and Oncology

DOI

10.3390/ijms25189777

PubMed

39337265

More information

Latest update

10/7/2024