A study published in Cancers looked at a new way to diagnose malignant pleural mesothelioma (MPM).
The usual way to diagnose MPM is through a biopsy or looking at fluid in the chest, but these methods are not very accurate. The researchers in this study used mass spectrometry-based proteomics to look for biomarkers that can pinpoint MPM in the body.
MPM is a serious cancer that is linked to exposure to asbestos and is usually found late, making it hard to treat. Pleural mesothelioma occurs in about 2,000 people in the United States every year. The prognosis is poor, with a median survival rate of 12 months.
Conventional treatment may include surgery, radiation therapy, and chemotherapy.
Improving the methods used to diagnose MPM could improve treatment options and the survival outcome of affected patients. The current methods include a physical exam, imaging tests, and tissue biopsies.
A developing diagnostic method is biomarkers. A biomarker is a molecule that shows something is happening in the body, like a disease. Researchers are looking for a reliable biomarker for MPM that doctors could use to easily identify the cancer.
Using New Methods
The researchers in this study used a new method called mass spectrometry-based proteomics to analyze fluid in the chest of 84 patients who were suspected of having MPM.
They found several proteins that can accurately identify MPM. Some of these promising biomarkers include proteins that are associated with the growth of cancer cells, like galactin-3 binding protein.
The researchers also confirmed previous findings of two proteins that are good markers for the disease. These two proteins are fibulin-3 and mesothelin.
The biomarkers identified in this study could be used to quickly and easily diagnose MPM. The researchers believe that this new method of finding biomarkers can provide a more accurate and objective way to diagnose MPM.
Palstrøm NB, Overgaard M, Licht P, Beck HC. Identification of Highly Sensitive Pleural Effusion Protein Biomarkers for Malignant Pleural Mesothelioma by Affinity-Based Quantitative Proteomics. Cancers (Basel). 2023;15(3):641. Published 2023 Jan 19. doi:10.3390/cancers15030641. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913626/