Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis.
Eithar MohamedDaniel J García MartínezMohammad-Salar HosseiniSi Qi YoongSimon HartBarbara-Ann GuinnPublished in: Carcinogenesis (2023)
Lung cancer (LC) causes few symptoms in the earliest stages, leading to one of the highest mortality rates among cancers. Low-dose computerised tomography (LDCT) is used to screen high-risk individuals, reducing the mortality rate by 20%. However, LDCT results in a high number of false positives and is associated with unnecessary follow-up and cost. Biomarkers with high sensitivities and specificities could assist in the early detection of LC, especially in patients with high-risk features. Carcinoembryonic antigen (CEA), cytokeratin 19 fragments, and cancer antigen 125 have been found to be highly expressed during the later stages of LC but have low sensitivity in the earliest stages. We determined the best biomarkers for the early diagnosis of LC, using a systematic review of eight databases. We identified 98 articles that focused on the identification and assessment of diagnostic biomarkers and achieved a pooled area under curve of 0.85 (95% CI 0.82-0.088), indicating that the diagnostic performance of these biomarkers when combined was excellent. Of the studies, 30 focussed on single/antigen panels, 22 on autoantibodies, 31 on miRNA and RNA panels, and 15 suggested the use of circulating DNA combined with CEA or NSE for early LC detection. Verification of blood biomarkers with high sensitivities (Ciz1, exoGCC2, ITGA2B), high specificities (CYFR21-1, antiHE4, OPNV), or both (HSP90α, CEA) along with miR-15b and miR-27b/miR-21 from sputum may improve early lung cancer detection. Further assessment is needed using appropriate sample sizes, control groups that include patients with non-malignant conditions, and standardised cut-off levels for each biomarker.
Keyphrases
- low dose
- simultaneous determination
- mass spectrometry
- cell proliferation
- cardiovascular events
- cystic fibrosis
- mycobacterium tuberculosis
- squamous cell carcinoma
- clinical trial
- coronary artery disease
- cardiovascular disease
- risk factors
- oxidative stress
- machine learning
- single molecule
- solid phase extraction
- heat shock protein
- electronic health record
- sensitive detection
- clinical decision support
- heat stress
- circulating tumor cells
- phase iii