Background: As the heterogeneity of CTCs is becoming increasingly better understood, it is clear that identifying particular subtypes of CTCs would be more relevant.Methods: We detected folate receptor (FR)-positive circulating tumor cells (FR+-CTCs) by a novel ligand-targeted polymerase chain reaction (LT-PCR) detection technique.Results: In the none-dynamic study, FR+-CTC levels of patients with lung cancer were significantly higher than controls (patients with benign lung diseases and healthy controls). With a threshold of 8.7 CTC units, FR+-CTC showed a sensitivity of 77.7% and specificity of 89.5% in the diagnosis of lung cancer. When compared with established clinical biomarkers including carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1), and neuron-specific enolase (NSE), FR+-CTC showed the highest diagnostic efficiency. Notably, the combination of FR+-CTC, CEA, NSE, and CYFRA21-1 could significantly improve the diagnostic efficacy in differentiating patients with lung cancer from benign lung disease. In our dynamic surveillance study, the CTC levels of 62 non-small cell lung cancer (NSCLC) patients decreased significantly after tumor resection.Conclusion: We established a LT-PCR-based FR+-CTC detection platform for patients with lung cancer that exhibits high sensitivity and specificity. This platform would be clinical useful in lung cancer diagnosis and treatment response assessment.

Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.

Mitochondrial (mt)DNA mutations have been revealed to be associated with Leber's hereditary optic neuropathy (LHON). The present study conducted clinical, genetic and molecular evaluations of two Han Chinese families. A total of 4 (3 men and 1 female) out of 14 matrilineal relatives in the families exhibited visual impairment with variable severity and age of onset. The average age of onset of visual loss was 20.5 years old. Molecular analysis of the complete mitochondrial genome in these pedigrees demonstrated that the three primary mutations associated with LHON were not detected; however, the homoplasmic m.5587T>C mutation was identified, which was localized at the end of the mitochondrially encoded transfer (t)RNA alanine gene and may alter the tertiary structure of this tRNA. Subsequently, this structural alteration may result in tRNA metabolism failure. In addition, distinct sets of mtDNA polymorphisms belonging to haplogroup F1 were detected in both families tested. The findings of the present study suggested that the m.5587T>C mutation may be involved in the pathogenesis of visual impairment. In addition, the mtDNA variant m.15024G>A(p.C93H) in the mitochondrially encoded cytochrome B gene was detected in both families, which exhibited evolutionary conservation, indicating it may serve a potential modifying role in the development of visual impairment associated with m.5587T>C mutation in these families. Furthermore, other modifying factors, including nuclear modifier genes, and environmental and personal factors may also contribute to the development of LHON in subjects carrying this mutation.

No abstract