The study aims to determine the efficacy and feasibility of a novel folate receptor (FR)-based circulating tumor cell (CTC) detection method in the diagnosis of non-small cell lung cancer (NSCLC). CTCs were collected from 3 ml of blood based on negative enrichment by immunomagnetic beads and then labeled by a conjugate of a tumor-specific ligand folate and an oligonucleotide. After washing off redundant conjugates, the bound conjugates were removed and analyzed by quantitative polymerase chain reaction. The captured cells were validated as tumor cells by immunofluorescence staining. In the evaluation of clinical utility, the results showed that the CTC levels of 153 patients with NSCLC were significantly higher than the controls (49 healthy donors and 64 patients with benign lung diseases; P < .001). With a threshold of 8.64 CTC units, the method showed a sensitivity of 73.2% and a specificity of 84.1% in the diagnosis of NSCLC, especially a sensitivity of 67.2% in stage I disease. Compared with the existing clinical biomarkers such as neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), cyfra21-1, and squamous cell carcinoma antigen (SCC Ag), the method showed the highest diagnostic efficiency (area under the curve, 0.823; 95% confidence interval, 0.773-0.874). Together, our results demonstrated that FR-positive CTCs were feasible diagnostic biomarkers in patients with NSCLC, as well as in early-stage tumors.

Non-small-cell lung cancer (NSCLC) is the most common cause of premature death among the malignant diseases worldwide. The current staging criteria do not fully capture the complexity of this disease. Molecular biology techniques, particularly gene expression microarrays, proteomics, and next-generation sequencing, have recently been developed to facilitate effectively its molecular classification. The underlying etiology, pathogenesis, therapeutics, and prognosis of NSCLC based on an improved molecular classification scheme may promote individualized treatment and improve clinical outcomes. This review focuses on the molecular classification of NSCLC based on gene expression microarray technology reported during the past decade, as well as their applications for improving the diagnosis, staging and treatment of NSCLC, including the discovery of prognostic markers or potential therapeutic targets. We highlight some of the recent studies that may refine the identification of NSCLC subtypes using novel techniques such as epigenetics, proteomics, or deep sequencing.

BackgroundClinical staging is essential for clinical decisions but remains imprecise. We purposed to construct a novel survival prediction model for improving clinical staging system (cTNM) for patients with esophageal adenocarcioma (EAC).MethodsA total of 4180 patients diagnosed with EAC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and included as the training cohort. Significant prognostic variables were identified for nomogram model development using multivariable Cox regression. The model was validated internally by bootstrap resampling, and then subjected to external validation with a separate cohort of 886 patients from 2 institutions in China. The prognostic performance was measured by concordance index (C-index), Akaike information criterion (AIC) and calibration plots. Different risk groups were stratified by the nomogram scores.ResultsA total of six variables were determined related with survival and entered into the nomogram construction. The calibration curves showed satisfied agreement between nomogram-predicted survival and actual observed survival for 1-, 3-, and 5-year overall survival. By calculating the AIC and C-index values, our nomogram presented superior discriminative and risk-stratifying ability than current TNM staging system. Significant distinctions in survival curves were observed between different risk subgroups stratified by nomogram scores.ConclusionThe established and validated nomogram presented better risk-stratifying ability than current clinical staging system, and could provide a convenient and reliable tool for individual survival prediction and treatment strategy making.

Poly ADP‐ribose polymerase inhibitors (PARPi) have shown promising therapeutic efficacy in triple‐negative breast cancer (TNBC) patients. However, resistance ultimately develops, preventing a curative effect from being attained. Extensive investigations have indicated the diversity in the mechanisms underlying the PARPi sensitivity of breast cancer. In this study, we found that DNA damage binding protein 2 (DDB2), a DNA damage‐recognition factor, could protect TNBC cells from PARPi by regulating DNA double‐strand break repair through the homologous recombination pathway, whereas the depletion of DDB2 sensitizes TNBC cells to PARPi. Furthermore, we found that DDB2 was able to stabilize Rad51 by physical association and disrupting its ubiquitination pathway‐induced proteasomal degradation. These findings highlight an essential role of DDB2 in modulating homologous recombination pathway activity and suggest a promising therapeutic target for TNBC.