Multidrug resistance (MDR) is an obstacle for cancer chemotherapy. It was reported that 20(S)-ginsenoside Rg3 (hereafter Rg3) was able to regulate MDR in mouse leukemia cells. The present study investigated the effect of Rg3 on the MDR of A549 lung cancer cells. A cell viability assay revealed that Rg3 treatment increased cisplatin (DDP) cytotoxicity in DDP resistant A549 cells (A549/DDP). Furthermore, Rg3 increases the antitumor effect of DDP on A549/DDP xenograft mice. The expression of MDR-mediated proteins, including P-glycoprotein (P-gp), multidrug resistance-associated protein (MPR1) and lung resistance protein 1 (LPR1), was detected in tumor tissue of A549/DDP xenograft mice. The results revealed that Rg3 treatment inhibited the expression of these MDR-associated proteins. Additionally, technetium-99m labeled hexakis-2-methoxyisobutylisonitrile (99mTc-MIBI) single-photon emission computed tomography was used to monitor the effect of Rg3 on cisplatin sensitivity of A549/DDP xenograft tumors. It was observed that uptake of 99mTc-MIBI was increased by Rg3 treatment, which indicated that Rg3 is able to effectively enhance chemotherapy sensitivity of A549/DDP xenograft tumors. Taken together, these results revealed that Rg3 may be able to reverse MDR of lung cancer via the downregulation of P-gp, MPR1 and LPR1.

Centromere protein M (CENPM) is essential for chromosome separation during mitosis. However, its roles in lung adenocarcinoma (LUAD) progression and metastasis remain unknown. In this study, we aimed to explore the effects of CENPM on LUAD progression as well as the underlying mechanisms. We analyzed the expression of CENPM and its correlation with clinicopathological characteristics using GEO LUAD chip datasets and TCGA dataset. We further investigated the impact of CENPM on LUAD in vitro and in vivo. In silico analysis and qRT-PCR revealed that CENPM is upregulated in LUAD compared with that in normal lung tissues. Via gain/loss-of-function assays, we further found that CENPM promotes the LUAD cell cycle, cell proliferation, migration and invasion, and inhibits cell apoptosis in vitro. The in vivo study showed that loss of CENPM inhibits the growth of A549 xenografts. Furthermore, we found that CENPM can promote the phosphorylation of mTOR rather than directly affect the mTOR content. Inhibition of mTOR activity abrogates the promoting effects of CENPM on cell cycle progression, cell proliferation, migration and invasion. Taken together, these results show that CENPM plays an important role in the growth and metastasis of LUAD and may be a promising therapeutic target in LUAD.

Objective: An increasing number of studies have shown the potential diagnostic value of cell-free DNA (cfDNA) as a new biomarker in the management of thyroid cancer (TC); however, the accuracy of research results is inconsistent. This meta-analysis is the first to synthesize published results and evaluate the application value of circulating cfDNA in the diagnosis of TC. Methods: A search strategy was developed according to PICO (P: Patient; I: Intervention; C: Comparison; O: Outcome) principles. We searched 5 databases until October 2022. Original studies that examined cfDNA for the diagnosis of TC and used pathology as the gold standard were included in this meta-analysis. A random-effects model was used to pool the data extracted from individual studies, including the number of patients and the numbers of true positives, false positives, true negatives, and false negatives. Results: A total of 622 patients with TC, 547 patients with benign thyroid nodules, and 98 healthy individuals were included in 20 studies reported in 14 articles. The types of cfDNA included in the research include specific mutations of cfDNA, methylation of cfDNA, the content of cfDNA, and cfDNA index. After rigorous statistical analysis, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve were 0.76 (95% confidence interval [CI] 0.62–0.85), 0.87 (95% CI 0.78–0.93), 5.08 (95% CI 3.3–10.3), 0.28 (95% CI 0.17–0.46), 21 (95% CI 9–49), and 0.89 (95% CI 0.86–0.91), respectively. The meta-regression results showed that the number of cfDNAs, cfDNA methylation status, and sample size were the sources of heterogeneity in the specificity of the study. A subgroup analysis showed that the quantitative analysis group (cfDNA level) had a higher diagnostic accuracy than that of the qualitative analysis group (cfDNA methylation, mutation, or integrity index), with a sensitivity of 0.84, specificity of 0.89, and area under the curve of 0.91. Conclusions: The results of this meta-analysis suggest that cfDNA has value as an adjunct for the diagnosis of TC. Quantitative detection of cfDNA can achieve relatively high diagnostic accuracy. However, due to heterogeneity, the test results based on cfDNA for TC should be interpreted with caution.

Background Patients with non-small cell lung cancer (NSCLC) who develop brain metastases (BM) have a poor prognosis. This study aimed to construct a clinical prediction model to determine the overall survival (OS) of NSCLC patients with BM. Methods A total of 300 NSCLC patients with BM at the Yunnan Cancer Centre were retrospectively analysed. The prediction model was constructed using the least absolute shrinkage and selection operator-Cox regression. The bootstrap sampling method was employed for internal validation. The performance of our prediction model was compared using recursive partitioning analysis (RPA), graded prognostic assessment (GPA), the update of the graded prognostic assessment for lung cancer using molecular markers (Lung-molGPA), the basic score for BM (BSBM), and tumour-lymph node-metastasis (TNM) staging. Results The prediction models comprising 15 predictors were constructed. The area under the curve (AUC) values for the 1-year, 3-year, and 5-year time-dependent receiver operating characteristic (curves) were 0.746 (0.678–0.814), 0.819 (0.761–0.877), and 0.865 (0.774–0.957), respectively. The bootstrap-corrected AUC values and Brier scores for the prediction model were 0.811 (0.638–0.950) and 0.123 (0.066-0.188), respectively. The time-dependent C-index indicated that our model exhibited significantly greater discrimination compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging. Similarly, the decision curve analysis demonstrated that our model displayed the widest range of thresholds and yielded the highest net benefit. Furthermore, the net reclassification improvement and integrated discrimination improvement analyses confirmed the enhanced predictive power of our prediction model. Finally, the risk subgroups identified by our prognostic model exhibited superior differentiation of patients’ OS. Conclusion The clinical prediction model constructed by us shows promise in predicting OS for NSCLC patients with BM. Its predictability is superior compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging.