Gold-containing compound BDG-I inhibits the growth of A549 lung cancer cells through the deregulation of miRNA expression

Anticancer Activity of Imidazolyl Gold(I/III) Compounds in Non-Small Cell Lung Cancer Cell Lines

Lung cancer is a leading cause of cancer-related death worldwide that needs updated therapies to contrast both the serious side effects and the occurrence of drug resistance. A panel of non-small cell lung cancer (NSCLC) cells were herein employed as cancer models. Eight structurally related gold(I) and gold(III) complexes with NHC and halides or triphenylphosphane ligands were investigated as lung cancer cell growth inhibitors. As expected, gold compounds with PPh3 were found to be more cytotoxic than homoleptic [(NHC)2-Au(I)]X or heteroleptic NHC-Au(I)X or NHC-Au(III)X3 complexes. Mixed ligand gold(I) compounds exhibiting the linear NHC-AuPPh3 (compound 7) or the trigonal NHC-Au(Cl)PPh3 (compound 8) arrangements at the central metal were found to be the best lung cancer cytotoxic compounds. Analysis of the TrxR residual activity of the treated cells revealed that these compounds efficiently inhibit the most accredited molecular target for gold compounds, the TrxR, with compound 8 reaching more than 80% activity reduction in lung cells. Some of the current cancer lung therapy protocols consist of specific lung cancer cell cytotoxic agents combined with antifolate drugs; interestingly, the herein gold compounds are both TrxR and antifolate inhibitors. The human DHFR was inhibited with IC50 ranging between 10-21 µM, depending on substrate concentrations, proceeding by a likely allosteric mechanism only for compound 8.

Organometallic gold(I) and gold(III) complexes for lung cancer treatment

Metal compounds, especially gold complexes, have recently gained increasing attention as possible lung cancer therapeutics. Some gold complexes display not only excellent activity in cisplatin-sensitive lung cancer but also in cisplatin-resistant lung cancer, revealing promising prospects in the development of novel treatments for lung cancer. This review summarizes examples of anticancer gold(I) and gold (III) complexes for lung cancer treatment, including mechanisms of action and approaches adopted to improve their efficiency. Several excellent examples of gold complexes against lung cancer are highlighted.

A Fast Linear Neighborhood Similarity-Based Network Link Inference Method to Predict MicroRNA-Disease Associations

Increasing evidences revealed that microRNAs (miRNAs) play critical roles in important biological processes. The identification of disease-related miRNAs is critical to understand the molecular mechanisms of human diseases. Most existing computational methods require diverse features to predict miRNA-disease associations. However, diverse features are not available for all miRNAs or diseases. In addition, most methods can't predict links for miRNAs or diseases without association information. In this paper, we propose a fast linear neighborhood similarity-based network link inference method, named FLNSNLI, to predict miRNA-disease associations. First, known miRNA-disease associations are formulated as a bipartite network, and miRNAs (or diseases) are expressed as association profiles. Second, miRNA-miRNA similarity and disease-disease similarity are calculated by fast linear neighborhood similarity measure and association profiles. Third, the label propagation algorithm is respectively implemented on two sides to score candidate miRNA-disease associations. Finally, FLNSNLI adopts the weighted average strategy and makes predictions. Moreover, we develop a link complementing approach, and extend FLNSNLI to predict links for miRNAs (or diseases) without known associations. In computational experiments, FLNSNLI produces high-accuracy performances, and outperforms other state-of-the-art methods. More importantly, FLNSNLI requires less information but performs well. Case studies on three popular diseases show that FLNSNLI is useful for the microRNA-disease association prediction.

MicroRNA-375 overexpression disrupts cardiac development of Zebrafish (Danio rerio) by targeting notch2

MicroRNAs are small noncoding RNAs that are important for proper cardiac development. In our previous study of fetuses with ventricular septal defects, we discovered that microRNA-375 (miR-375) is obviously upregulated compared with that in healthy controls. Our study also confirmed that miR-375 is crucial for cardiomyocyte differentiation. This research mainly focused on the biological significance and mechanism of miR-375 using a zebrafish model. We injected zebrafish embryos with 1-2 nl of a miR-375 mimic at various concentrations (0/2/4/8 μM) or with negative control. The deformation and mortality rates were separately assessed. The different expression levels of miR-375 and related genes were examined by qRT-PCR, and luciferase assays and in situ hybridization were used to clarify the mechanism of miR-375 during embryonic development. Overexpression of miR-375 disrupted the cardiac development of zebrafish embryos. Disruption of miR-375 led to a decreased heart rate, pericardial edema, and abnormal cardiac looping. Various genes involved in cardiac development were downregulated due to the overexpression of miR-375. Moreover, the NOTCH signaling pathway was affected, and the luciferase reporter gene assays confirmed notch2, which was predicted by bioinformatics analysis, as the target gene of miR-375. Our findings demonstrated that the overexpression of miR-375 is detrimental to embryonic development, including cardiac development, and can partially simulate a multisystemic disorder. MiR-375 has an important role during cardiac morphogenesis of zebrafish embryos by targeting notch2, indicating its potential as a diagnostic marker.

Diagnostic Value of Plasma MicroRNAs for Lung Cancer Using Support Vector Machine Model

Aim: Small single-stranded non-coding RNAs (miRNAs) play an important role in carcinogenesis through degrading target mRNAs. However, the diagnostic value of miRNAs was not explored in lung cancers. In this study, a support-vector-machine (SVM) model for diagnosis of lung cancer was established based on plasma miRNAs biomarkers, clinical symptoms and epidemiology material.

Methods: The expressions of plasma miRNA were examined with SYBR Green-based quantitative real-time PCR.

Results: We identified that the expressions of 10 plasma miRNAs (miR-21, miR-20a, miR-210, miR-145, miR-126, miR-223, miR-197, miR-30a, miR-30d, miR-25), smoking status, fever, cough, chest pain or tightness, bloody phlegm, haemoptysis, were significantly different between lung cancer and control groups (P<0.05). The accuracies of the combined SVM, miRNAs SVM, symptom SVM, combined Fisher, miRNAs Fisher and symptom Fisher were 96.34%, 80.49%, 84.15%, 84.15%, 75.61%, and 80.49%, respectively; AUC of these six model were 0.976, 0.841, 0.838, 0.865, 0.750, and 0.801, respectively. The accuracy and AUC of combined SVM were higher than the other 5 models (P<0.05).

Conclusions: Our findings indicate that SVM model based on plasma miRNAs biomarkers may serve as a novel, accurate, noninvasive method for auxiliary diagnosis of lung cancer.