Downregulation of lysosomal and further gene expression characterization in lung cancer patients with bone metastasis

Development of a graphical model of causal gene regulatory networks using medical big data and Bayesian machine learning

Background: Data collection from medicine and biomedical science is becoming a large task and increasingly complicated with each passing day. Machine learning methods have been applied to elucidate interactions between genes and genes and their environment.Current Concepts: Many machine learning methods have been used to determine the statistical meaning or relationship in the prediction or progression of diseases through the creation of causal networks based on medical big data. Through these analyses, the occurrence and progression of diseases have been shown to be related to several genes and environmental factors. However, these methods cannot identify the key upstream regulators inferred from genomic, clinical, and environmental medical data.Discussion and Conclusion: The causal Bayesian network (CBN) is a machine learning method that can be used to understand a causal network inferred from the gene expression data. The CBN can help identify the key upstream regulators through examining the causal network inferred from medical big data having genomic information. We can easily improve the clinical outcome through regulation of these identified key upstream factors. Therefore, the CBN may be a powerful and flexible tool in the era of precision medicine.

Proteomic analysis enables distinction of early- versus advanced-stage lung adenocarcinomas

Background

A gel‐free proteomic approach was utilized to perform in‐depth tissue protein profiling of lung adenocarcinoma (ADC) and normal lung tissues from early and advanced stages of the disease. The long‐term goal of this study is to generate a large‐scale, label‐free proteomics dataset from histologically well‐classified lung ADC that can be used to increase further our understanding of disease progression and aid in identifying novel biomarkers.

Methods and results

Cases of early‐stage (I‐II) and advanced‐stage (III‐IV) lung ADCs were selected and paired with normal lung tissues from 22 patients. The histologically and clinically stratified human primary lung ADCs were analyzed by liquid chromatography‐tandem mass spectrometry. From the analysis of ADC and normal specimens, 4863 protein groups were identified. To examine the protein expression profile of ADC, a peak area‐based quantitation method was used. In early‐ and advanced‐stage ADC, 365 and 366 proteins were differentially expressed, respectively, between normal and tumor tissues (adjusted P‐value < .01, fold change ≥ 4). A total of 155 proteins were dysregulated between early‐ and advanced‐stage ADCs and 18 were suggested as early‐specific stage ADC. In silico functional analysis of the upregulated proteins in both tumor groups revealed that most of the enriched pathways are involved in mRNA metabolism. Furthermore, the most overrepresented pathways in the proteins that were unique to ADC are related to mRNA metabolic processes.

Conclusions

Further analysis of these data may provide an insight into the molecular pathways involved in disease etiology and may lead to the identification of biomarker candidates and potential targets for therapy. Our study provides potential diagnostic biomarkers for lung ADC and novel stage‐specific drug targets for rational intervention.

Antiproliferative and Antimetastatic Effects of Praeruptorin C on Human Non-Small Cell Lung Cancer Through Inactivating ERK/CTSD Signalling Pathways

Praeruptorin C (PC) reportedly has beneficial effects in terms of antiinflammation, antihypertension, and antiplatelet aggregation, and it potentially has anticancer activity. However, the effect of PC on human non-small cell lung cancer (NSCLC) is largely unknown. Compared with the effects of praeruptorin A and praeruptorin B, we observed that PC significantly suppressed cell proliferation, colony formation, wound closure, and migration and invasion of NSCLC cells. It induced cell cycle arrest in the G0/G1 phase, downregulated cyclin D1 protein, and upregulated p21 protein. PC also significantly reduced the expression of cathepsin D (CTSD). In addition, the phosphorylation/activation of the ERK1/2 signalling pathway was significantly suppressed in PC-treated NSCLC cells. Cotreatment with PC and U0126 synergistically inhibited CTSD expression, cell migration, and cell invasion, which suggests that the ERK1/2 signalling pathway is involved in the downregulation of CTSD expression and invasion activity of NSCLC cells by PC. These findings are the first to demonstrate the inhibitory effects of PC in NSCLC progression. Therefore, PC may represent a novel strategy for treating NSCLC.

Multifunctional redox-responsive and CD44 receptor targeting polymer-drug nanomedicine based curcumin and alendronate: synthesis, characterization and in vitro evaluation

The traditional therapy of cancer has systemic side effects, and many cancers, such as human breast cancer and lung cancer easily metastasize to bones, leading to the formation of secondary tumours. This study was aimed at enhancing the anti-tumour effect of curcumin (CUR) and preventing tumour spread to the bone. A novel multifunctional redox-responsive and CD44 receptor targeting polymer-drug, poly alendronate-hyaluronan-S-S-curcumin copolymer (ALN-oHA-S-S-CUR) based CUR and alendronate (ALN) were synthesized successfully with the disulphide bond linker. The structure of ALN-oHA-S-S-CUR was characterized by ¹H-NMR. The nanomedicine had natural anti-tumour drugs (CUR) as the hydrophobic kernel, and targeting CD44 receptor oligosaccharides of hyaluronan (oHA) and other anti-tumour drugs (ALN) as hydrophilic shell, named ALN-oHA-S-S-CUR conjugates, which could self-assemble into micelle-like nano-spheres in water via a dialysis method with hydrodynamic diameters of 179 ± 23 nm. Interestingly, the cur-loaded ALN-oHA-S-S-CUR micelles were stable in PBS but were capable of releasing the drug under the reducing environment. The rate of drug release was proportional to the GSH concentration. The uptake and cytotoxicity of micelles were higher in MDA-MB-231 cells than in MCF-7 cells because of a higher expression of the CD44 receptor in the former cell line. And compared to the cur-loaded oHA-CUR micelles, the cur-loaded ALN-oHA-S-S-CUR micelles had a good cellular uptake in 2D cancer cell and penetrability in 3D cancer cell spheroids. These results indicated the active targeting redox-sensitive micelles were promising as intracellular drug delivery systems for cancer treatment.

Hyaluronic acid/polyethylene glycol nanoparticles for controlled delivery of mitoxantrone

Hyaluronic acid (HA) has inherent ability to target the CD44 receptors and internalize into tumour cells via receptor-mediated endocytosis. Therefore, conjugation of this natural linear polysaccharide to polymeric NPs or micelles, as one of the most promising approaches, could be useful for future clinical applications such as drug delivery. Accordingly, we report on the synthesis of mitoxantrone (MTX)-conjugated polymeric nanoparticles (NPs) composed of polyethylene glycol-HA (PEG-HA) for MTX delivery toward special tumour cells. To determine the size of the polymeric NPs, field emission scanning electron microscopy (FESEM) and particle size analyzer system Zetasizer_nanoZS were employed. The in vitro cytotoxicity analysis of MTX-loaded HA-PEG NPs and free MTX against two cell lines with different levels of CD44 expression (MDA-MB-231 (very high) and MCF-7 (low) was conducted by MTT assay. Also, computational molecular docking was employed to study in detail the active site residues and the critical interactions between HA-EDA-PEG-EDA-MTX NPs and CD44 receptor. The particle size analysis and electron microscopy showed the average size of polymeric NPs less than 350 nm. FT-IR spectrophotometry analysis and also NMR confirmed the conjugation of HA and MTX onto the PEG. Cytotoxicity assay revealed that the engineered polymeric NPs were able to specifically bind to and significantly inhibit the CD44 receptor-positive MDA-MB-231 cells, but not the CD44-negative MCF-7 cells. Furthermore, analysis of the binding modes revealed that for the best-docked pose nearly 10 conventional hydrogen bond can occur between the MTX-EDA-PEG-EDA-HA NPs and amino acids of CD44 receptor. Based on these findings, we suggest the HA-PEG-MTX NPs as an effective functional-targeted nanomedicine toward therapy of CD44-positive cancers.