Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking

Automated calibration for stability selection in penalised regression and graphical models

Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stability score and accommodating a priori-known block structure (e.g. multi-OMIC) data. It applies to [Least Absolute Shrinkage Selection Operator (LASSO)] penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the original calibration. Application to multi-block graphical LASSO on real (epigenetic and transcriptomic) data from the Norwegian Women and Cancer study reveals a central/credible and novel cross-OMIC role of LRRN3 in the biological response to smoking. Proposed approaches were implemented in the R package sharp.

Design and optimization various formulations of PEGylated niosomal nanoparticles loaded with phytochemical agents: potential anti-cancer effects against human lung cancer cells

BACKGROUND

Phytochemicals and their derivatives are good options to improve treatment efficiency in cancer patients. Artemisinin (ART) and metformin (MET) are widely used phytochemicals to treat various types of cancers. However, their application because of their dose-dependent side effects, and poor bioavailability brings several challenges. Niosome is a novel nanocarrier that is the best choice to encapsulate both lipophilic and hydrophilic drugs. In this study, we synthesized and characterized various formulations of PEGylated (polyethylene glycol) niosomal nanoparticles co-loaded with ART-MET and evaluated their anticancer effect on A549 lung cancer cells.

METHODS

Various formulations of PEGylated noisome were prepared by the thin-film hydration method and characterized in size, morphology, release pattern, and physicochemical structure. The cytotoxic effect of the free ART-MET and optimized PEGylated niosomal nanoparticles loaded with ART-MET on A549 cells were evaluated by MTT assay. Furthermore, the Real-time PCR (RT-PCR) technique used to evaluate apoptotic and anti-apoptotic gene expression.

RESULTS

The size, encapsulation efficiency (EE), and polydispersity index (PDI) of the optimized nanoparticles are 256 nm, 95%, and 0.202, respectively. Additionally, due to the PEGylation hydrophilic character, there is a major consideration of the high impact of PEGylation on reducing niosome size. According to the results of the MTT assay, free ART-MET and ART-MET-loaded niosomal nanoparticles showed dose-dependent toxicity and inhibits the growth of A549 lung cancer cells. Furthermore, the RT-PCR results indicated that ART-MET-loaded niosomal nanoparticles have a higher anti-proliferative effect by inhibiting anti-apoptotic and inducing apoptotic gene expression in A549 lung cancer cells.

CONCLUSIONS

Our study revealed that the simultaneous use of ART and MET in the optimized PEGylated niosomal nanoparticles delivery system could be an appropriate approach to improve the effectiveness of lung cancer treatment.

Associations between the urban exposome and type 2 diabetes: Results from penalised regression by least absolute shrinkage and selection operator and random forest models

BACKGROUND

Type 2 diabetes (T2D) is thought to be influenced by environmental stressors such as air pollution and noise. Although environmental factors are interrelated, studies considering the exposome are lacking. We simultaneously assessed a variety of exposures in their association with prevalent T2D by applying penalised regression Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Artificial Neural Networks (ANN) approaches. We contrasted the findings with single-exposure models including consistently associated risk factors reported by previous studies.

METHODS

Baseline data (n = 14,829) of the Occupational and Environmental Health Cohort study (AMIGO) were enriched with 85 exposome factors (air pollution, noise, built environment, neighbourhood socio-economic factors etc.) using the home addresses of participants. Questionnaires were used to identify participants with T2D (n = 676(4.6 %)). Models in all applied statistical approaches were adjusted for individual-level socio-demographic variables.

RESULTS

Lower average home values, higher share of non-Western immigrants and higher surface temperatures were related to higher risk of T2D in the multivariable models (LASSO, RF). Selected variables differed between the two multi-variable approaches, especially for weaker predictors. Some established risk factors (air pollutants) appeared in univariate analysis but were not among the most important factors in multivariable analysis. Other established factors (green space) did not appear in univariate, but appeared in multivariable analysis (RF). Average estimates of the prediction error (logLoss) from nested cross-validation showed that the LASSO outperformed both RF and ANN approaches.

CONCLUSIONS

Neighbourhood socio-economic and socio-demographic characteristics and surface temperature were consistently associated with the risk of T2D. For other physical-chemical factors associations differed per analytical approach.