Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer.

Lung cancer is referred to as the second most common cancer worldwide and is mainly associated with complex diagnostics and the absence of personalized therapy. Metabolomics may provide significant insights into the improvement of lung cancer diagnostics through identification of the specific biomarkers or biomarker panels that characterize the pathological state of the patient. We performed targeted metabolomic profiling of plasma samples from individuals with non-small cell lung cancer (NSLC, n = 100) and individuals without any cancer or chronic pathologies (n = 100) to identify the relationship between plasma endogenous metabolites and NSLC by means of modern comprehensive bioinformatics tools, including univariate analysis, multivariate analysis, partial correlation network analysis and machine learning. Through the comparison of metabolomic profiles of patients with NSCLC and noncancer individuals, we identified significant alterations in the concentration levels of metabolites mainly related to tryptophan metabolism, the TCA cycle, the urea cycle and lipid metabolism. Additionally, partial correlation network analysis revealed new ratios of the metabolites that significantly distinguished the considered groups of participants. Using the identified significantly altered metabolites and their ratios, we developed a machine learning classification model with an ROC AUC value equal to 0.96. The developed machine learning lung cancer model may serve as a prototype of the approach for the in-time diagnostics of lung cancer that in the future may be introduced in routine clinical use. Overall, we have demonstrated that the combination of metabolomics and up-to-date bioinformatics can be used as a potential tool for proper diagnostics of patients with NSCLC.

Method for correction of experimental action spectrum using actual overlapping spectra of radiation sources.

Experimental dependency of the photosystem's response on the wavelength of exciting radiation, also known as action spectrum, may be substantially affected by the spectrum shape of this radiation. This is especially important in the case, when different radiation sources are used for the investigation of action spectrum. For instance, too wide emission peaks of radiation sources can blur the scopes of actual action spectrum and distort information about the properties of photosystem at certain wavelength regions. Here, we propose a method for the correction of experimental action spectrum by the recalculation of experimental data of photoresponse according to actual spectra of exciting radiation. In the case of overlapping radiation spectra from different radiation sources, this method results in much better correlation of experimental action spectrum to actual action spectrum or absorption spectrum of photosystem. The data on photoactivity of several photocatalysts are presented to illustrate and validate the proposed method.•Activity of photosystem depends on the actual spectrum of the radiation source•Single-peak optical radiation sources with the same basic wavelength may cause a different photoactivity•Effect of actual spectrum of the light source on the photoactivity is to be considered.