Oblique rotation of factors: a novel pattern recognition strategy to classify fluorescence excitation-emission matrices of human blood plasma for early diagnosis of colorectal cancer

Multifarious analytical capabilities of the UV/Vis protein fluorescence in blood plasma

Autofluorescence of blood plasma has been broadly considered as a prospective disease screening method. However, the assessment of such intrinsic fluorescence is mostly phenomenological, and its origin is still not fully understood, complicating its use in the clinical practice. Here we present the detailed evaluation of analytical capabilities, variability, and formation of blood plasma protein fluorescence based on the open dataset of excitation-emission matrices measured for ∼300 patients with suspected colorectal cancer, and our supporting model experiments. Using high-resolution size-exclusion chromatography coupled with comprehensive spectral analysis, we demonstrate, for the first time, the dominant role of HSA in the formation of blood plasma fluorescence in the visible spectral range (excitation wavelength >350 nm), presumably caused by its oxidative modifications. Furthermore, the diagnostic value of the tryptophan emission, as well as of the tyrosine fluorescence and visible fluorescence of proteins is shown by building a tree-based classification model that uses a small subset of physically interpretable fluorescence features for distinguishing between the control group and cancer patients with >80% accuracy. The obtained results extend current understanding and approaches used for the analysis of blood plasma fluorescence and pave the way for novel autofluorescence-based disease screening methods.

Artificial intelligence in colorectal cancer management

Artificial intelligence (AI) is a new branch of computer science involving many disciplines and technologies. Since its application in the medical field, it has been constantly studied and developed. AI includes machine learning and neural networks to create new technologies or to improve existing ones. Various AI supporting systems are available for a personalized and novel strategy for the management of colorectal cancer (CRC). This mini-review aims to summarize the progress of research and possible clinical applications of AI in the investigation, early diagnosis, treatment, and management of CRC, to offer elements of knowledge as a starting point for new studies and future applications.

The emerging role of super enhancer-derived noncoding RNAs in human cancer

Super enhancers (SEs) are large clusters of adjacent enhancers that drive the expression of genes which regulate cellular identity; SE regions can be enriched with a high density of transcription factors, co-factors, and enhancer-associated epigenetic modifications. Through enhanced activation of their target genes, SEs play an important role in various diseases and conditions, including cancer. Recent studies have shown that SEs not only activate the transcriptional expression of coding genes to directly regulate biological functions, but also drive the transcriptional expression of non-coding RNAs (ncRNAs) to indirectly regulate biological functions. SE-derived ncRNAs play critical roles in tumorigenesis, including malignant proliferation, metastasis, drug resistance, and inflammatory response. Moreover, the abnormal expression of SE-derived ncRNAs is closely related to the clinical and pathological characterization of tumors. In this review, we summarize the functions and roles of SE-derived ncRNAs in tumorigenesis and discuss their prospective applications in tumor therapy. A deeper understanding of the potential mechanism underlying the action of SE-derived ncRNAs in tumorigenesis may provide new strategies for the early diagnosis of tumors and targeted therapy.

FTICR mass spectrometry-based multivariate analysis to explore distinctive metabolites and metabolic pathways: A comprehensive bioanalytical strategy toward time-course metabolic profiling of Thymus vulgaris plants responding to drought stress

In this research, metabolic profiling/pathways of Thymus vulgaris (thyme) plant were assessed during a water deficit stress using an FTICR mass spectrometry-based metabolomics strategy incorporating multivariate data analysis and bioinformatics techniques. Herein, differences of MS signals in specific time courses after water deficit stress and control cases without any timing period were distinguished significantly by common pattern recognition techniques, i.e., PCA, HCA-Heatmap, and PLS-DA. Subsequently, the results were compared with supervised Kohonen neural network (SKN) ones as a non-linear data visualization and capable mapping tool. The classification models showed excellent performance to predict the level of drought stress. By assessing variances contribution on the PCA-loadings of the MS data, the discriminant variables related to the most critical metabolites were identified and then confirmed by ANOVA. Indeed, FTICR MS-based multivariate analysis strategy could explore distinctive metabolites and metabolic pathways/profiles, grouped into three metabolism categories including amino acids, carbohydrates (i.e., galactose, glucose, fructose, sucrose, and mannose), and other metabolites (rosmarinic acid and citrate), to indicate biological mechanisms in response to drought stress for thyme. It was achieved and approved through the MS signals, genomics databases, and transcriptomics factors to interpret and predict the plant metabolic behavior. Eventually, a comprehensive pathway analysis was used to provide a pathway enrichment analysis and explore topological pathway characteristics dealing with the remarkable metabolites to demonstrate that galactose metabolism is the most significant pathway in the biological system of thyme.

LncRNA BDNF-AS suppresses colorectal cancer cell proliferation and migration by epigenetically repressing GSK-3β expression

This study was designed to investigate the molecular mechanism and biological roles of long non-coding RNA (lncRNA) brain-derived neurotrophic factor antisense (BDNF-AS) in colorectal cancer (CRC). The quantitative real-time PCR (qRT-PCR) and western blotting were performed to detect the expressions of lncRNA BDNF-AS and glycogen synthase kinase-3β (GSK-3β) in human CRC tissues and cell lines. The cell proliferation, transwell migration, and invasion assays were carried out to evaluate the effect of lncRNA BDNF-AS on the growth of CRC cells. RNA pull-down and RNA immunoprecipitation (RIP) assays were conducted to confirm the interaction between lncRNA BDNF-AS and enhancer of Zeste Homologue 2 (EZH2). Chromatin immunoprecipitation (ChIP) assay was used to verify the enrichment of EZH2 and histone H3 lysine 27 trimethylation (H3K27me3) in the promoter region of GSK-3β in CRC cells. LncRNA BDNF-AS expression was significantly decreased, while GSK-3β was highly expressed in human CRC tissues and cell lines. Moreover, lncRNA BDNF-AS induced inhibition of proliferation, migration, and invasion of CRC cells via inhibiting GSK-3β expression. Mechanistically, BDNF-AS led to GSK-3β promoter silencing in CRC cells through recruitment of EZH2. In conclusion, lncRNA BDNF-AS functioned as an oncogene in CRC and shed new light on lncRNA-directed therapeutics in CRC. SIGNIFICANCE OF THE STUDY: LncRNA BDNF-AS is recently reported to be remarkably downregulated in a variety of tumours and served as a tumour suppressor. However, the functions and underlying mechanism of lncRNA BDNF-AS in CRC pathogenesis have not been reported yet. Our study is the first to demonstrate the effect of lncRNA BDNF-AS in CRC and revealed that lncRNA BDNF-AS expression is negatively correlated with the aggressive biological behaviour of CRC. Further investigation demonstrated that lncRNA BDNF-AS functioned as a tumour suppressor in CRC progression by suppressing GSK-3β expression through binding to EZH2 and H3K27me3 with the GSK-3β promoter, shedding light on the diagnosis and therapy for CRC.

Determination of adulterated gasoline using fluorescence emission-excitation matrices and multivariate calibration

Methanol gasoline, known as a new energy, has a certain degree of damage to automobile. The aim of this work was to identify and quantify the methanol in methanol gasoline using three-dimensional fluorescence spectroscopy technique combined with second order chemometric methods. Parallel factor analysis (PARAFAC) and self-weighted alternating trilinear decomposition (SWATLD) methods were used to analyse artificial samples. However, the obtained results by PARAFAC were not satisfactory. On the other hand, excellent prediction results were obtained when SWATLD model was applied, with recovery rate between 98.7 and 102.8%, and between 97.4 and 101.9% for two and three factor respectively. In order to verify the accuracy of the method, four real samples were predicted using SWATLD model with RMSEP between 0.1 μg/mL and 0.23 μg/mL.