Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration.

Cancer is one of the most deadly diseases in the world. Accurate cancer subtype classification is critical for patient diagnosis, treatment, and prognosis. Ever-increasing multi-omics data describes the characteristics of the patients from different views and serves as complementary information to promote cancer subtype identification. However, omics data generally have different distributions and high dimensions. How to effectively integrate multiple omics data to classify cancer subtypes accurately is a challenge for researchers. This work proposes a method integrating multi-omics data based on supervised graph contrast learning (MCRGCN) to classify cancer subtypes. The method considers the unique feature distribution of each omics data and the interaction of different omics data features to improve the accuracy of cancer subtype classification. To achieve this, MCRGCN first constructs different sample networks based on the multi-omics data of the samples. Then, it puts the omics data and adjacency matrix of the sample into different residual graph convolution models to get multi-omics features of the samples, which are trained with a supervised comparison loss to maintain that the sample features of each omics should be as consistent as possible. Finally, we input the sample features combining multi-omics features into a classifier to obtain the cancer subtypes. We applied MCRGCN to the invasive breast carcinoma (BRCA) and glioblastoma multiforme (GBM) datasets, integrating gene expression, miRNA expression, and DNA methylation data. The results demonstrate that our model is superior to other methods in integrating multi-omics data. Moreover, the results of survival analysis experiments demonstrate that the cancer subtypes identified by our model have significant clinical features. Furthermore, our model can help to identify potential biomarkers and pathways associated with cancer subtypes.

Biochar synthesized via pyrolysis of Broussonetia papyrifera leaves: mechanisms and potential applications for phosphate removal.

In this study, Broussonetia papyrifera leaves collected from land near a restored manganese mine in the Hunan Province of China were converted into biochar under high-temperature anaerobic conditions, regeneration and utilization of agricultural and forest waste, and applied to the prevention of eutrophication. The physicochemical properties of the B. papyrifera biochar were characterized using Micromeritics 3Flex analyzer, scanning electron microscope (SEM), Fourier transform infrared spectrometer (FT-IR), thermogravimetric analyzer (TGA), X-ray photoelectron spectrometer (XPS), zeta potential meter (zeta), and X-ray diffraction (XRD). The effects of pH, ionic strength, coexisting ions, time, initial concentration, and temperature on the decontamination process of phosphate in water were studied. The results indicated that adsorption was enhanced under alkaline conditions. The pseudo-second-order model of adsorption kinetics was applied to illustrate the adsorption processes. The chemical adsorption reaction was the main rate-limiting step in the adsorption process. Isotherm experimental data were best fitted by the Freundlich model at 25 °C and by the Langmuir model at 35 °C. The phosphate combined with B. papyrifera biochar mainly in the forms of exchangeable phosphorus (Ex-P), Al-bound phosphorus (Al-P), and Fe-bound phosphorus (Fe-P). These results indicate that B. papyrifera biochar is a suitable candidate for the treatment of a eutrophic body of water.

Identification of Key Candidate Genes and Pathways in Endometrial Cancer by Integrated Bioinformatical Analysis

Endometrial Cancer is the most common female genital tract malignancy, its pathogenesis is complex, not yet fully described. To identify key genes of Endometrial Cancer we downloaded the gene chip GSE17025 from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified through the GEO2R analysis tool. Functional and pathway enrichment analysis were performed for DEGs using DAVID database. The network of protein­protein-interaction (PPI) was established by STRING website and visualized by Cytoscape. Then, functional and pathway enrichment analysis of DEGS were performed by DAVID database. A total of 1000 significant differences genes were obtained, contain 362 up-regulated genes and 638 down-regulated genes. PCDH10, SLC6A2, OGN, SFRP4, TRH, ANGPTL, FOSB are down-regulated genes. The gene of IGH, CCL20, ELF5, LTF, ASPM expression level in tumor patients are up-regulated. Biological function of enrichment include metabolism of xenobiotics by cytochrome P450, MAPK signaling pathway, Serotonergic synapse, Protein digestion and absorption, IL-17 signaling pathway, Chemokine signaling pathway, HIF-1 signaling pathway, p53 signaling pathway. All in all, the current study to determine endometrial differentially expressed genes and biological function, comprehensive analysis of intrauterine membrane carcinoma pathogenesis mechanism, and might be used as molecular targets and diagnostic biomarkers for the treatment of endometrial cancer.