Physical Characterization and Volatile Organic Compound Monitoring of Recycled Polyethylene Terephthalate under Mechanical Recycling

In this study, physical characterization and monitoring of volatile organic compounds (VOCs) were investigated on recycled polyethylene terephthalate (rPET) from a mechanical recycling process and rPET bottles made with different rPET contents, with the aim of tracing the source of rPET and assessing its safety when use as a food contact material. It was found that rPET had a similar thermal stability to that of virgin PET (vPET). rPET bottles did not show any significant changes in groups or structure and exhibit similar crystallization and melting behaviors to vPET. However, there were minor mechanical scratches in the surface micromorphology of rPET bottles, and the color of rPET bottles became darker, greener and yellower as the content of recycled material increased. The solid-state polycondensation process was found to play an important role in the removal of VOCs, as detected by headspace gas chromatography-mass spectrometry (HS-GC-MS), resulting in a very small amount of residual VOCs in rPET. Four VOCs (acetaldehyde, glycol and nonanal at levels less than 1.00 mg/kg; 2-methyl-1,3 dioxolane at levels of 1.72-5.76 mg/kg) were detected in the rPET bottles. This study shows that rPET bottles are qualified for reuse in food contact in terms of thermal properties, structure, morphology and VOC residues, although there is variability in color

Comprehensive Characterization of Somatic Mutations Impacting lncRNA Expression for Pan-Cancer

Somatic mutations have long been recognized as an important feature of cancer. However, analysis of somatic mutations, to date, has focused almost entirely on the protein coding regions of the genome. The potential roles of somatic mutations in human long noncoding RNAs (lncRNAs) are therefore largely unknown, particularly their functional significance across different cancer types. In this study, we characterized some lncRNAs whose expression was affected by somatic mutations (defined as MutLncs) and constructed global MutLnc landscapes across 17 cancer types by systematically integrating multiple levels of data. MutLncs were commonly downregulated and carried low mutation frequencies and non-silent mutations in most cancer types. Co-occurrence analysis in pan-cancer highlighted combined patterns of specific MutLncs, suggesting that a number of MutLncs influence diverse cancer types through combination effects. Several conserved and cancer-specific functions of MutLncs were determined. We further explored the somatic mutations affecting lncRNA expression via mixed and unmixed effects, which led to specific functions in pan-cancer. Survival analysis indicated that MutLncs and co-occurrence pairs can potentially serve as cancer biomarkers. Clarification of the specific roles of MutLncs in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types and developing the appropriate treatments

LnCeVar: a comprehensive database of genomic variations that disturb ceRNA network regulation

LnCeVar (http://www.bio-bigdata.net/LnCeVar/) is a comprehensive database that aims to provide genomic variations that disturb lncRNA-associated competing endogenous RNA (ceRNA) network regulation curated from the published literature and high-throughput data sets. LnCeVar curated 119 501 variation-ceRNA events from thousands of samples and cell lines, including: (i) more than 2000 experimentally supported circulating, drug-resistant and prognosis-related lncRNA biomarkers; (ii) 11 418 somatic mutation-ceRNA events from TCGA and COSMIC; (iii) 112 674 CNV-ceRNA events from TCGA; (iv) 67 066 SNP-ceRNA events from the 1000 Genomes Project. LnCeVar provides a user-friendly searching and browsing interface. In addition, as an important supplement of the database, several flexible tools have been developed to aid retrieval and analysis of the data. The LnCeVar-BLAST interface is a convenient way for users to search ceRNAs by interesting sequences. LnCeVar-Function is a tool for performing functional enrichment analysis. LnCeVar-Hallmark identifies dysregulated cancer hallmarks of variation-ceRNA events. LnCeVar-Survival performs COX regression analyses and produces survival curves for variation-ceRNA events. LnCeVar-Network identifies and creates a visualization of dysregulated variation-ceRNA networks. Collectively, LnCeVar will serve as an important resource for investigating the functions and mechanisms of personalized genomic variations that disturb ceRNA network regulation in human diseases