Effect of pyrolysis temperature on the binding characteristics of DOM derived from livestock manure biochar with Cu(II).

Biochar-derived dissolved organic matter (BDOM) has the potential to influence the environmental application of biochar and the behavior of heavy metals. In this study, the binding properties of BDOM derived from livestock manure biochar at different pyrolysis temperatures with Cu(II) were investigated based on a multi-analytical approach. The results showed that the DOC concentration, aromatics, and humification degree of BDOM were higher in the process of low pyrolysis of biochar. The pyrolysis temperature changed the composition of BDOM functional groups, which affected the binding mechanism of BDOM-Cu(II). Briefly, humic-like and protein-like substances dominated BDOM-Cu(II) binding at low and high pyrolysis temperatures, respectively. The higher binding capacity for Cu(II) was exhibited by BDOM derived from the lower pyrolysis temperature, due to the carboxyl as the main binding site in humic acid had high content and binding ability at low-temperature. The amide in proteins only participated in the BDOM-Cu(II) binding at high pyrolysis temperature, and polysaccharides also played an important role in the binding process. Moreover, the biochar underwent the secondary reaction at certain high temperatures, which led to condensation reaction of the aromatic structure and the conversion of large molecules into small molecules, affecting the BDOM-Cu(II) binding sites.

Chronic Lymphocytic Leukemia IGHV Somatic Hypermutation Detection by Targeted Capture Next-Generation Sequencing.

BACKGROUND: Somatic hypermutation (SHM) status of the immunoglobulin heavy variable (IGHV) gene plays a crucial role in determining the prognosis and treatment of patients with chronic lymphocytic leukemia (CLL). A common approach for determining SHM status is multiplex polymerase chain reaction and Sanger sequencing of the immunoglobin heavy locus; however, this technique is low throughput, is vulnerable to failure, and does not allow multiplexing with other diagnostic assays. METHODS: Here we designed and validated a DNA targeted capture approach to detect immunoglobulin heavy variable somatic hypermutation (IGHV SHM) status as a submodule of a larger next-generation sequencing (NGS) panel that also includes probes for ATM, BIRC3, CHD2, KLHL6, MYD88, NOTCH1, NOTCH2, POT1, SF3B1, TP53, and XPO1. The assay takes as input FASTQ files and outputs a report containing IGHV SHM status and V allele usage following European Research Initiative on CLL guidelines. RESULTS: We validated the approach on 35 CLL patient samples, 34 of which were characterized using Sanger sequencing. The NGS panel identified the IGHV SHM status of 34 of 35 CLL patients. We showed 100% sensitivity and specificity among the 33 CLL samples with both NGS and Sanger sequencing calls. Furthermore, we demonstrated that this panel can be combined with additional targeted capture panels to detect prognostically important CLL single nucleotide variants, insertions/deletions, and copy number variants (TP53 copy number loss). CONCLUSIONS: A targeted capture approach to IGHV SHM detection can be integrated into broader sequencing panels, allowing broad CLL prognostication in a single molecular assay.

Multisite clinical cross-validation and variant interpretation of a next generation sequencing panel for lymphoid cancer prognostication.

AIMS: Genomic sequencing of lymphomas is under-represented in routine clinical testing despite having prognostic and predictive value. Clinical implementation is challenging due to a lack of consensus on reportable targets and a paucity of reference samples. We organised a cross-validation study of a lymphoma-tailored next-generation sequencing panel between two College of American Pathologists (CAP)-accredited clinical laboratories to mitigate these challenges. METHODS: A consensus for the genomic targets was discussed between the two institutes based on recurrence in diffuse large B-cell lymphoma, follicular lymphoma, mantle cell lymphoma, chronic lymphocytic leukaemia and T-cell lymphomas. Using the same genomic targets, each laboratory ordered libraries independently and a cross-validation study was designed to exchange samples (8 cell lines and 22 clinical samples) and their FASTQ files. RESULTS: The sensitivity of the panel when comparing different library preparation and bioinformatic workflows was between 97% and 99% and specificity was 100% when a 5% limit of detection cut-off was applied. To evaluate how the current standards for variant classification of tumours apply to lymphomas, the Association for Molecular Pathology/American Society of Clinical Oncology/CAP and OncoKB classification systems were applied to the panel. The majority of variants were assigned a possibly actionable class or likely pathogenic due to more limited evidence in the literature. CONCLUSIONS: The cross-validation study highlights the benefits of sample and data exchange for clinical validation and provided a framework for reporting the findings in lymphoid malignancies.

A novel strategy for the study on molecular mechanism of prostate injury induced by 4,4'-sulfonyldiphenol based on network toxicology analysis.

The study aimed to investigate the underlying molecular mechanisms of prostate injury induced by 4,4'-sulfonyldiphenol (BPS) exposure and propose a novel research strategy to systematically explore the molecular mechanisms of toxicant-induced adverse health effects. By utilizing the ChEMBL, STITCH, and GeneCards databases, a total of 208 potential targets associated with BPS exposure and prostate injury were identified. Through screening the potential target network in the STRING database and Cytoscape software, we determined 21 core targets including AKT1, EGFR, and MAPK3. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses conducted through the DAVID database illustrated that the potential targets of BPS in prostatic toxicity were primarily enriched in cancer signaling pathways and calcium signaling pathways. These findings suggest that BPS may actively participate in the occurrence and development of prostate inflammation, prostatic hyperplasia, prostate cancer, and other aspects of prostate injury by regulating prostate cancer cell apoptosis and proliferation, activating inflammatory signaling pathways, and modulating prostate adipocytes and fibroblasts. This research provides a theoretical basis for understanding the molecular mechanism of underlying BPS-induced prostatic toxicity and establishes a foundation for the prevention and treatment of prostatic diseases associated with exposure to plastic products containing BPS and certain BPS-overwhelmed environments.

Efficient analysis of toxicity and mechanisms of environmental pollutants with network toxicology and molecular docking strategy: Acetyl tributyl citrate as an example.

The study aims to promote network toxicology strategy to efficiently investigate the putative toxicity and underlying molecular mechanisms of environmental pollutants through an example of exploring brain injury induced by ATBC exposure. By utilizing ChEMBL, STITCH, GeneCards, and OMIM databases, we identified 213 potential targets associated with ATBC exposure and brain injury. Further refinements via STRING and Cytoscape software highlight 23 core targets, including AKT1, CASP3, and HSP90AA1. GO and KEGG pathway analysis conducted through DAVID and FUMA databases reveal that core targets of ATBC-induced brain toxicity are predominantly enriched in cancer signaling and neuroactive ligand receptor interaction pathways. Molecular docking was performed with Autodock, which confirmed robust binding between ATBC and core targets. Together, these findings suggest that ATBC may impact the occurrence and development of brain cancer and brain related inflammation, whereas pose risks for cognitive impairment and neurodegeneration, by modulating the apoptosis and proliferation of brain cancer cells, activating inflammatory signaling pathways, and regulating neuroplasticity. This research provides a theoretical basis for understanding the molecular mechanism of ATBC-induced brain toxicity, as well as establishing a foundation for the prevention and treatment of prostatic diseases associated with exposure to plastic products containing ATBC and certain ATBC-overwhelmed environments. Moreover, our network toxicology approach also expedites the elucidation of toxicity pathways for uncharacterized environmental chemicals.

Development and validation of a prognostic 15-gene signature for stratifying HER2+/ER+ breast cancer.

Background: Human epidermal growth receptor 2-positive (HER2+) breast cancer (BC) is a heterogeneous subgroup. Estrogen receptor (ER) status is emerging as a predictive marker within HER2+ BCs, with the HER2+/ER+ cases usually having better survival in the first 5 years after diagnosis but have higher recurrence risk after 5 years compared to HER2+/ER-. This is possibly because sustained ER signaling in HER2+ BCs helps escape the HER2 blockade. Currently HER2+/ER+ BC is understudied and lacks biomarkers. Thus, a better understanding of the underlying molecular diversity is important to find new therapy targets for HER2+/ER+ BCs. Methods: In this study, we performed unsupervised consensus clustering together with genome-wide Cox regression analyses on the gene expression data of 123 HER2+/ER+ BC from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohort to identify distinct HER2+/ER+ subgroups. A supervised eXtreme Gradient Boosting (XGBoost) classifier was then built in TCGA using the identified subgroups and validated in another two independent datasets (Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) (accession number GSE149283)). Computational characterization analyses were also performed on the predicted subgroups in different HER2+/ER+ BC cohorts. Results: We identified two distinct HER2+/ER+ subgroups with different survival outcomes using the expression profiles of 549 survival-associated genes from the Cox regression analyses. Genome-wide gene expression differential analyses found 197 differentially expressed genes between the two identified subgroups, with 15 genes overlapping the 549 survival-associated genes.XGBoost classifier, using the expression values of the 15 genes, achieved a strong cross-validated performance (Area under the curve (AUC) = 0.85, Sensitivity = 0.76, specificity = 0.77) in predicting the subgroup labels. Further investigation partially confirmed the differences in survival, drug response, tumor-infiltrating lymphocytes, published gene signatures, and CRISPR-Cas9 knockout screened gene dependency scores between the two identified subgroups. Conclusion: This is the first study to stratify HER2+/ER+ tumors. Overall, the initial results from different cohorts showed there exist two distinct subgroups in HER2+/ER+ tumors, which can be distinguished by a 15-gene signature. Our findings could potentially guide the development of future precision therapies targeted on HER2+/ER+ BC.

Bioinformatics driven discovery of small molecule compounds that modulate the FOXM1 and PPARA pathway activities in breast cancer.

Our previous studies demonstrated that the FOXM1 pathway is upregulated and the PPARA pathway downregulated in breast cancer (BC), and especially in the triple negative breast cancer (TNBC) subtype. Targeting the two pathways may offer potential therapeutic strategies to treat BC, especially TNBC which has the fewest effective therapies available among all BC subtypes. In this study we identified small molecule compounds that could modulate the PPARA and FOXM1 pathways in BC using two methods. In the first method, data were initially curated from the Connectivity Map (CMAP) database, which provides the gene expression profiles of MCF7 cells treated with different compounds as well as paired controls. We then calculated the changes in the FOXM1 and PPARA pathway activities from the compound-induced gene expression profiles under each treatment to identify compounds that produced a decreased activity in the FOXM1 pathway or an increased activity in the PPARA pathway. In the second method, the CMAP database tool was used to identify compounds that could reverse the expression pattern of the two pathways in MCF7 cells. Compounds identified as repressing the FOXM1 pathway or activating the PPARA pathway by the two methods were compared. We identified 19 common compounds that could decrease the FOXM1 pathway activity scores and reverse the FOXM1 pathway expression pattern, and 13 common compounds that could increase the PPARA pathway activity scores and reverse the PPARA pathway expression pattern. It may be of interest to validate these compounds experimentally to further investigate their effects on TNBCs.

Facile Fabrication of 1-Methylimidazole/Cu Nanozyme with Enhanced Laccase Activity for Fast Degradation and Sensitive Detection of Phenol Compounds.

Facile construction of functional nanomaterials with laccase-like activity is important in sustainable chemistry since laccase is featured as an efficient and promising catalyst especially for phenolic degradation but still has the challenges of high cost, low activity, poor stability and unsatisfied recyclability. In this paper, we report a simple method to synthesize nanozymes with enhanced laccase-like activity by the self-assembly of copper ions with various imidazole derivatives. In the case of 1-methylimidazole as the ligand, the as-synthesized nanozyme (denoted as Cu-MIM) has the highest yield and best activity among the nanozymes prepared. Compared to laccase, the Km of Cu-MIM nanozyme to phenol is much lower, and the vmax is 6.8 times higher. In addition, Cu-MIM maintains excellent stability in a variety of harsh environments, such as high pH, high temperature, high salt concentration, organic solvents and long-term storage. Based on the Cu-MIM nanozyme, we established a method for quantitatively detecting phenol concentration through a smartphone, which is believed to have important applications in environmental protection, pollutant detection and other fields.

Predicting breast cancer drug response using a multiple-layer cell line drug response network model.

BACKGROUND: Predicting patient drug response based on a patient's molecular profile is one of the key goals of precision medicine in breast cancer (BC). Multiple drug response prediction models have been developed to address this problem. However, most of them were developed to make sensitivity predictions for multiple single drugs within cell lines from various cancer types instead of a single cancer type, do not take into account drug properties, and have not been validated in cancer patient-derived data. Among the multi-omics data, gene expression profiles have been shown to be the most informative data for drug response prediction. However, these models were often developed with individual genes. Therefore, this study aimed to develop a drug response prediction model for BC using multiple data types from both cell lines and drugs. METHODS: We first collected the baseline gene expression profiles of 49 BC cell lines along with IC50 values for 220 drugs tested in these cell lines from Genomics of Drug Sensitivity in Cancer (GDSC). Using these data, we developed a multiple-layer cell line-drug response network (ML-CDN2) by integrating a one-layer cell line similarity network based on the pathway activity profiles and a three-layer drug similarity network based on the drug structures, targets, and pan-cancer IC50 profiles. We further used ML-CDN2 to predict the drug response for new BC cell lines or patient-derived samples. RESULTS: ML-CDN2 demonstrated a good predictive performance, with the Pearson correlation coefficient between the observed and predicted IC50 values for all GDSC cell line-drug pairs of 0.873. Also, ML-CDN2 showed a good performance when used to predict drug response in new BC cell lines from the Cancer Cell Line Encyclopedia (CCLE), with a Pearson correlation coefficient of 0.718. Moreover, we found that the cell line-derived ML-CDN2 model could be applied to predict drug response in the BC patient-derived samples from The Cancer Genome Atlas (TCGA). CONCLUSIONS: The ML-CDN2 model was built to predict BC drug response using comprehensive information from both cell lines and drugs. Compared with existing methods, it has the potential to predict the drug response for BC patient-derived samples.

Erb-b2 Receptor Tyrosine Kinase 2 (ERBB2) Promotes ATG12-Dependent Autophagy Contributing to Treatment Resistance of Breast Cancer Cells.

The epidermal growth factor receptor (EGFR) family member erb-b2 receptor tyrosine kinase 2 (ERBB2) is overexpressed in many types of cancers leading to (radio- and chemotherapy) treatment resistance, whereas the underlying mechanisms are still unclear. Autophagy is known to contribute to cancer treatment resistance. In this study, we demonstrate that ERBB2 increases the expression of different autophagy genes including ATG12 (autophagy-related 12) and promotes ATG12-dependent autophagy. We clarify that lapatinib, a dual inhibitor for EGFR and ERBB2, promoted autophagy in cells expressing only EGFR but inhibited autophagy in cells expressing only ERBB2. Furthermore, breast cancer database analysis of 35 genes in the canonical autophagy pathway shows that the upregulation of ATG12 and MAP1LC3B is associated with a low relapse-free survival probability of patients with ERBB2-positive breast tumors following treatments. Downregulation of ERBB2 or ATG12 increased cell death induced by chemotherapy drugs in ERBB2-positive breast cancer cells, whereas upregulation of ERBB2 or ATG12 decreased the cell death in ERBB2-negative breast cancer cells. Finally, ERBB2 antibody treatment led to reduced expression of ATG12 and autophagy inhibition increasing drug or starvation-induced cell death in ERBB2-positive breast cancer cells. Taken together, this study provides a novel approach for the treatment of ERBB2-positive breast cancer by targeting ATG12-dependent autophagy.

The treatment of SARS-CoV2 with antivirals and mitigation of the cytokine storm syndrome: the role of gene expression.

In the absence of a vaccine, the treatment of SARS-CoV2 has focused on eliminating the virus with antivirals or mitigating the cytokine storm syndrome (CSS) that leads to the most common cause of death: respiratory failure. Herein we discuss the mechanisms of antiviral treatments for SARS-CoV2 and treatment strategies for the CSS. Antivirals that have shown in vitro activity against SARS-CoV2, or the closely related SARS-CoV1 and MERS-CoV, are compared on the enzymatic level and by potency in cells. For treatment of the CSS, we discuss medications that reduce the effects or expression of cytokines involved in the CSS with an emphasis on those that reduce IL-6 because of its central role in the development of the CSS. We show that some of the medications covered influence the activity or expression of enzymes involved in epigenetic processes and specifically those that add or remove modifications to histones or DNA. Where available, the latest clinical data showing the efficacy of the medications is presented. With respect to their mechanisms, we explain why some medications are successful, why others have failed, and why some untested medications may yet prove useful.

An integrative deep learning framework for classifying molecular subtypes of breast cancer.

Classification of breast cancer subtypes using multi-omics profiles is a difficult problem since the data sets are high-dimensional and highly correlated. Deep neural network (DNN) learning has demonstrated advantages over traditional methods as it does not require any hand-crafted features, but rather automatically extract features from raw data and efficiently analyze high-dimensional and correlated data. We aim to develop an integrative deep learning framework for classifying molecular subtypes of breast cancer. We collect copy number alteration and gene expression data measured on the same breast cancer patients from the Molecular Taxonomy of Breast Cancer International Consortium. We propose a deep learning model to integrate the omics datasets for predicting their molecular subtypes. The performance of our proposed DNN model is compared with some baseline models. Furthermore, we evaluate the misclassification of the subtypes using the learned deep features and explore their usefulness for clustering the breast cancer patients. We demonstrate that our proposed integrative deep learning model is superior to other deep learning and non-deep learning based models. Particularly, we get the best prediction result among the deep learning-based integration models when we integrate the two data sources using the concatenation layer in the models without sharing the weights. Using the learned deep features, we identify 6 breast cancer subgroups and show that Her2-enriched samples can be classified into more than one tumor subtype. Overall, the integrated model show better performance than those trained on individual data sources.

Cardiac-specific methylation patterns of circulating DNA for identification of cardiomyocyte death.

BACKGROUND: Correct detection of human cardiomyocyte death is essential for definitive diagnosis and appropriate management of cardiovascular diseases. Although current strategies have proven utility in clinical cardiology, they have some limitations. Our aim was to develop a new approach to monitor myocardial death using methylation patterns of circulating cell-free DNA (cf-DNA). METHODS: We first examined the methylation status of FAM101A in heart tissue and blood of individual donors using quantitative methylation-sensitive PCR (qMS-PCR). The concentrations and kinetics of cardiac cf-DNA in plasma from five congenital heart disease (CHD) children before and after they underwent cardiac surgery at serial time points were then investigated. RESULTS: We identified demethylated FAM101A specifically present in heart tissue. Importantly, our time course experiments demonstrated that the plasma cardiac cf-DNA level increased quickly during the early post-cardiac surgery phase, peaking at 4-6 h, decreased progressively (24 h) and returned to baseline (72 h). Moreover, cardiac cf-DNA concentrations pre- and post-operation were closely correlated with plasma troponin levels. CONCLUSIONS: We proposed a novel strategy for the correct detection of cardiomyocyte death, based on analysis of plasma cf-DNA carrying the cardiac-specific methylation signature. Our pilot study may lead to new tests for human cardiac pathologies.

DTF: Deep Tensor Factorization for predicting anticancer drug synergy.

MOTIVATION: Combination therapies have been widely used to treat cancers. However, it is cost and time consuming to experimentally screen synergistic drug pairs due to the enormous number of possible drug combinations. Thus, computational methods have become an important way to predict and prioritize synergistic drug pairs. RESULTS: We proposed a Deep Tensor Factorization (DTF) model, which integrated a tensor factorization method and a deep neural network (DNN), to predict drug synergy. The former extracts latent features from drug synergy information while the latter constructs a binary classifier to predict the drug synergy status. Compared to the tensor-based method, the DTF model performed better in predicting drug synergy. The area under precision-recall curve (PR AUC) was 0.58 for DTF and 0.24 for the tensor method. We also compared the DTF model with DeepSynergy and logistic regression models, and found that the DTF outperformed the logistic regression model and achieved similar performance as DeepSynergy using several performance metrics for classification task. Applying the DTF model to predict missing entries in our drug-cell-line tensor, we identified novel synergistic drug combinations for 10 cell lines from the 5 cancer types. A literature survey showed that some of these predicted drug synergies have been identified in vivo or in vitro. Thus, the DTF model could be a valuable in silico tool for prioritizing novel synergistic drug combinations. AVAILABILITY AND IMPLEMENTATION: Source code and data are available at https://github.com/ZexuanSun/DTF-Drug-Synergy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep graph embedding for prioritizing synergistic anticancer drug combinations.

Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein-protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico.

Germline Predisposition and Copy Number Alteration in Pre-stage Lung Adenocarcinomas Presenting as Ground-Glass Nodules.

Objective: Synchronous multiple ground-glass nodules (SM-GGNs) are a distinct entity of lung cancer which has been emerging increasingly in recent years in China. The oncogenesis molecular mechanisms of SM-GGNs remain elusive. Methods: We investigated single nucleotide variations (SNV), insertions and deletions (INDEL), somatic copy number variations (CNV), and germline mutations of 69 SM-GGN samples collected from 31 patients, using target sequencing (TRS) and whole exome sequencing (WES). Results: In the entire cohort, many known driver mutations were found, including EGFR (21.7%), BRAF (14.5%), and KRAS (6%). However, only one out of the 31 patients had the same somatic missense or truncated events within SM-GGNs, indicating the independent origins for almost all of these SM-GGNs. Many germline mutations with a low frequency in the Chinese population, and genes harboring both germline and somatic variations, were discovered in these pre-stage GGNs. These GGNs also bore large segments of copy number gains and/or losses. The CNV segment number tended to be positively correlated with the germline mutations (r = 0.57). The CNV sizes were correlated with the somatic mutations (r = 0.55). A moderate correlation (r = 0.54) was also shown between the somatic and germline mutations. Conclusion: Our data suggests that the precancerous unstable CNVs with potentially predisposing genetic backgrounds may foster the onset of driver mutations and the development of independent SM-GGNs during the local stimulation of mutagens.

Integrative Analysis Reveals Subtype-Specific Regulatory Determinants in Triple Negative Breast Cancer.

Different breast cancer (BC) subtypes have unique gene expression patterns, but their regulatory mechanisms have yet to be fully elucidated. We hypothesized that the top upregulated (Yin) and downregulated (Yang) genes determine the fate of cancer cells. To reveal the regulatory determinants of these Yin and Yang genes in different BC subtypes, we developed a lasso regression model integrating DNA methylation (DM), copy number variation (CNV) and microRNA (miRNA) expression of 391 BC patients, coupled with miRNA-target interactions and transcription factor (TF) binding sites. A total of 25, 20, 15 and 24 key regulators were identified for luminal A, luminal B, Her2-enriched, and triple negative (TN) subtypes, respectively. Many of the 24 TN regulators were found to regulate the PPARA and FOXM1 pathways. The Yin Yang gene expression mean ratio (YMR) and combined risk score (CRS) signatures built with either the targets of or the TN regulators were associated with the BC patients' survival. Previously, we identified FOXM1 and PPARA as the top Yin and Yang pathways in TN, respectively. These two pathways and their regulators could be further explored experimentally, which might help to identify potential therapeutic targets for TN.

An electrodialytic potassium hydroxide eluent generator suited to small bore ion chromatography.

An electrodialytic potassium hydroxide eluent generator (EDG) well-suited to small-bore (2 mm i.d.) ion chromatography (sIC) system is described. The basic configuration of EDG cartridge is much similar to that of conventional counterpart except lower void volume, which was measured to be ˜9 µL relative to ˜ 31 µL of conventional one. The corresponding dispersion volume was computed to be ˜58 µL and ˜106 µL, respectively. Lower void volume will improve the sharpness of a step gradient (or steeper rise curve), which is helpful to gradient elution. The device can bear the pressure up to at least 20.5 MPa and the effective KOH concentration that can be generated is ranging from 0.15 mM to 100 mM at the typical flow rate of sIC (0.4 mL/min). High purity of the produced KOH eluent and excellent running reproducibility of the device were observed, as indicated by the typical suppressed background (0.36 µS/cm) and <0.11% (or <0.23%) of the relative standard deviation of the retention times for common inorganic anions achieved under isocratic (or gradient) mode.

Overexpression of lncRNA LINC01793 acts as a potential predictor for progression and poor prognosis of gastric cancer.

OBJECTIVE: Accumulating evidence has shown that differentially expressed long non-coding RNA (lncRNA) is closely related to the development of gastric cancer. This study aims to explore the role of potential lncRNAs in the development of gastric cancer. METHODS: TCGA database of gastric cancer were analyzed by bioinformatics. LINC01793 was overexpressed in gastric cancer. Furthermore, LINC01793 level in 21 pairs of gastric cancer and paracancerous tissues were detected by qRT-PCR (quantitative real-time polymerase chain reaction). Prognostic analysis was performed to investigate the predictive effect of LINC01793 on the prognosis of patients with gastric cancer. GSEA analysis was performed to investigate the possible biological processes of LINC01793 involved in the development of gastric cancer. RESULTS: LINC01793 was overexpressed in gastric cancer tissues compared to paracancerous tissues. Moreover, LINC01793 overexpression in gastric cancer was closely related to patients with poor prognosis. GSEA analysis found that LINC01793 was closely related to apoptosis, cell cycle, invasion and metastasis of gastric cancer cells. CONCLUSIONS: Our study showed that overexpression of LINC01793 indicates the poor prognosis of gastric cancer patients. Therefore, LINC01793 may serve as a target for the diagnosis and treatment of gastric cancer.