Project description:Whole gene methylation profiles of U87 and U251 glioma cells before and after artesunate treatment, Infinium MethylationEPIC BeadChip, samples including 3 U87 cell normal group, 3 U251 cell normal group, 3 artesunate treated U87 cell group, U251 cell group was treated with 3 artesunate.
Project description:We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway-based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP, or environmental PAH mixtures (Mix 1-3) following a 2-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC >> BaP = Mix2 = Mix3 > Mix1 = Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared with tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (P < .05) for DNA damage, apoptosis, response to chemical stimulus, and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. These data further provide a 'source-to-outcome' model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action-based risk assessment could be employed for environmental PAH mixtures.
Project description:BackgroundThe prognostic and clinicopathological significance of POU Class 5 Homeobox 1 (POU5F1) among various cancers are disputable heretofore. The diagnostic value and functional mechanism of POU5F1 in liver hepatocellular carcinoma (LIHC) have not been studied thoroughly.MethodsAn integrative strategy of meta-analysis, bioinformatics, and wet-lab approach was used to explore the diagnostic and prognostic significance of POU5F1 in various types of tumors, especially in LIHC. Meta-analysis was utilized to investigate the impact of POU5F1 on prognosis and clinicopathological parameters in various cancers. The expression level and diagnostic value of POU5F1 were assessed by qPCR in plasma collected from LIHC patients and controls. The correlation between POU5F1 and tumor infiltrating immune cells (TIICs) in LIHC was evaluated by CIBERSORT. Gene set enrichment analysis (GSEA) was performed based on TCGA. Hub genes and related pathways were identified on the basis of co-expression genes of POU5F1.ResultsElevated POU5F1 was associated with poor OS, DFS, RFS, and DSS in various cancers. POU5F1 was confirmed as an independent risk factor for LIHC and correlated with tumor occurrence, stage, and invasion depth. The combination of POU5F1 and AFP in plasma was with high diagnostic validity (AUC = 0.902, p < .001). Specifically, the level of POU5F1 was correlated with infiltrating levels of B cells, T cells, dendritic cells, and monocytes in LIHC. GSEA indicated that POU5F1 participated in multiple cancer-related pathways and cell proliferation pathways. Moreover, CBX3, CCHCR1, and NFYC were filtered as the central hub genes of POU5F1.ConclusionOur study identified POU5F1 as a pan-cancer gene that could not only be a prognostic and diagnostic biomarker in various cancers, especially in LIHC, but functionally carcinogenic in LIHC.
Project description:BackgroundSingle cell sequencing can provide comprehensive information about gene expression in individual tumor cells, which can allow exploration of heterogeneity of malignant melanoma cells and identification of new anticancer therapeutic targets.MethodsSingle cell sequencing of 31 melanoma patients in GSE115978 was downloaded from the Gene Expression Omniniub (GEO) database. First, the limma package in R software was used to identify the differentially expressed metastasis related genes (MRGs). Next, we developed a prognostic MRGs biomarker in the cancer genome atlas (TCGA) by combining univariate cox analysis and the least absolute shrinkage and selection operator (LASSO) method and was further validated in another two independent datasets. The efficiency of MRGs biomarker in diagnosis of melanoma was also evaluated in multiple datasets. The pattern of somatic tumor mutation, immune infiltration, and underlying pathways were further explored. Furthermore, nomograms were constructed and decision curve analyses were also performed to evaluate the clinical usefulness of the nomograms.ResultsIn total, 41 MRGs were screened out from 1958 malignant melanoma cell samples in GSE115978. Next, a 5-MRGs prognostic marker was constructed and validated, which show more effective performance for the diagnosis and prognosis of melanoma patients. The nomogram showed good accuracies in predicting 3 and 5 years survival, and the decision curve of nomogram model manifested a higher net benefit than tumor stage and clark level. In addition, melanoma patients can be divided into high and low risk subgroups, which owned differential mutation, immune infiltration, and clinical features. The low risk subgroup suffered from a higher tumor mutation burden (TMB), and higher levels of T cells infiltrating have a significantly longer survival time than the high risk subgroup. Gene Set Enrichment Analysis (GSEA) revealed that the extracellular matrix (ECM) receptor interaction and epithelial mesenchymal transition (EMT) were the most significant upregulated pathways in the high risk group.ConclusionsWe identified a robust MRGs marker based on single cell sequencing and validated in multiple independent cohort studies. Our finding provides a new clinical application for prognostic and diagnostic prediction and finds some potential targets against metastasis of melanoma.
Project description:Metabolic disorders, such as obesity and type 2 diabetes have a large impact on global health, especially in industrialized countries. Tissue-specific chronic low-grade inflammation is a key contributor to complications in metabolic disorders. To support therapeutic approaches to these complications, it is crucial to gain a deeper understanding of the inflammatory dynamics and to monitor them on the individual level. To this end, blood-based biomarkers reflecting the tissue-specific inflammatory dynamics would be of great value. Here, we describe an in silico approach to select candidate biomarkers for tissue-specific inflammation by using a priori mechanistic knowledge from pathways and tissue-derived molecules. The workflow resulted in a list of candidate markers, in part consisting of literature confirmed biomarkers as well as a set of novel, more innovative biomarkers that reflect inflammation in the liver and adipose tissue. The first step of biomarker verification was on murine tissue gene-level by inducing hepatic inflammation and adipose tissue inflammation through a high-fat diet. Our data showed that in silico predicted hepatic markers had a strong correlation to hepatic inflammation in the absence of a relation to adipose tissue inflammation, while others had a strong correlation to adipose tissue inflammation in the absence of a relation to liver inflammation. Secondly, we evaluated the human translational value by performing a curation step in the literature using studies that describe the regulation of the markers in human, which identified 9 hepatic (such as Serum Amyloid A, Haptoglobin, and Interleukin 18 Binding Protein) and 2 adipose (Resistin and MMP-9) inflammatory biomarkers at the highest level of confirmation. Here, we identified and pre-clinically verified a set of in silico predicted biomarkers for liver and adipose tissue inflammation which can be of great value to study future development of therapeutic/lifestyle interventions to combat metabolic inflammatory complications.
Project description:We established cell models of hypoxia, and the number of autophagic vacuoles was observed by electron microscopy. Autophagy associated proteins and invasion were evaluated. RNA sequencing was used to identify DEmRNAs and DEmiRNAs to study the mechanism of hypoxia-induced autophagy in liver cancer cells. Results: We found that hypoxia might promote liver cancer cell invasion by inducing autophagy. In addition, a total of 407 shared DEmRNAs and 57 shared DEmiRNAs were identified in both HCCLM3 autophagy group and SMMC-7721 autophagy group compared with normal controls. Furthermore, 278 DEmRNAs were identified cancer autophagy-specific DEmRNAs and 24 DEmiRNAs were identified cancer autophagy-specific DEmRNA and DEmiRNAs. Finally, we obtained 19 DEmiRNAs with high degree based on the DEmiRNA-DEmRNA interaction network. Among them, hsa-miR-483-5p, hsa-miR-4739, has-miR-214-3p and has-miR-296-5p may be potential gene signatures related to liver cancer autophagy.
Project description:We established cell models of hypoxia, and the number of autophagic vacuoles was observed by electron microscopy. Autophagy associated proteins and invasion were evaluated. RNA sequencing was used to identify DEmRNAs and DEmiRNAs to study the mechanism of hypoxia-induced autophagy in liver cancer cells. Results: We found that hypoxia might promote liver cancer cell invasion by inducing autophagy. In addition, a total of 407 shared DEmRNAs and 57 shared DEmiRNAs were identified in both HCCLM3 autophagy group and SMMC-7721 autophagy group compared with normal controls. Furthermore, 278 DEmRNAs were identified cancer autophagy-specific DEmRNAs and 24 DEmiRNAs were identified cancer autophagy-specific DEmRNA and DEmiRNAs. Finally, we obtained 19 DEmiRNAs with high degree based on the DEmiRNA-DEmRNA interaction network. Among them, hsa-miR-483-5p, hsa-miR-4739, has-miR-214-3p and has-miR-296-5p may be potential gene signatures related to liver cancer autophagy.
Project description:This study investigated whether hospital-adopted health information technology (HIT) is associated with a reduction in the frequency of preventable emergency department (ED) visits for patients with Alzheimer's Disease and Related Dementias (ADRD). We used data from the 2015 State Emergency Department Databases, Area Health Resources File, and the American Hospital Association Annual Survey Information Technology Supplement. We employed multivariable logistic regression models to examine the variation of the likelihood of having preventable ED visits by hospitals' adoption of HIT functions and adjusted for patient, hospital, and county-level factors. We focused on hospital-HIT functions related to patient engagement, routine integration and availability of electronic clinical information, frequency of hospital reported use of electronic patient information, and the provision of electronic notification to the patient's primary care provider. Approximately 23% of ADRD patients went to a hospital that often used electronic records from outside providers, and 75% of ADRD patients went to a hospital that provided electronic notification to the patient's primary care provider. Regression results showed that hospital reported use of electronic patient health information from outside providers (OR = 0.88; p < 0.001), provision of electronic notification to the patient's primary care physician inside and outside of the system (OR = 0.91; p = 0.013), and hospital-HIT patient engagement functionalities (OR = 0.90; p < 0.001) were associated with significantly lower preventable ED visit rates. The results of our study suggest that certain types of HIT functionalities may be useful for reducing preventable ED visits for ADRD patients.
Project description:Liver cancer is one of the most common cancers worldwide with high morbidity and mortality. Its molecular mechanism hasn't been fully understood though many studies have been conducted and thus further researches are still needed to improve the prognosis of liver cancer. Firstly, differentially expressed genes (DEGs) between six Mdr2-knockout (Mdr2-KO) mutant mice samples (3-month-old and 12-month-old) and six control mice samples were identified. Then, the enriched GO terms and KEGG pathways of those DEGs were obtained using the Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.abcc.ncifcrf.gov/). Finally, protein-protein interactions (PPI) network of those DEGs were constructed using STRING database ( http://www.string-db.org/) and visualized by Cytoscape software, at the same time, genes with high degree were selected out. Several novel biomarkers that might play important roles in liver cancer were identified through the analysis of gene microarray in GEO. Also, some genes such as Tyrobp, Ctss and pathways such as Pathways in cancer, ECM-receptor interaction that had been researched previously were further confirmed in this study. Through the bioinformatics analysis of the gene microarray in GEO, we found some novel biomarkers of liver cancer and further confirmed some known biomarkers.
Project description:Colorectal cancer (CRC) is one of the most frequently occurring malignancy tumors with a high morbidity additionally, CRC patients may develop liver metastasis, which is the major cause of death. Despite significant advances in diagnostic and therapeutic techniques, the survival rate of colorectal liver metastasis (CRLM) patients remains very low. CRLM, as a complex cascade reaction process involving multiple factors and procedures, has complex and diverse molecular mechanisms. In this review, we summarize the mechanisms/pathophysiology, diagnosis, treatment of CRLM. We also focus on an overview of the recent advances in understanding the molecular basis of CRLM with a special emphasis on tumor microenvironment and promise of newer targeted therapies for CRLM, further improving the prognosis of CRLM patients.