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1.
Medicine (Baltimore) ; 100(14): e25369, 2021 Apr 09.
Artículo en Inglés | MEDLINE | ID: mdl-33832120

RESUMEN

ABSTRACT: Colon cancer patients suffer from high incidence and mortality rates worldwide. More novel molecular biomarkers should be used for the diagnosis and treatment of colon cancer. Long noncoding RNAs (lncRNAs) are found to be involved in colon cancer tumorigenesis and metastasis. This study aimed to identify novel lncRNAs in colon cancer.Two independent datasets (GSE70880 and GSE110715) were downloaded from the Gene Expression Omnibus database and merged with the sva package. R software was used to distinguish differentially expressed lncRNAs and mRNAs in the merged dataset. The competing endogenous RNA (ceRNA) network was constructed using differentially expressed lncRNAs and mRNAs with Cytoscape. Differentially expressed RNAs in the ceRNA network were further verified using the Cancer Genome Atlas database. Gene oncology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment and survival analysis were also performed to identify hub genes.A total of 99 differentially expressed lncRNAs and 95 differentially expressed mRNAs were identified in the merged database. Ten lncRNAs, 8 miRNAs, and 6 mRNAs were involved in the ceRNA network, in which LINC00114 and UCA1 were highly expressed in colon cancer. They were both associated with early tumor stages and might be used for the early diagnosis of colon cancer. High expression of LINC00114 can lead to poor overall survival of colon cancer patients. Furthermore, new pathways such as LINC00114/miR-107/PCKS5, UCA1/miR-107/PCKS5, and UCA1/miR-129-5p/SEMA6A were identified.Two novel lncRNAs (LINC00114 and UCA1) in colon cancer were identified by bioinformatics analysis. They might contribute to the occurrence and development of colon cancer. In addition, LINC00114 may be involved in the overall survival of colon cancer patients.


Asunto(s)
Neoplasias del Colon/genética , Biología Computacional/métodos , ARN Largo no Codificante/genética , ARN Mensajero/genética , Anciano , Biomarcadores de Tumor/genética , Carcinogénesis/genética , Carcinogénesis/patología , Neoplasias del Colon/epidemiología , Neoplasias del Colon/mortalidad , Bases de Datos Genéticas/estadística & datos numéricos , Femenino , Regulación Neoplásica de la Expresión Génica/genética , Humanos , Incidencia , Masculino , MicroARNs/genética , Persona de Mediana Edad , Metástasis de la Neoplasia/genética , Metástasis de la Neoplasia/patología , Estadificación de Neoplasias/métodos , Análisis de Supervivencia
2.
Nat Commun ; 12(1): 1983, 2021 03 31.
Artículo en Inglés | MEDLINE | ID: mdl-33790270

RESUMEN

Inferring a phylogenetic tree is a fundamental challenge in evolutionary studies. Current paradigms for phylogenetic tree reconstruction rely on performing costly likelihood optimizations. With the aim of making tree inference feasible for problems involving more than a handful of sequences, inference under the maximum-likelihood paradigm integrates heuristic approaches to evaluate only a subset of all potential trees. Consequently, existing methods suffer from the known tradeoff between accuracy and running time. In this proof-of-concept study, we train a machine-learning algorithm over an extensive cohort of empirical data to predict the neighboring trees that increase the likelihood, without actually computing their likelihood. This provides means to safely discard a large set of the search space, thus potentially accelerating heuristic tree searches without losing accuracy. Our analyses suggest that machine learning can guide tree-search methodologies towards the most promising candidate trees.


Asunto(s)
Algoritmos , Evolución Molecular , Aprendizaje Automático , Filogenia , Animales , Bases de Datos Genéticas/estadística & datos numéricos , Bases de Datos de Proteínas/estadística & datos numéricos , Humanos , Modelos Genéticos
3.
Sci Rep ; 11(1): 7380, 2021 04 01.
Artículo en Inglés | MEDLINE | ID: mdl-33795722

RESUMEN

The spread of SARS-CoV-2 created a pandemic crisis with > 150,000 cumulative cases in > 65 countries within a few months. The reproductive number (R) is a metric to estimate the transmission of a pathogen during an outbreak. Preliminary published estimates were based on the initial outbreak in China. Whole genome sequences (WGS) analysis found mutational variations in the viral genome; however, previous comparisons failed to show a direct relationship between viral genome diversity, transmission, and the epidemic severity. COVID-19 incidences from different countries were modeled over the epidemic curve. Estimates of the instantaneous R (Wallinga and Teunis method) with a short and standard serial interval were done. WGS were used to determine the populations genomic variation and that underpinned creation of the pathogen genome identity (GENI) score, which was merged with the outbreak curve in four distinct phases. Inference of transmission time was based on a mutation rate of 2 mutations/month. R estimates revealed differences in the transmission and variable infection dynamics between and within outbreak progression for each country examined. Outside China, our R estimates observed propagating dynamics indicating that other countries were poised to move to the takeoff and exponential stages. Population density and local temperatures had no clear relationship to the outbreak progression. Integration of incidence data with the GENI score directly predicted increases in cases as the genome variation increased that led to new variants. Integrating the outbreak curve, dynamic R, and SNP variation found a direct association between increasing cases and transmission genome evolution. By defining the epidemic curve into four stages and integrating the instantaneous country-specific R with the GENI score, we directly connected changes in individual outbreaks based on changes in the virus genome via SNPs. This resulted in the ability to forecast potential increases in cases as well as mutations that may defeat PCR screening and the infection process. By using instantaneous R estimations and WGS, outbreak dynamics were defined to be linked to viral mutations, indicating that WGS, as a surveillance tool, is required to predict shifts in each outbreak that will provide actionable decision making information. Integrating epidemiology with genome sequencing and modeling allows for evidence-based disease outbreak tracking with predictive therapeutically valuable insights in near real time.


Asunto(s)
/patología , Genoma Viral , /genética , Alelos , /transmisión , Bases de Datos Genéticas , Humanos , Mutación , Secuenciación Completa del Genoma
4.
Int J Mol Sci ; 22(5)2021 Mar 05.
Artículo en Inglés | MEDLINE | ID: mdl-33807682

RESUMEN

The Coronavirus Disease 2019 (COVID-19) pandemic has become a global health emergency with no effective medical treatment and with incipient vaccines. It is caused by a new positive-sense RNA virus called severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2). G-quadruplexes (G4s) are nucleic acid secondary structures involved in the control of a variety of biological processes including viral replication. Using several G4 prediction tools, we identified highly putative G4 sequences (PQSs) within the positive-sense (+gRNA) and negative-sense (-gRNA) RNA strands of SARS-CoV-2 conserved in related betacoronaviruses. By using multiple biophysical techniques, we confirmed the formation of two G4s in the +gRNA and provide the first evidence of G4 formation by two PQSs in the -gRNA of SARS-CoV-2. Finally, biophysical and molecular approaches were used to demonstrate for the first time that CNBP, the main human cellular protein bound to SARS-CoV-2 RNA genome, binds and promotes the unfolding of G4s formed by both strands of SARS-CoV-2 RNA genome. Our results suggest that G4s found in SARS-CoV-2 RNA genome and its negative-sense replicative intermediates, as well as the cellular proteins that interact with them, are relevant factors for viral genes expression and replication cycle, and may constitute interesting targets for antiviral drugs development.


Asunto(s)
G-Cuádruplex , Proteínas de Unión al ARN/metabolismo , /metabolismo , Dicroismo Circular , Biología Computacional/métodos , Bases de Datos Genéticas , Ensayo de Cambio de Movilidad Electroforética , Genoma Viral/fisiología , Humanos , Espectroscopía de Protones por Resonancia Magnética , Replicación Viral/fisiología
5.
Front Immunol ; 12: 597399, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33796097

RESUMEN

There exists increasing evidence that people with preceding medical conditions, such as diabetes and cancer, have a higher risk of infection with SARS-CoV-2 and are more vulnerable to severe disease. To get insights into the possible role of the immune system upon COVID-19 infection, 2811 genes of the gene ontology term "immune system process GO: 0002376" were selected for coexpression analysis of the human targets of SARS-CoV-2 (HT-SARS-CoV-2) ACE2, TMPRSS2, and FURIN in tissue samples from patients with cancer and diabetes mellitus. The network between HT-SARS-CoV-2 and immune system process genes was analyzed based on functional protein associations using STRING. In addition, STITCH was employed to determine druggable targets. DPP4 was the only immune system process gene, which was coexpressed with the three HT-SARS-CoV-2 genes, while eight other immune genes were at least coexpressed with two HT-SARS-CoV-2 genes. STRING analysis between immune and HT-SARS-CoV-2 genes plotted 19 associations of which there were eight common networking genes in mixed healthy (323) and pan-cancer (11003) tissues in addition to normal (87), cancer (90), and diabetic (128) pancreatic tissues. Using this approach, three commonly applicable druggable connections between HT-SARS-CoV-2 and immune system process genes were identified. These include positive associations of ACE2-DPP4 and TMPRSS2-SRC as well as a negative association of FURIN with ADAM17. Furthermore, 16 drugs were extracted from STITCH (score <0.8) with 32 target genes. Thus, an immunological network associated with HT-SARS-CoV-2 using bioinformatics tools was identified leading to novel therapeutic opportunities for COVID-19.


Asunto(s)
Diabetes Mellitus/metabolismo , Neoplasias/metabolismo , /metabolismo , Glicoproteína de la Espiga del Coronavirus/metabolismo , /genética , Antivirales/química , Antivirales/farmacología , /genética , /metabolismo , Bases de Datos Genéticas , Diabetes Mellitus/genética , Diabetes Mellitus/inmunología , Diabetes Mellitus/virología , Dipeptidil Peptidasa 4/genética , Dipeptidil Peptidasa 4/metabolismo , Furina/genética , Furina/metabolismo , Regulación de la Expresión Génica/inmunología , Ontología de Genes , Estudio de Asociación del Genoma Completo , Genómica , Humanos , Linfocitos/inmunología , Linfocitos/metabolismo , Neoplasias/genética , Neoplasias/inmunología , Neoplasias/virología , Páncreas/inmunología , Páncreas/metabolismo , Páncreas/virología , Mapas de Interacción de Proteínas/genética , Mapas de Interacción de Proteínas/inmunología , /inmunología , Serina Endopeptidasas/genética , Serina Endopeptidasas/metabolismo
6.
Medicine (Baltimore) ; 100(15): e25553, 2021 Apr 16.
Artículo en Inglés | MEDLINE | ID: mdl-33847684

RESUMEN

BACKGROUND: Acute myocardial infarction (AMI) is a common disease leading threat to human health around the world. Here we aimed to explore new biomarkers and potential therapeutic targets in AMI through adopting integrated bioinformatics tools. METHODS: The gene expression Omnibus (GEO) database was used to obtain genes data of AMI and no-AMI whole blood. Furthermore, differentially expressed genes (DEGs) were screened using the "Limma" package in R 3.6.1 software. Functional and pathway enrichment analyses of DEGs were performed via "Bioconductor" and "GOplot" package in R 3.6.1 software. In order to screen hub DEGs, the STRING version 11.0 database, Cytoscape and molecular complex detection (MCODE) were applied. Correlation among the hub DEGs was evaluated using Pearson's correlation analysis. RESULTS: By performing DEGs analysis, 289 upregulated and 62 downregulated DEGs were successfully identified from GSE66360, respectively. And they were mainly enriched in the terms of neutrophil activation, immune response, cytokine, nuclear factor kappa-B (NF-κB) signaling pathway, IL-17 signaling pathway, and tumor necrosis factor (TNF) signaling pathway. Based on the data of protein-protein interaction (PPI), the top 10 hub genes were ranked, including interleukin-8 (CXCL8), TNF, N-formyl peptide receptor 2 (FPR2), growth-regulated alpha protein (CXCL1), transcription factor AP-1 (JUN), interleukin-1 beta (IL1B), platelet basic protein (PPBP), matrix metalloproteinase-9 (MMP9), toll-like receptor 2 (TLR2), and high affinity immunoglobulin epsilon receptor subunit gamma (FCER1G). What's more, the results of correlation analysis demonstrated that there was positive correlation between the 10 hub DEGs. CONCLUSION: Ten DEGs were identified as potential candidate diagnostic biomarkers for patients with AMI in present study. However, further experiments are needed to confirm the functional pathways and hub genes associated with AMI.


Asunto(s)
Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , Infarto del Miocardio/genética , Biomarcadores/análisis , Correlación de Datos , Citocinas/metabolismo , Bases de Datos Genéticas , Humanos , Inmunidad/genética , Activación Neutrófila/genética , Mapas de Interacción de Proteínas/genética , Transducción de Señal/genética
7.
Int J Mol Sci ; 22(6)2021 Mar 17.
Artículo en Inglés | MEDLINE | ID: mdl-33802922

RESUMEN

Enhancers are short genomic regions exerting tissue-specific regulatory roles, usually for remote coding regions. Enhancers are observed in both prokaryotic and eukaryotic genomes, and their detections facilitate a better understanding of the transcriptional regulation mechanism. The accurate detection and transcriptional regulation strength evaluation of the enhancers remain a major bioinformatics challenge. Most of the current studies utilized the statistical features of short fixed-length nucleotide sequences. This study introduces the location information of each k-mer (SeqPose) into the encoding strategy of a DNA sequence and employs the attention mechanism in the two-layer bi-directional long-short term memory (BD-LSTM) model (spEnhancer) for the enhancer detection problem. The first layer of the delivered classifier discriminates between enhancers and non-enhancers, and the second layer evaluates the transcriptional regulation strength of the detected enhancer. The SeqPose-encoded features are selected by the Chi-squared test, and 45 positions are removed from further analysis. The existing studies may focus on selecting the statistical DNA sequence descriptors with large contributions to the prediction models. This study does not utilize these statistical DNA sequence descriptors. Then the word vector of the SeqPose-encoded features is obtained by using the word embedding layer. This study hypothesizes that different word vector features may contribute differently to the enhancer detection model, and assigns different weights to these word vectors through the attention mechanism in the BD-LSTM model. The previous study generously provided the training and independent test datasets, and the proposed spEnhancer is compared with the three existing state-of-the-art studies using the same experimental procedure. The leave-one-out validation data on the training dataset shows that the proposed spEnhancer achieves similar detection performances as the three existing studies. While spEnhancer achieves the best overall performance metric MCC for both of the two binary classification problems on the independent test dataset. The experimental data shows that the strategy of removing redundant positions (SeqPose) may help improve the DNA sequence-based prediction models. spEnhancer may serve well as a complementary model to the existing studies, especially for the novel query enhancers that are not included in the training dataset.


Asunto(s)
Algoritmos , Biología Computacional/métodos , Elementos de Facilitación Genéticos , Secuencia de Bases , Bases de Datos Genéticas
8.
Nat Commun ; 12(1): 2337, 2021 04 20.
Artículo en Inglés | MEDLINE | ID: mdl-33879782

RESUMEN

While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-onset phenotypes in a sampling scheme that facilitates biobank-scale time-to-event analyses. We show in extensive simulation work the benefits BayesW provides in terms of number of discoveries, model performance and genomic prediction. In the UK Biobank, we find many thousands of common genomic regions underlying the age-at-onset of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of onset reflecting the underlying genetic liability to disease. Age-at-menopause and age-at-menarche are also highly polygenic, but with higher variance contributed by low frequency variants. Genomic prediction into the Estonian Biobank data shows that BayesW gives higher prediction accuracy than other approaches.


Asunto(s)
Edad de Inicio , Genoma Humano , Modelos Genéticos , Herencia Multifactorial , Factores de Edad , Algoritmos , Teorema de Bayes , Enfermedades Cardiovasculares/genética , Simulación por Computador , Bases de Datos Genéticas , Diabetes Mellitus Tipo 2/genética , Estonia , Femenino , Estudios de Asociación Genética , Estudio de Asociación del Genoma Completo , Genómica , Humanos , Hipertensión/genética , Menarquia/genética , Menopausia/genética , Fenotipo , Polimorfismo de Nucleótido Simple , Reino Unido
9.
Nat Commun ; 12(1): 2345, 2021 04 20.
Artículo en Inglés | MEDLINE | ID: mdl-33879792

RESUMEN

Age is the most important risk factor for cancer, as cancer incidence and mortality increase with age. However, how molecular alterations in tumours differ among patients of different age remains largely unexplored. Here, using data from The Cancer Genome Atlas, we comprehensively characterise genomic, transcriptomic and epigenetic alterations in relation to patients' age across cancer types. We show that tumours from older patients present an overall increase in genomic instability, somatic copy-number alterations (SCNAs) and somatic mutations. Age-associated SCNAs and mutations are identified in several cancer-driver genes across different cancer types. The largest age-related genomic differences are found in gliomas and endometrial cancer. We identify age-related global transcriptomic changes and demonstrate that these genes are in part regulated by age-associated DNA methylation changes. This study provides a comprehensive, multi-omics view of age-associated alterations in cancer and underscores age as an important factor to consider in cancer research and clinical practice.


Asunto(s)
Envejecimiento/genética , Neoplasias/etiología , Neoplasias/genética , Factores de Edad , Envejecimiento/metabolismo , Variaciones en el Número de Copia de ADN , Metilación de ADN , Bases de Datos Genéticas , Epigénesis Genética , Femenino , Duplicación de Gen , Perfilación de la Expresión Génica , Regulación Neoplásica de la Expresión Génica , Genoma Humano , Inestabilidad Genómica , Genómica , Humanos , Pérdida de Heterocigocidad , Masculino , Mutación , Neoplasias/metabolismo , Oncogenes , Factores de Riesgo , Transducción de Señal/genética , Secuenciación Completa del Genoma
10.
Int J Mol Sci ; 22(5)2021 Mar 04.
Artículo en Inglés | MEDLINE | ID: mdl-33806327

RESUMEN

Personalised medicine is the future and hope for many patients, including those with cancers. Early detection, as well as rapid, well-selected treatment, are key factors leading to a good prognosis. MicroRNA mediated gene regulation is a promising area of development for new diagnostic and therapeutic methods, crucial for better prospects for patients. Bladder cancer is a frequent neoplasm, with high lethality and lacking modern, advanced therapeutic modalities, such as immunotherapy. MicroRNAs are involved in bladder cancer pathogenesis, proliferation, control and response to treatment, which we summarise in this perspective in response to lack of recent review publications in this field. We further performed a correlation-based analysis of microRNA and gene expression data in bladder cancer (BLCA) TCGA dataset. We identified 27 microRNAs hits with opposite expression profiles to genes involved in immune response in bladder cancer, and 24 microRNAs hits with similar expression profiles. We discuss previous studies linking the functions of these microRNAs to bladder cancer and assess if they are good candidates for personalised medicine therapeutics and diagnostics. The discussed functions include regulation of gene expression, interplay with transcription factors, response to treatment, apoptosis, cell proliferation and angiogenesis, initiation and development of cancer, genome instability and tumour-associated inflammatory reaction.


Asunto(s)
/genética , MicroARNs/genética , Neoplasias de la Vejiga Urinaria/genética , Neoplasias de la Vejiga Urinaria/inmunología , Biomarcadores de Tumor/genética , Neoplasias de la Mama/genética , Neoplasias de la Mama/inmunología , Bases de Datos Genéticas , Femenino , Perfilación de la Expresión Génica , Regulación Neoplásica de la Expresión Génica , Redes Reguladoras de Genes , Humanos , Sinapsis Inmunológicas/genética , Modelos Genéticos , ARN Mensajero/genética , ARN Neoplásico/genética
11.
Medicine (Baltimore) ; 100(17): e25596, 2021 Apr 30.
Artículo en Inglés | MEDLINE | ID: mdl-33907110

RESUMEN

BACKGROUND: As the most common type of cerebrovascular disease, ischemic stroke is the disturbance of cerebrovascular circulation caused by various factors, with complex pathogenesis. At present, the molecular mechanism of ischemic stroke is still unclear, and there lacks early diagnostic markers. Therefore, there is an urgent need to find effective preventive measures, active diagnostic methods and rapid treatment measures. In recent years, related studies have displayed that long noncoding RNAs (lncRNAs) is related to the prognosis of ischemic stroke. However, the results are not supported by some evidence. Therefore, in this study, meta-analysis was used to analyze the relationship between lncRNAs and the prognosis of ischemic stroke. In addition, we carried out bioinformatics analysis to study the action mechanism and related pathways of lncRNAs in ischemic stroke. METHODS: Literature search was operated on databases up to March 2021, including China National Knowledge Infrastructure, Chinese Biomedical literature Database, Chinese Scientific and Journal Database, Wan Fang database, Web of Science, PubMed, and EMBASE. The relationship between lncRNAs expression and survival outcome was estimated by hazard ratio (HR) and 95% confidence interval (CI). Meta-analysis was conducted on the Stata 16.0. Starbase v2.0 software predicts microRNAs (miRNAs) that interacts with lncRNAs. In addition, HMDD v2.0 database filters out miRNAs related to ischemic stroke. Furthermore, Consite transcription factor database was used to predict the transcription factors of each lncRNAs and miRNA. At the same time, the transcription factors related to ischemic stroke were screened out after intersection. miRwalk online software was applied to predict the target mRNA of each miRNA, and the common target genes were screened by consistent method. The molecular regulatory network map of lncRNAs in ischemic stroke was drawn. Based on the overlapping target genes, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) network analysis were carried out to explore the possible mechanism. RESULTS: The results of this meta-analysis would be submitted to peer-reviewed journals for publication. CONCLUSION: This study will provide evidence-based medical evidence for the relationship between lncRNA and the prognosis of ischemic stroke. What is more, bioinformatics analysis will provide ideas for the study of ischemic stroke mechanism. ETHICS AND DISSEMINATION: The private information from individuals will not be published. This systematic review also should not damage participants' rights. Ethical approval is not available. The results may be published in a peer-reviewed journal or disseminated in relevant conferences. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/QBZW6.


Asunto(s)
Biología Computacional/métodos , ARN Largo no Codificante/sangre , Biomarcadores/sangre , Bases de Datos Genéticas , Ontología de Genes , Humanos , /mortalidad , Metaanálisis como Asunto , MicroARNs/sangre , Pronóstico , Modelos de Riesgos Proporcionales , Mapeo de Interacción de Proteínas , Proyectos de Investigación , Análisis de Supervivencia , Factores de Transcripción/sangre
12.
World J Surg Oncol ; 19(1): 123, 2021 Apr 17.
Artículo en Inglés | MEDLINE | ID: mdl-33865399

RESUMEN

BACKGROUND: Pancreatic cancer (PAC) is one of the most devastating cancer types with an extremely poor prognosis, characterized by a hypoxic microenvironment and resistance to most therapeutic drugs. Hypoxia has been found to be one of the factors contributing to chemoresistance in PAC, but also a major driver of the formation of the tumor immunosuppressive microenvironment. However, the method to identify the degree of hypoxia in the tumor microenvironment (TME) is incompletely understood. METHODS: The mRNA expression profiles and corresponding clinicopathological information of PAC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, respectively. To further explore the effect of hypoxia on the prognosis of patients with PAC as well as the tumor immune microenvironment, we established a hypoxia risk model and divided it into high- and low-risk groups in line with the hypoxia risk score. RESULTS: We established a hypoxia risk model according to four hypoxia-related genes, which could be used to demonstrate the immune microenvironment in PAC and predict prognosis. Moreover, the hypoxia risk score can act as an independent prognostic factor in PAC, and a higher hypoxia risk score was correlated with poorer prognosis in patients as well as the immunosuppressive microenvironment of the tumor. CONCLUSIONS: In summary, we established and validated a hypoxia risk model that can be considered as an independent prognostic indicator and reflected the immune microenvironment of PAC, suggesting the feasibility of hypoxia-targeted therapy for PAC patients.


Asunto(s)
Hipoxia/genética , Neoplasias Pancreáticas/genética , Microambiente Tumoral , Biomarcadores de Tumor/genética , Bases de Datos Genéticas , Perfilación de la Expresión Génica/métodos , Regulación Neoplásica de la Expresión Génica/genética , Humanos , Neoplasias Pancreáticas/patología , Pronóstico
13.
Medicine (Baltimore) ; 100(16): e25246, 2021 Apr 23.
Artículo en Inglés | MEDLINE | ID: mdl-33879658

RESUMEN

ABSTRACT: Lung adenocarcinoma (LUAD) is a lethal malignancy worldwide and a major public health concern. We explored the potential clinical significance for LUAD of ATP-binding cassette (ABC), sub-family C, consisting of ABCC1-6, 8-12, and cystic fibrosis transmembrane conductance regulator (CFTR).Five hundred LUAD patients from The Cancer Genome Atlas database were used for analysis, including differential expression and diagnostic and prognostic significance. Oncomine and MERAV databases were used to validate differential expression and diagnostic significance. A risk score model was constructed using prognosis-related ABCC members. Prognosis-related genes were further explored to correlate their expression with tumor stage progression. Interaction networks, including biological processes and metabolic pathways, were constructed using Cytoscape software and STRING website.ABCC1-3 consistently showed high expression in tumor tissues (all P ≤ 0.05). Most datasets indicated that ABCC5, 10, and 11 were highly expressed in tumor tissues whereas ABCC6, 9, and CFTR were highly expressed in nontumor tissues (all P ≤ 0.05). Diagnostic significance of ABCC3 and ABCC5 was consistently assessed and validated in three datasets (all area under the curve > 0.700) whereas ABCC6, 8, 10, 11, and CFTR were assessed in The Cancer Genome Atlas dataset and validated in one dataset (all area under the curve > 0.700). Prognostic analysis indicated that ABCC2, 6, and 8 mRNA expression was associated with survival of LUAD (all adjusted P ≤ .037). The risk score model constructed using ABCC2, 6, and 8 suggested prognostic significance for survival predictions. ABCC2 expression was associated with tumor stage, whereas ABCC6 and 8 were not. Interaction networks indicated that they were involved in establishment of localization, ion transport, plasma membrane, apical plasma membrane, adenylyl nucleotide binding, ABC transporters, ABC transporter disorders, ABC-family-protein-mediated transport, and bile secretion.Differentially expressed ABCC2 and ABCC5 might be diagnostic whereas ABCC2, 6, and 8 may be prognostic biomarkers for LUAD, possibly through ABC-family-mediated transporter disorders.


Asunto(s)
Adenocarcinoma del Pulmón/genética , Adenocarcinoma del Pulmón/mortalidad , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/mortalidad , Proteínas Asociadas a Resistencia a Múltiples Medicamentos/metabolismo , Anciano , Biomarcadores de Tumor/genética , Regulador de Conductancia de Transmembrana de Fibrosis Quística/metabolismo , Bases de Datos Genéticas , Progresión de la Enfermedad , Femenino , Regulación Neoplásica de la Expresión Génica/genética , Humanos , Masculino , Persona de Mediana Edad , Estadificación de Neoplasias , Valor Predictivo de las Pruebas , Pronóstico , Medición de Riesgo , Factores de Riesgo , Análisis de Supervivencia
14.
Medicine (Baltimore) ; 100(16): e25414, 2021 Apr 23.
Artículo en Inglés | MEDLINE | ID: mdl-33879671

RESUMEN

ABSTRACT: Single-cell RNA-seq has become a powerful tool to understand tumor cell heterogenicity. This study tried to screen prognosis-related genes in basal-like breast tumors and evaluate their correlations with cellular states at the single-cell level.Bulk RNA-seq data of basal-like tumor cases from The Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) and single-cell RNA-seq from GSE75688 were retrospectively reviewed. Kaplan-Meier survival curves, univariate and multivariate analysis based on Cox regression model were conducted for survival analysis. Gene set enrichment analysis (GSEA) and single-cell cellular functional state analysis were performed.Twenty thousand five hundred thirty genes with bulk RNA-seq data in TCGA were subjected to screening. Preliminary screening identified 10 candidate progression-related genes, including CDH19, AQP5, SDR16C5, NCAN, TTYH1, XAGE2, RIMS2, GZMB, LY6D, and FAM3B. By checking their profiles using single-cell RNA-seq data, only CDH19, SDR16C5, TTYH1, and RIMS2 had expression in primary triple-negative breast cancer (TNBC) cells. Prognostic analysis only confirmed that RIMS2 expression was an independent prognostic indicator of favorable progression free survival (PFS) (HR: 0.78, 95%: 0.64-0.95, P  = .015). GSEA analysis showed that low RIMS2 group expression had genes significantly enriched in DNA Repair, and MYC Targets V2. Among the 89 basal-like cells, RIMS2 expression was negatively correlated with DNA repair and epithelial-to-mesenchymal transition (EMT).RIMS2 expression was negatively associated with DNA repair capability of basal-like breast tumor cells and might serve as an independent indicator of favorable PFS.


Asunto(s)
Neoplasias de la Mama/genética , Proteínas de la Membrana/genética , Neoplasias Basocelulares/genética , RNA-Seq , Proteínas de Unión al GTP rab3/genética , Adulto , Biomarcadores de Tumor/genética , Simulación por Computador , Reparación del ADN/genética , Bases de Datos Genéticas , Detección Precoz del Cáncer/métodos , Transición Epitelial-Mesenquimal/genética , Femenino , Regulación Neoplásica de la Expresión Génica/genética , Humanos , Estimación de Kaplan-Meier , Persona de Mediana Edad , Pronóstico , Supervivencia sin Progresión , Modelos de Riesgos Proporcionales , Estudios Retrospectivos , Análisis de Supervivencia , Neoplasias de la Mama Triple Negativas/genética
15.
Medicine (Baltimore) ; 100(16): e25541, 2021 Apr 23.
Artículo en Inglés | MEDLINE | ID: mdl-33879700

RESUMEN

ABSTRACT: Thyroid cancer is a common endocrine malignancy; however, surgery remains its primary treatment option. A novel targeted drug for the development and application of targeted therapy in thyroid cancer treatment remain underexplored.We obtained RNA sequence data of thyroid cancer from The Cancer Genome Atlas database and identified differentially expressed genes (DEGs). Then, we constructed co-expression network with DEGs and combined it with differentially methylation analysis to screen the key genes in thyroid cancer. PockDrug-Server, an online tool, was applied to predict the druggability of the key genes. Finally, we constructed protein-protein interaction (PPI) network to observe potential targeted drugs for thyroid cancer.We identified 3 genes correlated with altered DNA methylation level and oncogenesis of thyroid cancer. According to the druggable analysis and PPI network, we predicted TRAF2 and NCK-interacting protein kinase (TNIK) sever as the drug targeted for thyroid cancer. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that genes in protein-protein interaction network of TNIK enriched in mitogen-activated protein kinase signaling pathway. For drug repositioning, we identified a targeted drug of genes in PPI network.Our study provides a bioinformatics method for screening drug targets and provides a theoretical basis for thyroid cancer targeted therapy.


Asunto(s)
Desarrollo de Medicamentos/métodos , Proteínas Serina-Treonina Quinasas/genética , Factor 2 Asociado a Receptor de TNF/genética , Neoplasias de la Tiroides/tratamiento farmacológico , Neoplasias de la Tiroides/genética , Biomarcadores de Tumor/genética , Carcinogénesis/genética , Biología Computacional/métodos , Metilación de ADN/genética , Bases de Datos Genéticas , Regulación Neoplásica de la Expresión Génica/genética , Ontología de Genes , Humanos , Sistema de Señalización de MAP Quinasas/genética , Mapas de Interacción de Proteínas/genética
16.
Medicine (Baltimore) ; 100(16): e25603, 2021 Apr 23.
Artículo en Inglés | MEDLINE | ID: mdl-33879726

RESUMEN

ABSTRACT: Gliomas have the highest incidence among primary brain tumors, and the extracellular matrix (ECM) plays a vital role in tumor progression. We constructed a risk signature using ECM-related genes to predict the prognosis of patients with gliomas.mRNA and clinical data from glioma patients were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Chinese Glioma Genome Atlas (CGGA) databases. Differentially expressed ECM-related genes were screened, and a risk signature was built using least absolute shrinkage and selection operator (LASSO) Cox regression. Cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to assess immune infiltration in different risk groups. Gene set enrichment analysis (GSEA) was performed to explore the molecular mechanisms of the genes employed in the risk score.Differentially expressed ECM-related genes were identified, and their associated regulatory mechanisms were predicted via analysis of protein-protein interaction (PPI), transcription factor (TF) regulatory and TF coexpression networks. The established risk signature considered 17 ECM-related genes. The prognosis of the high-risk group was significantly worse than that of the low-risk group. We used the CGGA database to validate the signature. CIBERSORT indicated that the levels of naive B cells, activated memory CD4 T cells, regulatory T cells, gamma delta T cells, activated NK cells, monocytes, activated dendritic cells and activated mast cells were higher in the high-risk group. The levels of plasma cells, CD8 T cells, naive CD4 T cells, resting memory CD4 T cells, M0 macrophages, M1 macrophages, resting mast cells, and neutrophils were lower in the high-risk group. Ultimately, GSEA showed that the terms intestinal immune network for IgA production, primary immunodeficiency, and ECM receptor interaction were the top 3 terms enriched in the high-risk group. The terms Wnt signaling pathway, ErbB signaling pathway, mTOR signaling pathway, and calcium signaling pathway were enriched in the low-risk group.We built a risk signature to predict glioma prognosis using ECM-related genes. By evaluating immune infiltration and biofunctions, we gained a further understanding of this risk signature. This risk signature could be an effective tool for predicting glioma prognosis.This study did not require ethical approval. We will disseminate our findings by publishing results in a peer-reviewed journal.


Asunto(s)
Neoplasias Encefálicas/genética , Matriz Extracelular/genética , Glioma/genética , Medición de Riesgo/normas , Adulto , Biomarcadores de Tumor/genética , Neoplasias Encefálicas/mortalidad , Bases de Datos Genéticas , Femenino , Perfilación de la Expresión Génica , Regulación Neoplásica de la Expresión Génica/genética , Glioma/mortalidad , Humanos , Masculino , Persona de Mediana Edad , Valor Predictivo de las Pruebas , Pronóstico , Modelos de Riesgos Proporcionales , Mapas de Interacción de Proteínas/genética , ARN Mensajero/genética , Reproducibilidad de los Resultados , Factores de Riesgo , Transducción de Señal/genética , Factores de Transcripción/genética
17.
Eur Rev Med Pharmacol Sci ; 25(7): 3122-3131, 2021 04.
Artículo en Inglés | MEDLINE | ID: mdl-33877681

RESUMEN

OBJECTIVE: Transcriptome data related to severe acute respiratory syndrome-related coronavirus 2 (a novel coronavirus discovered in 2019, SARS-CoV-2) in GEO database were downloaded. Based on the data, influence of SARS-CoV-2 on human cells was analyzed and potential therapeutic compounds against the SARS-CoV-2 were screened. MATERIALS AND METHODS: R package "DESeq2" was used for differential gene analysis on the data of cells infected or non-infected with SARS-CoV-2. The "ClusterProfiler" package was used for GO functional annotation and KEGG pathway enrichment analysis of the differentially expressed genes (DEGs). A protein-protein interaction (PPI) network of the DEGs was constructed through STRING website, and the key subset in the PPI network was identified after visualization by Cytoscape software. Connectivity Map (CMap) database was used to screen known compounds that caused genomic change reverse to that caused by SARS-CoV-2. RESULTS: By intersecting DEGs in two datasets, a total of 145 DEGs were screened out, among which 136 genes were upregulated and 9 genes were downregulated in SARS-CoV-2-infected cells. Functional enrichment analyses revealed that these genes were mainly associated with the pathways involved in viral infection, inflammatory response, and immunity. The CMap research found that there were three compounds with a median_tau_score less than -90, namely triptolide, tivozanib and daunorubicin. CONCLUSIONS: SARS-CoV-2 can cause abnormal changes in a large number of molecules and related signaling pathways in human cells, among which IL-17 and TNF signaling pathways may play a key role in pathogenic process of SARS-CoV-2. Here, three compounds that may be effective for the treatment of SARS-CoV-2 were screened, which would provide new options for improving treatment of patients infected with SARS-CoV-2.


Asunto(s)
/tratamiento farmacológico , Descubrimiento de Drogas , Perfilación de la Expresión Génica , Bases de Datos Genéticas , Bases de Datos Farmacéuticas , Daunorrubicina , Diterpenos , Regulación hacia Abajo , Compuestos Epoxi , Ontología de Genes , Redes Reguladoras de Genes , Humanos , Terapia Molecular Dirigida , Fenantrenos , Compuestos de Fenilurea , Mapas de Interacción de Proteínas , Quinolinas , Transducción de Señal/genética , Regulación hacia Arriba
18.
Sci Rep ; 11(1): 8570, 2021 04 21.
Artículo en Inglés | MEDLINE | ID: mdl-33883570

RESUMEN

Although a defective vitamin D endocrine system has been widely suspected to be associated in SARS-CoV-2 pathobiology, the status of the vitamin D endocrine system and vitamin D-modulated genes in lung cells of patients infected with SARS-CoV-2 remains unknown. To understand the significance of the vitamin D endocrine system in SARS-CoV-2 pathobiology, computational approaches were applied to transcriptomic datasets from bronchoalveolar lavage fluid (BALF) cells of such patients or healthy individuals. Levels of vitamin D receptor, retinoid X receptor, and CYP27A1 in BALF cells of patients infected with SARS-CoV-2 were found to be reduced. Additionally, 107 differentially expressed, predominantly downregulated genes, as potentially modulated by vitamin D endocrine system, were identified in transcriptomic datasets from patient's cells. Further analysis of differentially expressed genes provided eight novel genes with a conserved motif with vitamin D-responsive elements, implying the role of both direct and indirect mechanisms of gene expression by the dysregulated vitamin D endocrine system in SARS-CoV-2-infected cells. Protein-protein interaction network of differentially expressed vitamin D-modulated genes were enriched in the immune system, NF-κB/cytokine signaling, and cell cycle regulation as top predicted pathways that might be affected in the cells of such patients. In brief, the results presented here povide computational evidence to implicate a dysregulated vitamin D endocrine system in the pathobiology of SARS-CoV-2 infection.


Asunto(s)
Líquido del Lavado Bronquioalveolar/química , Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , Redes Reguladoras de Genes , Vitamina D/metabolismo , Células A549 , Estudios de Casos y Controles , Línea Celular , Colestanotriol 26-Monooxigenasa/genética , Bases de Datos Genéticas , Regulación hacia Abajo , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Mapas de Interacción de Proteínas , Receptores de Calcitriol/genética , Receptores X Retinoide/genética
19.
BMC Bioinformatics ; 22(1): 149, 2021 Mar 23.
Artículo en Inglés | MEDLINE | ID: mdl-33757430

RESUMEN

BACKGROUND: A common approach for sequencing studies is to do joint-calling and store variants of all samples in a single file. If new samples are continually added or controls are re-used for several studies, the cost and time required to perform joint-calling for each analysis can become prohibitive. RESULTS: We present ATAV, an analysis platform for large-scale whole-exome and whole-genome sequencing projects. ATAV stores variant and per site coverage data for all samples in a centralized database, which is efficiently queried by ATAV to support diagnostic analyses for trios and singletons, as well as rare-variant collapsing analyses for finding disease associations in complex diseases. Runtime logs ensure full reproducibility and the modularized ATAV framework makes it extensible to continuous development. Besides helping with the identification of disease-causing variants for a range of diseases, ATAV has also enabled the discovery of disease-genes by rare-variant collapsing on datasets containing more than 20,000 samples. Analyses to date have been performed on data of more than 110,000 individuals demonstrating the scalability of the framework. To allow users to easily access variant-level data directly from the database, we provide a web-based interface, the ATAV data browser ( http://atavdb.org/ ). Through this browser, summary-level data for more than 40,000 samples can be queried by the general public representing a mix of cases and controls of diverse ancestries. Users have access to phenotype categories of variant carriers, as well as predicted ancestry, gender, and quality metrics. In contrast to many other platforms, the data browser is able to show data of newly-added samples in real-time and therefore evolves rapidly as more and more samples are sequenced. CONCLUSIONS: Through ATAV, users have public access to one of the largest variant databases for patients sequenced at a tertiary care center and can look up any genes or variants of interest. Additionally, since the entire code is freely available on GitHub, ATAV can easily be deployed by other groups that wish to build their own platform, database, and user interface.


Asunto(s)
Genética de Población/instrumentación , Genómica , Programas Informáticos , Secuenciación del Exoma Completo , Bases de Datos Genéticas , Humanos , Fenotipo , Reproducibilidad de los Resultados
20.
Sci Rep ; 11(1): 6625, 2021 03 23.
Artículo en Inglés | MEDLINE | ID: mdl-33758205

RESUMEN

Coronavirus disease 2019 (COVID-19) has emerged in December 2019 when the first case was reported in Wuhan, China and turned into a pandemic with 27 million (September 9th) cases. Currently, there are over 95,000 complete genome sequences of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing COVID-19, in public databases, accompanying a growing number of studies. Nevertheless, there is still much to learn about the viral population variation when the virus is evolving as it continues to spread. We have analyzed SARS-CoV-2 genomes to identify the most variant sites, as well as the stable, conserved ones in samples collected in the Netherlands until June 2020. We identified the most frequent mutations in different geographies. We also performed a phylogenetic study focused on the Netherlands to detect novel variants emerging in the late stages of the pandemic and forming local clusters. We investigated the S and N proteins on SARS-CoV-2 genomes in the Netherlands and found the most variant and stable sites to guide development of diagnostics assays and vaccines. We observed that while the SARS-CoV-2 genome has accumulated mutations, diverging from reference sequence, the variation landscape is dominated by four mutations globally, suggesting the current reference does not represent the virus samples circulating currently. In addition, we detected novel variants of SARS-CoV-2 almost unique to the Netherlands that form localized clusters and region-specific sub-populations indicating community spread. We explored SARS-CoV-2 variants in the Netherlands until June 2020 within a global context; our results provide insight into the viral population diversity for localized efforts in tracking the transmission of COVID-19, as well as sequenced-based approaches in diagnostics and therapeutics. We emphasize that little diversity is observed globally in recent samples despite the increased number of mutations relative to the established reference sequence. We suggest sequence-based analyses should opt for a consensus representation to adequately cover the genomic variation observed to speed up diagnostics and vaccine design.


Asunto(s)
/patología , /genética , /virología , Bases de Datos Genéticas , Evolución Molecular , Genoma Viral , Humanos , Mutación , Tasa de Mutación , Países Bajos , Filogenia , /aislamiento & purificación , Secuenciación Completa del Genoma
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