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1.
Ann Palliat Med ; 10(8): 9206-9214, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34488406

RESUMO

BACKGROUND: Psoriasis is a chronic inflammatory dermatosis. The hyperproliferation and hyperkeratosis of keratinocytes is a key step in the pathogenesis of psoriasis. Long non-coding RNAs (lncRNAs) and mRNAs regulate gene expression in various biological process, including the function of keratinocytes. This research investigated the expression profile of lncRNAs and mRNAs in keratinocytes of patients with psoriasis vulgaris. METHODS: The expression of lncRNAs and mRNAs in keratinocytes from patients with psoriasis vulgaris and healthy patients was examined and compared using microarrays. Quantitative polymerase chain reaction (qPCR) and bioinformatic analysis was also performed. DAVID and KEGG were used to analyze the gene function. The competing endogenous RNA (ceRNA) network was also constructed. RESULTS: A total of 48 lncRNAs and 17 mRNAs were differentially expressed in keratinocytes of psoriasis vulgaris. Quantitative PCR data showed that the expression of lnc-AGXT2L1-2:2 (P=0.009) and NR_027032 (P=0.033) was up-regulated in psoriasis vulgaris. The lncRNA-miRNA-mRNA interaction network was established. The mRNA showing the most connections with the lncRNAs and miRNAs was CEP104. The miRNA showing the most connections with the lncRNAs and mRNAs was miR-484. The lncRNA showing the most connections with miRNAs and mRNAs was ENST00000494887. CONCLUSIONS: The identification of the differentially expressed lncRNAs and mRNAs in psoriasis vulgaris provides significant insights into the pathogenesis of the disease.


Assuntos
Psoríase , RNA Longo não Codificante , Redes Reguladoras de Genes/genética , Humanos , Queratinócitos , Psoríase/genética , RNA Longo não Codificante/genética , RNA Mensageiro/genética
2.
Int J Mol Sci ; 22(14)2021 Jul 09.
Artigo em Inglês | MEDLINE | ID: mdl-34299019

RESUMO

Myocardial infarction (MI) is one of the most common cardiovascular diseases. Although previous studies have shown that histidine decarboxylase (HDC), a histamine-synthesizing enzyme, is involved in the stress response and heart remodeling after MI, the mechanism underlying it remains unclear. In this study, using Hdc-deficient mice (Hdc-/- mice), we established an acute myocardial infarction mouse model to explore the potential roles of Hdc/histamine in cardiac immune responses. Comprehensive analysis was performed on the transcriptomes of infarcted hearts. Differentially expressed gene (DEG) analysis identified 2126 DEGs in Hdc-deficient groups and 1013 in histamine-treated groups. Immune related pathways were enriched in Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Then we used the ssGSEA algorithm to evaluate 22 kinds of infiltrated immunocytes, which indicated that myeloid cells and T memory/follicular helper cells were tightly regulated by Hdc/histamine post MI. The relationships of lncRNAs and the Gene Ontology (GO) functions of protein-coding RNAs and immunocytes were dissected in networks to unveil immune-associated lncRNAs and their roles in immune modulation after MI. Finally, we screened out and verified four lncRNAs, which were closely implicated in tuning the immune responses after MI, including ENSMUST00000191157, ENSMUST00000180693 (PTPRE-AS1), and ENSMUST-00000182785. Our study highlighted the HDC-regulated myeloid cells as a driving force contributing to the government of transmission from innate immunocytes to adaptive immunocytes in the progression of the injury response after MI. We identified the potential role of the Hdc/histamine-lncRNAs network in regulating cardiac immune responses, which may provide novel promising therapeutic targets for further promoting the treatment of ischemic heart disease.


Assuntos
Histidina Descarboxilase/metabolismo , Infarto do Miocárdio/imunologia , Infarto do Miocárdio/metabolismo , RNA Longo não Codificante/metabolismo , Transcriptoma/genética , Algoritmos , Animais , Modelos Animais de Doenças , Perfilação da Expressão Gênica , Regulação da Expressão Gênica/genética , Regulação da Expressão Gênica/imunologia , Ontologia Genética , Redes Reguladoras de Genes/genética , Redes Reguladoras de Genes/imunologia , Histidina Descarboxilase/genética , Camundongos , Camundongos Endogâmicos BALB C , Camundongos Knockout , Células Mieloides/imunologia , Infarto do Miocárdio/genética , Infarto do Miocárdio/patologia , Células RAW 264.7 , RNA Longo não Codificante/genética , Reação em Cadeia da Polimerase em Tempo Real , Linfócitos T Auxiliares-Indutores/imunologia
3.
Biomolecules ; 11(6)2021 06 18.
Artigo em Inglês | MEDLINE | ID: mdl-34207065

RESUMO

Induced granulocytic differentiation of human leukemic cells under all-trans-retinoid acid (ATRA) treatment underlies differentiation therapy of acute myeloid leukemia. Knowing the regulation of this process it is possible to identify potential targets for antileukemic drugs and develop novel approaches to differentiation therapy. In this study, we have performed transcriptomic and proteomic profiling to reveal up- and down-regulated transcripts and proteins during time-course experiments. Using data on differentially expressed transcripts and proteins we have applied upstream regulator search and obtained transcriptome- and proteome-based regulatory networks of induced granulocytic differentiation that cover both up-regulated (HIC1, NFKBIA, and CASP9) and down-regulated (PARP1, VDR, and RXRA) elements. To verify the designed network we measured HIC1 and PARP1 protein abundance during granulocytic differentiation by selected reaction monitoring (SRM) using stable isotopically labeled peptide standards. We also revealed that transcription factor CEBPB and LYN kinase were involved in differentiation onset, and evaluated their protein levels by SRM technique. Obtained results indicate that the omics data reflect involvement of the DNA repair system and the MAPK kinase cascade as well as show the balance between the processes of the cell survival and apoptosis in a p53-independent manner. The differentially expressed transcripts and proteins, predicted transcriptional factors, and key molecules such as HIC1, CEBPB, LYN, and PARP1 may be considered as potential targets for differentiation therapy of acute myeloid leukemia.


Assuntos
Diferenciação Celular/fisiologia , Redes Reguladoras de Genes/genética , Leucemia Mieloide/metabolismo , Apoptose/efeitos dos fármacos , Linhagem Celular Tumoral , Sobrevivência Celular/efeitos dos fármacos , Expressão Gênica/genética , Perfilação da Expressão Gênica/métodos , Regulação Leucêmica da Expressão Gênica/genética , Humanos , Leucemia Mieloide/genética , Leucemia Mieloide/patologia , Leucemia Mieloide Aguda/genética , Leucemia Mieloide Aguda/metabolismo , Proteômica/métodos , Fatores de Transcrição/metabolismo
4.
J Endod ; 47(9): 1365-1375, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34260959

RESUMO

INTRODUCTION: Molecular diagnosis may overcome the limitations of clinical and histologic diagnosis in pulpitis, thereby benefiting many treatment techniques, such as vital pulp therapies. In this study, integrated microarray data on pulpitis were used to obtain a list of normalized differentially expressed (DE) genes for analyzing the molecular mechanisms underlying pulpitis and identifying potential diagnostic biomarkers. METHODS: A systematic search of public microarray and sequencing databases was performed to obtain expression data of pulpitis. Robust rank aggregation (RRA) was used to obtain DE gene lists (RRA_DEmRNAs and RRA_DElncRNAs) between inflamed pulp and normal samples. DE genes were evaluated by functional enrichment analyses, correlation analyses for inflammation-related RRA_DEmRNAs, and protein-protein interaction and competing endogenous RNA network construction. Quantitative real-time polymerase chain reaction validation was applied in snap-frozen pulp tissues. RESULTS: Using the GSE77459 and GSE92681 data sets, 280 RRA_DEmRNAs and 90 RRA_DElncRNAs were identified. RRA_DEmRNAs were significantly enriched in inflammation-related biological processes and osteoclast differentiation and tumor necrosis factor, chemokine, and B-cell receptor signaling pathways. The molecular complex detection and cytoHubba methods identified 2 clusters and 10 hub genes in the protein-protein interaction network. The competing endogenous RNA network was composed of 2 long noncoding RNAs (ADAMTS9-AS2 and LINC00290), 2 microRNAs (hsa-miR-30a-5p and hsa-miR-128-3p), and 3 messenger RNAs (ABCA1, FBLN5, and SOCS3). The expression between most top inflammation-related RRA_DEmRNAs in pulpitis showed positive correlations. Quantitative real-time polymerase chain reacation validated the expression trends of selected genes, including ITGAX, TREM1, CD86, FCGR2A, ADAMTS9-AS2, LINC00290, hsa-miR-30a-5p, hsa-miR-128-3p, RASGRP3, IL3RA, CCDC178, CRISPLD1, LINC01857, AC007991.2, ARHGEF26-AS1, and AL021408.1. CONCLUSIONS: The identified biomarkers provide insight into the pathology and may aid in the molecular diagnosis of pulpitis.


Assuntos
MicroRNAs , Pulpite , RNA Longo não Codificante , Redes Reguladoras de Genes/genética , Humanos , MicroRNAs/genética , Pulpite/genética , RNA Mensageiro
5.
Methods Mol Biol ; 2328: 1-11, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34251616

RESUMO

Recent progress in transcriptomics and co-expression networks have enabled us to predict the inference of the biological functions of genes with the associated environmental stress. Microarrays and RNA sequencing (RNA-seq) are the most commonly used high-throughput gene expression platforms for detecting differentially expressed genes between two (or more) phenotypes. Gene co-expression networks (GCNs) are a systems biology method for capturing transcriptional patterns and predicting gene interactions into functional and regulatory relationships. Here, we describe the procedures and tools used to construct and analyze GCN and investigate the integration of transcriptional data with GCN to provide reliable information about the underlying biological mechanism.


Assuntos
Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Regulação da Expressão Gênica/genética , Redes Reguladoras de Genes/genética , Análise em Microsséries/métodos , Algoritmos , Arabidopsis , Ontologia Genética , Fenótipo , Análise de Componente Principal , Mapas de Interação de Proteínas , Software
6.
Methods Mol Biol ; 2328: 13-23, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34251617

RESUMO

Gene coexpression networks (GCNs) are useful tools for inferring gene functions and understanding biological processes when properly constructed. Traditional microarray analysis is being more frequently replaced by bulk-based RNA-sequencing as a method for quantifying gene expression. This new technology requires improved statistical methods for generating GCNs. This chapter explores several popular methods for constructing GCNs using bulk-based RNA-Seq data, such as distribution-based methods and normalization techniques, implemented using the statistical programming language R.


Assuntos
Perfilação da Expressão Gênica/métodos , Regulação da Expressão Gênica/genética , Redes Reguladoras de Genes/genética , Algoritmos , Modelos Estatísticos , Modelos Teóricos , RNA-Seq/métodos , Software
7.
Methods Mol Biol ; 2328: 25-46, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34251618

RESUMO

Chromatin accessibility is directly linked with transcription in eukaryotes. Accessible regions associated with regulatory proteins are highly sensitive to DNase I digestion and are termed DNase I hypersensitive sites (DHSs). DHSs can be identified by DNase I digestion, followed by high-throughput DNA sequencing (DNase-seq). The single-base-pair resolution digestion patterns from DNase-seq allows identifying transcription factor (TF) footprints of local DNA protection that predict TF-DNA binding. The identification of differential footprinting between two conditions allows mapping relevant TF regulatory interactions. Here, we provide step-by-step instructions to build gene regulatory networks from DNase-seq data. Our pipeline includes steps for DHSs calling, identification of differential TF footprints between treatment and control conditions, and construction of gene regulatory networks. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be adapted to work with DNase-seq data from any organism with a sequenced genome.


Assuntos
Cromatina/metabolismo , Mapeamento Cromossômico/métodos , Pegada de DNA/métodos , Desoxirribonuclease I/metabolismo , Regulação da Expressão Gênica/genética , Redes Reguladoras de Genes/genética , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Arabidopsis/genética , Arabidopsis/metabolismo , Cromatina/genética , Genômica , Ligação Proteica , Software , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo
8.
Methods Mol Biol ; 2328: 47-65, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34251619

RESUMO

Gene expression data analysis and the prediction of causal relationships within gene regulatory networks (GRNs) have guided the identification of key regulatory factors and unraveled the dynamic properties of biological systems. However, drawing accurate and unbiased conclusions requires a comprehensive understanding of relevant tools, computational methods, and their workflows. The topics covered in this chapter encompass the entire workflow for GRN inference including: (1) experimental design; (2) RNA sequencing data processing; (3) differentially expressed gene (DEG) selection; (4) clustering prior to inference; (5) network inference techniques; and (6) network visualization and analysis. Moreover, this chapter aims to present a workflow feasible and accessible for plant biologists without a bioinformatics or computer science background. To address this need, TuxNet, a user-friendly graphical user interface that integrates RNA sequencing data analysis with GRN inference, is chosen for the purpose of providing a detailed tutorial.


Assuntos
Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Regulação da Expressão Gênica/genética , Redes Reguladoras de Genes/genética , Algoritmos , Motivos de Aminoácidos/genética , Análise por Conglomerados , Família Multigênica , RNA-Seq/métodos , Software , Análise Espaço-Temporal , Fluxo de Trabalho
9.
Methods Mol Biol ; 2328: 99-113, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34251621

RESUMO

The cell expresses various genes in specific contexts with respect to internal and external perturbations to invoke appropriate responses. Transcription factors (TFs) orchestrate and define the expression level of genes by binding to their regulatory regions. Dysregulated expression of TFs often leads to aberrant expression changes of their target genes and is responsible for several diseases including cancers. In the last two decades, several studies experimentally identified target genes of several TFs. However, these studies are limited to a small fraction of the total TFs encoded by an organism, and only for those amenable to experimental settings. Experimental limitations lead to many computational techniques having been proposed to predict target genes of TFs. Linear modeling of gene expression is one of the most promising computational approaches, readily applicable to the thousands of expression datasets available in the public domain across diverse phenotypes. Linear models assume that the expression of a gene is the sum of expression of TFs regulating it. In this chapter, I introduce mathematical programming for the linear modeling of gene expression, which has certain advantages over the conventional statistical modeling approaches. It is fast, scalable to genome level and most importantly, allows mixed integer programming to tune the model outcome with prior knowledge on gene regulation.


Assuntos
Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Regulação da Expressão Gênica/genética , Redes Reguladoras de Genes/genética , Programação Linear , Fatores de Transcrição/metabolismo , Bases de Dados Genéticas , Modelos Teóricos , Software , Fatores de Transcrição/genética
10.
Methods Mol Biol ; 2328: 115-138, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34251622

RESUMO

With the popularity of high-throughput transcriptomic techniques like RNAseq, models of gene regulatory networks have been important tools for understanding how genes are regulated. These transcriptomic datasets are usually assumed to reflect their associated proteins. This assumption, however, ignores post-transcriptional, translational, and post-translational regulatory mechanisms that regulate protein abundance but not transcript abundance. Here we describe a method to model cross-regulatory influences between the transcripts and proteins of a set of genes using abundance data collected from a series of transgenic experiments. The developed model can capture the effects of regulation that impacts transcription as well as regulatory mechanisms occurring after transcription. This approach uses a sparse maximum likelihood algorithm to determine relationships that influence transcript and protein abundance. An example of how to explore the network topology of this type of model is also presented. This model can be used to predict how the transcript and protein abundances will change in novel transgenic modification strategies.


Assuntos
Perfilação da Expressão Gênica/métodos , Regulação da Expressão Gênica/genética , Redes Reguladoras de Genes/genética , Metabolômica/métodos , Proteínas/metabolismo , Proteômica/métodos , Transcriptoma/genética , Algoritmos , Biologia Computacional/métodos , Modelos Teóricos , Populus/genética , Populus/metabolismo , Proteínas/genética
11.
Methods Mol Biol ; 2328: 153-170, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34251624

RESUMO

Single-cell RNAseq is an emerging technology that allows the quantification of gene expression in individual cells. In plants, single-cell sequencing technology has been applied to generate root cell expression maps under many experimental conditions. DAP-seq and ATAC-seq have also been used to generate genome-scale maps of protein-DNA interactions and open chromatin regions in plants. In this protocol, we describe a multistep computational pipeline for the integration of single-cell RNAseq data with DAP-seq and ATAC-seq data to predict regulatory networks and key regulatory genes. Our approach utilizes machine learning methods including feature selection and stability selection to identify candidate regulatory genes. The network generated by this pipeline can be used to provide a putative annotation of gene regulatory modules and to identify candidate transcription factors that could play a key role in specific cell types.


Assuntos
Sequenciamento de Cromatina por Imunoprecipitação/métodos , Biologia Computacional/métodos , Redes Reguladoras de Genes/genética , Aprendizado de Máquina , RNA-Seq/métodos , Análise de Sequência de DNA/métodos , Análise de Célula Única/métodos , Cromatina/metabolismo , Linguagens de Programação , Software
12.
Methods Mol Biol ; 2328: 139-151, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34251623

RESUMO

In this book chapter, we introduce a pipeline to mine significant biomedical entities (or bioentities) in biological networks. Our focus is on prioritizing both bioentities themselves and the associations between bioentities in order to reveal their biological functions. We will introduce three tools BEERE, WIPER, and PAGER 2.0 that can be used together for network analysis and function interpretation: (1) BEERE is a network analysis tool for "Biomedical Entity Expansion, Ranking and Explorations," (2) WIPER is an entity-to-entity association ranking tool, and (3) PAGER 2.0 is a service for gene enrichment analysis.


Assuntos
Mineração de Dados/métodos , Redes Reguladoras de Genes/genética , Mapas de Interação de Proteínas/genética , Algoritmos , Humanos , Linguagens de Programação
13.
Methods Mol Biol ; 2328: 171-182, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34251625

RESUMO

With the advent of recent next-generation sequencing (NGS) technologies in genomics, transcriptomics, and epigenomics, profiling single-cell sequencing became possible. The single-cell RNA sequencing (scRNA-seq) is widely used to characterize diverse cell populations and ascertain cell type-specific regulatory mechanisms. The gene regulatory network (GRN) mainly consists of genes and their regulators-transcription factors (TF). Here, we describe the lightning-fast Python implementation of the SCENIC (Single-Cell reEgulatory Network Inference and Clustering) pipeline called pySCENIC. Using single-cell RNA-seq data, it maps TFs onto gene regulatory networks and integrates various cell types to infer cell-specific GRNs. There are two fast and efficient GRN inference algorithms, GRNBoost2 and GENIE3, optionally available with pySCENIC. The pipeline has three steps: (1) identification of potential TF targets based on co-expression; (2) TF-motif enrichment analysis to identify the direct targets (regulons); and (3) scoring the activity of regulons (or other gene sets) on single cell types.


Assuntos
Redes Reguladoras de Genes/genética , RNA-Seq/métodos , Análise de Célula Única/métodos , Fatores de Transcrição/metabolismo , Algoritmos , Motivos de Aminoácidos/genética , Análise por Conglomerados , Linguagens de Programação , Fatores de Transcrição/genética
14.
Methods Mol Biol ; 2328: 183-189, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34251626

RESUMO

Plant immunity is a highly dynamic process and requires dynamic modeling to capture the events of complexity mediated by the interaction between plant host and the attacking pathogen. The events of recognition are invoked by pathogen-based epitopes, while the subversion of host defenses are orchestrated by pathogen-originated effector molecules. The pathogen constitutes an immune signaling network inside the host cells. We model plant immune dynamics by using JIMENA-package, which is a java-based genetic regulatory network (GRN) simulation framework. It can efficiently compute network behavior and system states mediated by pathogenic perturbations. Here, we describe a step-by-step protocol to introduce the application of JIMENA-package to quantify immune dynamics in plant-pathogen interaction networks.


Assuntos
Simulação por Computador , Redes Reguladoras de Genes/genética , Interações Hospedeiro-Patógeno/genética , Doenças das Plantas/imunologia , Imunidade Vegetal , Plantas/imunologia , Modelos Imunológicos , Software
15.
Methods Mol Biol ; 2328: 261-275, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34251632

RESUMO

Competing endogenous RNAs (ceRNAs) are transcripts with the ability to competitively titrate microRNAs (miRNAs) against miRNA repressing target genes to post-transcriptionally regulate the expression of corresponding miRNAs. It is a newly discovered gene regulation pattern between longer RNA and miRNA molecules. Recent research has gradually revealed the functional significance of ceRNAs in regulating normal development and stress response processes in plants and animals, as well as in cancer genesis and metastasis. Therefore, ceRNA identification is an important and necessary step to deepen our understanding of the regulation mechanisms of various biological processes. Here, we provide a pipeline used to computationally identify plant ceRNAs and reconstruct ceRNA regulatory networks based on RNA-seq and small RNA-seq data.


Assuntos
Redes Reguladoras de Genes/genética , MicroRNAs/metabolismo , Plantas/metabolismo , RNA Longo não Codificante/metabolismo , RNA-Seq/métodos , Bases de Dados Genéticas , Regulação da Expressão Gênica de Plantas/genética , MicroRNAs/genética , Plantas/genética , RNA Circular/genética , RNA Circular/metabolismo , RNA Longo não Codificante/genética , Pequeno RNA não Traduzido/genética , Pequeno RNA não Traduzido/metabolismo , Software
16.
Nat Commun ; 12(1): 4487, 2021 07 23.
Artigo em Inglês | MEDLINE | ID: mdl-34301922

RESUMO

Testicular germ cell tumors (TGCT) are the most common tumor in young white men and have a high heritability. In this study, the international Testicular Cancer Consortium assemble 10,156 and 179,683 men with and without TGCT, respectively, for a genome-wide association study. This meta-analysis identifies 22 TGCT susceptibility loci, bringing the total to 78, which account for 44% of disease heritability. Men with a polygenic risk score (PRS) in the 95th percentile have a 6.8-fold increased risk of TGCT compared to men with median scores. Among men with independent TGCT risk factors such as cryptorchidism, the PRS may guide screening decisions with the goal of reducing treatment-related complications causing long-term morbidity in survivors. These findings emphasize the interconnected nature of two known pathways that promote TGCT susceptibility: male germ cell development within its somatic niche and regulation of chromosomal division and structure, and implicate an additional biological pathway, mRNA translation.


Assuntos
Predisposição Genética para Doença/genética , Estudo de Associação Genômica Ampla/métodos , Neoplasias Embrionárias de Células Germinativas/genética , Polimorfismo de Nucleotídeo Único , Neoplasias Testiculares/genética , Linhagem Celular Tumoral , Mapeamento Cromossômico , Redes Reguladoras de Genes/genética , Genótipo , Humanos , Desequilíbrio de Ligação , Masculino , Metanálise como Assunto , Neoplasias Embrionárias de Células Germinativas/metabolismo , Mapas de Interação de Proteínas/genética , Neoplasias Testiculares/metabolismo
17.
Aging (Albany NY) ; 13(13): 17302-17315, 2021 07 05.
Artigo em Inglês | MEDLINE | ID: mdl-34226298

RESUMO

The molecular mechanism of bone metastasis in breast cancer is largely unknown. Herein, we aimed to identify the key genes and long non-coding RNAs (lncRNAs) related to the bone metastasis of breast cancer using a bioinformatics approach. We screened differentially expressed genes and lncRNAs between normal breast and breast cancer bone metastasis samples using the GSE66206 dataset from the Gene Expression Omnibus. We also constructed a differentially expressed lncRNA-mRNA interaction network and analyzed the node degrees to identify the driving genes. After finding potential pathogenic modules of breast cancer bone metastasis, we identified breast cancer bone metastasis-related modules and functional enrichment analysis of the genes and lncRNAs in the modules. Based on the above analysis, we constructed a differentially expressed lncRNA-mRNA network related to bone metastasis in breast cancer and identified core driver genes, including BNIP3 and the lncRNA RP11-317-J19.1. The role of core driver genes and lncRNAs in the network implies their biological functions in regulating bone development and remodeling. Thus, targeting the core driver genes and lncRNAs in the network may be a promising therapeutic strategy to manage bone metastasis.


Assuntos
Neoplasias Ósseas/genética , Neoplasias Ósseas/secundário , Neoplasias da Mama/genética , Neoplasias da Mama/patologia , Biologia Computacional , Regulação Neoplásica da Expressão Gênica/genética , Genes Neoplásicos/genética , RNA Longo não Codificante/genética , Desenvolvimento Ósseo/genética , Remodelação Óssea/genética , Bases de Dados Genéticas , Feminino , Perfilação da Expressão Gênica , Redes Reguladoras de Genes/genética , Humanos , Proteínas de Membrana/genética , Proteínas Proto-Oncogênicas/genética
18.
Aging (Albany NY) ; 13(13): 17442-17461, 2021 07 06.
Artigo em Inglês | MEDLINE | ID: mdl-34229299

RESUMO

Metastatic cancer especially bone metastasis (BM) is the lethal end-stage of castration-resistant prostate cancer (CRPC). To understand the possible molecular mechanisms underlying the development of the distant metastasis is of potential clinical value. We sought to identify differentially expressed genes between patient-matched primary and bone metastatic CRPC tumors. Functional enrichment, protein-protein interaction networks, and survival analysis of DEGs were performed. DEGs with a prognostic value considered as candidate genes were evaluated, followed by genetic analysis of tumor infiltrating immune cells based on Wilcoxon test and immunofluorescence identification. Expression profiles analysis showed that 381 overlapping genes were screened as differentially expressed genes (DEGs), of which 16 DEGs were randomly selected to be validated and revealed that most of these genes showed a transcriptional profile similar to that seen in the datasets (Pearson's r = 0.76). Six core genes were found to be involved in regulation of extracellular matrix receptor interaction and chemotactic activity, and four of them were significantly correlated with the survival of PCa patients with bone metastases. Immune infiltration analysis showed that the expressions levels of COL3A1, RAC1, FN1, and SDC2 in CD4+T cells were significantly higher than those in tumor cells, especially regulatory T cell infiltration was significantly increased in BM tumors. We analyzed gene expression signatures specifically associated with the development of bone metastases of CRPC patients. Characterization of genes associated with BM of mCRPC is critical for identification of predictive biomarkers and potential therapeutic targets.


Assuntos
Neoplasias Ósseas/patologia , Neoplasias Ósseas/secundário , Neoplasias da Próstata/patologia , Linfócitos T Reguladores/patologia , Idoso , Neoplasias Ósseas/genética , Linfócitos T CD4-Positivos , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica/genética , Redes Reguladoras de Genes/genética , Humanos , Estimativa de Kaplan-Meier , Masculino , Pessoa de Meia-Idade , Prognóstico , Neoplasias da Próstata/genética , Mapas de Interação de Proteínas , Transdução de Sinais/genética , Análise de Sobrevida
19.
Aging (Albany NY) ; 13(13): 17607-17628, 2021 07 08.
Artigo em Inglês | MEDLINE | ID: mdl-34237706

RESUMO

The function of competitive endogenous RNA (ceRNA) network in the immune regulation of hepatocellular carcinoma (HCC) is unclear. Our study aimed to construct an immune-related ceRNA network and develop an immune-related long noncoding RNA (lncRNA) signature to assess the prognosis of HCC patients and to optimize the treatment methods. We firstly constructed a ceRNA regulatory network for HCC using differentially expressed lncRNAs, mRNAs and microRNAs (miRNAs) from the Cancer Genome Atlas. A signature was constructed by 11 immune-related prognostic lncRNAs from the ceRNA network. The survival analysis and receiver operating characteristic analysis validated the reliability of the signature. Multivariate Cox regression analysis revealed that the signature could act an independent prognostic indicator. This signature also showed high association with immune cell infiltration and immune check blockades. LINC00491 was identified as the hub lncRNA in the signature. In vitro and in vivo evidence demonstrated that silencing of LINC00491 significantly inhibited HCC growth. Finally, 59 lncRNAs, 21 miRNAs, and 26 mRNAs were obtained to build the immune-related ceRNA network for HCC. In conclusion, our novel immune-related lncRNA prognostic signature and the immune-related ceRNA network might provide in-depth insights into tumor-immune interaction of HCC and promote better individual treatment strategies in HCC patients.


Assuntos
Biomarcadores Tumorais/análise , Carcinoma Hepatocelular/diagnóstico , Carcinoma Hepatocelular/genética , Neoplasias Hepáticas/diagnóstico , Neoplasias Hepáticas/genética , RNA Longo não Codificante/análise , RNA Longo não Codificante/genética , Animais , Carcinoma Hepatocelular/imunologia , Redes Reguladoras de Genes/genética , Inativação Gênica , Genômica , Humanos , Neoplasias Hepáticas/imunologia , Camundongos , Camundongos Endogâmicos BALB C , Prognóstico , Curva ROC , Reprodutibilidade dos Testes , Análise de Sobrevida , Ensaios Antitumorais Modelo de Xenoenxerto
20.
Int J Mol Sci ; 22(14)2021 Jul 18.
Artigo em Inglês | MEDLINE | ID: mdl-34299294

RESUMO

Nitrogen (N) is an essential nutrient for plant growth and development. The root system architecture is a highly regulated morphological system, which is sensitive to the availability of nutrients, such as N. Phenotypic characterization of roots from LY9348 (a rice variety with high nitrogen use efficiency (NUE)) treated with 0.725 mM NH4NO3 (1/4N) was remarkable, especially primary root (PR) elongation, which was the highest. A comprehensive analysis was performed for transcriptome and proteome profiling of LY9348 roots between 1/4N and 2.9 mM NH4NO3 (1N) treatments. The results indicated 3908 differential expression genes (DEGs; 2569 upregulated and 1339 downregulated) and 411 differential abundance proteins (DAPs; 192 upregulated and 219 downregulated). Among all DAPs in the proteome, glutamine synthetase (GS2), a chloroplastic ammonium assimilation protein, was the most upregulated protein identified. The unexpected concentration of GS2 from the shoot to the root in the 1/4N treatment indicated that the presence of an alternative pathway of N assimilation regulated by GS2 in LY9348 corresponded to the low N signal, which was supported by GS enzyme activity and glutamine/glutamate (Gln/Glu) contents analysis. In addition, N transporters (NRT2.1, NRT2.2, NRT2.3, NRT2.4, NAR2.1, AMT1.3, AMT1.2, and putative AMT3.3) and N assimilators (NR2, GS1;1, GS1;2, GS1;3, NADH-GOGAT2, and AS2) were significantly induced during the long-term N-deficiency response at the transcription level (14 days). Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis demonstrated that phenylpropanoid biosynthesis and glutathione metabolism were significantly modulated by N deficiency. Notably, many transcription factors and plant hormones were found to participate in root morphological adaptation. In conclusion, our study provides valuable information to further understand the response of rice roots to N-deficiency stress.


Assuntos
Glutamato-Amônia Ligase/metabolismo , Nitrogênio/deficiência , Oryza/genética , Expressão Gênica/genética , Perfilação da Expressão Gênica/métodos , Redes Reguladoras de Genes/genética , Glutamato-Amônia Ligase/genética , Nitrogênio/metabolismo , Oryza/enzimologia , Oryza/crescimento & desenvolvimento , Oryza/metabolismo , Reguladores de Crescimento de Plantas/metabolismo , Folhas de Planta/genética , Proteínas de Plantas/genética , Raízes de Plantas/genética , Proteômica/métodos , Estresse Fisiológico/genética , Fatores de Transcrição/metabolismo , Transcriptoma/genética
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