جستجوی مقالات مرتبط با کلیدواژه « پروفایل بیان ژن » در نشریات گروه « پزشکی »

 West Nile virus is an infection transmitted through blood-sucking arthropods. Ocular manifestations are one of the characteristics of West Nile virus infection. They occur mostly in connection with severe neurological disease. In this study، using bioinformatics analysis، we investigated and identified the genes in the pathways related to West Nile virus infection in the retina.

 In this study, using bioinformatics methods and referring to the GEO dataset, the appropriate dataset was selected for analysis. This dataset included gene expression profiles in West Nile virus infection with retinal pigment cells. Gene clusters with high and low expression were categorized. For more accurate data evaluation,  databases such as Enrichr، STRING and Network analyst were used. The candidate genes were isolated and their protein relationship was also measured using cytoescape software version 3.7.1. The obtained values with P-Value˂0.05 were selected as significant data.

 The number of 830 genes with high expression and 500 genes with low expression was involved in the development pathways of West Nile virus infection in the eye. Apoptosis، necroptosis، AMPK and MAPK autophagy pathways were clearly observed. After evaluating the relationship between protein networks، RIPK1، STAT1/3 and JAK2 increased expression and GSK3B، MAPK8 and TSC2 genes had decreased expression.

 The present study showed that important proteins and genes played a major role in strengthening West Nile virus inflammation in the retina, among which RIPK1,TSC2, STAT1 and JAK2 showed a more prominent role in this pathway.

People with weakened immune system are more prone to opportunistic infections (OIs). Finding the association of these viruses and the transmission pathways associated with these viruses, such as EBV and CMV, play a significant role in investigating their association with helper T lymphocytes. In this study, bioinformatics analysis was used to examine and candidate genes in pathways associated with opportunistic viruses with helper T lymphocytes.

In this study, by referring to the GEO database, a suitable database was selected for analysis. This dataset included gene expression profiles in EBV and CMV virus infections. Gene clusters were classified into high and low expression. Rich databases such as Enrichr, STRING, and Networkanalyst were used to evaluate the data more accurately. Finally, the candidate genes were isolated and their protein binding was measured.

964 high-expression genes and 837 low-expression genes are involved in the progression of opportunistic viral infections with lymphocytes. Cell cycle pathways, oxidative stress, RNA synthesis and TGFB were significantly observed.

The present study showed that important proteins and genes played a major role in enhancing the inflammation of opportunistic viruses such as Epstein-Barr and Cytomegalovirus, among which CDK2, CCNB1, GSK3B, SRC, and SMAD3 showed a more prominent role in this pathway.

Studies have shown that up to 68% of infants may be infected with respiratory syncytial virus (RSV) in their first year of life, and almost all children are infected by the age of two. In this study, using bioinformatics analysis, the genes in the pathways related to respiratory syncytial virus (RSV) infection were examined and nominated.

By referring to the GEO database, the appropriate dataset was selected for analysis. This dataset included gene expression profiles in respiratory syncytial virus (RSV) infection. Gene clusters with high and low expression were categorized. Rich databases such as Enrichr, STRING, and Panther were used for more accurate data evaluation. Finally, the candidate genes were isolated, and their protein relationship was also measured.

A total of 740 and 822 genes had high and low expressions, respectively. These genes can play a role in RSV infection pathways in children. The NOD-like receptor, Epstein-Barr-influenza, tuberculosis, leishmaniasis, necroptosis, and salmonella pathways are highly expressed, and the pathways of coronavirus, herpes virus, immune system deficiency, Th1 lymphocyte cell receptor, ribosome, NFKappa B, and PDL1 had low expression at checkpoints of the cell cycle.

The present study revealed that important proteins and genes played a major role in strengthening RSV inflammation in children, among which STAT3, TLR4, RPL26A, STAT1, and MAPK4 showed a more prominent role in this pathway.

Non-small cell lung carcinoma is the most prevalent type of lung cancer which causes about one million death worldwide. Although patients with the early stage of NSCLC have better responses to drugs, the overall five-year survival remains poor for the patients with the late stages of this cancer. Therefore, understanding the molecular mechanisms underlying the progression of NSCLC, like alterations in expression profile, is essential to find new therapeutic targets and increase the patient’s survival. Bioinformatic tools and microarray together with next-generation sequencing studies have important roles in finding hub genes in the network of differentially expressed genes. Thus, this study aims to find hub genes which are associated with NSCLC and study their correlations with the patients’ survival.In this study, two microarray studies of GSE21933 and GSE21933 from GEO database were selected. To find differentially expressed genes in tumor samples versus controls, the R software was used. Gene ontology and KEGG gene enrichment analysis were performed by GSEA website. Cytoscape software and Cytohubba plugin were used to identify the hub genes. The correlation between each canonical gene and patients’ survival was extracted from the Kaplan Meier plotter website.The results showed that the hub genes in the network of late stage-specific differentially expressed genes are EGF, CCT2, CCT5, PDIA6, AR, WDR12, PNPLA2, PENK, NUP37 and SNAI2. All genes except PNPLA2 and SNAI2, showed a significant correlation with the patients’ overall survival.This study introduced hub genes which are associated with the late-stage NSCLC. Possibly, they could be targeted to inhibit NSCLC progression and increase the survival of patients with NSCLC.

Cervical cancer is one of the most prevalent cancers among women. Accurate diagnosis and treatment of complex diseases require precise identification of molecular characteristics of the disease. Transcriptome profiles provide valuable information on gene expression of the studied cells. Applying meta-analysis approache along with network-based approaches provides precise and valuable information about studied data, which can be used in developing new diagnostic and therapeutic methods. The aim of this study was meta-analysis investigation of cervical cancer transcriptome using a network approach in order to identify key genes in the disease.
 

In the current study, three data-sets including 189 cervical cancer samples, were selected for cervical cancer. The data sets were analyzed separately and the results were integrated to obtain genes with the same expression pattern. These genes were used to construct gene regulatory network by STRING database. Network parameters (including degree and betweenness centrality), were used to explore key elements in the network, which are generalizable to cervical cancer.
 

In this study, 194 genes with same expression pattern, were identified in the three data-sets. Moreover, the obtained network analysis led to identification of 12 key genes in the gene expression regulatory network. Some of these genes have been previously reported as oncogenes and are involved in cell cycle regulation and DNA repair pathways.
 

According to the results of this study, these genes can be considered as the potential diagnostic and therapeutic markers for the treatment of cervical cancer.

Multiple sclerosis (MS) is the most common central nervous system (CNS) demyelinating disease. New methods are substantive in order to have a better understanding its pathogenesis and also identifying new therapeutic targets for the disease. The aim of this study was to use network biology approach to identify potential key markers involved in MS pathogenesis.
In this study, gene expression profile of cerebrospinal fluid (CSF) samples from 26 newly diagnosed MS patients and 18 controls, which obtained from Array Express Database, were analyzed. Differentially expressed genes (DEG) were integrated with protein-protein interaction (PPI) data to construct PPI sub network. The sub network underwent further topological analysis by Cytoscape software.
Gene expression profile analysis unraveled 3062 differential expressed genes (FDR Therefore, network-based analysis could identify new markers which can be further explored as potential therapeutic targets for MS

In this study, we aimed to improve the reliability and biological interpretability of gene signatures selected from microarrays by efficient usage of computational models and mathematical algorithms.
At the first step, a good model with high accuracy was chosen to predict cancer recurrence in microarray gene expression data on breast tumors. In this regard, microarray gene expression data of breast tumor in 1271 cancer patients (379 with recurrence and 892 people without recurrence) were utilized to construct an appropriate predictive model for recurrence by comparing the performance of multiple classifiers. In the pre-processing stage, different methods like correlation-based feature selection (CFS), principal component analysis (PCA), independent component analysis (ICA), and genetic algorithm as well as a random selection method were used to reduce the dimensions and choose the most appropriate genes (features).
Findings: A total of five gene signatures were selected by combining genetic algorithm, top scoring set (TSS), and random selection method, which showed the best results in most classification models. The final indicator genes were TRIP13, KIF20A, NEK2, RACGAP1 and TYMS, which had significant contribution in the structure of microtubules and spindle and also regulated the attachment of spindle microtubules to kinetochore.
By using hybrid models, we can avoid overfitting in training and achieve acceptable accuracy with biologically interpretable genes.