Unraveling molecular similarities between colorectal polyps and colorectal cancer: a systems biology approach.

BACKGROUND/AIMS: Colorectal cancer (CRC) and colorectal polyps are intimately linked, with polyps acting as precursors to CRC. Understanding the molecular mechanisms governing their development is crucial for advancing diagnosis and treatment. Employing a systems biology approach, we investigated the molecular similarities between polyp and CRC. METHODS: We analyzed gene expression profiles, protein-protein interactions, transcription factors, and gene ontology to identify common differentially expressed genes (DEGs) and unravel shared molecular pathways. RESULTS: Our analysis revealed 520 commonly dysregulated genes in polyps and CRC, serving as potential biomarkers and pivotal contributors to disease progression. Gene ontology analysis elucidated distinct biological processes associated with upregulated and downregulated DEGs in both conditions, highlighting common pathways, including signal transduction, cell adhesion, and positive regulation of cell proliferation. Moreover, protein-protein interaction networks shed light on subnetworks involved in rRNA processing, positive regulation of cell proliferation, mRNA splicing, and cell division. Transcription factor analysis identified major regulators and differentially expressed transcription factors in polyp and CRC. Notably, we identified common differentially expressed transcription factors, including ZNF217, NR3C1, KLF5, GATA6, and STAT3, with STAT3 and NR3C1 exhibiting increased expression. CONCLUSIONS: This comprehensive analysis enriches our understanding of the molecular mechanisms underlying polyp formation and CRC development, providing potential targets for further investigation and therapeutic intervention. Our findings contribute substantively to crafting personalized strategies for refining the diagnosis and treatment of polyps and CRC.

Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease that can cause dementia. We aim to screen out the hub genes involved in AD based on microarray datasets. METHODS: Gene expression profiles GSE5281 and GSE28146 were retrieved from Gene Expression Omnibus database to acquire differentially expressed genes (DEGs). Gene Ontology and pathway enrichment were conducted using DAVID online tool. The STRING database and Cytoscape tools were employed to analyze protein-protein interactions and identify hub genes. The predictive value of hub genes was assessed by principal component analysis and receiver operating characteristic curves. AD mice model was constructed, and histology was then observed by hematoxylin-eosin staining. Gene expression levels were finally determined by real-time quantitative PCR. RESULTS: We obtained 197 overlapping DEGs from GSE5281 and GSE28146 datasets. After constructing protein-protein interaction network, three highly interconnected clusters were identified and 6 hub genes (RBL1, BUB1, HDAC7, KAT5, SIRT2, and ITGB1) were selected. The hub genes could be used as basis to predict AD. Histological abnormalities of brain were observed, suggesting successful AD model was constructed. Compared with the control group, the mRNA expression levels of RBL1, BUB1, HDAC7, KAT5 and SIRT2 were significantly increased, while the mRNA expression level of ITGB1 was significantly decreased in AD groups. CONCLUSION: RBL1, BUB1, HDAC7, KAT5, SIRT2 and ITGB1 are promising gene signatures for diagnosis and therapy of AD.

Protein Biomarkers of New-Onset Heart Failure: Insights From the Heart Omics and Ageing Cohort, the Atherosclerosis Risk in Communities Study, and the Framingham Heart Study.

BACKGROUND: We sought to identify protein biomarkers of new-onset heart failure (HF) in 3 independent cohorts (HOMAGE cohort [Heart Omics and Ageing], ARIC study [Atherosclerosis Risk in Communities], and FHS [Framingham Heart Study]) and assess if and to what extent they improve HF risk prediction compared to clinical risk factors alone. METHODS: A nested case-control design was used with cases (incident HF) and controls (without HF) matched on age and sex within each cohort. Plasma concentrations of 276 proteins were measured at baseline in ARIC (250 cases/250 controls), FHS (191/191), and HOMAGE cohort (562/871). RESULTS: In single protein analysis, after adjusting for matching variables and clinical risk factors (and correcting for multiple testing), 62 proteins were associated with incident HF in ARIC, 16 in FHS, and 116 in HOMAGE cohort. Proteins associated with incident HF in all cohorts were BNP (brain natriuretic peptide), NT-proBNP (N-terminal pro-B-type natriuretic peptide), eukaryotic translation initiation factor 4E-BP1 (4E-binding protein 1), hepatocyte growth factor (HGF), Gal-9 (galectin-9), TGF-alpha (transforming growth factor alpha), THBS2 (thrombospondin-2), and U-PAR (urokinase plasminogen activator surface receptor). The increment in C-index for incident HF based on a multiprotein biomarker approach, in addition to clinical risk factors and NT-proBNP, was 11.1% (7.5%-14.7%) in ARIC, 5.9% (2.6%-9.2%) in FHS, and 7.5% (5.4%-9.5%) in HOMAGE cohort, all P<0.001), each of which was a larger increase than that for NT-proBNP on top of clinical risk factors. Complex network analysis revealed a number of overrepresented pathways related to inflammation (eg, tumor necrosis factor and interleukin) and remodeling (eg, extracellular matrix and apoptosis). CONCLUSIONS: A multiprotein biomarker approach improves prediction of incident HF when added to natriuretic peptides and clinical risk factors.

Prognostic biomarkers and molecular pathways mediating Helicobacter pylori-induced gastric cancer: a network-biology approach.

Cancer of the stomach is the second most frequent cancer-related death worldwide. The survival rate of patients with gastric cancer (GC) remains fragile. There is a requirement to discover biomarkers for prognosis approaches. Helicobacter pylori in the stomach is closely associated with the progression of GC. We identified the genes associated with poor/favorable prognosis in H. pylori-induced GC. Multivariate statistical analysis was applied on the Gene Expression Omnibus (GEO) dataset GSE54397 to identify differentially expressed miRNAs (DEMs) in gastric tissues with H. pylori-induced cancer compared with the H. pylori-positive with non-cancerous tissue. A protein interaction map (PIM) was built and subjected to DEMs targets. The enriched pathways and biological processes within the PIM were identified based on substantial clusters. Thereafter, the most critical genes in the PIM were illustrated, and their prognostic impact in GC was investigated. Considering p-value less than 0.01 and |Log2 fold change| as >1, five microRNAs demonstrated significant changes among the two groups. Gene functional analysis revealed that the ubiquitination system, neddylation pathway, and ciliary process are primarily involved in H. pylori-induced GC. Survival analysis illustrated that the overexpression of DOCK4, GNAS, CTGF, TGF-b1, ESR1, SELE, TIMP3, SMARCE1, and TXNIP was associated with poor prognosis, while increased MRPS5 expression was related to a favorable prognosis in GC patients. DOCK4, GNAS, CTGF, TGF-b1, ESR1, SELE, TIMP3, SMARCE1, TXNIP, and MRPS5 may be considered prognostic biomarkers for H. pylori-induced GC. However, experimental validation is necessary in the future.

Skin proteomic profiling of irradiation-induced fibrosis and its modulation by low molecular weight fucoidan via tight junction pathway.

Fucoidans, sulfated and fucosylated polysaccharides extracted from brown seaweed, were found to inhibit radiotherapy-induced cell damage and fibrosis through the TGF-ß1 pathway. However, the comprehensive molecular response during irradiation-induced fibrosis and fucoidan-assisted recovery still remain unclear. Rat hind limbs were irradiated and smeared with low molecular weight fucoidan (LMF). Protein profiles were examined by a mass spectrometry-based proteomics analysis. Out of a total of 4625 proteins, 233 were found to be significantly up-regulated after irradiation and down-regulated after LMF treatment. Pathway and protein-protein interaction network analyses further indicated that four proteins including Actb, Ezr, Msn and Cdc42 were clustered into the tight junction and regulation of actin cytoskeleton pathways. These four proteins may serve as biomarkers for the detection of skin fibrosis induced by irradiation or TGF-ß1, and for the recovery following LMF treatment.

Molecular mechanisms of Biyu decoction as treatment for psoriasis: A network pharmacology and molecular docking study.

BACKGROUND: The therapeutic effects of a combination of Chinese medicines called Biyu decoction have been clinically verified, although its molecular targets in psoriasis remain unknown. AIM: To explore the molecular mechanisms of Biyu decoction for psoriasis treatment. METHODS: In this network pharmacology and molecular docking study, the Traditional Chinese Medicine Systems Pharmacology database was searched for Biyu decoction active ingredients. GeneCards, Online Mendelian Inheritance in Man, PharmGkb, Therapeutic Target Database, and DrugBank databases were searched for psoriasis-related genes. The genes targeted by the decoction's active ingredient and disease genes were intersected to obtain predictive targets of the drug during psoriasis treatment. Cytoscape 3.8.0 was used to construct a drug component/ target disease network. The The functional protein association networks database and Cytoscape were used to construct a protein-protein interaction network and streamline the core network. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for pathway enrichment analysis. Molecular docking technology was used to verify the drug component/target disease network. RESULTS: We screened 117 major active ingredients, including quercetin, kaempferol, naringenin, and acetyl-shikonin, and identified 213 gene targets, such as MAPK3, JUN, FOS, MYC, MAPK8, STAT3, and NFKBIA. Using a molecular docking analysis, the main active ingredients demonstrated good binding to the core targets. The Gene Ontology analysis showed that these ingredients were significantly associated with biological activities, such as transcription factor DNA binding, RNA polymerase II-specific DNA binding of transcription factors, and cytokine receptor binding; responses to lipopolysaccharides, molecules of bacterial origin, and oxidative stress; and were mainly distributed in membrane rafts, microdomains, and regions. The Kyoto Encyclopedia of Genes and Genomes analysis showed that decoction ingredients act on Th17 cell differentiation, tumor necrosis factor and mitogen-activated protein signaling pathways, the interleukin-17 signaling pathway, and the PI3K-Akt signaling pathway. CONCLUSION: Biyu decoction may be effective against psoriasis through multi-component, multi-target, and multi-channel synergy.

The plant endoplasmic reticulum UPRome: A repository and pathway browser for genes involved in signaling networks linked to the endoplasmic reticulum.

The endoplasmic reticulum (ER) houses sensors that respond to environmental stress and underly plants' adaptative responses. These sensors transduce signals that lead to changes in nuclear gene expression. The ER to nuclear signaling pathways are primarily attributed to the unfolded protein response (UPR) and are also integrated with a wide range of development, hormone, immune, and stress signaling pathways. Understanding the role of the UPR in signaling network mechanisms that associate with particular phenotypes is crucially important. While UPR-associated genes are the subject of ongoing investigations in a few model plant systems, most remain poorly annotated, hindering the identification of candidates across plant species. This open-source curated database provides a centralized resource of peer reviewed knowledge of ER to nuclear signaling pathways for the plant community. We provide a UPRome interactive viewer for users to navigate through the pathways and to access annotated information. The plant ER UPRome website is located at http://uprome.tamu.edu. We welcome contributions from the researchers studying the ER UPR to incorporate additional genes into the database through the "contact us" page.

Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis.

BACKGROUND: The onset and progression of type 1 diabetes mellitus (T1DM) is closely related to autoimmunity. Effective monitoring of the immune system and developing targeted therapies are frontier fields in T1DM treatment. Currently, the most available tissue that reflects the immune system is peripheral blood mononuclear cells (PBMCs). Thus, the aim of this study was to identify key PBMC biomarkers of T1DM. METHODS: Common differentially expressed genes (DEGs) were screened from the Gene Expression Omnibus (GEO) datasets GSE9006, GSE72377, and GSE55098, and PBMC mRNA expression in T1DM patients was compared with that in healthy participants by GEO2R. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) network analyses of DEGs were performed using the Cytoscape, DAVID, and STRING databases. The vital hub genes were validated by reverse transcription-polymerase chain reaction using clinical samples. The disease-gene-drug interaction network was built using the Comparative Toxicogenomics Database (CTD) and Drug Gene Interaction Database (DGIdb). RESULTS: We found that various biological functions or pathways related to the immune system and glucose metabolism changed in PBMCs from T1DM patients. In the PPI network, the DEGs of module 1 were significantly enriched in processes including inflammatory and immune responses and in pathways of proteoglycans in cancer. Moreover, we focused on four vital hub genes, namely, chitinase-3-like protein 1 (CHI3L1), C-X-C motif chemokine ligand 1 (CXCL1), matrix metallopeptidase 9 (MMP9), and granzyme B (GZMB), and confirmed them in clinical PBMC samples. Furthermore, the disease-gene-drug interaction network revealed the potential of key genes as reference markers in T1DM. CONCLUSION: These results provide new insight into T1DM pathogenesis and novel biomarkers that could be widely representative reference indicators or potential therapeutic targets for clinical applications.

Identification of Significant Genes and Pathways Associated with Tenascin-C in Cancer Progression by Bioinformatics Analysis.

Background: Tenascin-C (TNC) is a large glycoprotein of the extracellular matrix which associated with poor clinical outcomes in several malignancies. TNC over-expression is repeatedly observed in several cancer tissues and promotes several processes in tumor progression. Until quite recently, more needs to be known about the potential mechanisms of TNC as a key player in cancer progression and metastasis. Materials and Methods: In the present study, we performed a bioinformatics analysis of breast and colorectal cancer expression microarray data to survey TNC role and function with holistic view. Gene expression profiles were analyzed to identify differentially expressed genes (DEGs) between normal samples and cancer biopsy samples. The protein-protein interaction (PPI) networks of the DEGs with CluePedia plugin of Cytoscape software were constructed. Furthermore, after PPI network construction, gene-regulatory networks analysis was performed to predict long noncoding RNAs and microRNAs associated with TNC and cluster analysis was performed. Using the Clue gene ontology (GO) plugin of Cytoscape software, the GO and pathway enrichment analysis were performed. Results: PPI and DEGs-miRNA-lncRNA regulatory networks showed TNC is a significant node in a huge network, and one of the main gene with high centrality parameters. Furthermore, from the regulatory level perspective, TNC could be significantly impressed by miR-335-5p. GO analysis results showed that TNC was significantly enriched in cancer-related biological processes. Conclusions: It is important to identify the TNC underlying molecular mechanisms in cancer progression, which may be clinically useful for tumor-targeting strategies. Bioinformatics analysis provides an insight into the significant roles that TNC plays in cancer progression scenarios.

Identification of Prognostic Biomarkers in Papillary Thyroid Cancer and Developing Non-Invasive Diagnostic Models Through Integrated Bioinformatics Analysis.

BACKGROUND: Papillary thyroid cancer (PTC) is the most frequent subtype of thyroid carcinoma, mainly detected in patients with benign thyroid nodules (BTN). Due to the invasiveness of accurate diagnostic tests, there is a need to discover applicable biomarkers for PTC. So, in this study, we aimed to identify the genes associated with prognosis in PTC. Besides, we performed a machine learning tool to develop a non-invasive diagnostic approach for PTC. METHODS: For the study purposes, the miRNA dataset GSE130512 was downloaded from the GEO database and then analyzed to identify the common differentially expressed miRNAs in patients with non-metastatic PTC (nm-PTC)/metastatic PTC (m-PTC) compared with BTNs. The SVM was also applied to differentiate patients with PTC from those patients with BTN using the common DEMs. A protein-protein interaction network was also constructed based on the targets of the common DEMs. Next, functional analysis was performed, the hub genes were determined, and survival analysis was then executed. RESULTS: A total of three common miRNAs were found to be differentially expressed among patients with nm-PTC/m-PTC compared with BTNs. In addition, it was established that the autophagosome maturation, ciliary basal body-plasma membrane docking, antigen processing as ubiquitination & proteasome degradation, and class I MHC mediated antigen processing & presentation are associated with the pathogenesis of PTC. Furthermore, it was illustrated that RPS6KB1, CCNT1, SP1, and CHD4 might serve as new potential biomarkers for PTC prognosis. CONCLUSION: RPS6KB1, CCNT1, SP1, and CHD4 may be considered new potential biomarkers used for prognostic aims in PTC. However, performing validation tests is inevitable in the future.

A protein interactions map of multiple organ systems associated with COVID-19 disease.

Coronavirus disease 2019 (COVID-19) is an on-going pandemic disease infecting millions of people across the globe. Recent reports of reduction in antibody levels and the re-emergence of the disease in recovered patients necessitated the understanding of the pandemic at the core level. The cases of multiple organ failures emphasized the consideration of different organ systems while managing the disease. The present study employed RNA sequencing data to determine the disease associated differentially regulated genes and their related protein interactions in several organ systems. It signified the importance of early diagnosis and treatment of the disease. A map of protein interactions of multiple organ systems was built and uncovered CAV1 and CTNNB1 as the top degree nodes. A core interactions sub-network was analyzed to identify different modules of functional significance. AR, CTNNB1, CAV1, and PIK3R1 proteins were unfolded as bridging nodes interconnecting different modules for the information flow across several pathways. The present study also highlighted some of the druggable targets to analyze in drug re-purposing strategies against the COVID-19 pandemic. Therefore, the protein interactions map and the modular interactions of the differentially regulated genes in the multiple organ systems would incline the scientists and researchers to investigate in novel therapeutics for the COVID-19 pandemic expeditiously.

Fibrinogen Dysregulation is a Prominent Process in Fatal Conditions of COVID-19 Infection; a Proteomic Analysis.

INTRODUCTION: Molecular pathophysiology of COVID-19 is not completely known. Expression changes in patients' plasma proteins have revealed new information about the disease. Introducing the key targeted plasma protein in fatal conditions of COVID-19 infection is the aim of this study. METHODS: Significant differentially expressed proteins (DEPs) in the plasma of cases with a fatal condition of COVID-19 were extracted from an original article. These proteins were included in a network via STRING database along with 100 first neighbor proteins to determine central nodes of the network for analyzing. RESULTS: Queried and added proteins were included in a scale free network. Three hub nodes were identified as critical target proteins. The top queried hub proteins were chains of fibrinogen; Fibrinogen Alpha chain (FGA), Fibrinogen gamma chain (FGG), and Fibrinogen beta chain (FGB), which are related to the coagulation process. CONCLUSIONS: It seems that fibrinogen dysregulation has a deep impact on the fatality of COVID-19 infection.

Bioinformatics Investigation and Contribution of Other Chromosomes Besides Chromosome 21 in the Risk of Down Syndrome Development.

INTRODUCTION: Down syndrome as a genetic disorder is a popular research topic in molecular studies. One way to study Down syndrome is via bioinformatics. METHODS: In this study, a gene expression profile from a microarray study was selected for more investigation. RESULTS: The study of Down syndrome patients shows specific genes with differential expression and network centrality properties. These genes are introduced as RHOA, FGF2, FYN, and CD44, and their level of expression is high in these patients. CONCLUSION: This study suggests that besides chromosomes 21, there are additional contributing chromosomes to the risk of Down syndrome development. Besides, these genes could be used for clinical studies after more analysis.

Integrated Network and Gene Ontology Analysis Identifies Key Genes and Pathways for Coronary Artery Diseases.

BACKGROUND: The prevalence of Coronary Artery Disease (CAD) in developing countries is on the rise, owing to rapidly changing lifestyle. Therefore, it is imperative that the underlying genetic and molecular mechanisms be understood to develop specific treatment strategies. Comprehensive disease network and Gene Ontology (GO) studies aid in prioritizing potential candidate genes for CAD and also give insights into gene function by establishing gene and disease pathway relationships. METHODS: In the present study, CAD-associated genes were collated from different data sources and protein-protein interaction network was constructed using STRING. Highly interconnected network clusters were inferred and GO analysis was performed. RESULTS: Interrelation between genes and pathways were analyzed on ClueGO and 38 candidates were identified from 1475 CAD-associated genes, which were significantly enriched in CAD-related pathways such as metabolism and regulation of lipid molecules, platelet activation, macrophage derived foam cell differentiation, and blood coagulation and fibrin clot formation. DISCUSSION: Integrated network and ontology analysis enables biomarker prioritization for common complex diseases such as CAD. Experimental validation and future studies on the prioritized genes may reveal valuable insights into CAD development mechanism and targeted treatment strategies.

Exploring the Potential Mechanism of Shufeng Jiedu Capsule for Treating COVID-19 by Comprehensive Network Pharmacological Approaches and Molecular Docking Validation.

OBJECTIVE: Shufeng Jiedu capsule (SFJDC) is a well-known Chinese patent drug that is recommended as a basic prescription and applied widely in the clinical treatment of COVID-19. However, the exact molecular mechanism of SFJDC remains unclear. The present study aims to determine the potential pharmacological mechanisms of SFJDC in the treatment of COVID-19 based on network pharmacology. METHODS: The network pharmacology-based strategy includes collection and analysis of active compounds and target genes, network construction, identification of key compounds and hub target genes, KEGG and GO enrichment, recognition and analysis of main modules, as well as molecule docking. RESULTS: A total of 214 active chemical compounds and 339 target genes of SFJDC were collected. Of note, 5 key compounds (ß -sitosterol, luteolin, kaempferol, quercetin, and stigmasterol) and 10 hub target genes (TP53, AKT1, NCOA1, EGFR, PRKCA, ANXA1, CTNNB1, NCOA2, RELA and FOS) were identified based on network analysis. The hub target genes mainly enriched in pathways including MAPK signaling pathway, PI3K-Akt signaling pathway and cAMP signaling pathway, which could be the underlying pharmacological mechanisms of SFJDC for treating COVID-19. Moreover, the key compounds had high binding activity with three typical target proteins including ACE2, 2OFZ, and 1SSK. CONCLUSION: By network pharmacology analysis, SFJDC was found to effectively improve immune function and reduce inflammatory responses based on its key compounds, hub target genes, and the relevant pathways. These findings may provide valuable evidence for explaining how SFJDC exerting the therapeutic effects on COVID-19, providing a holistic view for further clinical application.

Identification of Pathways and Key Genes in Venous Remodeling After Arteriovenous Fistula by Bioinformatics Analysis.

The arteriovenous fistula (AVF) is the first choice for vascular access for hemodialysis of renal failure patients. Venous remodeling after exposure to high fistula flow is important for AVF to mature but the mechanism underlying remodeling is still unknown. The objective of this study is to identify the molecular mechanisms that contribute to venous remodeling after AVF. To screen and identify the differentially expressed genes (DEGs) that may involve venous remodeling after AVF, we used bioinformatics to download the public microarray data (GSE39488) from the Gene Expression Omnibus (GEO) and screen for DEGs. We then performed gene ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA) for the functional annotation of DEGs. The protein-protein interaction (PPI) network was constructed and the hub genes were carried out. Finally, we harvested 12 normal vein samples and 12 AVF vein samples which were used to confirm the expressions of the hub genes by immunohistochemistry. A total of 45 DEGs were detected, including 32 upregulated and 13 downregulated DEGs. The biological process (BP) of the GO analysis were enriched in the extrinsic apoptotic signaling pathway, cGMP-mediated pathway signaling, and molting cycle. The KEGG pathway analysis showed that the upregulated DEGs were enriched in glycosaminoglycan biosynthesis and purine metabolism, while the downregulated DEGs were mainly enriched in pathways of glycosaminoglycan biosynthesis, antifolate resistance, and ABC transporters. The GSEA analysis result showed that the top three involved pathways were oxidative phosphorylation, TNFA signaling via NF-K B, and the inflammatory response. The PPI was constructed and the hub genes found through the method of DMNC showed that INHBA and NR4A2 might play an important role in venous remodeling after AVF. The integrated optical density (DOI) examined by immunohistochemistry staining showed that the expression of both INHBA and NR4A2 increased in AVF compared to the control group. Our research contributes to the understanding of the molecular mechanism of venous remodeling after exposure to high fistula flow, which may be useful in treating AVF failure.

Breast Cancer Interaction Network Concept from Mostly Related Components.

BACKGROUND: Management of breast cancer (BC) as a heterogeneous disease is very challenging. Biomarker discovery has been shown promising for this aim. Protein interaction mapping could provide further knowledge of the vital roles of these markers. MATERIALS AND METHODS: Cytoscape and its plug-ins are used for network construction and evaluation. The plug-ins used in this study are STRING, Network Analyzer, GeneMANIA, and CluePedia. RESULTS: The central proteins are enriched in transcription regulatory region DNA binding, regulatory region nucleic acid binding, regulatory region DNA binding, Fc receptor signaling pathway, cell cycle arrest, and immune response-regulating cell surface receptor signaling pathway. CONCLUSION: The introduced biomarkers and their related biological processes may show useful for the breast cancer diagnosis and monitoring; however, has to encounter more validation studies to be clinically applicable.

Identification of the Key Genes of Autism Spectrum Disorder Through Protein-Protein Interaction Network.

BACKGROUND: Currently, the prevalence of autism spectrum disorder (ASD) is increasing, which widely spurs the interest in the molecular investigation. Thereby, a better understanding of the given disorder mechanisms is likely to be achieved. Bioinformatics suiting protein-protein interactions analysis via the application of high-throughput studies, such as protein array, is one of these achievements. MATERIALS AND METHODS: The gene expression data from Gene Expression Omnibus (GEO) database were downloaded, and the expression profile of patients with developmental delay and autistic features were analyzed via Cytoscape and its relevant plug-ins. RESULTS: Our findings indicated that EGFR, ACTB, RHOA, CALM1, MAPK1, and JUN genes as the hub-bottlenecks and their related terms could be important in ASD risk. In other words, any expression modification in these genes could trigger dysfunctions in the corresponding biological processes. CONCLUSION: We suggest that differentially expressed genes could be used as suitable targets for ASD after being validated.

Introducing Genes With Significant Role in Migraine: An Interactomic Approach.

INTRODUCTION: Migraine is a severe kind of headache with the chance hereditary of 50%. Molecular studies can promote understanding of migraine pathophysiology. One of which is bioinformatics approach that could provide additional information related to the identified biomarkers. METHODS: In this research, migraine genes are studies in terms of interaction pattern to introduce important individuals. Through STRING database Plug-in in Cytoscape, candidate genes for migraine were retrieved and analyzed by related applications. Based on centrality and action types (expression, activation, and inhibition) genes were screened. RESULTS: Numbers of 33 central genes including seven hub-bottlenecks were identified which 70% of central genes were involved in expression action with each other. Activation was dominate action relative to inhibition between the central genes. CONCLUSION: The finding indicates that insulin is the most important gene relative to migraine. It seems regulation of metabolism play critical role in control of migraine.

New Molecular Aspects of Cardiac Arrest; Promoting Cardiopulmonary Resuscitation Approaches.

INTRODUCTION: Cardiopulmonary resuscitation (CPR) is a method to improve survival of patients with cardiac arrest. This study aimed to identify the key genes affected five minutes after cardiac arrest, hoping to elevate the efficacy of CPR. METHODS: In this bioinformatics study differentially expressed genes of six pigs were downloaded from GEO and screened. The significant and characterized genes were analyzed via calculating fold change and protein-protein interaction (PPI) networks. The crucial nodes were determined based on centrality parameters and their related biological processes were investigated via ClueGO. RESULTS: 17 significant up-regulated (LogFC ≥ 2) and 22 down-regulated (LogFC < -0.5) genes were detected. Transthyretin (TTR logFC = 4.59) and Gonadotropin-releasing hormone receptor (GNRHR logFC = 3.84) had higher logFC among up-regulated and down-regulated genes, respectively. The critical genes including four up-regulated and five down-regulated genes were detected from network analysis. GNRHR and Prolactin precursor (PRL) were among the most important down res 5 minutes after cardiac arrest and Beta-2 adrenergic receptor and Cadherin-1 were among the most important up regulated gens. CONCLUSION: The introduced potential biomarkers could reveal a new molecular aspect for CPR performance and pituitary gland protection was highlighted in this respect.