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.

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.

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.

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.

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.

Serum Proteomic Study of Women With Obsessive-Compulsive Disorder, Washing Subtype

Introduction

Many genetic studies are conducted on Obsessive-Compulsive Disorder (OCD). however, a high-throughput examination of proteome profile of this severe disease has not been performed yet.

Methods

Here, the proteomic study of OCD patients' serum samples was conducted by the application of Two-Dimensional Electrophoresis (2DE) followed by Mass Spectrometry (MALDI-TOF-TOF).

Results

A total of 240 protein spots were detected and among them, five significant differentially expressed protein spots with the fold change of ≥1.5 were considered for further evaluations. These proteins include IGKC, GC, HPX, and two isoforms of HP. While IGKC and HP show down-regulation, GC and HPX indicate up-regulation. Moreover, a validation study of overall HP levels in patients' serum via nephelometric quantification confirmed the lower levels of this protein in the serum of OCD patients. Additionally, enrichment analysis and validation test revealed that inflammation is one of most dominant processes in OCD.

Conclusion

It is suggested that these candidate proteins and their underlying processes (especially, inflammation) may be linked to OCD pathophysiology and can promise a clinical use after extensive validation studies.

Barrett's esophagus transits to a cancer condition via potential biomarkers

AIM

In this study, the transcriptome profile of Barrett's esophagus (BE) was examined for identification potential related biomarkers in view of interacting charactering.

BACKGROUND

Since BE is known as a precursor of esophageal cancer, the molecular studies of this condition could be essential.

METHODS

Gene expression data of BE in comparison with normal cases, GSE34619 was retrieved from Gene Expression Omnibus. Differentially expressed genes (DEGs) were determined applying GEO2R online software. The DEGs then were analyzed in terms of centrality properties via constructing an interaction network.

RESULTS

The data indicate that there are two sets of hub-bottlenecks panels with distinguishable values in BE. The first group shows that BE is very susceptible to develop cancer, and the second one implied on central characteristic of some DEGs as previously were also reported for BE pathogenicity. In addition, these genes are also implicated in cancer shift from certain conditions.

CONCLUSION

On the whole, taking together these findings explain and support the cancerous origin of BE and introduced a panel of nominated biomarkers that could be more specific for BE rather than other types of esophageal problems. However, a complementary study to support this claim is suggested.

Insulin dysregulation plays a critical role in colon inflammation: a bioinformatics approach

AIM

Evaluating and screening of genes related to colorectal inflammation of mice for finding critical ones in this disease was the aim of this study.

BACKGROUND

Many studies are shown direct relationship between inflammation and colorectal cancer onset and development. Several molecular aspects of inflammation are investigated to discover molecular mechanism of this disease.

METHODS

Profiles of differentially expressed genes (DEGs) of mice inflamed colorectal tissue in comparison with normal samples are obtained from Gene Expression Omnibus (GEO) database. The significant and characterized DEGs were screened via protein-protein interaction (PPI) network. Hubs of the network were determined and backbone network was constructed. Moreover, action network for the critical nodes was constructed and analyzed.

RESULTS

Eight central genes including IL6, ALB, PRDM10, AKT1, GAPDH, IL8, INS and TNF were determined as hub nodes. Findings indicate that insulin plays critical role in regulation of hub genes. This finding shows association between inflammation and metabolism dysregulation. Except PRDM10 and GAPDH, the other hubs show considerable regulatory effects on each other.

CONCLUSION

Inflammation of colorectal tissue is strongly depended on metabolism especially to insulin function.

Comparative analyses of the proteins from Mycobacterium tuberculosis and human genomes: Identification of potential tuberculosis drug targets

Tuberculosis, one of the major infectious diseases affecting human beings is caused by the bacillus Mycobacterium tuberculosis. Increased resistance to known drugs commonly used for the treatment of tuberculosis has created an urgent need to identify new targets for validation and to develop drugs. In this study, we have used various bioinformatics tools, to compare the protein sequences from twenty-three M. tuberculosis genome strains along with the known human protein sequences, in order to identify the 'conserved' M. tuberculosis proteins absent in human. Further, based on the analysis of protein interaction networks, we selected one-hundred and forty proteins that were predicted as potential M. tuberculosis drug targets and prioritized according to the ranking of 'clusters' of interacting proteins. Comparison of the predicted 140 TB targets with literature indicated that 46 of them were previously reported, thereby increasing the confidence in our predictions of the remaining 94 targets too. The analyses of the structures and functions corresponding to the predicted potential TB drug targets indicated a diverse range of proteins that included ten 'druggable' targets with some of the known drugs.

Nodes with high centrality in protein interaction networks are responsible for driving signaling pathways in diabetic nephropathy

In spite of huge efforts, chronic diseases remain an unresolved problem in medicine. Systems biology could assist to develop more efficient therapies through providing quantitative holistic sights to these complex disorders. In this study, we have re-analyzed a microarray dataset to identify critical signaling pathways related to diabetic nephropathy. GSE1009 dataset was downloaded from Gene Expression Omnibus database and the gene expression profile of glomeruli from diabetic nephropathy patients and those from healthy individuals were compared. The protein-protein interaction network for differentially expressed genes was constructed and enriched. In addition, topology of the network was analyzed to identify the genes with high centrality parameters and then pathway enrichment analysis was performed. We found 49 genes to be variably expressed between the two groups. The network of these genes had few interactions so it was enriched and a network with 137 nodes was constructed. Based on different parameters, 34 nodes were considered to have high centrality in this network. Pathway enrichment analysis with these central genes identified 62 inter-connected signaling pathways related to diabetic nephropathy. Interestingly, the central nodes were more informative for pathway enrichment analysis compared to all network nodes and also 49 differentially expressed genes. In conclusion, we here show that central nodes in protein interaction networks tend to be present in pathways that co-occur in a biological state. Also, this study suggests a computational method for inferring underlying mechanisms of complex disorders from raw high-throughput data.

Identifying functional modules for coronary artery disease by a prior knowledge-based approach

Until recently, the underlying genetic mechanisms for coronary artery disease (CAD) have been largely unknown, with just a list of genes identified accounting for very little of the disease in the population. Hence, a systematic dissection of the sophisticated interplays between these individual disease genes and their functional involvements becomes essential. Here, we presented a novel knowledge-based approach to identify the functional modules for CAD. First, we selected 266 disease genes in CADgene database as the initial seed genes, and used PPI knowledge as a guide to expand these genes into a CAD-specific gene network. Then, we used Newman's algorithm to decompose the primary network into 14 compact modules with high modularity. By analysis of these modules, we further identified 114 hub genes, all either directly or indirectly associated with CAD. Finally, by functional analysis of these modules, we revealed several novel pathogenic mechanisms for CAD (for examples, some yet rarely concerned like peptide YY receptor activity, Fc gamma R-mediated phagocytosis and actin cytoskeleton regulation etc.).