Molecular Complex Detection Algorithm Research Articles

Investigating the molecular mechanisms between type 1 diabetes and mild cognitive impairment using bioinformatics analysis, with a focus on immune response

The immune system is involved in the development and progression of several diseases. Type 1 diabetes mellitus (T1DM), an autoimmune disorder, influences the progression of several other conditions; however, the link between T1DM and mild cognitive impairment (MCI) remains unclear. This study investigated the underlying immune response mechanisms that contribute to the development and progression of T1DM and MCI. Microarray datasets for MCI (GSE63060) and T1DM (GSE30208) were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the limma package. To explore the functional implications of these DEGs, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using ClusterProfiler. Protein–protein interaction networks for the DEGs were constructed using the STRING database and visualized using Cytoscape. The Molecular Complex Detection algorithm was used to analyze DEGs. Immune cell infiltration in patients with T1DM and MCI was analyzed using the xCell method. Gene set enrichment analysis was used to gain in-depth insights into the functional characteristics of T1DM and MCI. Immune-related genes were obtained from the GeneCards and ImmPort databases. Machine learning algorithms were used to identify potential hub genes associated with immunity for T1DM and MCI diagnosis, and the diagnostic value was assessed using the receiver operating characteristic curve. The identified genes were validated using quantitative polymerase chain reaction. In the T1DM and MCI datasets, 610 DEGs showed consistent trends, of which 232 and 378 were upregulated and downregulated, respectively. Immune response analysis revealed significant changes in the immune cells associated with MCI and T1DM. Using immune-related genes, DEGs, and machine learning techniques, we identified CD3D in the MCI and T1DM groups. We observed a gradual decline in the cognitive function of mice with T1DM as the disease progressed. CD3D expression increased with increasing disease severity; CD3D primarily affected CD4+ T cells. This study revealed a complex interaction between T1DM and MCI, providing novel insights into the intricate relationship between immune dysregulation and cognitive impairment and expanding our understanding of these two interconnected disorders. These findings will facilitate the development of therapeutic interventions and identification of potential therapeutic targets.

Identification of hub genes and key pathways in arsenic-treated rice (Oryza sativa L.) based on 9 topological analysis methods of CytoHubba.

Arsenic is a toxic metalloid that can cause acute and chronic adverse health problems. Unfortunately, rice, the primary staple food for more than half of the world's population, is generally regarded as a typical arsenic-accumulating crop plant. Evidence indicates that arsenic stress can influence the growth and development of the rice plant, and lead to high concentrations of arsenic in rice grain. But the underlying mechanisms remain unclear. In the present research, the possible molecules and pathways involved in rice roots in response to arsenic stress were explored using bioinformatics methods. Datasets that involving arsenic-treated rice root and the "study type" that was restricted to "Expression profiling by array" were selected and downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between the arsenic-treated group and the control group were obtained using the online web tool GEO2R. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to investigate the functions of DEGs. The protein-protein interactions (PPI) network and the molecular complex detection algorithm (MCODE) of DEGs were analyzed using STRING and Cystoscope, respectively. Important nodes and hub genes in the PPI network were predicted and explored using the Cytoscape-cytoHubba plug-in. Two datasets, GSE25206 and GSE71492, were downloaded from Gene Expression Omnibus (GEO) database. Eighty common DEGs from the two datasets, including sixty-three up-regulated and seventeen down-regulated genes, were then selected. After functional enrichment analysis, these common DEGs were enriched mainly in 10 GO items, including glutathione transferase activity, glutathione metabolic process, toxin catabolic process, and 7 KEGG pathways related to metabolism. After PPI network and MCODE analysis, 49 nodes from the DEGs PPI network were identified, filtering two significant modules. Next, the Cytoscape-cytoHubba plug-in was used to predict important nodes and hub genes. Finally, five genes [Os01g0644000, PRDX6 (Os07g0638400), PRX112 (Os07g0677300), ENO1(Os06g0136600), LOGL9 (Os09g0547500)] were verified and could serve as the best candidates associated with rice root in response to arsenic stress. In summary, we elucidated the potential pathways and genes in rice root in response to arsenic stress through a comprehensive bioinformatics analysis.

Molecular pathways and gene networks in fetal hemoglobin as a novel protein target for beta-thalassemia

Abstract Background: Beta thalassemia is a condition in which the body cannot produce the beta subunit of hemoglobin due to harmful mutations in the globin gene that cause inadequate development of adult hemoglobin (HbA). In beta-thalassemic individuals, fetal hemoglobin (HbF), consisting of two and two subunits, is a potential replacement for HbA with significant therapeutic importance. HbF increase is a powerful and essential therapeutic tool to overcome the problem of beta-thalassemia. Materials and Methods: The GEO2R statistical tool and the GSE96060 dataset from the gene expression omnibus database were used to determine the differentially expressed genes (DEGs). The search tool for the retrieval of interacting genes program was used to reveal connections between these DEGs in samples, followed by applying the molecular complex detection algorithm to identify clusters of genes within these interaction networks. Using ClueGo and CluePedia, the discovered DEGs were subjected to functional annotation, including gene ontology (GO) and enriched molecular pathway analysis. Results: We searched the top 200 DEGs that met the criteria for significance (P-value 0.05; fold two change >1 or 1). Myeloid cell differentiation, erythrocyte differentiation, cellular detoxification, and heme binding are only a few examples of the biological processes and molecular pathways the GO analysis of DEGs identified as having significant alterations. The link between the DEGs in heme biosynthesis and transcriptional dysregulation in cancer was discovered by studying enriched Kyoto encyclopedia of genes and genomes pathways. To find possible targets for beta thalassemia treatments, we looked for the genes Krüppel-like factors 1 (KLF1) and mouse double minute 2 (MDM2). By activating both beta-globin and HbF gamma-globin genes, KLF1 encourages HbF repression, which is regulated by changing myeloblastosis expression. Conclusion: This study shows that the genes KLF1 and MDM2 are related to dysregulated molecular pathways, contribute to the development of beta thalassemia, and may be exploited as a platform for the induction of fetal hemoglobin in the development of therapeutics.

Identification of CCL20 as a Key Biomarker of Inflammatory Responses in the Pathogenesis of Intracerebral Hemorrhage.

Inflammatory responses after intracerebral hemorrhage (ICH) contribute to severe secondary brain injury, leading to poor clinical outcomes. However, the responsible genes for effective anti-inflammation treatment in ICH remain poorly elucidated. The differentially expressed genes (DEGs) of human ICH were explored by online GEO2R. Go and KEGG were used to explore the biological function of DEGs. Protein-protein interactions (PPI) were built in the String database. Critical modules of PPI were identified by a molecular complex detection algorithm (MCODE). Cytohubba was used to determine the hub genes. The mRNA-miRNA interaction network was built in the miRWalk database. The rat ICH model was applied to validate the key genes. A total of 776 DEGs were identified in ICH. Go and KEGG analyses indicated that DEGs were mainly involved in neutrophil activation and the TNF signaling pathway. GSEA analysis presented that DEGs were significantly enriched in TNF signaling and inflammatory response. PPI network was constructed in the 48 differentially expressed inflammatory response-related genes. The critical module of the PPI network was constructed by 7 MCODE genes and functioned as the inflammatory response. The top 10 hub genes with the highest degrees were identified in the inflammatory response after ICH. CCL20 was confirmed as a key gene and mainly expressed in neurons in the rat ICH model. The regulatory network between CCL20 and miR-766 was built, and the miR-766 decrease was confirmed in a human ICH dataset. CCL20 is a key biomarker of inflammatory response after intracerebral hemorrhage, providing a potential target for inflammatory intervention in ICH.

WDR43 is a potential diagnostic biomarker and therapeutic target for osteoarthritis complicated with Parkinson’s disease

Osteoarthritis (OA) and Parkinson's disease (PD) are on the rise and greatly impact the quality of individuals' lives. Although accumulating evidence indicates a relationship between OA and PD, the particular interactions connecting the two diseases have not been thoroughly examined. Therefore, this study explored the association through genetic characterization and functional enrichment. Four datasets (GSE55235, GSE12021, GSE7621, and GSE42966) were chosen for assessment and validation from the Gene Expression Omnibus (GEO) database. Weighted Gene Co-Expression Network Analysis (WGCNA) was implemented to determine the most relevant genes for clinical features. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to explore the biological processes of common genes, and to display the interrelationships between common genes, the STRING database and the application Molecular Complex Detection Algorithm (MCODE) of Cytoscape software were leveraged to get hub genes. By intersecting the common genes with the differentially expressed genes (DEGs) acquired from GSE12021 and GSE42966, the hub genes were identified. Finally, we validated the diagnostic efficacy of hub genes and explored their correlation with 22 immune infiltrating cells. As a consequence, we discovered 71 common genes, most of which were functionally enriched in antigen processing and presentation, mitochondrial translation, the mRNA surveillance pathway, and nucleocytoplasmic transport. Furthermore, WDR43 was found by intersecting eight hub genes with 28 DEGs from the two validation datasets. Receiver Operating Characteristic (ROC) implied the diagnostic role of WDR43 in OA and PD. Immune infiltration research revealed that T-cell regulatory (Tregs), monocytes, and mast cells resting were associated with the pathogenesis of OA and PD. WDR43 may provide key insights into the relationship between OA and PD.

EPSTI1 as an immune biomarker predicts the prognosis of patients with stage III colon cancer.

The poor prognosis and heterogeneity of stage III colon cancer (CC) suggest the need for more prognostic biomarkers. The tumor microenvironment (TME) plays a crucial role in tumor progression. We aimed to explore novel immune infiltration-associated molecules that serve as potential prognostic and therapeutic targets. TME immune scores were calculated using "TMEscore" algorithm. Differentially expressed genes between the high and low TME immune score groups were identified and further investigated through a protein-protein interaction network and the Molecular Complex Detection algorithm. Cox regression, meta-analysis and immunohistochemistry were applied to identify genes significantly correlated with relapse-free survival (RFS). We estimated immune infiltration using three different algorithms (TIMER 2.0, CIBERSORTx, and TIDE). Single-cell sequencing data were processed by Seurat software. Poor RFS was observed in the low TME immune score groups (log-rank P < 0.05). EPSTI1 was demonstrated to be significantly correlated with RFS (P < 0.05) in stage III CC. Meta-analysis comprising 547 patients revealed that EPSTI1 was a protective factor (HR = 0.79, 95% CI, 0.65-0. 96; P < 0.05)). More immune infiltrates were observed in the high EPSTI1 group, especially M1 macrophage and myeloid dendritic cell infiltration (P < 0.05). The TME immune score is positively associated with better survival outcomes. EPSTI1 could serve as a novel immune prognostic biomarker for stage III CC.

Comprehensive Network Analysis Reveals the Targets and Potential Multitarget Drugs of Type 2 Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) is a metabolic disease with increasing prevalence and mortality year by year. The purpose of this study was to explore new therapeutic targets and candidate drugs for multitargets by single-cell RNA expression profile analysis, network pharmacology, and molecular docking. Single-cell RNA expression profiling of islet β cell samples between T2DM patients and nondiabetic controls was conducted to identify important subpopulations and the marker genes. The potential therapeutic targets of T2DM were identified by the overlap analysis of insulin-related genes and diabetes-related genes, the construction of protein-protein interaction network, and the molecular complex detection (MCODE) algorithm. The network distance method was employed to determine the potential drugs of the target. Molecular docking and molecular dynamic simulations were carried out using AutoDock Vina and Gromacs2019, respectively. Eleven cell clusters were identified by single-cell RNA sequencing (scRNA-seq) data, and three of them (C2, C8, and C10) showed significant differences between T2DM samples and normal samples. Eight genes from differential cell clusters were found from differential cell clusters to be associated with insulin activity and T2DM. The MCODE algorithm built six key subnetworks, with five of them correlating with inflammatory pathways and immune cell infiltration. Importantly, CCR5 was a gene within the key subnetworks and was differentially expressed between normal samples and T2DM samples, with the highest area under the ROC curve (AUC) of 82.5% for the diagnosis model. A total of 49 CCR5-related genes were screened, and DB05494 was identified as the most potential drug with the shortest distance to CCR5-related genes. Molecular docking illustrated that DB05494 stably bound with CCR5 (-8.0 kcal/mol) through multiple hydrogen bonds (LYS26, TYR37, TYR89, CYS178, and GLN280) and hydrophobic bonds (TRP86, PHE112, ILE198, TRP248, and TYR251). This study identified CCR5 as a potential therapeutic target and screened DB05494 as a potential drug for T2DM treatment.

TMEM92 acts as an immune-resistance and prognostic marker in pancreatic cancer from the perspective of predictive, preventive, and personalized medicine.

Pancreatic cancer presents extremely poor prognosis due to the difficulty of early diagnosis, low resection rate, and high rates of recurrence and metastasis. Immune checkpoint blockades have been widely used in many cancer types but showed limited efficacy in pancreatic cancer. The current study aimed to evaluate the landscape of tumor microenvironment (TME) of pancreatic cancer and identify the potential markers of prognosis and immunotherapy efficacy which might contribute to improve the targeted therapy strategy and efficacy in pancreatic cancer in the context of predictive, preventive, and personalized medicine (PPPM). In the current study, a total of 382 pancreatic samples from the datasets of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were selected. LM22 gene signature matrix was applied to quantify the fraction of immune cells based on "CIBERSORT" algorithm. Weighted Gene Co-expression Network Analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm was applied to confirm the hub-network of immune-resistance phenotype. A nomogram model based on COX and Logistic regression was constructed to evaluate the prognostic value and the predictive value of hub-gene in immune-response. The role of transmembrane protein 92 (TMEM92) in regulating cell proliferation was evaluated by MTS assay. Western blot and Real-time PCR were applied to assess the biological effects of PD-L1 inhibition by TMEM92. Moreover, the effect of TMEM92 in immunotherapy was evaluated with PBMC co-culture and by MTS assay. Two tumor-infiltrating immune cell (TIIC) phenotypes were identified and a weighted gene co-expression network was constructed to confirm the 167 gene signatures correlated with immune-resistance TIIC subtype. TMEM92 was further identified as a core gene of 167 gene signature network based on MCODE algorithm. High TMEM92 expression was significantly correlated with unfavorable prognosis, characterizing by immune resistance. A nomogram model and external validation confirmed that TMEM92 was an independent prognostic factor in pancreatic cancer. An elevated tumor mutation burden (TMB), mostly is consistent with commonly mutations of KRAS and TP53, was found in the high TMEM92 group. The predictive role of TMEM92 in immunotherapeutic response was also confirmed by IMvigor210 datasets. In addition, the specific biological roles of TMEM92 in cancer was explored in vitro. The results showed that abnormal overexpression of TMEM92 was significantly associated with the poor survival rate of pancreatic cancer. Moreover, we demonstrated that TMEM92 inhibit tumour immune responses of the anti-PD-1 antibody with PBMC co-culture. The current study explored for the first time the immune-resistance phenotype of pancreatic cancer and identified TMEM92 as an innovative marker in predicting clinical outcomes and immunotherapeutic efficacy. These findings not only help to recognize high-risk and immune-resistance population which could be supplied targeted prevention, but also provide personalized medical services by intervening TMEM92 function to improve the prognosis of pancreatic cancer. In addition, the biological role of TMEM92 might reveal the potential molecular mechanisms of pancreatic cancer and lead to a novel sight for development of a PPPM approach for pancreatic cancer management. The online version contains supplementary material available at 10.1007/s13167-022-00287-0.

Identification of potential therapeutic targets associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis

Colorectal cancer (CRC) is the most common malignancy of digestive system with significant mortality rate. CRC patients with comparable clinical symptoms or at similar stages of the disease have different outcomes. This underlying clinical result is almost inevitably due to genetic heterogeneity. Therefore, the current study aimed to highlight gene signatures during CRC and unveil their potential mechanisms through bioinformatic analysis. The gene expression profiles (GSE28000, GSE33113, GSE44861, and GSE37182) were downloaded from the Gene Expression Omnibus database, and the differential expressed genes (DEGs) were identified in normal tissues and tumor tissue samples of CRC patients. In total, 8931 DEGs were identified in CRC, including 411 up-regulated genes and 166 down-regulated genes. Further, a protein-protein interaction network was constructed and the highly related genes were clustered using the Molecular Complex Detection algorithm (MCODE) to retrieve the core interaction in different genes’ crosstalk. The screened hub genes were subjected to functional enrichment analysis. GO analysis results showed that up-regulated DEGs were significantly enriched in biological processes (BP), including cell division, cell cycle, and cell proliferation; the down-regulated DEGs were significantly enriched in BP, including cellular homeostasis, detoxification, defense response, intracellular signaling cascade. Additionally, KEGG pathway analysis displayed the up-regulated DEGs were enriched in the cell cycle, TNF signaling, chemokine signaling pathway, while the down-regulated DEGs were enriched in NF-kB signaling, mineral reabsorption. Furthermore, the overall survival and expression levels of hub genes were detected by the UALCAN database and were further validated using Human Protein Atlas database. Taken together the identified DEGs (MT2A, CCNB1, DLGAP5, CCNA2, CXCL2, and RACGAP1) enhance our understanding of the molecular pathways that underpin CRC pathogenesis and could be exploited as molecular targets and diagnostic biomarkers for CRC therapy.

Kikuchi-Fujimoto disease is mediated by an aberrant type I interferon response

Kikuchi-Fujimoto disease (KFD) is a reactive lymphadenitis of unclear etiology. To understand the pathogenesis of KFD, we performed targeted RNA sequencing of a well-characterized cohort of 15 KFD specimens with 9 non-KFD lymphadenitis controls. Two thousand and three autoimmunity-related genes were evaluated from archived formalin-fixed paraffin-embedded lymph node tissue and analyzed by a bioinformatics approach. Differential expression analysis of KFD cases compared to controls revealed 44 significantly upregulated genes in KFD. Sixty-eight percent of these genes were associated with the type I interferon (IFN) response pathway. Key component of the pathway including nucleic acid sensors, IFN regulatory factors, IFN-induced antiviral proteins, IFN transcription factors, IFN-stimulated genes, and IFN-induced cytokines were significantly upregulated. Unbiased gene expression pathway analysis revealed enrichment of IFN signaling and antiviral pathways in KFD. Protein–protein interaction analysis and a molecular complex detection algorithm identified a densely interacting 15-gene module of type I IFN pathway genes. Apoptosis regulator IFI6 was identified as a key seed gene. Transcription factor target analysis identified enrichment of IFN-response elements and IFN-response factors. T-cell-associated genes were upregulated while myeloid and B-cell-associated genes were downregulated in KFD. CD123+ plasmacytoid dendritic cells (PDCs) and activated T cells were noted in KFD. In conclusion, KFD is mediated by an aberrant type I interferon response that is likely driven by PDCs and T cells.

Identification of potential novel biomarkers for abdominal aortic aneurysm based on comprehensive analysis of circRNA-miRNA-mRNA networks.

Abdominal aortic aneurysm (AAA) is a life-threatening disorder and, therefore, investigation into its underlying mechanisms in light of the competing endogenous RNAs (ceRNAs) hypothesis has gradually increased. However, there is still lacking systematic analysis on AAA-associated circular RNA (circRNA)-microRNA (miRNA/miR)-messenger RNA (mRNA) interaction networks based on bioinformatics methods. The present study attempted to identify novel molecular biomarkers for AAA by profiling circRNA-miRNA-mRNA networks using three public microarray datasets (GSE7084, GSE57691 and GSE144431). A total of 135 differentially expressed genes (DEGs) and 142 differentially expressed circRNAs were detected using the limma R package with the statistical threshold of P<0.05 and |log2fold change (FC)| >1.5. In addition, 12 circRNA-miRNA-mRNA axes were identified to construct upregulated and downregulated ceRNA networks using Cytoscape. Based on molecular complex detection algorithm, (hsa_circ_0057691/0092108/0006845/0082182)- miR-330-5p-calponin 1 (CNN1) and (hsa_circ_0061482/0011450/0008351/0004121)-miR-326-CD8a molecule (CD8A) were recognized as the center axes in ceRNA networks. Reverse transcription-quantitative PCR results verified the significant downregulation of CNN1 and upregulation of CD8A in human AAA tissues (P<0.05). In addition, four upregulated circRNA/mRNA axes, and five downregulated circRNA/mRNA axes were revealed to have possible biological functions in the pathogenesis of AAA using the Cytoscape software. Receiver operating characteristic analysis demonstrated the accuracy of these nine DEGs involved in these axes for AAA diagnosis with area under the curves >0.80. The present study revealed novel circRNA-miRNA-mRNA networks associated with AAA, especially for CNN1 and CD8A axes with the potential function of ‘focal adhesion’ and ‘immune response’, respectively. Overall, the present findings may provide evidence to explore the implicated ceRNAs in the molecular mechanisms and as novel biomarkers for AAA.

Identification of Key Candidate Genes Related to Inflammatory Osteolysis Associated with Vitamin E-Blended UHMWPE Debris of Orthopedic Implants by Integrated Bioinformatics Analysis and Experimental Confirmation.

PurposeThis study aims to identify differentially expressed genes (DEGs) in macrophages exposed to ultra-high-molecular-weight polyethylene (UHMWPE) or vitamin E-blended UHMWPE (VE-UHMWPE) particles, thereby providing potential targets for the treatment of inflammatory osteolysis.MethodsThe GSE104589 dataset of genome expression in macrophages exposed to UHMWPE and VE-UHMWPE was downloaded from the Gene Expression Omnibus database to identify DEGs. Functional enrichment analysis was performed using DAVID, and the corresponding protein–protein interaction (PPI) network was constructed from the STRING database. Important modules were selected using the molecular complex detection algorithm, and hub genes were identified in cytoHubba. MicroRNAs targeting these DEGs were obtained from the TarBase, miRTarBase, and miRecords databases, while transcription factors (TFs) targeting DEGs were predicted from the ENCODE database. Finally, the top five DEGs were validated by quantitative real‐time polymerase chain reaction (qRT-PCR).ResultsA total of 112 DEGs (44 upregulated and 68 downregulated DEGs) were screened. Immune and inflammatory responses were significantly related in gene ontology analysis, and 18 signaling pathways were enriched according to Kyoto Encyclopedia of Genes and Genomes pathway analysis. The PPI network involving 85 nodes and 266 protein pairs indicated that IL1β, CXCL1, ICAM1, CCL5 and CCL4 showed higher degrees. qRT-PCR analysis of the top five DEGs revealed a decreasing trend in the VE-UHMWPE group compared with the UHMWPE group. Key microRNAs (hsa-miR-144, hsa-miR-21, and hsa-miR-221) and TFs (RELA and NFKB1) were predicted to be correlated with the pathogenesis of inflammatory osteolysis through microRNA-TF regulatory network analysis.ConclusionThe present study helps shed light on the molecular mechanisms underlying the changes in the wear-induced inflammatory process after blending vitamin E with UHMWPE. Hub genes including IL1β, CXCL1, ICAM1, CCL5, and CCL4, key microRNAs (hsa-miR-144, hsa-miR-21, and hsa-miR-221) and TFs (RELA and NFKB1) may serve as prognostic and therapeutic targets of inflammatory osteolysis.

Bioinformatics analysis and identification of genes and molecular pathways involved in Parkinson's disease in patients with mutations in the glucocerebrosidase gene.

Glucocerebrosidase (GBA) mutations occur frequently in Parkinson's disease (PD) patients. This study aims to identify potential crucial genes and pathways associated with GBA mutations in patients with PD and to further analyze new molecular mechanisms related to the occurrence of gene mutations from the perspective of bioinformatics. Gene expression profiles of datasets GSE53424 and GSE99142 were acquired from the Gene Expression Ominibus database. Differentially expressed genes (DEGs) were detected, using the 'limma' package in R, comparing IDI-PD 1 (idiopathic PD patients) and GBA-PD 1 [PD patients with heterozygous GBA mutations (GBA N370S)] group samples. The functions of top modules were assessed using the DAVID, whereas gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed. Protein-protein interaction networks were assembled with Cytoscape software and separated into subnetworks using the Molecular Complex Detection Algorithm. Data from GSE53424 and GSE99142 were also extracted to verify our findings. There were 283 DEGs identified in PD patients heterozygous for GBA mutations. Module analysis revealed that GBA mutations in PD patients were associated with significant pathways, including Calcium signaling pathway, Rap1 signaling pathway and Cytokine-cytokine receptor interaction. Hub genes of the two modules were corticotropin-releasing hormone (CRH) and Melatonin receptor 1B (MTNR1B). The expression of CRH was downregulated, whereas that of MTNR1B was upregulated in PD patients with GBA mutations. The expression of CRH and MTNR1B has diagnostic value for PD patients with heterozygous GBA mutations. Novel DEGs and pathways identified herein might provide new insights into the underlying molecular mechanisms of heterozygous GBA mutations in PD patients.

Study of miRNA interactome in active rheumatoid arthritis patients reveals key pathogenic roles of dysbiosis in the infection-immune network.

ObjectivesTo characterize serum microRNA (miR) and the miR interactome of active RA patients in RA aetiology and pathogenesis.MethodsThe differentially expressed miRs (DEmiRs) in serum of naïve active RA patients (NARAPs, n = 9, into three pools) vs healthy controls (HCs, n = 15, into five pools) were identified with Agilent human miR microarray analysis. Candidate driver genes in epigenetic and pathogenic signalling pathway modules for RA were analysed using miRTarBase and a molecular complex detection algorithm. The interactome of these DEmiRs in RA pathogenesis were further characterized with gene ontology and Kyoto Encyclopaedia of Genes and Genomes.ResultsThree upregulated DEmiRs (hsa-miR-187-5p, -4532, -4516) and eight downregulated DEmiRs (hsa-miR-125a-3p, -575, -191-3p, -6865-3p, -197-3p, -6886-3p, -1237-3p, -4436b-5p) were identified in NARAPs. Interactomic analysis from heterogeneous experimentally validated sources yielded 1719 miR–target interactions containing 5.67% strong and 94.33% less strong experimental evidence. Gene ontology and Kyoto Encyclopaedia of Genes and Genomes analyses allocated the upregulated DEmiRs in the infection modules and the downregulated DEmiRs in the immune signalling pathways. Specifically, these DEmiRs revealed the significant contributions of the intestinal microbiome dysbiosis in the infection–inflammation–immune network for activation of T cells, immune pathways of IL-17, Toll-like receptor, TNF, Janus kinase-signal transducer and activator of transcription, osteoclast cell differentiation pathway and IgA production to the active RA pathogenesis.ConclusionsOur experiment-based interactomic study of DEmiRs in serum of NARAPs revealed novel clinically relevant miRs interactomes in the infection–inflammation–immune network of RA. These results provide valuable resources for understanding the integrated function of the miR network in RA pathogenesis and the application of circulating miRs as biomarkers for early aetiologic RA diagnosis.

Identification of differentially expressed and methylated genes associated with rheumatoid arthritis based on network

Rheumatoid arthritis (RA) is a multi-systemic inflammatory autoimmune disease involving peripheral joints, and the pathogenesis is not clear. Studies showed that DNA methylation and expression might also be involved in the pathogenesis of RA. This study integrated three expression profile datasets (GSE55235, GSE12021, and GSE55457) and one methylation profile dataset GSE111942 to elucidate the potential essential candidate genes and pathways in RA. Differentially expressed genes (DEGs) and differentially methylation genes (DMGs) were identified by R programming software, using Limma package and ChAMP package, respectively. DAVID performed gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Functional annotation and construction of a protein–protein interaction (PPI) network and the Molecular Complex Detection Algorithm (MCODE) were analysed by STRING and Cystoscope, respectively. Then the connection analysis of DEGs and DMGs was carried out, and further to analyse the relationship between methylation and gene expression, aiming to screen out the potential genes. In this study, 288 DEGs and 228 DMGs were identified, and the majority of DEGs were up-regulated. Enrichment analysis represented that DEGs mainly involved immune response and participated in the Cytokine–cytokine receptor interaction signal pathway. 282 nodes were identified from DEGs PPI network and MCODE, filtering the most significant 2 modules, 23 core node genes were identified and most of them are involved in the T cell receptor signalling pathway and chemokine-mediated signalling pathway. Cross-analysis revealed 4 genes [KNTC1 (cg 01277763), LRRC8D (cg 07600884), DHRS9 (cg 05961700), and UCP2 (cg 05205664)] that exhibited differential expression and methylation in RA simultaneously. Therefore, the four genes could be used as the target for RA.

Identification of potential hub genes via bioinformatics analysis combined with experimental verification in colorectal cancer.

Colorectal cancer (CRC) is a kind of malignant cancer with high morbidity and mortality. The purpose of this study was to explore potential regulated key genes involved in CRC through bioinformatics analysis and experimental verification. The gene expression profile data were downloaded from the Gene Expression Omnibus, and the differential expression genes were detected in cancerous and paracancerous samples of CRC patients, respectively. Then functional enrichment analysis, such as the Kyoto Encyclopedia of Genes and Genomes pathway analysis as well as the protein-protein interaction network were constructed, and the highly related genes were clustered by Molecular COmplex DEtection algorithm to find out the core interaction in different genes' crosstalk. The genes affecting CRC prognosis were screened by the Human Protein Atlas database. In addition, the expression level of core genes was detected by GEPIA database, and the core genes' changes in large-scale cancer genome data set were directly analyzed by cBioPortal database. The expression of the predicted hub genes DSN1, AHCY, and ERCC6L was verified by reverse-transcription quantitative polymerase chain reaction in CRC cells. The gene function of DSN1 was analyzed by wound healing and colony formation assays. The results showed that silencing of DSN1 could significantly reduce the migration and proliferation of CRC cells. Further, BUB1B, the potential interacting protein of DSN1, was also predicted via bioinformatics analysis. Above all, this study shows that bioinformatics analysis combined with experimental method verification provide more potential vital genes for the prevention and therapy of CRC.

Identification of key pathways and genes in colorectal cancer to predict the prognosis based on mRNA interaction network.

The aim of the present study was to identify key genes in colorectal cancer (CRC) that could be used to reliably diagnose this disease and to explore the potential underlying mechanisms in silico. The gene expression profiles of primary human cancer datasets GSE21510 and GSE32323 were downloaded from the Gene Expression Omnibus database. The limma R software package was used to identify differentially expressed (DE) genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on DE genes using the Database for Annotation, Visualization and Integrated Discovery. The Search Tool for the Retrieval of Interacting Genes/Proteins database was used to construct a protein-protein interaction (PPI) network of the DE genes. Survival rate was analyzed and visualized using The Cancer Genome Atlas (TCGA). A total of 1,126 genes were significantly DE in the present study. All DE genes were enriched in KEGG pathways including ‘cell cycle’, ‘mineral absorption’, ‘pancreatic secretion’, ‘pathways in cancer’, ‘metabolic pathways’, ‘aldosterone-regulated sodium reabsorption’ and ‘Wnt signaling pathway’. A total of 5 hub genes enriched in cell cycle and tumor-associated pathways, including E2F2, SKP2, MYC, CDKN1A and CDKN2B, were significantly DE and validated between tumor and normal tissues. CDKN1A and CDKN2B were identified within the PPI network using the Molecular Complex Detection algorithm. Survival and content distribution analyses of 362 clinical samples from TCGA revealed that CDKN1A effectively predicted the prognosis of patients. The present study identified key genes and potential signaling pathways involved in CRC. These findings may provide new insights for survival assessment during the clinical diagnosis of CRC.

Pathway-based network analyses and candidate genes associated with Kashin-Beck disease.

To perform a comprehensive analysis focusing on the biological functions and interactions of Kashin-Beck disease (KBD)-related genes to provide information towards understanding the pathogenesis of KBD.A retrospective, integrated bioinformatics analysis was designed and conducted. First, by reviewing the literature deposited in PubMed, we identified 922 genes genetically associated with KBD. Then, biological function and network analyses were conducted with Cytoscape software. Moreover, KBD specific molecular network analysis was conducted by Cytocluster using the Molecular Complex Detection Algorithm (MCODE).The biological function enrichment analysis suggested that collagen catabolic process, protein activation cascade, cellular response to growth factor stimulus, skeletal system development, and extrinsic apoptosis played important roles in KBD development. The apoptosis pathway, NF-kappa B signaling pathway, and the glutathione metabolism pathway were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway network, suggesting that these pathways may play key roles in KBD occurrence and development. MCODE clusters showed that in top 3 clusters, 54 of KBD-related genes were included in the network and 110 candidate genes were discovered might be potentially related to KBD.The 110 candidate genes discovered in the current study may be related to the development of KBD. The expression changes of apoptosis and oxidative stress-related genes might serve as biomarkers for early diagnosis and treatment of KBD.

Potential candidate biomarkers associated with osteoarthritis: Evidence from a comprehensive network and pathway analysis.

Osteoarthritis (OA) is one of the most common forms of arthritis world widely. Some key genes and diagnostic markers have been reported due to the development of modern molecular biology technologies. However, the etiology and pathogenesis of OA remains unknown. In this study, an integrated network and pathway analysis towards the biological function of OA-associated genes was conducted to provide valuable information to further explore the etiology and pathogenesis of OA. A total of 2,548 genes which reported a statistically significant association with OA were screened. An integrated network and pathway analysis was performed to identify the pathways and genes most associated to OA. Moreover, OA-specific protein-protein interaction (PPI) network was constructed by cytocluster based on the Molecular Complex Detection Algorithm (MCODE) to screen its candidate biomarkers. Quantitative real-time polymerase chain reaction was used to confirm the expression levels and to validate the results of MCODE cluster analysis by six genes. The pathway networks suggested that extracellular matrix (ECM) organization, collagen degradation and collagen formation showed important associations with OA. In top two PPI clusters, 61 of the OA-associated genes were included in the OA-specific PPI network, which also included 23 candidate genes that are likely to be highly associated with OA based on MCODE clusters. Analysis of mRNA showed that the expression levels of COL9A1, COL9A2, ITGA3, COL9A3, ITGA2, and LAMA1 in the peripheral blood mononuclear cells of OA patients were significantly lower than those of the normal controls (p<0.005). To our knowledge, this is the first comprehensive and systematic report based on OA-related genes demonstrating that the functional destruction of collagen in cartilage may be a very important contributing factor to OA. Quantitative detection of collagen synthesis may be of great help in early identification and prediction of OA. Maintaining the quality and quantity of collagen can be a potential target for clinical treatment of OA in the future practice.

Identification of novel genes involved in gingival epithelial cells responding to Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis infections

ObjectiveThis study aimed to identify the differentially expressed genes (DEGs) in gingiva epithelial cells responding to Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis infections using bioinformatics method. Study designGSE9723 dataset was downloaded from Gene Expression Omnibus, and DEGs between the infected cells and controls were identified using unpaired t-test. Overlapping DEGs in responding to Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis infections were extracted. Protein-protein interaction networks were constructed and functional modules were isolated using Molecular Complex Detection algorithm. Key genes in protein-protein interaction network and Molecular Complex Detection modules were subjected to functional enrichment analyses. In addition, the transcriptional factors were predicted. ResultsA total of 533 co-up-regulated and 202 co-down-regulated genes were identified. The up-regulated genes, including IL6, CCL19, EDN1, ADCY9, and BCL2 and the down-regulated genes, including CCNB1, PLK1, and CCNA2 were the key genes in the protein-protein interaction network and modules. They were intensively enriched in chemokine signaling pathway, calcium signaling pathway and cell cycle. Finally, two transcriptional factors, E12 and NRSF, targeting to the up-regulated genes and one transcriptional factor, NRP1, targeting the down-regulated genes, were predicted. ConclusionsCCNB1, PLK1, and CCNA2 might play important roles in the response of host epithelial cells to Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis.