Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease

In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.

Integrated transcriptomics explored the cancer-promoting genes CDKN3 in esophageal squamous cell cancer

Background and objectives Each individual studies is limited to multi-factors and potentially lead to a significant difference of results among them. The present study aim to explore the critical genes related to the development of Esophageal squamous cell carcinoma (ESCC) by integrated transcriptomics and to investigate the clinical significance by experimental validation. Methods Datasets of protein-coding genes expression which involved in ESCC were downloaded from Gene Expression Omnibus (GEO) database. The “Robustrankaggreg” package in language was used for data integration, and the different expression genes (DEGs) were identified based the cut-off criteria as follows: adjust p-value < 0.05, |fold change (FC)| ≥ 1.5; The protein expression of seed gene in 184 cases of primary ESCC tissues and 50 tumor adjacent normal tissues (at least 5 cm away from the tumor, and defind as the controls) were detected by immunohistochemistry; The relationship between the expression level of seed genes and clinical parameter were analyze. Enumeration data were represented by frequency or percentage (%) and were tested by x2 test. The P value of less than 0.05 was considered statistically significant. Results A total of 244 DEGs were identified by comparing gene expression patterns between ESCC patients and the controls based on integrating dataset of GSE77861, GSE77861, GSE100942, GSE26886, GSE17351, GSE38129, GSE33426, GSE20347 and GSE23400; The Cyclin-dependent kinase inhibitor 3 (CDKN3) were identified the top 1 seed gene of top cluster by use of protein-protein Interaction network and plug-in Molecular Complex Detection; The level of CDKN3 mRNA was significantly increased in ESCC patients compared to controls; The positive expression rate of CDKN3 protein in ESCC tissue samples was 32 and 61.4% in control, respectively. The correlations between the expression level of CDKN3 and lymph node metastasis or clinical staging of ESCC patients are statistically significant. Conclusion Integrated transcriptomics is an efficient approach to system biology. By this procedure, our study improved the understanding of the transcriptome status of ESCC.

Integrated Transcriptomics Explored the Cancer-Promoting Genes CDKN3 in Esophageal Squamous Cell Cancer

Background and objectives: The aims of the present study were to explore the critical genes that related to development of ESCC by integrated transcriptomics and investigate the clinical significance by experimental validation. Methods: The datasets of protein-coding genes expression which involved in ESCC were downloaded from GEO database. The "Robustrankaggreg" package was used for data integration, and the different expression genes (DEGs) were identified based the cut-off criteria as follows: adjust p-value < 0.05, |Log2 fold change (FC)| ≥ 1.5; The protein expression of seed gene in 184 cases of primary ESCC were detected by immunohistochemistry; The relationship between the expression level of seed genes and clinical significance were analyze. Results: A total of 244 DEGs were identified by comparing gene expression patterns between ESCC patients and the controls based on integrating dataset of GSE77861, GSE77861, GSE100942, GSE26886, GSE17351, GSE38129, GSE33426, GSE20347 and GSE23400; The CDKN3 were identified the seed gene of top cluster by use of PPI network and plug-in MCODE; The level of CDKN3 mRNA was significantly increased in ESCC patients compared to controls; The positive expression rate of CDKN3 protein in ESCC tissue samples was 32.0% and 61.4% in control, respectively, differences were statistically significant; There was significant correlation between the expression level of CDKN3 and lymph node metastasis or clinical staging of ESCC patients. Conclusion: Integrated transcriptomics is an efficient approach to system biology. By this procedure, our study improved the understanding of the transcriptome status of ESCC.

Dissecting the genetic basis of comorbid epilepsy phenotypes in neurodevelopmental disorders

Background Neurodevelopmental disorders (NDDs) such as autism spectrum disorder, intellectual disability, developmental disability, and epilepsy are characterized by abnormal brain development that may affect cognition, learning, behavior, and motor skills. High co-occurrence (comorbidity) of NDDs indicates a shared, underlying biological mechanism. The genetic heterogeneity and overlap observed in NDDs make it difficult to identify the genetic causes of specific clinical symptoms, such as seizures. Methods We present a computational method, MAGI-S, to discover modules or groups of highly connected genes that together potentially perform a similar biological function. MAGI-S integrates protein-protein interaction and co-expression networks to form modules centered around the selection of a single “seed” gene, yielding modules consisting of genes that are highly co-expressed with the seed gene. We aim to dissect the epilepsy phenotype from a general NDD phenotype by providing MAGI-S with high confidence NDD seed genes with varying degrees of association with epilepsy, and we assess the enrichment of de novo mutation, NDD-associated genes, and relevant biological function of constructed modules. Results The newly identified modules account for the increased rate of de novo non-synonymous mutations in autism, intellectual disability, developmental disability, and epilepsy, and enrichment of copy number variations (CNVs) in developmental disability. We also observed that modules seeded with genes strongly associated with epilepsy tend to have a higher association with epilepsy phenotypes than modules seeded at other neurodevelopmental disorder genes. Modules seeded with genes strongly associated with epilepsy (e.g., SCN1A, GABRA1, and KCNB1) are significantly associated with synaptic transmission, long-term potentiation, and calcium signaling pathways. On the other hand, modules found with seed genes that are not associated or weakly associated with epilepsy are mostly involved with RNA regulation and chromatin remodeling. Conclusions In summary, our method identifies modules enriched with de novo non-synonymous mutations and can capture specific networks that underlie the epilepsy phenotype and display distinct enrichment in relevant biological processes. MAGI-S is available at https://github.com/jchow32/magi-s.

A FLEXIBLE NONPARAMETRIC APPROACH TO FIND CANDIDATE GENES ASSOCIATED WITH DISEASE IN MICROARRAY EXPERIMENTS

Very often biologists are interested to know the biological function of a particular gene. Its true biological function may depend on other genes. Finding other genes in the same biological pathway of that gene may enhance further understanding of its biological function. Therefore, we are interested in finding other candidate genes whose expression values are highly correlated with that of a "seed" gene. The "seed" gene, which is known and associated with a disease, is used as a reference to extract candidate genes from microarray experiments and enriched pathways. We propose a nonparametric procedure for selecting the candidate genes. The expression levels for these candidate genes are correlated with that of a "seed" gene in microarray experiments. The proposed test statistic compares two Area Under Receiver Operating Characteristic Curves (AUC) for gene pairs, taking implicit correlation between two AUCs into account. The performance of our method is compared to the other well-known methods through the use of simulation and real data analysis.