Graph Signal Processing For Cancer Gene Co-Expression Network Analysis

Cancer heterogeneity arises from complex molecular interactions. Elucidating systems-level properties of gene interaction networks distinguishing cancer from normal cells is critical for understanding disease mechanisms and developing targeted therapies. Previous works focused only on identifying differences in network structures. In this study, we used graph frequency analysis of cancer genetic signals defined on a co-expression network to describe the spectral properties of underlying cancer systems. We demonstrated that cancer cells exhibit distinctive signatures in the graph frequency content of their gene expression signals. Applying graph frequency filtering, graph Fourier transforms, and its inverse to gene expression from different cancer stages resulted in significant improvements in average F-statistics of the genes compared to using their unfiltered expression levels. We propose graph spectral properties of cancer genetic signals defined on gene co-expression networks as cancer hallmarks with potential application for differential co-expression analysis

Weighted Gene Co-expression Network Analysis of Glioblastoma Gene Expression Microarray Data

Glioblastoma is a highly aggressive and lethal form of brain cancer characterized by its complex molecular landscape. Understanding the underlying gene expression patterns and their relationships is essential for unraveling the mechanisms driving this disease. In this study, we conducted a Weighted Gene Co-expression Network Analysis (WGCNA) on Glioblastoma gene expression microarray data to identify co-expressed gene modules and potential key regulatory genes associated with the disease. Utilizing a comprehensive dataset of Glioblastoma samples, we performed quality control and preprocessing to ensure the reliability of the data. WGCNA was employed to construct a weighted gene co-expression network, enabling the identification of modules of co-expressed genes. The correlation between these modules and clinical characteristics such as patient survival, tumor grade, and other relevant factors was assessed. Additionally, we conducted functional enrichment analysis to gain insights into the biological processes and pathways associated with the identified gene modules. Our findings revealed distinct gene modules associated with Glioblastoma progression and patient outcomes. Notably, we identified key hub genes within these modules, which may serve as potential biomarkers or therapeutic targets. Furthermore, functional enrichment analysis provided a comprehensive understanding of the biological processes and pathways influenced by these co-expressed gene modules. In conclusion, our Weighted Gene Co-expression Network analysis of Glioblastoma gene expression microarray data has shed light on the complex gene interactions and regulatory networks underlying this aggressive brain cancer. This knowledge may ultimately contribute to the development of novel diagnostic and therapeutic strategies, improving the prognosis for Glioblastoma patients

Gene co-expression network analysis associated with carcass traits in Nellore steers.

Carcass traits are influenced by a complex network of gene interactions in muscle, so elucidating the relationships between genes and how these genes influence these traits is crucial for understanding the muscle development in animals

Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia

<p>Abstract</p> <p>Background</p> <p>Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgV_H) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgV_H status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgV_H mutational status which can accurately predict the survival outcome are yet to be discovered.</p> <p>Results</p> <p>In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO) and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgV_H mutation status from the ZAP70 co-expression network.</p> <p>Conclusions</p> <p>We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgV_H mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.</p

Regulation of immune responses in primary biliary cholangitis: a transcriptomic analysis of peripheral immune cells

BACKGROUND AIMS: In patients with primary biliary cholangitis (PBC), the serum liver biochemistry measured during treatment with ursodeoxycholic acid-the UDCA response-accurately predicts long-term outcome. Molecular characterization of patients stratified by UDCA response can improve biological understanding of the high-risk disease, thereby helping to identify alternative approaches to disease-modifying therapy. In this study, we sought to characterize the immunobiology of the UDCA response using transcriptional profiling of peripheral blood mononuclear cell subsets. METHODS: We performed bulk RNA-sequencing of monocytes and TH1, TH17, TREG, and B cells isolated from the peripheral blood of 15 PBC patients with adequate UDCA response ("responders"), 16 PBC patients with inadequate UDCA response ("nonresponders"), and 15 matched controls. We used the Weighted Gene Co-expression Network Analysis to identify networks of co-expressed genes ("modules") associated with response status and the most highly connected genes ("hub genes") within them. Finally, we performed a Multi-Omics Factor Analysis of the Weighted Gene Co-expression Network Analysis modules to identify the principal axes of biological variation ("latent factors") across all peripheral blood mononuclear cell subsets. RESULTS: Using the Weighted Gene Co-expression Network Analysis, we identified modules associated with response and/or disease status (q<0.05) in each peripheral blood mononuclear cell subset. Hub genes and functional annotations suggested that monocytes are proinflammatory in nonresponders, but antiinflammatory in responders; TH1 and TH17 cells are activated in all PBC cases but better regulated in responders; and TREG cells are activated-but also kept in check-in responders. Using the Multi-Omics Factor Analysis, we found that antiinflammatory activity in monocytes, regulation of TH1 cells, and activation of TREG cells are interrelated and more prominent in responders. CONCLUSIONS: We provide evidence that adaptive immune responses are better regulated in patients with PBC with adequate UDCA response

Regulation of immune responses in primary biliary cholangitis: a transcriptomic analysis of peripheral immune cells

BACKGROUND AIMS: In patients with primary biliary cholangitis (PBC), the serum liver biochemistry measured during treatment with ursodeoxycholic acid-the UDCA response-accurately predicts long-term outcome. Molecular characterization of patients stratified by UDCA response can improve biological understanding of the high-risk disease, thereby helping to identify alternative approaches to disease-modifying therapy. In this study, we sought to characterize the immunobiology of the UDCA response using transcriptional profiling of peripheral blood mononuclear cell subsets. METHODS: We performed bulk RNA-sequencing of monocytes and TH1, TH17, TREG, and B cells isolated from the peripheral blood of 15 PBC patients with adequate UDCA response ("responders"), 16 PBC patients with inadequate UDCA response ("nonresponders"), and 15 matched controls. We used the Weighted Gene Co-expression Network Analysis to identify networks of co-expressed genes ("modules") associated with response status and the most highly connected genes ("hub genes") within them. Finally, we performed a Multi-Omics Factor Analysis of the Weighted Gene Co-expression Network Analysis modules to identify the principal axes of biological variation ("latent factors") across all peripheral blood mononuclear cell subsets. RESULTS: Using the Weighted Gene Co-expression Network Analysis, we identified modules associated with response and/or disease status (q<0.05) in each peripheral blood mononuclear cell subset. Hub genes and functional annotations suggested that monocytes are proinflammatory in nonresponders, but antiinflammatory in responders; TH1 and TH17 cells are activated in all PBC cases but better regulated in responders; and TREG cells are activated-but also kept in check-in responders. Using the Multi-Omics Factor Analysis, we found that antiinflammatory activity in monocytes, regulation of TH1 cells, and activation of TREG cells are interrelated and more prominent in responders. CONCLUSIONS: We provide evidence that adaptive immune responses are better regulated in patients with PBC with adequate UDCA response

Gene co-expression network analysis for identifying modules and functionally enriched pathways in SCA2

Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disease caused by CAG repeat expansion in the ATXN2 gene. The repeat resides in an encoded region of the gene resulting in polyglutamine (polyQ) expansion which has been assumed to result in gain of function, predominantly, for the ATXN2 protein. We evaluated temporal cerebellar expression profiles by RNA sequencing of ATXN2Q127 mice versus wild-type (WT) littermates. ATXN2Q127 mice are characterized by a progressive motor phenotype onset, and have progressive cerebellar molecular and neurophysiological (Purkinje cell firing frequency) phenotypes. Our analysis revealed previously uncharacterized early and progressive abnormal patterning of cerebellar gene expression. Weighted Gene Coexpression Network Analysis revealed four gene modules that were significantly correlated with disease status, composed primarily of genes associated with GTPase signaling, calcium signaling and cell death. Of these genes, few overlapped with differentially expressed cerebellar genes that we identified in Atxn2−/− knockout mice versus WT littermates, suggesting that loss-of-function is not a significant component of disease pathology. We conclude that SCA2 is a disease characterized by gain of function for ATXN2

Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data

<p>Abstract</p> <p>Background</p> <p>Using gene co-expression analysis, researchers were able to predict clusters of genes with consistent functions that are relevant to cancer development and prognosis. We applied a weighted gene co-expression network (WGCN) analysis algorithm on glioblastoma multiforme (GBM) data obtained from the TCGA project and predicted a set of gene co-expression networks which are related to GBM prognosis.</p> <p>Methods</p> <p>We modified the Quasi-Clique Merger algorithm (QCM algorithm) into edge-covering Quasi-Clique Merger algorithm (eQCM) for mining weighted sub-network in WGCN. Each sub-network is considered a set of features to separate patients into two groups using K-means algorithm. Survival times of the two groups are compared using log-rank test and Kaplan-Meier curves. Simulations using random sets of genes are carried out to determine the thresholds for log-rank test p-values for network selection. Sub-networks with p-values less than their corresponding thresholds were further merged into clusters based on overlap ratios (>50%). The functions for each cluster are analyzed using gene ontology enrichment analysis.</p> <p>Results</p> <p>Using the eQCM algorithm, we identified 8,124 sub-networks in the WGCN, out of which 170 sub-networks show p-values less than their corresponding thresholds. They were then merged into 16 clusters.</p> <p>Conclusions</p> <p>We identified 16 gene clusters associated with GBM prognosis using the eQCM algorithm. Our results not only confirmed previous findings including the importance of cell cycle and immune response in GBM, but also suggested important epigenetic events in GBM development and prognosis.</p

Integrated Weighted Gene Co-expression Network Analysis with an Application to Chronic Fatigue Syndrome

<p>Abstract</p> <p>Background</p> <p>Systems biologic approaches such as Weighted Gene Co-expression Network Analysis (WGCNA) can effectively integrate gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of genetic marker data allows one to characterize network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set.</p> <p>Results</p> <p>We combine WGCNA with genetic marker data to identify a disease-related pathway and its causal drivers, an analysis which we refer to as "Integrated WGCNA" or IWGCNA. Specifically, we present the following IWGCNA approach: 1) construct a co-expression network, 2) identify trait-related modules within the network, 3) use a trait-related genetic marker to prioritize genes within the module, 4) apply an integrated gene screening strategy to identify candidate genes and 5) carry out causality testing to verify and/or prioritize results. By applying this strategy to a CFS data set consisting of microarray, SNP and clinical trait data, we identify a module of 299 highly correlated genes that is associated with CFS severity. Our integrated gene screening strategy results in 20 candidate genes. We show that our approach yields biologically interesting genes that function in the same pathway and are causal drivers for their parent module. We use a separate data set to replicate findings and use Ingenuity Pathways Analysis software to functionally annotate the candidate gene pathways.</p> <p>Conclusion</p> <p>We show how WGCNA can be combined with genetic marker data to identify disease-related pathways and the causal drivers within them. The systems genetics approach described here can easily be used to generate testable genetic hypotheses in other complex disease studies.</p