Navigating the Gene Co-Expression Network and Drug Repurposing Opportunities for Brain Disorders Associated with Neurocognitive Impairment

Abstract

Neurocognitive impairment refers to a spectrum of disorders characterized by a decline in cognitive functions such as memory, attention, and problem-solving, which are often linked to structural or functional abnormalities in the brain. While its exact etiology remains elusive, genetic factors play a pivotal role in disease onset and progression. This study aimed to identify highly correlated gene clusters (modules) and key hub genes shared across neurocognition-impairing diseases, including Alzheimer’s disease (AD), Parkinson’s disease with dementia (PDD), HIV-associated neurocognitive disorders (HAND), and glioma. Herein, the microarray datasets AD (GSE5281), HAND (GSE35864), glioma (GSE15824), and PD (GSE7621) were used to perform Weighted Gene Co-expression Network Analysis (WGCNA) to identify highly preserved modules across the studied brain diseases. Through gene set enrichment analysis, the shared modules were found to point towards processes including neuronal transcriptional dysregulation, neuroinflammation, protein aggregation, and mitochondrial dysfunction, hallmarks of many neurocognitive disorders. These modules were used in constructing protein-protein interaction networks to identify hub genes shared across the diseases of interest. These hub genes were found to play pivotal roles in processes including protein homeostasis, cell cycle regulation, energy metabolism, and signaling, all associated with brain and CNS diseases, and were explored for their drug repurposing experiments. Drug repurposing based on gene signatures highlighted drugs including Dorzolamide and Oxybuprocaine, which were found to modulate the expression of the hub genes in play and may have therapeutic implications in neurocognitive disorders. While both drugs have traditionally been used for other medical purposes, our study underscores the potential of a combined WGCNA and drug repurposing strategy for searching for new avenues in the simultaneous treatment of different diseases that have similarities in gene co-expression networks.

Modular Hub Genes in DNA Microarray Suggest Potential Signaling Pathway Interconnectivity in Various Glioma Grades

“…The vast heterogeneity of gliomas per grade and cell origin is one of the significant factors contributing to its difficulty responding well to treatment [27]. Through WGCNA, this study focused on identifying points at which the distinct glioma grades align genetically, since several studies have demonstrated the effectiveness of this approach in determining highly correlated genes in related diseases from a systems biology point of view [28][29][30]. Furthermore, it is able to determine pathways involved in disease networks due to its ability to investigate the interplay among the highly preserved genes.…”

Theoretical Studies of DNA Microarray Present Potential Molecular and Cellular Interconnectivity of Signaling Pathways in Immune System Dysregulation

“…Using a deep-split parameter of 1 to obtain larger modules while maintaining efficiency in the sensitivity of the algorithm [49], we obtained 25 co-expression modules that are represented by various colors, as seen in Figure 4. The turquoise module contains 1476 genes, the blue module contains 1356 genes, the brown module contains 951 genes, the yellow module contains 761 genes, the green module contains 730 genes, the red module contains 619 genes, the black module contains 523 genes, the pink module contains 358 genes, the magenta module contains 327 genes, the purple module contains 285 genes, the green-yellow module contains 268 genes, the tan module contains 248 genes, the cyan module contains 245 genes, the salmon module contains 245 genes, the light-cyan module contains 229 genes, the midnight-blue module contains 229 genes, the grey60 module contains 209 genes, the light-green module contains 200 genes, the light-yellow module contains 183 genes, the royal-blue module contains 164 genes, the dark-red module contains 128 genes, the dark-green module contains 116 genes, the gold module contains 100 genes, the dark-turquoise module contains 78 genes, and the dark-grey module contains 44 genes.…”

Transcriptomic Analysis of Hub Genes Reveals Associated Inflammatory Pathways in Estrogen-Dependent Gynecological Diseases

“…In approximating the scale-free network, the pickSoftThreshold function of the WGCNA R [17] package (v4.3.1.) was first used to plot the scale-free topology fit versus power index (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) to estimate the appropriate soft-thresholding power (β), which is the lowest power where the scale-free topology criterion is met. This is when the distribution of the number of gene connections follows the power-law distribution, and it is usually evaluated by plotting the scale-free topology fit versus soft-thresholding power to measure the relativity of the co-expression network to the linear relationship between log of connectivity and log of connectivity probability.…”

“…Concerning this, estrogen plays a major role in regulating immune and inflammatory responses, further making it plausible to investigate the molecular underpinnings of inflammation and estrogen production in gynecological diseases. This can be made possible through weighted gene co-expression network analysis (WGCNA) [ 9 , 10 ], which is a systems biology approach that characterizes the gene associations in the gene expression data of several diseases to identify significant gene clusters (modules) and elucidate the key hub genes and main pathways that are dysregulated. The identification of upregulated and downregulated hub genes further allows for the scanning of potential already-existing drugs that can induce countering effects based on the provided gene signature while determining significant pathways opens therapeutic opportunities.…”