Prediction and validation of GUCA2B as the hub-gene in colorectal cancer based on co-expression network analysis: In-silico and in-vivo study.

BACKGROUND: Several serious attempts to treat colorectal cancer have been made in recent decades. However, no effective treatment has yet been discovered due to the complexities of its etiology. METHODS: we used Weighted Gene Co-expression Network Analysis (WGCNA) to identify key modules, hub-genes, and mRNA-miRNA regulatory networks associated with CRC. Next, enrichment analysis of modules has been performed using Cluepedia. Next, quantitative real-time PCR (RT-qPCR) was used to validate the expression of selected hub-genes in CRC tissues. RESULTS: Based on the WGCNA results, the brown module had a significant positive correlation (r = 0.98, p-value=9e-07) with CRC. Using the survival and DEGs analyses, 22 genes were identified as hub-genes. Next, three candidate hub-genes were selected for RT-qPCR validation, and 22 pairs of cancerous and non-cancerous tissues were collected from CRC patients referred to the Gastroenterology and Liver Clinic. The RT-qPCR results revealed that the expression of GUCA2B was significantly reduced in CRC tissues, which is consistent with the results of differential expression analysis. Finally, top miRNAs correlated with GUCA2B were identified, and ROC analyses revealed that GUCA2B has a high diagnostic performance for CRC. CONCLUSIONS: The current study discovered key modules and GUCA2B as a hub-gene associated with CRC, providing references to understand the pathogenesis and be considered a novel candidate to CRC target therapy.

The expression pattern of VISTA in the PBMCs of relapsing-remitting multiple sclerosis patients: A single-cell RNA sequencing-based study.

Multiple sclerosis (MS) is an inflammatory disease of the central nervous system (CNS). Dysregulated immune responses have been implicated in MS development. Growing evidence has indicated that inhibitory immune checkpoint molecules can substantially regulate immune responses and maintain immune tolerance. V-domain Ig suppressor of T cell activation (VISTA) is a novel inhibitory immune checkpoint molecule that can suppress immune responses; however, its expression pattern in the peripheral blood mononuclear cells (PBMCs) of relapsing-remitting multiple sclerosis (RRMS) has not thoroughly been studied. Herein, we evaluated Vsir expression in PBMCs of RRMS patients and characterized the expression pattern of the Vsir in the PBMCs of MS patients. Besides, we investigated the effect of fingolimod, IFNß-1α, glatiramer acetate (GA), and dimethyl fumarate (DMF) on Vsir expression in PBMCs of RRMS patients. Our results have shown that Vsir expression is significantly downregulated in the PBMCs of patients with RRMS. Besides, the single-cell RNA sequencing results have demonstrated that Vsir expression is downregulated in classical monocyte, intermediate monocytes, non-classical monocytes, myeloid DCs (mDC), Plasmacytoid dendritic cells (pDCs), and naive B-cells of PBMCs of MS patients compared to the control. In addition, DMF, IFNß-1α, and GA have significantly upregulated Vsir expression in the PBMCs of RRMS patients. Collectively, the current study has shed light on Vsir expression in the PBMCs of MS patients; however, further studies are needed to elucidate the significance of VISTA in the mentioned immune cells.

Tumor necrosis factor­α in systemic lupus erythematosus: Structure, function and therapeutic implications (Review).

Tumor necrosis factor­α (TNF­α) is a pleiotropic pro­inflammatory cytokine that contributes to the pathophysiology of several autoimmune diseases, such as multiple sclerosis, inflammatory bowel disease, rheumatoid arthritis, psoriatic arthritis and systemic lupus erythematosus (SLE). The specific role of TNF­α in autoimmunity is not yet fully understood however, partially, in a complex disease such as SLE. Through the engagement of the TNF receptor 1 (TNFR1) and TNF receptor 2 (TNFR2), both the two variants, soluble and transmembrane TNF­α, can exert multiple biological effects according to different settings. They can either function as immune regulators, impacting B­, T­ and dendritic cell activity, modulating the autoimmune response, or as pro­inflammatory mediators, regulating the induction and maintenance of inflammatory processes in SLE. The present study reviews the dual role of TNF­α, focusing on the different effects that TNF­α may have on the pathogenesis of SLE. In addition, the efficacy and safety of anti­TNF­α therapies in preclinical and clinical trials SLE are discussed.

Exploiting systems biology to investigate the gene modules and drugs in ovarian cancer: A hypothesis based on the weighted gene co-expression network analysis.

BACKGROUND: Ovarian cancer (OC) is one of the worrisome gynecological cancers worldwide. Given its considerable mortality rate, it is necessary to investigate its oncogenesis. METHODS: In this study, we used systems biology approaches to describe the key gene modules, hub genes, and regulatory drugs associated with serous OC as the novel biomarkers using weighted gene co-expression network analysis (WGCNA). FINDINGS: Our findings have demonstrated that the blue module genes (r = 0.8, p-value = 1e-16) are involved in OC progression. Based on gene enrichment analysis, the genes in this module are frequently involved in biological processes such as the Cyclic adenosine monophosphate (cAMP) signaling pathway and the cellular response to transforming growth factor-beta stimulation. The co-expression network has been built using the correlated module's top hub genes, which are ADORA1, ANO9, CD24P4, CLDN3, CLDN7, ELF3, KLHL14, PRSS8, RASAL1, RIPK4, SERINC2, and WNT7A. Finally, a drug-target network has been built to show the interaction of the FDA-approved drugs with hub genes. CONCLUSIONS: Our results have discovered that ADORA1, ANO9, SERINC2, and KLHL14 are hub genes associated with serous OC. These genes can be considered as novel candidate target genes for treating OC.

Weighted Gene Co-Expression Network Analysis Combined with Machine Learning Validation to Identify Key Modules and Hub Genes Associated with SARS-CoV-2 Infection.

The coronavirus disease-2019 (COVID-19) pandemic has caused an enormous loss of lives. Various clinical trials of vaccines and drugs are being conducted worldwide; nevertheless, as of today, no effective drug exists for COVID-19. The identification of key genes and pathways in this disease may lead to finding potential drug targets and biomarkers. Here, we applied weighted gene co-expression network analysis and LIME as an explainable artificial intelligence algorithm to comprehensively characterize transcriptional changes in bronchial epithelium cells (primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells) during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our study detected a network that significantly correlated to the pathogenicity of COVID-19 infection based on identified hub genes in each cell line separately. The novel hub gene signature that was detected in our study, including PGLYRP4 and HEPHL1, may shed light on the pathogenesis of COVID-19, holding promise for future prognostic and therapeutic approaches. The enrichment analysis of hub genes showed that the most relevant biological process and KEGG pathways were the type I interferon signaling pathway, IL-17 signaling pathway, cytokine-mediated signaling pathway, and defense response to virus categories, all of which play significant roles in restricting viral infection. Moreover, according to the drug-target network, we identified 17 novel FDA-approved candidate drugs, which could potentially be used to treat COVID-19 patients through the regulation of four hub genes of the co-expression network. In conclusion, the aforementioned hub genes might play potential roles in translational medicine and might become promising therapeutic targets. Further in vitro and in vivo experimental studies are needed to evaluate the role of these hub genes in COVID-19.

From Oncogenic Signaling Pathways to Single-Cell Sequencing of Immune Cells: Changing the Landscape of Cancer Immunotherapy.

Over the past decade, there have been remarkable advances in understanding the signaling pathways involved in cancer development. It is well-established that cancer is caused by the dysregulation of cellular pathways involved in proliferation, cell cycle, apoptosis, cell metabolism, migration, cell polarity, and differentiation. Besides, growing evidence indicates that extracellular matrix signaling, cell surface proteoglycans, and angiogenesis can contribute to cancer development. Given the genetic instability and vast intra-tumoral heterogeneity revealed by the single-cell sequencing of tumoral cells, the current approaches cannot eliminate the mutating cancer cells. Besides, the polyclonal expansion of tumor-infiltrated lymphocytes in response to tumoral neoantigens cannot elicit anti-tumoral immune responses due to the immunosuppressive tumor microenvironment. Nevertheless, the data from the single-cell sequencing of immune cells can provide valuable insights regarding the expression of inhibitory immune checkpoints/related signaling factors in immune cells, which can be used to select immune checkpoint inhibitors and adjust their dosage. Indeed, the integration of the data obtained from the single-cell sequencing of immune cells with immune checkpoint inhibitors can increase the response rate of immune checkpoint inhibitors, decrease the immune-related adverse events, and facilitate tumoral cell elimination. This study aims to review key pathways involved in tumor development and shed light on single-cell sequencing. It also intends to address the shortcomings of immune checkpoint inhibitors, i.e., their varied response rates among cancer patients and increased risk of autoimmunity development, via applying the data from the single-cell sequencing of immune cells.