Comprehensive Gene expression meta-analysis and integrated bioinformatic approaches reveal shared signatures between thrombosis and myeloproliferative disorders

In silico exploration of anti-prostate cancer compounds from differential expressed genes

Prostate cancer (PCa) is a complex and biologically diverse disease with no curative treatment options at present. This study aims to utilize computational methods to explore potential anti-PCa compounds based on differentially expressed genes (DEGs), with the goal of identifying novel therapeutic indications or repurposing existing drugs. The methods employed in this study include DEGs-to-drug prediction, pharmacokinetics prediction, target prediction, network analysis, and molecular docking. The findings revealed a total of 79 upregulated DEGs and 110 downregulated DEGs in PCa, which were used to identify drug compounds capable of reversing the dysregulated conditions (dexverapamil, emetine, parthenolide, dobutamine, terfenadine, pimozide, mefloquine, ellipticine, and trifluoperazine) at a threshold probability of 20% on several molecular targets, such as serotonin receptors 2a/2b/2c, HERG protein, adrenergic receptors alpha-1a/2a, dopamine D3 receptor, inducible nitric oxide synthase (iNOS), epidermal growth factor receptor erbB1 (EGFR), tyrosine-protein kinases, and C-C chemokine receptor type 5 (CCR5). Molecular docking analysis revealed that terfenadine binding to inducible nitric oxide synthase (-7.833 kcal.mol-1) and pimozide binding to HERG (-7.636 kcal.mol-1). Overall, binding energy ΔGbind (Total) at 0 ns was lower than that of 100 ns for both the Terfenadine-iNOS complex (-101.707 to -103.302 kcal.mol-1) and Ellipticine-TOPIIα complex (-42.229 to -58.780 kcal.mol-1). In conclusion, this study provides insight on molecular targets that could possibly contribute to the molecular mechanisms underlying PCa. Further preclinical and clinical studies are required to validate the therapeutic effectiveness of these identified drugs in PCa disease.

Identification of key genes and biological regulatory mechanisms in diabetic nephropathy: Meta-analysis of gene expression datasets

BACKGROUND

Diabetic nephropathy (DN) which refers to the cases with biopsy proven kidney lesions, is one of the main complications of diabetes all around the world; however, the underlying biological changes causing DN remain to be understood. Studying the alterations in gene expression profiles could give a holistic view of the molecular pathogenicity of DN and aid to discover key molecules as potential therapeutic targets. Here, we performed a meta-analysis study that included microarray gene expression profiles coming from glomerular samples of DN patients in order to acquire a list of consensus Differentially Expressed Genes (meta-DEGs) correlated with DN.

METHODS

After quality control and normalization steps, five gene expression datasets (GES1009, GSE30528, GSE47183, GSE104948, and GSE93804) were entered into the meta-analysis. The meta-analysis was performed by random effect size method and the meta-DEGs were put through network analysis and different pathway enrichment analyses steps. MiRTarBase and TRRUST databases were utilized to predict the meta-DEGs related miRNAs and transcription factors. A co-regulatory network including DEGs, transcription factors and miRNAs was constructed by Cytoscape, and top molecules were identified based on centrality scores in the network.

RESULTS

The identified meta-DEGs were 1364 DEGs including 665 downregulated and 669 upregulated DEGs. The results of pathway enrichment analysis showed, "immune system", "extracellular matrix organization", "hemostasis", "signal transduction", and "platelet activation" to be the top enriched terms with involvement of the meta-DEGs. After construction of the multilayer regulatory network, several top DEGs (TP53, MYC, BTG2, VEGFA, PTEN, etc.), as well as top miRNAs (miR-335, miR-16, miR-17, miR-20a, and miR-93), and transcription factors (SP1, STAT3, NF-KB1, RELA, E2F1), were introduced as potential therapeutic targets in DN. Among the regulatory molecules, miR-335-5p and SP1 were the most interactive miRNA and transcription factor molecules with the highest degree scores in the constructed network.

CONCLUSION

By performing a meta-analysis of available DN-related transcriptomics datasets, we reached a consensus list of DEGs for this complicated disorder. Further enrichment and network analyses steps revealed the involved pathways in the DN pathogenesis and marked the most potential therapeutic targets in this disease.

Molecular crosstalk between COVID-19 and Alzheimer's disease using microarray and RNA-seq datasets: A system biology approach

Objective

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The clinical and epidemiological analysis reported the association between SARS-CoV-2 and neurological diseases. Among neurological diseases, Alzheimer's disease (AD) has developed as a crucial comorbidity of SARS-CoV-2. This study aimed to understand the common transcriptional signatures between SARS-CoV-2 and AD.

Materials and methods

System biology approaches were used to compare the datasets of AD and COVID-19 to identify the genetic association. For this, we have integrated three human whole transcriptomic datasets for COVID-19 and five microarray datasets for AD. We have identified differentially expressed genes for all the datasets and constructed a protein-protein interaction (PPI) network. Hub genes were identified from the PPI network, and hub genes-associated regulatory molecules (transcription factors and miRNAs) were identified for further validation.

Results

A total of 9,500 differentially expressed genes (DEGs) were identified for AD and 7,000 DEGs for COVID-19. Gene ontology analysis resulted in 37 molecular functions, 79 cellular components, and 129 biological processes were found to be commonly enriched in AD and COVID-19. We identified 26 hub genes which includes AKT1, ALB, BDNF, CD4, CDH1, DLG4, EGF, EGFR, FN1, GAPDH, INS, ITGB1, ACTB, SRC, TP53, CDC42, RUNX2, HSPA8, PSMD2, GFAP, VAMP2, MAPK8, CAV1, GNB1, RBX1, and ITGA2B. Specific miRNA targets associated with Alzheimer's disease and COVID-19 were identified through miRNA target prediction. In addition, we found hub genes-transcription factor and hub genes-drugs interaction. We also performed pathway analysis for the hub genes and found that several cell signaling pathways are enriched, such as PI3K-AKT, Neurotrophin, Rap1, Ras, and JAK-STAT.

Conclusion

Our results suggest that the identified hub genes could be diagnostic biomarkers and potential therapeutic drug targets for COVID-19 patients with AD comorbidity.

Identification of therapeutic miRNAs from the Arsenic induced gene expression profile of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, with a rising worldwide burden due to a lack of efficient treatment techniques and diagnosis after it has metastasized. Therefore, small non-coding RNA (miRNAs) as protein translation inhibitors are gaining attention that degrades or suppress specific gene transcripts, making it a prime strategy for oncogenes or tumor suppression. Systematic research with miRNAs in combination with Arsenic, which has been employed as a drug to treat several diseases, including cancer, was focused on cellular responses through interacting with multiple biological targets. The differential gene expression of the DNA microarray dataset (GSE48441) revealed the association of sterol, cholesterol, and lipid metabolic processes. With the aid of the network pharmacology approach, hsa-mir-335-5p was uncovered to negatively regulate the important nodes driving the transport and utilization of essential compounds for the rapid growth and proliferation of cancer cells. The binding energies of the duplexes were validated by the minimal free energies of the mRNAs for hsa-mir-335-5p, indicating energetically desirable binding association. The molecular interactions between hsa-mir-335-5p, which interacts with the Argonaute protein in the RNA induced silencing complex, and the target-specific genes were also investigated, revealing its susceptibility to be employed in in vitro studies.

Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

Background: Ovarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray-based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets.

Methods: In the present study, the data of gene expression in ovarian cancer were downloaded from Gene Expression Omnibus (GEO) and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation.

Results: A total of 972 DEGs with P-value < 0.001 were identified in ovarian cancer, including 541 up-regulated genes and 431 down-regulated genes, among which 92 additional DEGs were found as gained DEGs. Top five up- and down-regulated genes were selected for the validation of gene expression profiling. Among these genes, up-regulated CD24 molecule (CD24), SRY (sex determining region Y)-box transcription factor 17 (SOX17), WFDC2, epithelial cell adhesion molecule (EPCAM), innate immunity activator (INAVA), and down-regulated aldehyde oxidase 1 (AOX1) were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFDC2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer.

Conclusion: Our study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.

Dysregulated NF-κB signal promotes the hub gene PCLAF expression to facilitate nasopharyngeal carcinoma proliferation and metastasis

BACKGROUND

Nasopharyngeal carcinoma (NPC) is common in Southern China. The molecular mechanism underlying NPC genesis and progression has been comprehensively investigated, but the key gene (s) or pathway (s) pertaining to NPC are unidentified.

METHODS

We explored some key genes and pathways involved in NPC through using meta-analysis of deposited expression of microarray data of NPC. The expression of proliferating cell nuclear antigen clamp associated factor (PCLAF) was determined by real-time PCR and western blots. CCK-8 assay, colony formation assay, transwell migration assay, cell wound healing assay, cell cycle analysis and cell apoptosis were carried out to assess biological behaviors caused by downregulation and overexpression of PCLAF in vitro. CHIP was utilized to determine the direct upstream regulatory transcription factors of PCLAF.

RESULTS

PCLAF was the key gene of NPC, which was significantly up-regulated in NPC cell line compared to the normal nasopharyngeal cell line. Additionally, in vitro assay has demonstrated the down-regulation and overexpression of PCLAF, resulted in significantly suppressed and enhanced NPC proliferation, metastasis and invasion respectively. Furthermore, the up-regulation of PCLAF in NPC is induced by direct binding of dysregulated NF-κB p50/RelB complex to the promoter of PCLAF.

CONCLUSION

Our results offer a strategy for re-using the deposited data to find the key genes and pathways involved in pathogenesis of cancer. Our study has provided evidence of supporting the role of PCLAF in NPC genesis and progression.

Construction of the gene regulatory network identifies MYC as a transcriptional regulator of SWI/SNF complex

Precise positioning of nucleosomes at the gene regulatory elements mediated by the SWI/SNF family of remodelling complex is important for the transcriptional regulation of genes. A wide set of genes are either positively or negatively regulated by SWI/SNF. In higher eukaryotes, around thirty genes were found to code for SWI/SNF subunits. The construction of a gene regulatory network of SWI/SNF subunits identifies MYC as a common regulator for many of the SWI/SNF subunit genes. A meta-analysis study was conducted to investigate the MYC dependent regulation of SWI/SNF remodelling complex. Subunit information and the promoter sequences of the subunit genes were used to find the canonical E-box motif and its variants. Detailed analysis of mouse and human ChIP-Seq at the SWI/SNF subunit loci indicates the presence of MYC binding peaks overlapping with E-boxes. The co-expression correlation and the differential expression analysis of wt vs. MYC perturbed MEFs indicate the MYC dependent regulation of some of the SWI/SNF subunits. The extension of the analysis was done on MYC proficient and MYC deficient embryonic fibroblast cell lines, TGR1 and HO15, and in one of the MYC amplified cancer types, Medulloblastoma. A transcriptional regulatory feedback loop between MYC and SWI/SNF could be a major factor contributing to the aggressiveness of MYC dependent cancers.

Available Software for Meta-analyses of Genome-wide Expression Studies

Advances in transcriptomic methods have led to a large number of published Genome-Wide Expression Studies (GWES), in humans and model organisms. For several years, GWES involved the use of microarray platforms to compare genome-expression data for two or more groups of samples of interest. Meta-analysis of GWES is a powerful approach for the identification of differentially expressed genes in biological topics or diseases of interest, combining information from multiple primary studies. In this article, the main features of available software for carrying out meta-analysis of GWES have been reviewed and seven packages from the Bioconductor platform and five packages from the CRAN platform have been described. In addition, nine previously described programs and four online programs are reviewed. Finally, advantages and disadvantages of these available programs and proposed key points for future developments have been discussed.

Meta-Analysis of HTLV-1-Infected Patients Identifies CD40LG and GBP2 as Markers of ATLL and HAM/TSP Clinical Status: Two Genes Beat as One

Human T-lymphotropic virus 1 (HTLV-1) was the first recognized human retrovirus. Infection can lead to two main symptomatologies: adult T-cell lymphoma/leukemia (ATLL) and HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP). Each manifestation is associated with distinct characteristics, as ATLL presents as a leukemia-like disease, while HAM/TSP presents as severe inflammation in the central nervous system, leading to paraparesis. Previous studies have identified molecules associated with disease development, e.g., the downregulation of Foxp3 in Treg cells was associated with increased risk of HAM/TSP. In addition, elevated levels of CXCL10, CXCL9, and Neopterin in cerebrospinal fluid also present increased risk. However, these molecules were only associated with specific patient groups or viral strains. Furthermore, the majority of studies did not jointly compare all clinical manifestations, and robust analysis entails the inclusion of both ATLL and HAM/TSP. The low numbers of samples also pose difficulties in conducting gene expression analysis to identify specific molecular relationships. To address these limitations and increase the power of manifestation-specific gene associations, meta-analysis was performed using publicly available gene expression data. The application of supervised learning techniques identified alterations in two genes observed to act in tandem as potential biomarkers: GBP2 was associated with HAM/TSP, and CD40LG with ATLL. Together, both molecules demonstrated high sample-classification accuracy (AUC values: 0.88 and 1.0, respectively). Next, other genes with expression correlated to these genes were identified, and we attempted to relate the enriched pathways identified with the characteristic of each clinical manifestation. The present findings contribute to knowledge surrounding viral progression and suggest a potentially powerful new tool for the molecular classification of HTLV-associated diseases.

Identification of differentially expressed genes in pancreatic ductal adenocarcinoma and normal pancreatic tissues based on microarray datasets

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignant tumor with rapid progression and poor prognosis. In the present study, 11 high‑quality microarray datasets, comprising 334 tumor samples and 151 non‑tumor samples from the Gene Expression Omnibus, were screened, and integrative meta‑analysis of expression data was used to identify gene signatures that differentiate between PDAC and normal pancreatic tissues. Following the identification of differentially expressed genes (DEGs), two‑way hierarchical clustering analysis was performed for all DEGs using the gplots package in R software. Hub genes were then determined through protein‑protein interaction network analysis using NetworkAnalyst. In addition, functional annotation and pathway enrichment analyses of all DEGs were conducted in the Database for Annotation, Visualization, and Integrated Discovery. The expression levels and Kaplan‑Meier analysis of the top 10 upregulated and downregulated genes were verified in The Cancer Genome Atlas. A total of 1,587 DEGs, including 1,004 upregulated and 583 downregulated genes, were obtained by comparing PDAC with normal tissues. Of these, hematological and neurological expressed 1, integrin subunit α2 (ITGA2) and S100 calcium‑binding protein A6 (S100A6) were the top upregulated genes, and kinesin family member 1A, Dymeclin and β‑secretase 1 were the top downregulated genes. Reverse transcription‑quantitative PCR was performed to examine the expression levels of S100A6, KRT19 and GNG7, and the results suggested that S100A6 was significantly upregulated in PDAC compared with normal pancreatic tissues. ITGA2 overexpression was significantly associated with shorter overall survival times, whereas family with sequence similarity 46 member C overexpression was strongly associated with longer overall survival times. In addition, network‑based meta‑analysis confirmed growth factor receptor‑bound protein 2 and histone deacetylase 5 as pivotal hub genes in PDAC compared with normal tissue. In conclusion, the results of the present meta‑analysis identified PDAC‑related gene signatures, providing new perspectives and potential targets for PDAC diagnosis and treatment.

A common molecular signature of patients with sickle cell disease revealed by microarray meta-analysis and a genome-wide association study

A chronic inflammatory state to a large extent explains sickle cell disease (SCD) pathophysiology. Nonetheless, the principal dysregulated factors affecting this major pathway and their mechanisms of action still have to be fully identified and elucidated. Integrating gene expression and genome-wide association study (GWAS) data analysis represents a novel approach to refining the identification of key mediators and functions in complex diseases. Here, we performed gene expression meta-analysis of five independent publicly available microarray datasets related to homozygous SS patients with SCD to identify a consensus SCD transcriptomic profile. The meta-analysis conducted using the MetaDE R package based on combining p values (maxP approach) identified 335 differentially expressed genes (DEGs; 224 upregulated and 111 downregulated). Functional gene set enrichment revealed the importance of several metabolic pathways, of innate immune responses, erythrocyte development, and hemostasis pathways. Advanced analyses of GWAS data generated within the framework of this study by means of the atSNP R package and SIFT tool identified 60 regulatory single-nucleotide polymorphisms (rSNPs) occurring in the promoter of 20 DEGs and a deleterious SNP, affecting CAMKK2 protein function. This novel database of candidate genes, transcription factors, and rSNPs associated with SCD provides new markers that may help to identify new therapeutic targets.

Identification of Shared Molecular Signatures Indicate the Susceptibility of Endometriosis to Multiple Sclerosis

Women with endometriosis (EMS) appear to be at a higher risk of developing other autoimmune diseases predominantly multiple sclerosis (MS). Though EMS and MS are evidently diverse in their phenotype, they are linked by a common autoimmune condition or immunodeficiency which could play a role in the expansion of endometriosis and possibly increase the risk of developing MS in women with EMS. However, the common molecular links connecting EMS with MS are still unclear. We conducted a meta-analysis of microarray experiments focused on EMS and MS with their respective controls. The GEO2R web application discovered a total of 711 and 1516 genes that are differentially expressed across the experimental conditions in EMS and MS, respectively with 129 shared DEGs between them. The functional enrichment analysis of DEGs predicts the shared gene expression signatures as well as the overlapping biological processes likely to infer the co-occurrence of EMS with MS. Network based meta-analysis unveiled six interaction networks/crosstalks through overlapping edges between commonly dysregulated pathways of EMS and MS. The PTPN1, ERBB3, and CDH1 were observed to be the highly ranked hub genes connected with disease-related genes of both EMS and MS. Androgen receptor (AR) and nuclear factor-kB p65 (RelA) were observed to be the most enriched transcription factor in the upstream of shared down-regulated and up-regulated genes, respectively. The two disease sample sets compared through crosstalk interactions between shared pathways revealed commonly up- and down-regulated expressions of 10 immunomodulatory proteins as probable linkers between EMS and MS. This study pinpoints the number of shared genes, pathways, protein kinases, and upstream regulators that may help in the development of biomarkers for diagnosis of MS and endometriosis at the same time through improved understanding of shared molecular signatures and crosstalk.

Mechanotransduction signaling in podocytes from fluid flow shear stress

Recently, we and others have found that hyperfiltration-associated increase in biomechanical forces, namely, tensile stress and fluid flow shear stress (FFSS), can directly and distinctly alter podocyte structure and function. The ultrafiltrate flow over the major processes and cell body generates FFSS to podocytes. Our previous work suggests that the cyclooxygenase-2 (COX-2)-PGE2-PGE2 receptor 2 (EP2) axis plays an important role in mechanoperception of FFSS in podocytes. To address mechanotransduction of the perceived stimulus through EP2, cultured podocytes were exposed to FFSS (2 dyn/cm2) for 2 h. Total RNA from cells at the end of FFSS treatment, 2-h post-FFSS, and 24-h post-FFSS was used for whole exon array analysis. Differentially regulated genes ( P < 0.01) were analyzed using bioinformatics tools Enrichr and Ingenuity Pathway Analysis to predict pathways/molecules. Candidate pathways were validated using Western blot analysis and then further confirmed to be resulting from a direct effect of PGE2 on podocytes. Results show that FFSS-induced mechanotransduction as well as exogenous PGE2 activate the Akt-GSK3β-β-catenin (Ser552) and MAPK/ERK but not the cAMP-PKA signal transduction cascades. These pathways are reportedly associated with FFSS-induced and EP2-mediated signaling in other epithelial cells as well. The current regimen for treating hyperfiltration-mediated injury largely depends on targeting the renin-angiotensin-aldosterone system. The present study identifies specific transduction mechanisms and provides novel information on the direct effect of FFSS on podocytes. These results suggest that targeting EP2-mediated signaling pathways holds therapeutic significance for delaying progression of chronic kidney disease secondary to hyperfiltration.

Pathogenesis of thrombosis: cellular and pharmacogenetic contributions

Our understanding of thrombosis formation has evolved significantly ever since physician Rudolf Virchow proposed his "triad" theory in 1856. Modern science has elucidated the mechanisms of stasis, hypercoagulability, and endothelial dysfunction. Today, we have a firm understanding of the key molecular factors involved in the coagulation cascade and fibrinolytic system, as well as the underlying genetic influences. This knowledge of cellular and genetic contributors has been translated into diverse pharmaceutical interventions. Here, we examine the molecular and cellular mechanisms of thrombosis and its associated pathologies. We also review the current state of pharmacologic interventions, including pro- and anti-thrombotics, direct oral anticoagulants, and anti-platelet therapies. The pharmacogenetic factors that guide clinical decision making and prognosis are described in detail. Finally, we explore new approaches to thrombosis drug discovery, repurposing, and diagnostics. We argue that network biology tools will enable a systems pharmacology revolution in the next generation of interventions, facilitating precision medicine applications and ultimately leading to improved patient outcomes.

Complications and management of coagulation disorders in leukemia patients

Patients with leukemia are predisposed to various coagulation abnormalities. Thrombosis and bleeding continue to be a major cause of morbidity and mortality in leukemias. The pathophysiology of these disorders is unique, and not only the disease but also the treatment and other factors play a role. There has been an increase in the understanding of these disorders in leukemias. However, it is still difficult to predict when and which patients will have these complications. The evidence for the management of coagulation abnormalities in leukemias is still evolving and not as established as in solid malignancies. The management of these disorders is complex, and making clinical decisions is often challenging. In the era of specialization, where there are different hematologists looking after benign- and malignant-hematology patients, opinions of thrombosis experts are often sought by leukemia specialists. This review aims to bridge the gap in the knowledge of these disorders between these specialists.

Gene expression meta-analysis in diffuse low-grade glioma and the corresponding histological subtypes

Diffuse low-grade glioma (DLGG) is a well-differentiated, slow-growing tumour with an inherent tendency to progress to high-grade glioma. The potential roles of genetic alterations in DLGG development have not yet been fully delineated. Therefore, the current study performed an integrated gene expression meta-analysis of eight independent, publicly available microarray datasets including 291 DLGGs and 83 non-glioma (NG) samples to identify gene expression signatures associated with DLGG. Using INMEX, 708 differentially expressed genes (DEGs) (385 upregulated and 323 downregulated genes) were identified in DLGG compared to NG. Furthermore, 497 DEGs (222 upregulated and 275 downregulated genes) corresponding to two histological types were identified. Of these, high expression of HIP1R significantly correlated with increased overall survival, whereas high expression of TBXAS1 significantly correlated with decreased overall survival. Additionally, network-based meta-analysis identified FN1 and APP as the key hub genes in DLGG compared with NG. PTPN6 and CUL3 were the key hub genes identified in the astrocytoma relative to the oligodendroglioma. Further immunohistochemical validation revealed that MTHFD2 and SPARC were positively expressed in DLGG, whereas RBP4 was positively expressed in NG. These findings reveal potential molecular biomarkers for diagnosis and therapy in patients with DLGG and provide a rich and novel candidate reservoir for future studies.