Drug-gene Interaction Analysis Research Articles

Bioinformatic analysis of ferroptosis related biomarkers and potential therapeutic targets in vitiligo

Vitiligo is a complex autoimmune skin disorder characterized by depigmentation and immune dysregulation. To elucidate the role of ferroptosis-related genes (FRGs) in vitiligo, we conducted a comprehensive analysis of gene expression data from the GSE53146 and GSE65127 datasets obtained from the GEO database. We identified 31 differentially expressed FRGs (DE-FRGs), with 21 genes upregulated and 10 downregulated. Functional enrichment analysis revealed that these DE-FRGs are significantly involved in oxidative stress, immune regulation, and vitiligo-associated signaling pathways. Utilizing machine learning approaches, including LASSO and SVM-RFE, we identified four key marker genes (ALOX5, SNCA, SLC1A4, and IL33) with strong diagnostic potential. Immune landscape analysis demonstrated that these marker genes influence immune cell composition, particularly showing correlations with CD8 + T cells and regulatory T cells. Furthermore, drug-gene interaction analysis proposed potential therapeutic targets, while ceRNA network analysis uncovered intricate regulatory relationships involving miRNAs and lncRNAs. Collectively, our findings provide novel insights into the molecular mechanisms underpinning vitiligo and suggest new avenues for diagnostic and therapeutic development.

Smoking, Genetic Susceptibility and Early Menopause: Unveiling Biological Mechanisms and Potential Therapy Targets.

To explore the association between smoking, genetic susceptibility and early menopause (EM) and clarify the potential mechanisms underlying this relationship. An observational and Transcriptome-wide association analysis (TWAS) study. UK Biobank and public summary statistics. 139 869 women with full baseline and menopause data, and no gynaecological surgery history. Adjusted modified Poisson regression models were developed to determine the smoking and genetic risk effects on EM. TWAS was used to identify gene expression between smoking and EM, with Mendelian randomisation (MR) to infer causality. Enrichment analysis explored regulatory networks of transcription factors, microRNAs and potential therapeutic targets. Small molecule drugs were predicted using drug-gene interaction analysis. EM prevalence and common gene expression patterns. Women with over 30 pack-years of smoking had about 1.5 times higher EM risk, with RRs of 1.39 (95%CI, 1.23-1.56), 1.45 (1.33-1.59) and 1.45 (1.36-1.55) in the low, intermediate and high genetic risk groups. TWAS identified hub genes such as IMMP2L, BMPR2 and HMGN1. MR confirmed daily cigarette consumption as a causal factor in early menopause. Several potential therapeutic targets (e.g., SP600125, INCB18424 and ruxolitinib) were identified. Smoking reduction significantly lowered the risk of EM. Hub genes and therapeutic targets identified provided new avenues for mitigating harmful effects of smoking.

Identification of PANoptosis-Related Genes in Community-Acquired Pneumonia Diagnosis.

This study aimed to identify and characterize novel PANoptosis biomarkers for community-acquired pneumonia (CAP) diagnosis. Transcriptomic data from training set GSE196399 and validation sets GSE94916 and GSE202947 were utilized. A PANoptosis gene set was identified by intersecting DEGs linked to CAP, WGCNA hub genes, and PANoptosis-related genes. GO and KEGG analyses were conducted for enrichment analysis. PANoptosis scores were calculated via ssGSEA. Feature genes were identified using SVM-RFE, LASSO regression, and RF methods. Diagnostic performance was assessed via ROC analysis. Immune cell infiltration was evaluated using CIBERSORT. A PPI network was constructed, and a nomogram was developed for CAP prediction. Drug-gene interactions were investigated. qRT-PCR was conducted to confirm feature gene alterations in clinical samples. We identified 7555 DEGs associated with CAP from the GSE196399 dataset. Through WGCNA, a PANoptosis gene set of 39 genes was found, showing significant enrichment in pathways related to apoptosis and inflammation. CAP patients exhibited significantly reduced PANoptosis scores compared to healthy controls, with a marked upregulation in the majority of the PANoptosis gene set in high-score individuals. Four feature genes (ZNF304, AKT3, MAPK8, and ARHGAP10) were identified as potential biomarkers, exhibiting high diagnostic accuracy with AUCs generally above 0.8. These genes also showed significant correlations with M0 macrophages and neutrophils. Drug-gene interaction analysis revealed potential therapeutic agents targeting MAPK8 and AKT3. Validation in clinical samples confirmed gene expression alterations in CAP patients. The identified PANoptosis feature genes demonstrate high diagnostic accuracy for CAP, serving as potential biomarkers and therapeutic targets for CAP.

Utilizing an integrated bioinformatics and machine learning approach to uncover biomarkers linking ulcerative colitis to purine metabolism-related genes

BackgroundUlcerative colitis (UC) is an increasing incidence of inflammatory disorder in the colon mucosa. One of the current research focuses is the alteration of metabolic networks in UC. One of the important aspects of this metabolic shift is the expression of purine metabolism genes (PMGs) vital for nucleic acid synthesis. Nevertheless, the precise function of PMGs in the pathophysiology of UC is not yet fully known. MethodsTo this end, this study used state-of-the-art bioinformatics tools and approaches to discover and confirm the PMGs involved in UC. All the 114 candidate PMGs were compared for their expression levels. GSEA and GSVA were applied to define the functional and pathway implications of these PMGs. Lasso regression and SVM-RFE approaches were used for the identification of hub genes and to assess the diagnostic potential of eight PMGs in UC classification. The relationship between these critical PMGs and clinical features was also systematically evaluated as well. The expression levels of these eight PMGs were validated using datasets GSE206285 and GSE179285. ResultsUsing bioinformatics and machine learning, this work seeks to establish the involvement of PMGs in UC. From the LASSO and SVM models, 114 DE PMGs were selected and investigated to build a stable predictive model. Based on these studies, the following genes: IMPDH1, GUK1, POLE3, ADCY3, ADCY4, PDE6B, PNPT1 and PDE4D were suggested as potential biomarkers of UC. Gene ontology enrichment analysis revealed that these genes are implicated in the biological processes of particular relevance to immune and inflammatory responses. The study also provided a lot of information on the interaction between immune cells and PMGs indicating that these genes may control some immune-related pathways in UC. Moreover, drug-gene interaction analysis presents potential therapeutic opportunities for potential drug targets which were further confirmed through molecular docking. Mendelian randomization analysis revealed that ADCY4 and PDAZN are involved in PMG-related processes, thus opening new possibilities for treatment. ConclusionsThis work reveals eight PMGs closely related to UC and provides new perspectives on possible markers of this inflammatory disease. These findings not only increase the understanding of the pathogenesis of UC but also offer potential for improving the surveillance of disease and its progression.

Identification of host immune-related biomarkers in active tuberculosis: A comprehensive analysis of differentially expressed genes

Tuberculosis (TB) is a serious public health issue in India. Numerous molecular mechanisms and immunological responses play significant roles in the pathogenesis of tuberculosis. This study aimed to identify host immune-related biomarkers that are significantly differentially expressed in active TB and that play a vital role in disease progression. The methodology employed in this study included data collection, pre-processing, analysis, and interpretation of the results. Six microarray datasets were used to identify differentially expressed genes (DEGs), and only the common DEGs were used for further downstream analysis, such as hub gene identification, gene ontology, pathway enrichment analysis, and drug-gene interaction analysis. The study identified 1728 DEGs, including 906 upregulated and 822 downregulated genes. Five hub genes were identified that were: STAT1, GBP5, GBP1, FCGR1A, and BATF2. Gene ontology and pathway enrichment revealed that most of the genes were involved in interferon-gamma signaling. In addition, through drug-gene interactions, known drugs have been identified for STAT1, FCGR1A and GBP1. The findings of this study may contribute to early detection and treatment of active TB.

Strength of spatial correlation between gray matter connectivity and patterns of proto-oncogene and neural network construction gene expression is associated with diffuse glioma survival.

Like other forms of neuropathology, gliomas appear to spread along neural pathways. Accordingly, our group and others have previously shown that brain network connectivity is highly predictive of glioma survival. In this study, we aimed to examine the molecular mechanisms of this relationship via imaging transcriptomics. We retrospectively obtained presurgical, T1-weighted MRI datasets from 669 adult patients, newly diagnosed with diffuse glioma. We measured brain connectivity using gray matter networks and coregistered these data with a transcriptomic brain atlas to determine the spatial co-localization between brain connectivity and expression patterns for 14 proto-oncogenes and 3 neural network construction genes. We found that all 17 genes were significantly co-localized with brain connectivity (p < 0.03, corrected). The strength of co-localization was highly predictive of overall survival in a cross-validated Cox Proportional Hazards model (mean area under the curve, AUC = 0.68 +/- 0.01) and significantly (p < 0.001) more so for a random forest survival model (mean AUC = 0.97 +/- 0.06). Bayesian network analysis demonstrated direct and indirect causal relationships among gene-brain co-localizations and survival. Gene ontology analysis showed that metabolic processes were overexpressed when spatial co-localization between brain connectivity and gene transcription was highest (p < 0.001). Drug-gene interaction analysis identified 84 potential candidate therapies based on our findings. Our findings provide novel insights regarding how gene-brain connectivity interactions may affect glioma survival.

Identification of potential crucial genes and therapeutic targets for epilepsy

BackgroundEpilepsy, a central neurological disorder, has a complex genetic architecture. There is some evidence suggesting that genetic factors play a role in both the occurrence of epilepsy and its treatment. However, the genetic determinants of epilepsy are largely unknown. This study aimed to identify potential therapeutic targets for epilepsy.MethodsDifferentially expressed genes (DEGs) were extracted from the expression profiles of GSE44031 and GSE1834. Gene co-expression analysis was used to confirm the regulatory relationship between newly discovered epilepsy candidate genes and known epilepsy genes. Expression quantitative trait loci analysis was conducted to determine if epilepsy risk single-nucleotide polymorphisms regulate DEGs’ expression in human brain tissue. Finally, protein–protein interaction analysis and drug–gene interaction analysis were performed to assess the role of DEGs in epilepsy treatment.ResultsThe study found that the protein tyrosine phosphatase receptor-type O gene (PTPRO) and the growth arrest and DNA damage inducible alpha gene (GADD45A) were significantly upregulated in epileptic rats compared to controls in both datasets. Gene co-expression analysis revealed that PTPRO was co-expressed with RBP4, NDN, PAK3, FOXG1, IDS, and IDS, and GADD45A was co-expressed with LRRK2 in human brain tissue. Expression quantitative trait loci analysis suggested that epilepsy risk single-nucleotide polymorphisms could be responsible for the altered PTPRO and GADD45A expression in human brain tissue. Moreover, the protein encoded by GADD45A had a direct interaction with approved antiepileptic drug targets, and GADD45A interacts with genistein and cisplatin.ConclusionsThe results of this study highlight PTPRO and GADD45A as potential genes for the diagnosis and treatment of epilepsy.

Text Mining and Drug Discovery Analysis: A Comprehensive Approach to Investigate Diabetes-Induced Osteoporosis.

Purpose: Osteoporosis (OP) and diabetes are prevalent diseases in orthopedic and endocrinology departments, with OP potentially arising as a complication of diabetes. However, the mechanism underlying diabetes-induced osteoporosis (DOP) remains enigmatic, and drug discovery in this domain is restricted, hindering research into the DOP's etiology and treatment. With the ultimate goal of preventing OP in diabetic patients, the objective of this study is to mine the genes and pathways linked to DOP using bioinformatics and databases. Method: The present study employed text mining as the initial approach to retrieve genes commonly associated with diabetes and OP. Subsequently, functional annotation was conducted to investigate the roles and functionalities. In order to explore the interactions between proteins relevant to DOP, we constructed protein-protein interaction (PPI) networks. Furthermore, to obtain key genes and candidate drugs for DOP treatment, we conducted drug-gene interaction (DGI) analysis, complemented by a thorough examination of transcriptional factors (TFs)-miRNA co-regulation. Results: The results through text mining revealed 110 genes that are commonly associated with both diabetes and OP. Subsequent enrichment analysis narrowed down the list to 95 symbols that were involved in PPI analysis. After DGI analysis, we identified 7 genes targeted by 11 drugs, which represent candidates for treating DOP. Conclusion: This study unveils ANDECALIXIMAB, SILTUXIMAB, OLOKIZUMAB, SECUKINUMAB, and IXEKIZUMAB as promising potential drugs for DOP treatment, demonstrating the significance of utilizing text mining and pathway analysis to investigate disease mechanisms and explore existing therapeutic options.

Exploring causal correlations between plasma proteins and peripheral neuropathy: a Mendelian randomization.

Peripheral neuropathy (PN) is a common neurological disorder, and circulating plasma proteins with causal genetic evidence are a major source of therapeutic targets. This study identifies several potential plasma proteins that are causally related to PN risk, providing new insights into protein-mediated pathogenesis of PN and potential targets for novel therapies. To identify potential therapeutic targets for PN, we employed two-sample Mendelian randomization (MR) to identify plasma proteins associated with six common PN. First, we screened for proteins related to PN using genome-wide association studies (GWAS), obtaining genetic data on plasma proteomes from 35,559 Icelanders. Summary data for six common PN, including Carpal Tunnel Syndrome (CTS), Trigeminal Neuralgia (TN), Alcoholic Neuropathy (AIP), Drug-induced Neuropathy (DIP), Diabetic Neuropathy (DP), and Guillain-Barré syndrome (GBS), were obtained from the FinnGen database. Two-sample MR and colocalization analyses were then conducted to further identify protein-PN pairs with presumed causal relationships. Enrichment analysis of positive proteins revealed potential biological processes and pathways. Based on drug-gene interaction analysis, we ultimately identified causal proteins associated with PN that could serve as potential drug targets for treating PN. Through MR analysis, we identified eight proteins (UBC12, SEM4C, IL23R, Prothrombin, CBS, Microglobulin, MATN4, COLEC12) with causal relationships to PN. We found that UBC12 is a protective factor for DP and CTS, while the remaining proteins are risk factors. Further colocalization analysis showed a posterior probability of hypothesis 4 (PPH4) less than 0.75, indicating no positive colocalization results were found. From the pathway enrichment analysis, we discovered that the proteins were mainly concentrated in pathways related to defense response to bacterium, receptor signaling pathway via STAT, cell killing, negative regulation of cytokine production, and leukocyte mediated immunity. Finally, in Drug-Gene Interaction database (DGIdb), we identified three protein-coding genes (IL23R, F2, CBS) as potential drug targets for PN. Mendelian randomization studies confirm the causal relationship between genetically predicted PN-related risk and genetically predicted plasma protein abundance. Plasma proteins, as biomarkers associated with PN, can provide potential drug targets for etiological intervention research in PN.

Comparing Pertubagens from Differential Gene Expression Data Analysis of ASD using Random Forest and Statistical Test

Abstract Differentially Expressed Genes (DEGs) are treated as candidate biomarkers, and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches like machine learning and statistical analysis. In this study, we used a combination of the machine learning algorithm and statistical tests to identify the top 300 genes that are differentially expressed in ASD compared to Typically Developed (TD). Initially, we extracted microarray gene expression data of 15 ASD and 15 TD from NCBI GEO database and used a standard pipeline to preprocess the data. Further, Random Forest (RF) was used to discriminate genes between ASD and TD. We, then analyzed the upregulated and downregulated genes using the logFC value to gain insights into their potential roles in the development of ASD. We further used drug-gene interaction analysis from ConnectivityMap to identify drugs that can inhibit the expression of these genes. Our results show that the proposed RF model yields average 5-fold cross-validation accuracy, sensitivity, and specificity of 96.67%. Further, we obtained precision and F-measure scores of 97.5% and 96.57%, respectively. Our analysis identified several novel genes that are dysregulated in ASD, including genes (such as proliferation-inducing protein 38 and germinal centre expressed transcript 32) involved in synaptic transmission, neural development, and immune function. We also identified several drugs (such as ATPase_Inhibitor, kinase inhibitors, and histone deacetylase inhibitors) that can potentially be used to treat ASD. Our findings provide new insights into the molecular mechanisms of ASD and suggest potential targets for drug development. These findings may lead to new therapeutic approaches for the treatment of ASD.

Deciphering the genetic landscape of obesity: a data-driven approach to identifying plausible causal genes and therapeutic targets

ObjectivesGenome-wide association studies (GWAS) have successfully revealed numerous susceptibility loci for obesity. However, identifying the causal genes, pathways, and tissues/cell types responsible for these associations remains a challenge, and standardized analysis workflows are lacking. Additionally, due to limited treatment options for obesity, there is a need for the development of new pharmacological therapies. This study aimed to address these issues by performing step-wise utilization of knowledgebase for gene prioritization and assessing the potential relevance of key obesity genes as therapeutic targets.Methods and resultsFirst, we generated a list of 28,787 obesity-associated SNPs from the publicly available GWAS dataset (approximately 800,000 individuals in the GIANT meta-analysis). Then, we prioritized 1372 genes with significant in silico evidence against genomic and transcriptomic data, including transcriptionally regulated genes in the brain from transcriptome-wide association studies. In further narrowing down the gene list, we selected key genes, which we found to be useful for the discovery of potential drug seeds as demonstrated in lipid GWAS separately. We thus identified 74 key genes for obesity, which are highly interconnected and enriched in several biological processes that contribute to obesity, including energy expenditure and homeostasis. Of 74 key genes, 37 had not been reported for the pathophysiology of obesity. Finally, by drug-gene interaction analysis, we detected 23 (of 74) key genes that are potential targets for 78 approved and marketed drugs.ConclusionsOur results provide valuable insights into new treatment options for obesity through a data-driven approach that integrates multiple up-to-date knowledgebases.

Identification of novel gene signatures and immune cell infiltration in intervertebral disc degeneration using bioinformatics analysis.

Background: Intervertebral disc degeneration (IDD) is the leading cause of lower back pain, and an overall understanding of the molecular mechanisms related to IDD is still lacking. The purpose of this study was to explore gene signatures and immune cell infiltration related to IDD via bioinformatics analysis. Methods: A total of five expression profiles of mRNA and non-coding RNA were downloaded from the Gene Expression Omnibus (GEO) database. The potentially involved lncRNA/circRNA-miRNA-mRNA networks and protein-protein interaction networks were constructed by miRNet, circBank, STRING, and the Cytoscape database. Gene ontology, Kyoto Encyclopaedia of Genes and Genomes Analysis, Gene Set Enrichment Analysis, Gene Set Variation Analysis, Immune Infiltration Analysis, and Drug-Gene Interaction were used to analyse the top 20 hub genes. RT-qPCR was conducted to confirm the 12 differential expressions of genes both in the nucleus pulposus and annulus fibrosus tissues Results: There were 346 differentially expressed mRNAs, 12 differentially expressed miRNAs, 883 differentially expressed lncRNAs, and 916 differentially expressed circRNAs in the GEO database. Functional and enrichment analyses revealed hub genes associated with platelet activation, immune responses, focal adhesion, and PI3K-Akt signalling. The apoptotic pathway, the reactive oxygen species pathway, and oxidative phosphorylation play an essential role in IDD. Immune infiltration analysis demonstrated that the Treg cells had significant infiltration, and three levels of immune cells, including dendritic cells, Th2 cells, and tumour-infiltrating lymphocytes, were inhibited in IDD. Drug-gene interaction analysis showed that COL1A1 and COL1A2 were targeted by collagenase clostridium histolyticum, ocriplasmin, and PDGFRA was targeted by 66 drugs or molecular compounds. Finally, 24 cases of IDD tissues and 12 cases of normal disc tissues were collected, and the results of RT-qPCR were consistent with the bioinformatics results. Conclusion: Our data indicated that the 20 hub genes and immune cell infiltration were involved in the pathological process of IDD. In addition, the PDGFRA and two potential drugs were found to be significant in IDD development.

Integrative transcriptomics analysis and experimental validation reveal immunomodulatory patterns in keratoconus

Keratoconus is a progressive disorder of the cornea and is typically considered a noninflammatory disease. However, increasing evidence indicates that immune disorders play an essential role in keratoconus progression, but the immune-related etiology remains elusive. Here, we comprehensively utilized bioinformatics approaches and experimental methods to explore the potential immunoregulatory mechanism of keratoconus progression. Transcriptomics data containing two keratoconus patient groups was derived from the public dataset GSE151631. The intersection of genes and known immunological genes was used to obtain differentially expressed immune-related genes. We utilized various protein clustering algorithms to screen out and validated the hub immune-related genes, and further explored their potential biological functions via gene annotation and pathway enrichment analyses. Moreover, the underlying immune landscape and drug targets were predicted by immune cell infiltration analysis and drug-gene interaction analysis. Furthermore, keratoconus-related immunoregulatory competitive endogenous RNA networks were constructed and experimentally validated. After filtering and experimental validation, nine keratoconus-associated immune-related genes were credible. Infiltrated monocytes might play an essential role in the progression of keratoconus. Moreover, eleven intersecting drugs targeting four genes, CCR2, CCR5, F2RL1, and ADORA1, were considered as potential druggable molecular targets for keratoconus. Furthermore, in the competitive endogenous RNA network, we identified several lncRNAs and miRNAs as critical noncoding RNAs regulating the hub genes. Overall, our data indicated that the immunomodulatory patterns had undergone changes in the pathogenesis of keratoconus, which might facilitate the understanding of keratoconus-related immune processes and provide novel insights into developing new immunotherapies for keratoconus.

Computational Drug Discovery in Ankylosing Spondylitis–Induced Osteoporosis Based on Data Mining and Bioinformatics Analysis

Ankylosing spondylitis (AS) and osteoporosis (OP) are both prevalent illnesses in spine surgery, with OP being a possible consequence of AS. However, the mechanism of AS-induced OP (AS-OP) remains unknown, limiting etiologic research and therapy of the illness. To mine targetable medicine for the prevention and treatment of AS-OP, this study analyzes public data sets using bioinformatics to identify genes and biological pathways relevant to AS-OP. First, text mining was used to identify common genes associated with AS and OP, after which functional analysis was carried out. The STRING database and Cytoscape software were used to create protein-protein interaction networks. Hub genes and potential drugs were discovered using drug-gene interaction analysis and transcription factors-gene interaction analysis. The results of text mining showed 241 genes common to AS and OP, from which 115 key symbols were sorted out by functional analysis. As options for treating AS-OP, protein-protein interaction analysis yielded 20 genes, which may be targeted by 13 medications. Carlumab, bermekimab, rilonacept, rilotumumab, and ficlatuzumab were first identified as the potential drugs for the treatment of AS-OP, proving the value of text mining and pathway analysis in drug discovery.

Integrated bioinformatic analysis to understand the association between phthalate exposure and breast cancer progression

Phthalates have been extensively used as plasticizers while manufacturing plastic-based consumer products. Estradiol mimicking properties and association studies suggest phthalates may contribute to breast cancer (BC). We performed an in-silico analysis and functional studies to understand the association between phthalate exposure and BC progression. Search for phthalate-responsive genes using the comparative toxicogenomics database identified 20 genes as commonly altered in response to multiple phthalates exposure. Of the 20 genes, 12 were significantly differentially expressed between normal and BC samples. In BC samples, 9 out of 20 genes showed a negative correlation between promoter methylation and its expression. AHR, BAX, BCL2, CAT, ESR2, IL6, and PTGS2 expression differed significantly between metastatic and non-metastatic BC samples. Gene set enrichment analysis identified metabolism, ATP-binding cassette transporters, insulin signaling, and type II diabetes as highly enriched pathways. The diagnostic assessment based on 20 genes expression suggested a sensitivity and a specificity >0.91. The aberrantly expressed phthalate interactive gene influenced the overall survival of BC patients. Drug-gene interaction analysis identified 14 genes and 523 candidate drugs, including 19 BC treatment-approved drugs. Di(2-ethylhexyl) phthlate (DEHP) exposure increased the growth, proliferation, and migration of MCF-7 and MDA-MB-231 cells in-vitro. DEHP exposure induced morphological changes, actin cytoskeletal remodeling, increased ROS content, reduced basal level lipid peroxidation, and induced epithelial to mesenchymal transition (EMT). The present approach can help to explore the potentially damaging effects of environmental agents on cancer risk and understand the underlined pathways and molecular mechanisms.

Identification of potential drug targets for vascular dementia and carotid plaques by analyzing underlying molecular signatures shared by them.

BackgroundVascular dementia (VaD) and carotid atherosclerotic plaques are common in the elderly population, conferring a heavy burden on families and society. Accumulating evidence indicates carotid atherosclerotic plaques to be a risk factor for VaD. However, the underlying mechanisms for this association are mainly unknown.Materials and methodsWe analyzed temporal cortex gene expression data of the GSE122063 dataset and gene expression data of the GSE163154 dataset to identify commonly differentially expressed genes (DEGs). Then we performed functional enrichment analysis, immune cell infiltration and evaluation, correlation analysis between differentially expressed immune-related genes (DEIRGs) and immune cells, receiver operating characteristic (ROC) analysis, and drug-gene analysis.ResultsWe identified 41 overlapped DEGs between the VaD and carotid atherosclerosis plaque datasets. Functional enrichment analyses revealed that these overlapped DEGs were mainly enriched in inflammatory and immune-related processes. Immunocyte infiltration and evaluation results showed that M0 macrophages, M2 macrophages, and T cells gamma delta had a dominant abundance in carotid atherosclerosis plaque samples, and M0 macrophages showed a significantly different infiltration percentage between the early and advanced stage plaques group. Resting CD4 memory T cells, M2 macrophages, and naive B cells were the top three highest infiltrating fractions in VaD. Furthermore, B cells and NK cells showed a different infiltration percentage between VaD and matched controls. We identified 12 DEIRGs, and the result of correlation analysis revealed that these DEIRGs were closely related to differentially expressed immune cells. We identified five key DEIRGs based on ROC analysis. The drug-gene interaction analysis showed that four drugs (avacopan, CCX354, BMS-817399, and ASK-8007) could be potential drugs for VaD and carotid atherosclerotic plaques treatment.ConclusionCollectively, these findings indicated that inflammatory and immune-related processes be a crucial common pathophysiological mechanism shared by VaD and carotid plaques. This study might provide new insights into common molecular mechanisms between VaD and carotid plaques and potential targets for the treatment.

Identification of hub genes and small molecule therapeutic drugs related to simple steatosis with secondary analysis of existing microarray data

This study aimed to explore the candidate genes associated with simple steatosis (SS) using integrated bioinformatics analysis. Data in GSE89632 were used to identify differentially expressed genes (DEGs) between SS and healthy controls (HC). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs was conducted. Protein–protein interactions (PPI) network and transcription factor (TF)-gene network were constructed, followed by pathway analysis. Drug-gene interactions were also predicted. Finally, the expression level of hub genes was verified using GSE126848 and quantitative real-time PCR (qRT-PCR). A total of 418 (139 up-regulated and 279 down-regulated) DEGs were identified between SS and HC groups. PPI analysis revealed that FOS, MYC, IL1B, and EGR1 might be considered as hub genes. Pathway analysis showed that these genes were mainly involved in TNF signaling pathway. Drug-gene interaction analysis identified six gene targets (PTGS2, IL1B, MMP9, CCL2, JUN, and IL6) and 38 small molecules. Further experimental verification confirmed that the expression level of FOS, MYC, IL1B, and EGR1 was significantly down-regulated in the SS samples. In conclusion, these data may provide new insights into the pathogenesis and treatment of SS. These hub genes and candidate drugs may improve the individualized diagnosis and treatment of SS.

Genetic association and Mendelian randomization for hypothyroidism highlight immune molecular mechanisms

SummaryWe carried out a genome-wide association analysis including 51,194 cases of hypothyroidism and 443,383 controls. In total, 139 risk loci were associated to hypothyroidism with genes involved in lymphocyte function. Candidate genes associated with hypothyroidism were identified by using molecular quantitative trait loci, colocalization, and enhancer-promoter chromatin looping. Mendelian randomization (MR) identified 42 blood expressed genes and circulating proteins as candidate causal molecules in hypothyroidism. Drug-gene interaction analysis provided evidence that immune checkpoint and tyrosine kinase inhibitors used in cancer therapy increase the risk of hypothyroidism. Hence, integrative mapping and MR support that expression of genes and proteins enriched in lymphocyte function are associated with the risk of hypothyroidism and provide genetic evidence for drug-induced hypothyroidism and identify actionable potential drug targets.

Identification of candidate biomarkers associated with gastric cancer prognosis based on an integrated bioinformatics analysis.

BackgroundThis study sought to identify candidate biomarkers associated with gastric cancer (GC) prognosis based on an integrated bioinformatics analysis.MethodsFirst, the GSE54129 and GSE79973 data sets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) identified between the 2 data sets were screened using the limma software package in R, and the intersection DEGs were obtained by a Venn analysis. Subsequently, gene clustering and a functional analysis were performed to explore the roles of the DEGs. The protein-protein interaction (PPI) network of the genes in clusters was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins. A survival analysis evaluated the associations between the candidate genes and the overall survival of GC patients. A drug-gene interaction analysis and an external data set analysis were conducted using The Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD) data set to validate the prognostic genes.ResultsWe extracted 421 intersection DEGs from the 2 GEO data sets. There were 5 gene clusters, and the functional analysis revealed that they were mainly associated with the extracellular matrix-receptor interaction pathway. The PPI interaction analysis identified the top 36 hub genes. The survival analysis revealed that 7 upregulated genes [i.e., platelet-derived growth factor receptor beta (PDGFRB), angiopoietin 2 (ANGPT2), vascular endothelial growth factor C (VEGFC), collagen type IV alpha 2 chain (COL4A2), collagen type IV alpha 1 chain (COL4A1), thrombospondin 1 (THBS1), and fibronectin 1 (FN1)] were associated with the survival prognosis of GC patients. The 20 drug-gene interaction pairs among the 4 genes and 18 drugs were obtained. Finally, TCGA-STAD data set was used to validate the expression levels of COL4A1, PDGFRB, and FN1.ConclusionsWe found that 7 upregulated genes (i.e., PDGFRB, ANGPT2, VEGFC, COL4A2, COL4A1, THBS1, and FN1) were promising markers of prognosis in GC patients.

Drug discovery in spinal cord injury-induced osteoporosis: a text mining-based study.

BackgroundSpinal cord injury (SCI) and osteoporosis (OP) are common diseases in spine surgery, and OP could be the complication of SCI. However, SCI-induced OP is a complex pathologic process and drug discovery is limited, which restricts the study in the mechanism and treatment of the disease. This study aims to identify the genes and molecular pathways related to SCI-induced OP through computational tools and public datasets, and to explore drug targeting therapy, ultimately preventing the occurrence of OP after SCI.MethodsIn this study, common genes related to SCI and OP were obtained by text mining, then which conducted the functional analysis. Protein-protein interaction (PPI) networks were constructed by STRING online and Cytoscape software. Finally, core genes and potential drugs were performed after undergoing drug-gene interaction analysis which also completed functional analysis.ResultsA total of 371 genes common to ‘SCI’ and ‘OP’ were identified by text mining. After functional analysis, 207 significant genes were screened out. Subsequently, PPI analysis yielded 23 genes targetable by 13 drugs which were the candidate to treat SCI-induced OP.ConclusionsTaken together, siltuximab, olokizumab, clazakizumab and BAN2401 were first discovered to become the potential drugs for the treatment of SCI-induced OP. Drug discovery using text mining and pathway analysis is a significant way to investigate the pathomechanism of the disease while exploring existing drugs to treat the disease.