Drug-Gene Interaction Database Research Articles

Molecular mechanisms of acquired idiopathic generalized anhidrosis: differential gene expression and potential therapeutic targets

Eccrine sweat glands are essential for thermoregulation. Acquired idiopathic generalized anhidrosis (AIGA) is a rare disorder characterized by the absence of sweating, leading to severe complications like heatstroke and skin dryness. The underlying etiology remains unknown, complicating diagnosis and treatment. This study aimed to elucidate the genetic mechanisms of AIGA by analysing the gene expression in eccrine sweat glands using the GSE193125 dataset from the NCBI Gene Expression Omnibus (GEO). This study identified differentially expressed genes (DEGs) (genes that show statistically significant differences in expression levels in AIGA), with JUN and CCN1 significantly downregulated in anhidrotic lesions of AIGA patients. Receiver operating characteristic (ROC) curve demonstrated the accuracy of these genes in classification (JUN = 0.88 and CCN1 = 0.77). Gene and protein interaction networks which are defined as networks of physical interactions between genes and proteins, constructed using GeneMANIA and STRING, revealed substantial physical interactions among them. Gene set enrichment analysis highlighted the neuroinflammatory and estrogen receptor signaling pathways are associated with this condition, suggesting hormonal regulation’s role in the anhidrosis mechanism. Key miRNAs, including hsa-let-7b-5p, hsa-miR-10b-5p, and hsa-miR-124-3p, are associated with the DEGs, underscoring the importance of post-transcriptional and transcriptional regulation. Through the Drug Gene Interaction Database (DGIdb), ciprofibrate is identified as a potential therapeutic agent targeting the JUN gene. Molecular docking confirmed a strong binding affinity between ciprofibrate and JUN, suggesting ciprofibrate could modulate JUN activity and regulate AIGA symptoms. ADMET analysis, which refers to the assessment of absorption, distribution, metabolism, excretion, and toxicity of a drug or compound, is crucial for evaluating its pharmacokinetic and safety profile. The analysis indicated that ciprofibrate has favourable pharmacokinetic properties and a relatively safe toxicity profile. This comprehensive bioinformatics approach, integrating gene expression analysis, miRNA identification, and drug interaction, provides valuable insights into anhidrosis molecular pathology and highlights ciprofibrate’s potential as a targeted therapeutic strategy. Despite limitations, including a small sample size and reliance on computational predictions, this study paves the way for further research and potential therapeutic interventions for AIGA.

Genomics-driven integrative analysis highlights immune-related plasma proteins for psychiatric disorders

BackgroundGenome-wide association studies (GWAS) have identified numerous variants associated with psychiatric disorders. However, it remains largely unknown on how GWAS risk variants contribute to psychiatric disorders. MethodsThrough integrating two largest, publicly available, independent protein quantitative trait loci datasets of plasma protein and nine large-scale GWAS summary statistics of psychiatric disorders, we first performed proteome-wide association study (PWAS) to identify psychiatric disorders-associated plasma proteins, followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we conducted Mendelian randomization (MR) and Bayesian colocalization (COLOC) analyses, with both discovery and parallel replication datasets, to further identify protein-disorder pairs with putatively causal relationships. We finally prioritized the potential drug targets using Drug Gene Interaction Database. ResultsPWAS totally identified 112 proteins, which were significantly enriched in biological processes relevant to immune regulation and response to stimulus including regulation of immune system process (adjusted P = 1.69 × 10−7) and response to external stimulus (adjusted P = 4.13 × 10−7), and viral infection related pathways, including COVID-19 (adjusted P = 2.94 × 10−2). MR and COLOC analysis further identified 26 potentially causal protein-disorder pairs in both discovery and replication analysis. Notably, eight protein-coding genes were immune-related, such as IRF3, CSK, and ACE, five among 16 druggable genes were reported to interact with drugs, including ACE, CSK, PSMB4, XPNPEP1, and MICB. ConclusionsOur findings highlighted the immunological hypothesis and identified potentially causal plasma proteins for psychiatric disorders, providing biological insights into the pathogenesis and benefit the development of preventive or therapeutic drugs for psychiatric disorders.

Mendelian randomization analysis of causal and druggable circulating inflammatory proteins in schizophrenia.

Schizophrenia (SZ) is a severe mental disorder with complex origins. Observational studies suggested that inflammatory factors may play a role in the pathophysiology of SZ and we aim to investigate the potential genetic connection between them by examining the causal impact of circulating inflammatory proteins on SZ. We utilized Mendelian randomization (MR) analysis to assess the causal relationship between circulating inflammatory proteins and SZ and the GWAS summary datasets were sourced from public databases. The SZ dataset comprised 74,776 cases and 101,023 controls, while the summary results for 91 plasma proteins in 14,824 participants were obtained through the Olink Target platform. Moreover, to identify and evaluate potential drug targets, we searched the Drug-Gene Interaction Database (DGIdb). The results of the MR study confirmed that nine inflammatory proteins had a causal effect on SZ. Among these proteins, IL1A (OR: 0.93), TNFB (OR: 0.94), TNFSF14 (OR: 0.96), and CD40 (OR: 0.95) exhibited protective effects against SZ. Conversely, CCL23 (OR: 1.04), CCL19 (OR: 1.04), 4EBP1 (OR: 1.06), TWEAK (OR: 1.08), and DNER (OR: 1.10) were associated with an increased risk of SZ. The MR-Egger and weighted median methods also supported the direction of these effects. According to the Gene-Drug analysis, LTA, IL1A, CD40, and 4EBP1 can serve as drug targets. Our study established causal relationships between circulating inflammatory proteins and SZ. It may be beneficial to personalize the treatment of SZ by incorporating inflammation management into the treatment regimen.

Genetic and therapeutic for oral lichen planus and diabetes mellitus: a comprehensive study

BackgroundThis study employed a bidirectional Mendelian Randomization (MR) approach to explore the causal relationships between Oral Lichen Planus (OLP), diabetes mellitus (DM), and glycemic control. It also aims to identify potential pharmacological and herbal treatments that effectively address both OLP and the metabolic dysfunctions associated with DM.MethodsThis study employs a two-way MR approach to investigate the potential causal relationships between diabetes type and glycated hemoglobin (HbA1c) levels, and the risk of OLP. We analyzed differentially expressed genes from the OLP dataset in the Genomics Expression Omnibus (GEO) database, cross-referencing these with HbA1c-related genes for enrichment analysis. Additionally, the Drug-Gene Interaction Database (DGIdb) and Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) were utilized to assess the effectiveness of specific drugs, herbs, and ingredients in treating OLP while managing blood glucose levels.ResultsThe MR analysis revealed a significant association between Type 1 Diabetes mellitus (T1DM) and an increased risk of OLP, unlike Type 2 Diabetes mellitus (T2DM). This finding indicates a unique immunological interaction in T1DM that may predispose individuals to OLP. The drug prediction analysis focused on core targets linked to OLP and HbA1c, evaluating the therapeutic potential of retinoic acid, prednisone, and thalidomide for treating OLP and regulating blood glucose levels. Additionally, herbal medicines such as Ecliptae herbaand Amygdalus communis vas, along with herbal ingredients like quercetin, luteolin, and 17-beta-estradiol, were identified for their anti-inflammatory properties and potential to mitigate metabolic dysfunction in diabetes.ConclusionThe study highlighted a complex interplay between diabetes and OLP, underscoring the efficacy of integrated therapeutic strategies that target both conditions. The findings suggest that both pharmaceutical and herbal treatments can effectively manage the clinical manifestations of OLP and associated metabolic challenges. This holistic approach to treatment could significantly enhance patient outcomes by addressing the interconnected aspects of these chronic conditions.

Familial adenomatous polyposis gene MUTYH mutations are identified in anaplastic thyroid carcinoma

Abstract Introduction/Objective Anaplastic thyroid carcinoma (ATC) is a rare aggressive disease unresponsive to treatment. Next-Generation Sequencing (NGS) allows identification of targets for precision therapies, such as BRAF, with implications in treating ATC. Our prior work identified ATC clusters with characteristic gene mutations and multiple previously unreported mutations. One of these is MUTYH, which is associated with colon (CRC), ovarian, breast, bladder, and duodenal carcinomas in MUTYH-associated polyposis (MAP). Its association with ATC is not defined. Methods/Case Report Exploratory analysis of pathogenic mutations in 727 ATC cases using a 324-gene panel (FoundationOneCDx) and an interactive visualization of association rules was conducted for informative maximal frequent patterns among mutated gene combinations to identify gene clusters including genes not commonly associated with ATC. TCGA database was reviewed for frequency of MUTYH mutations. My Cancer Genome and the Drug-Gene Interaction Database were used to identify potential targeted drugs for MUTYH gene-mutated cancers. Results (if a Case Study enter NA) NGS showed MUTYH short-variant mutations in 17 cases (2.34%). Germline MUTYH variants incidence is 1.43% in normal samples. 5 cases harbored only Y165C and 7 had only G382D, with 1 case having both Y165C and G382D. The other 4 cases contained sporadic truncating events in MUTYH. All instances of Y165C and G382D were predicted to be germline. 6 patients (35%) had BRAF co-mutations, correlating with expected frequency of ATC thought to transform from papillary carcinoma (PC). One case had only MUTYH mutation while a second had MUTYH with TERT and TP53. TCGA database contained 6(0.2%) cases of PC with MUTYH mutations (total 2871 MUTYH-mutated carcinomas), but no cases of ATC or other thyroid tumors were described. Conclusion MUTYH is a DNA repair enzyme linked to CRC and other cancers. MUTYH mutations occur in 6.7% of MAP patients and 1–2.2% of sporadic CRC cases that are mutually exclusive of BRAF mutations. While MAP increases the lifetime risk of CRC by over 60%, the link of MUTYH mutations to PC has only been observed in MAP and not been described outside of it. To our knowledge this is the first description of MUTYH mutations in ATC, especially without concurrent BRAF mutation (11 cases), suggesting MUTYH may independently contribute to pathogenesis of ATC. PARP inhibitors, PD-1/PD-L1 inhibitors, and ATR inhibitors demonstrate promising results in clinical trials of MUTYH-mutated cancers, warranting further research.

Identification of potential biomarkers for lung adenocarcinoma: a study based on bioinformatics analysis combined with validation experiments.

The prognosis for lung adenocarcinoma (LUAD) remains dismal, with a 5-year survival rate of <20%. Therefore, the purpose of this study was to identify potentially reliable biomarkers in LUAD by machine learning combination with Mendelian randomization (MR). TCGA-LUAD, GSE40791, and GSE31210 were employed this study. Key module differential genes were identified through differentially expressed analysis and weighted gene co-expression network analysis (WGCNA). Furthermore, candidate biomarkers were derived from protein-protein interaction network (PPI) and machine learning. Ultimately, biomarkers were confirmed using MR analysis. In addition, immunohistochemistry was used to detect the expression levels of genes that have a causal relationship to LUAD in the LUAD group and the control group. Cell experiments were conducted to validate the effect of screening genes on proliferation, migration, and apoptosis of LUAD cells. The correlation between the screened genes and immune infiltration was determined by CIBERSORT algorithm. In the end, the gene-related drugs were predicted through the Drug-Gene Interaction database. In total, 401 key module differential genes were obtained by intersecting of 5,702 differentially expressed genes (DEGs) and 406 key module genes. Thereafter, GIMAP6, CAV1, PECAM1, and TGFBR2 were identified. Among them, only TGFBR2 had a significant causal relationship with LUAD (p=0.04, b=-0.06), and it is a protective factor for LUAD. Subsequently, sensitivity analyses showed that there were no heterogeneity and horizontal pleiotropy in the univariate MR results, and the results were not overly sensitive to individual SNP loci, further validating the reliability of univariate Mendelian randomization (UVMR) results. However, no causal relationship was found between them by reverse MR analysis. Meanwhile, TGFBR2 expression was decreased in LUAD group through immunohistochemistry. TGFBR2 can inhibit proliferation and migration of lung adenocarcinoma cell line A549 and promote apoptosis of A549 cells. Immune infiltration analysis suggested a potential link between TGFBR2 expression and immune infiltration. Finally, Irinotecan and Hesperetin were predicted through DGIDB database. In this study, TGFBR2 was identified as a biomarker of LUAD, which provided a new idea for the treatment strategy of LUAD and may aid in the development of personalized immunotherapy strategies.

Exploring potential therapeutic strategy for hepatocellular carcinoma and COVID-19 using bioinformatics analysis

BackgroundHepatocellular carcinoma (HCC) constitutes an important contributor to fatalities. Coronavirus disease 2019 (COVID-19) frequently presents with complications such as respiratory distress, systemic inflammatory responses, and damage to various organs. Several studies have investigated the relationship between COVID-19 and mortality in patients with liver cancer, but there are few research on the relationship between them. This study is to explore the correlation between the two diseases and drugs treating them.MethodsThe Gene Expression Omnibus (GEO) database provides gene datasets of COVID-19 patients and HCC patients. Through differential gene analysis and weighted gene co-expression network analysis, we determined 223 genes represented in HCC and COVID-19. We then used functional annotation, protein–protein interaction network construction, predictive model development and verification, prognostic value analysis, and miRNA–gene network construction. Besides, we created a drug–hub–gene network by predicting possible medications that interact with hub genes using the Drug–Gene Interaction Database (DGIdb). Ultimately, we applied immunohistochemistry to ascertain the hub genes expression.ResultsThis study revealed that eight core genes (RRM2, TPX2, DTL, CDT1, TYMS, CDCA5, CDC25C, and HJURP) co-existed in both HCC and COVID-19 and were differentially expressed in both HCC and normal tissues.CDC25C, RRM2, CDCA5, and HJURP had diagnostic value (AUC > 0.8) and prognostic value (adjusted P-value < 0.05). Genome enrichment analysis indicated that eight genes may function in liver cancer through engagement in the cell cycle, DNA replication, etc. In liver cancer samples, these genes were significantly and adversely associated with plasma cells while RRM2 was positively associated with neutrophil and NK cell activation and with dendritic cell resting. Using the miRNAnet database and DGIdb, 9 transcription factors, 7 miRNAs, and 51 drugs or molecular compounds were predicted to interact with the hub genes. Finally, RRM2 expression showed significant variation in clinical specimens, and analysis of the association of RRM2 with immunomodulators indicated that RRM2 was closely connected to MICB and CD276.ConclusionsOur study revealed several metabolic genes related to HCC and COVID-19. Moreover, potential drugs related to central genes were predicted. These findings may provide new ideas for treating COVID-19 and HCC.

Identification of essential genes and drug discovery in bladder cancer and inflammatory bowel disease via text mining and bioinformatics analysis.

Bladder cancer (BCa) is the most common malignancy of the urinary system. Inflammation is critical in the occurrence and development of BCa. The purpose of this study was to identify key genes and pathways of inflammatory bowel disease in BCa through text mining technology and bioinformatics technology, and to explore potential therapeutic drugs for BCa. Genes associated with BCa and Crohn's disease (CD) were detected using the text mining tool GenClip3, and analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape, and modular analysis was performed using the Molecular Complex Detection plugin (MCODE). Finally, the genes clustered in the first two modules were selected as core genes, and the drug-gene interaction database was used to discover potential therapeutic drugs. We identified 796 genes shared by "Bladder cancer" and " Crohn's disease " by text mining. Gene function enrichment analysis yielded 18 enriched GO terms and the 6 most relevant KEGG pathways. A PPI network with 758 nodes and 4014 edges was constructed, and 20 gene modules were obtained using MCODE. We selected the top two gene clusters as core candidate genes. We found that 3 out of 55 selected core genes could be targeted by 26 existing drugs. The results indicated that CXCL12, FGF2 and FSCN1 are potential key genes involved in CD with BCa. Additionally, 26 drugs were identified as potential therapeutics for BCa treatment and management.

Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction

IntroductionAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. MethodsGenes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. ResultsSNP-based fine-mapping, TWAS and PWAS identified 118 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified six drugs significantly enriched for interactions with ALS associated genes, though directionality could not be determined. Additionally, drug class enrichment analysis showed gene signatures linked to calcium channel blockers may reduce ALS risk, whereas antiepileptic drugs may increase ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R2 = 5.1 %; p-value = 3.2 × 10−27) and clinical characteristics. ConclusionsFunctionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.

Necroptosis in recurrent implantation failure: A bioinformatics analysis of key genes and therapeutic targets.

Recurrent implantation failure (RIF), a major issue in assisted reproductive technology (ART), may be influenced by necroptosis, a form of cell death linked to several diseases. This study was aimed at investigating the involvement of necroptosis in RIF. Using RNA-sequencing data from the Gene Expression Omnibus database, we identified differentially expressed necroptosis-related genes (DENRGs) in RIF patients compared with those in controls. Functional enrichment, protein-protein interaction (PPI) networks, and transcription factor (TF) regulatory networks were analyzed to identify key genes. Immune cell infiltration was analyzed using the single-sample gene set enrichment analysis (ssGSEA) algorithm. Finally, potential therapeutic drugs targeting key genes were explored using a Drug Gene Interaction Database. In total, 20 DENRGs associated with RIF were identified, with a focus on 6 key genes (MLKL, FASLG, XIAP, CASP1, BIRC3, and TLR3) implicated in necroptosis and immune processes. These genes were used to develop a predictive model for RIF, which was validated in 2 datasets. The model and TF network analysis underscored the importance of TLR3. Immune infiltration analysis showed reduced levels of 16 immune cells in RIF patients, highlighting immune system alterations. Several drugs targeting CASP1, such as nivocasan and emricasan, were identified as potential treatments. The study sheds light on the role of necroptosis in RIF, identifying key genes and immune alterations that could serve as biomarkers and therapeutic targets. These findings pave the way for future experimental research and clinical applications targeting necroptosis in RIF treatment.

Integrative Bioinformatics Analysis for Targeting Hub Genes in Hepatocellular Carcinoma Treatment

Background: The damage in the liver and hepatocytes is where the primary liver cancer begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches. Objective: This study aimed to identify potential drug target(s) hubs mediating HCC progression using computational approaches through gene expression and protein-protein interaction datasets. Methodology: Four datasets related to HCC were acquired from the GEO database, and Differentially Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using the Fun Rich tool, functional associations among the genes were identified. Further, protein-protein interaction networks were predicted using STRING, and hub genes were identified using Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival analysis and functional enrichment studies for the identified hub genes. The up-regulating genes were further studied for immunohistopathological studies using HPA to identify gene/protein expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database were employed to find the reported drug status and targets. Finally, STITCH was performed to identify the functional association between the drugs and the identified hub genes. Results: The GEO2R analysis for the considered datasets identified 735 upregulating and 284 downregulating DEGs. Functional gene associations were identified through the Fun Rich tool. Further, PPIN network analysis was performed using STRING. A comparative study was carried out between the experimental evidence and the other seven data evidence in STRING, revealing that most proteins in the network were involved in protein-protein interactions. Further, through Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional enrichment studied for the hub genes. The HPA database studies further found that upregulating genes were involved in changes in protein expression in Normal vs HCC tissues. These findings indicated that hub genes were certainly involved in the progression of HCC. STITCH database studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub genes could also be considered as potential and promising drug targets as they are involved in the gene-chemical interaction networks. Conclusion: The present study involved various integrated bioinformatics approaches, analyzing gene expression and protein-protein interaction datasets, resulting in the identification of 20 topranked hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets. The current study underscores the significance of targeting these hub genes and utilizing existing molecules to generate new molecules to combat liver cancer effectively and can be further explored in terms of drug discovery research to develop treatments for HCC.

Exploration of the Shared Gene Signatures and Molecular Mechanisms between Chronic Bronchitis and Antineutrophil Cytoplasmic Antibody-associated Glomerulonephritis: Evidence from Transcriptome Data

Background: Chronic Bronchitis (CB) is a recurrent and persistent pulmonary inflammation disease. Growing evidence suggests an association between CB and Anti-neutrophil Cytoplasmic Antibody-associated Glomerulonephritis (ANCA-GN). However, the precise mechanisms underlying their association remain unclear. Aims: The purpose of this study was to further explore the molecular mechanism of the occurrence of chronic bronchitis (CB) associated with anti-neutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA- GN). Objective: Our study aimed to investigate the potential shared pathogenesis of CB-associated ANCA-GN. Methods: Datasets of ANCA (GSE108113 and GSE104948) and CB (GSE151052 and GSE162635) were obtained from the Gene Expression Omnibus (GEO) datasets. Firstly, GSE108113 and GSE151052 were analyzed to identify common differentially expressed genes (DEGs) by Limma package. Based on common DEGs, protein-protein interaction (PPI) network and functional enrichment analyses, including GO, KEGG, and GSEA, were performed. Then, hub genes were identified by degree algorithm and validated in GSE104948 and GSE162635. Further PPI network and functional enrichment analyses were performed on hub genes. Additionally, a competitive ceRNA network was constructed through miRanda and spongeScan. Transcription factors (TFs) were predicted and verified using the TRRUST database. Furthermore, the CIBERSORT algorithm was employed to explore immune cell infiltration. The Drug Gene Interaction Database (DGIDB) was utilized to predict small-molecular compounds of CB and ANCA-GN. Result: A total of 963 DEGs were identified in the integrated CB dataset, and 610 DEGs were identified in the integrated ANCA-GN dataset. Totally, we identified 22 common DEGs, of which 10 hub genes (LYZ, IRF1, PIK3CG, IL2RG, NT5E, ARG2, HBEGF, NFATC2, ALPL, and FKBP5) were primarily involved in inflammation and immune responses. Focusing on hub genes, we constructed a ceRNA network composed of 323 miRNAs and 348 lncRNAs. Additionally, five TFs (SP1, RELA, NFKB1, HIF1A, and SP3) were identified to regulate the hub genes. Furthermore, immune cell infiltration results revealed immunoregulation in CB and ANCA-GN. Finally, some small-molecular compounds (Daclizumab, Aldesleukin, and NT5E) were predicted to predominantly regulate inflammation and immunity, especially IL-2. Conclusion: Our study explores the inflammatory-immune pathways underlying CB-associated ANCA-GN and emphasizes the importance of NETs and lymphocyte differentiation, providing novel insights into the shared pathogenesis and therapeutic targets.

Potential Mechanism and Therapeutic Targets of Polymyositis.

To investigate the variability in the expression profile of genes associated with polymyositis (PM), explore the potential molecular mechanisms underlying PM, and predict novel targets for intervention. Descriptive study. Place and Duration of the Study: Department of Rheumatology, Taizhou Municipal Hospital, Taizhou, China, from August to November 2023. Three microarray datasets (GSE3112, GSE39454, and GSE128470) were extracted from the gene expression omnibus (GEO). The analysis of this research involved identifying the differentially expressed genes (DEGs) in PM compared to normal samples. Enrichment analysis, gene-microRNA, gene-transcription factor (TF), and protein-protein interaction (PPI) network studies were conducted to identify hub genes and relevant pathways. Additionally, the drug-gene interaction database (DGIdb) was used to predict therapeutic medications. Eighty-eight DEGs were identified. The enrichment analysis results highlighted the significant involvement of downregulated DEGs in antigen processing and presentation. Based on the PPI networks, seven hub genes with high connectivity degrees were selected including a cluster of differentiation 74 (CD74), human leukocyte antigen (HLA)-DPA1, HLA-B, guanylate-binding protein 1 (GBP1), recombinant 2', 5'-oligoadenylate synthetase 1 (OAS1), HLA-C, and HLA-E. This research screened-out core genes, projected prospective therapeutic medications, discovered DEGs between PM and normal samples, and offered fresh perspectives for additional research into the possible mechanism and therapeutic targets of PM. Polymyositis, DEGs, Hub genes, Bioinformatics, Potential therapeutic agents.

Comprehensive analysis of cuproptosis-related genes involved in immune infiltration and their use in the diagnosis of hepatic ischemia‒reperfusion injury: an experimental Study.

Hepatic ischemia reperfusion injury (HIRI) is a common injury not only during liver transplantation but also during major hepatic surgery. HIRI causes severe complications and affects the prognosis and survival of patients. Cuproptosis, a newly identified form of cell death, plays an important role in a variety of illnesses. However, its role in HIRI remains unknown. The GSE151648 dataset was mined from the Gene Expression Omnibus (GEO) database, and differences were analyzed for intersections. Based on the differentially expressed genes (DEGs), functional annotation, differentially expressed cuproptosis-related genes (DE-CRGs) identification and lasso logistic regression were conducted. Correlation analysis of DE-CRGs and immune infiltration was further conducted, and DE-CRGs were applied to construct an HIRI diagnostic model. The hierarchical clustering method was used to classify the specimens of HIRI, and functional annotation was conducted to verify the accuracy of these DE-CRGs in predicting HIRI progression. The GSE14951 microarray dataset and GSE171539 single-cell sequencing dataset were chosen as validation datasets. At the same time, the significance of DE-CRGs was verified using a mouse model of HIRI with cuproptosis inhibitors and inducers. Finally, a network of transcription-factor-DE-CRGs and miRNA-DE-CRGs was constructed to reveal the regulation mechanisms. And potential drugs for DE-CRGs were predicted using Drug Gene Interaction Database (DGIdb). Overall, 2390 DEGs and 19 DE-CRGs were identified. Through machine learning algorithms, 8 featured DE-CRGs (GNL3, ALAS1, TSC22D2, KLF5, GTF2B, DNTTIP2, SLFN11 and HNRNPU) were screened, and 2 cuproptosis-related subclusters were defined. Based on the 8 DE-CRGs obtained from the HIRI model (AUC=0.97), the nomogram model demonstrated accuracy in predicting HIRI. Eight DE-CRGs were highly expressed in HIRI samples and were negatively related to immune cell infiltration. A higher level of immune infiltration and expression of CRG group B was found in the HIRI population. Differences in cell death and immune regulation were found between the 2 groups. The diagnostic value of the 8 DE-CRGs was confirmed in the validation of two datasets. The identification of 7 DE-CRGs (SLFN11 excluded) by HIRI animal model experiments was also confirmed. Using hTFtarget, miRWalk and DGIDB database, we predicted that 17 transcription factors, 192 miRNAs and 10 drugs might interact with the DE-CRGs. This study shows that cuproptosis may occur in HIRI and is correlated with immune infiltration. Additionally, a cuproptosis-related predictive model was constructed for studying the causes of HIRI and developing targeted treatment options for HIRI.

Genetic associations in ankylosing spondylitis: circulating proteins as drug targets and biomarkers.

Ankylosing spondylitis (AS) is a complex condition with a significant genetic component. This study explored circulating proteins as potential genetic drug targets or biomarkers to prevent AS, addressing the need for innovative and safe treatments. We analyzed extensive data from protein quantitative trait loci (pQTLs) with up to 1,949 instrumental variables (IVs) and selected the top single-nucleotide polymorphism (SNP) associated with AS risk. Utilizing a two-sample Mendelian randomization (MR) approach, we assessed the causal relationships between identified proteins and AS risk. Colocalization analysis, functional enrichment, and construction of protein-protein interaction networks further supported these findings. We utilized phenome-wide MR (phenMR) analysis for broader validation and repurposing of drugs targeting these proteins. The Drug-Gene Interaction database (DGIdb) was employed to corroborate drug associations with potential therapeutic targets. Additionally, molecular docking (MD) techniques were applied to evaluate the interaction between target protein and four potential AS drugs identified from the DGIdb. Our analysis identified 1,654 plasma proteins linked to AS, with 868 up-regulated and 786 down-regulated. 18 proteins (AGER, AIF1, ATF6B, C4A, CFB, CLIC1, COL11A2, ERAP1, HLA-DQA2, HSPA1L, IL23R, LILRB3, MAPK14, MICA, MICB, MPIG6B, TNXB, and VARS1) that show promise as therapeutic targets for AS or biomarkers, especially MAPK14, supported by evidence of colocalization. PhenMR analysis linked these proteins to AS and other diseases, while DGIdb analysis identified potential drugs related to MAPK14. MD analysis indicated strong binding affinities between MAPK14 and four potential AS drugs, suggesting effective target-drug interactions. This study underscores the utility of MR analysis in AS research for identifying biomarkers and therapeutic drug targets. The involvement of Th17 cell differentiation-related proteins in AS pathogenesis is particularly notable. Clinical validation and further investigation are essential for future applications.

Identifying potential drug targets for varicose veins through integration of GWAS and eQTL summary data.

Varicose veins (VV) are a common chronic venous disease that is influenced by multiple factors. It affects the quality of life of patients and imposes a huge economic burden on the healthcare system. This study aimed to use integrated analysis methods, including Mendelian randomization analysis, to identify potential pathogenic genes and drug targets for VV treatment. This study conducted Summary-data-based Mendelian Randomization (SMR) analysis and colocalization analysis on data collected from genome-wide association studies and cis-expression quantitative trait loci databases. Only genes with PP.H4 > 0.7 in colocalization were chosen from the significant SMR results. After the above analysis, we screened 12 genes and performed Mendelian Randomization (MR) analysis on them. After sensitivity analysis, we identified four genes with potential causal relationships with VV. Finally, we used transcriptome-wide association studies and The Drug-Gene Interaction Database data to identify and screen the remaining genes and identified four drug targets for the treatment of VV. We identified four genes significantly associated with VV, namely, KRTAP5-AS1 [Odds ratio (OR) = 1.08, 95% Confidence interval (CI): 1.05-1.11, p = 1.42e-10] and PLEKHA5 (OR = 1.13, 95% CI: 1.06-1.20, p = 6.90e-5), CBWD1 (OR = 1.05, 95% CI: 1.01-1.11, p = 1.42e-2) and CRIM1 (OR = 0.87, 95% CI: 0.81-0.95, p = 3.67e-3). Increased expression of three genes, namely, KRTAP5-AS1, PLEKHA5, and CBWD1, was associated with increased risk of the disease, and increased expression of CRIM1 was associated with decreased risk of the disease. These four genes could be targeted for VV therapy. We identified four potential causal proteins for varicose veins with MR. A comprehensive analysis indicated that KRTAP5-AS1, PLEKHA5, CBWD1, and CRIM1 might be potential drug targets for varicose veins.

Functional Investigation and Two-sample Mendelian Randomization Study of Inguinal Hernia Hub Genes Obtained by Bioinformatics Analysis.

Inguinal hernia in adults is a common and frequent disease in surgery, prone to occur in the elderly or in those with a weak abdominal wall. Despite its prevalence, Molecular mechanisms underlying inguinal hernia formation are unclear. This study aims to identify potential gene markers for inguinal hernia and available drugs. Pubmed2Ensembl text mining was used to identify genes related to "inguinal hernia" keywords. The GeneCodis system was used to specify GO biological process terms and KEGG pathways defined in the Kyoto Encyclopedia of Genes and Genomes (KEGG). The STRING tool was used to construct protein-protein interaction networks, which were then visualized using Cytoscape.CytoHubba and Molecular Complex Detection were utilized to analyze the module (MCODE). A GO and KEGG analysis of gene modules was conducted using the DAVID platform database. Hub genes are those that are concentrated in prominent modules. The druggene interaction database was also used to identify potential drugs for inguinal hernia patients based on their interactions between the hub genes. Finally, a Mendelian randomization study was conducted based on genome-wide association studies to determine whether hub genes cause inguinal hernias. The identification of 96 genes associated with inguinal hernia was carried out using text mining techniques. It was constructed using PPI networks with 80 nodes and 476 edges, and the sequence of the genes was performed using CytoHubba. MCODE analysis identified three gene modules. Three modules contain 37 genes clustered as hub candidate genes associated with inguinal hernia patients. The PI3K-Akt, MAPK, AGE-RAGE, and HIF-1 pathways were found to be enriched in signaling pathways. Sixteen of the 37 genes were found to be targetable by 30 existing drugs. The relationship between hub genes and inguinal hernia was examined using Mendelian randomization. The research revealed nine genes that may be connected with inguinal hernia, such as POMC, CD40LG, TFRC, VWF, LOX, IGF2, BRCA1, TNF, and HGF in the plasma. By inverse variance weighting, ALB was associated with an increased risk of inguinal hernia with an OR of 1.203 (OR [95%] = 1,04 [1.012 to 1.089], p = 0.008). We identified potential hub genes for inguinal hernia, predicted potential drugs for inguinal hernia, and reverse-validated potential genes by Mendelian randomization. This may provide further insights into asymptomatic pre-diagnostic methods and contribute to studies to understand the molecular mechanisms of risk genes associated with inguinal hernia.

Network-based identification of key proteins and repositioning of drugs for non-small cell lung cancer.

NSCLC is a lethal cancer that is highly prevalent and accounts for 85% of cases of lung cancer. Conventional cancer treatments, such as chemotherapy and radiation, frequently exhibit limited efficacy and notable adverse reactions. Therefore, a drug repurposing method is proposed for effective NSCLC treatment. This study aims to evaluate candidate drugs that are effective for NSCLC at the clinical level using a systems biology and network analysis approach. Differentially expressed genes in transcriptomics data were identified using the systems biology and network analysis approaches. A network of gene co-expression was developed with the aim of detecting two modules of gene co-expression. Following that, the Drug-Gene Interaction Database was used to find possible drugs that target important genes within two gene co-expression modules linked to non-small cell lung cancer (NSCLC). The use of Cytoscape facilitated the creation of a drug-gene interaction network. Finally, gene set enrichment analysis was done to validate candidate drugs. Unlike previous research on repositioning drugs for NSCLC, which uses a gene co-expression network, this project is the first to research both gene co-expression and co-occurrence networks. And the co-occurrence network also accounts for differentially expressed genes in cancer cells and their adjacent normal cells. For effective management of non-small cell lung cancer (NSCLC), drugs that show higher gene regulation and gene affinity within the drug-gene interaction network are thought to be important. According to the discourse, NSCLC genes have a lot of control over medicines like vincristine, fluorouracil, methotrexate, clotrimazole, etoposide, tamoxifen, sorafenib, doxorubicin, and pazopanib. Hence, there is a possibility of repurposing these drugs for the treatment of non-small-cell lung cancer.

Identification of potential cell death-related biomarkers for diagnosis and treatment of osteoporosis

BackgroundThis study aimed to identify potential biomarkers for the diagnosis and treatment of osteoporosis (OP).MethodsData sets were downloaded from the Gene Expression Omnibus database, and differentially programmed cell death-related genes were screened. Functional analyses were performed to predict the biological processes associated with these genes. Least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF) machine learning algorithms were used to screen for characteristic genes, and receiver operating characteristics were used to evaluate the diagnosis of disease characteristic gene values. Gene set enrichment analysis (GSEA) and single-sample GSEA were conducted to analyze the correlation between characteristic genes and immune infiltrates. Cytoscape and the Drug Gene Interaction Database (DGIdb) were used to construct the mitochondrial RNA-mRNA-transcription factor network and explore small-molecule drugs. Reverse transcription real-time quantitative PCR (RT-qPCR) analysis was performed to evaluate the expression of biomarker genes in clinical samples.ResultsIn total, 25 differential cell death genes were identified. Among these, two genes were screened using the LASSO, SVM, and RF algorithms as characteristic genes, including BRSK2 and VPS35. In GSE56815, the area under the receiver operating characteristic curve of BRSK2 was 0.761 and that of VPS35 was 0.789. In addition, immune cell infiltration analysis showed that BRSK2 positively correlated with CD56dim natural killer cells and negatively correlated with central memory CD4 + T cells. Based on the data from DGIdb, hesperadin was associated with BRSK2, and melagatran was associated with VPS35. BRSK2 and VPS35 were expectably upregulated in OP group compared with controls (all p < 0.05).ConclusionsBRSK2 and VPS35 may be important diagnostic biomarkers of OP.

Abstract 3482: Single-cell transcriptome analysis reveals that PLK4 signature genes are highly associated with cancer proliferation and hotspot mutations in non-small cell lung cancer (NSCLC)

Abstract Background: Polo-like kinase 4 (PLK4) is a member of the polo-like kinase family known for its precise regulation during the cell cycle and its key role in centriole duplication, which is essential for proper cell division. Dysregulation of PLK4 is associated with abnormal centriole number, genomic instability and cancer development. In our previous study, we proposed PLK4 as a potential therapeutic target and biomarker for LUAD patients with TP53 mutations by bulk RNA-seq analysis and confirmed its association with drug resistance. In this study, we analyzed the expression of PLK4 and its association with cell cycle at the cellular level by scRNA-seq in NSCLC data. Methods: In a previous study, we analyzed NSCLC data from The Cancer Genome Atlas (TCGA) database using the UCSC Xena data portal. In this study, we analyzed E-MTAB-6653 dataset from the single cell expression atlas(https://www.ebi.ac.uk/gxa/sc/home). Results: In the previous study, we demonstrated that PLK4 expression is higher in primary tumors compared to normal tissues in NSCLC. Higher PLK4 expression in patients is associated with an unfavorable prognosis. Using genes highly correlated with PLK4, we confirmed a significant association between PLK4 and TP53 mutation by statistical analysis. Using the Drug Gene Interaction database, we observed an association between the drug resistance-related genes PBK and BIRC5 with PLK4, and these genes were also correlated with TP53 mutation. In this study, we investigated the association between PLK4 and the cell cycle using single cell RNA-seq. First, we performed data pre-processing to selectively isolate NSCLC cells. Using a set of 87 PLK4 signature genes obtained from previous research, we stratified the samples into PLK4 high and low groups based on the expression of these genes. We then analyzed cell cycle related genes in both groups and observed a higher expression of G2/M phase related genes in the PLK4 high group compared to the PLK4 low group. We also examined the expression of PLK4 signature genes based on pathological differences and found higher expression in tumor tissue compared to normal tissue. There was also an increasing expression gradient from the tumor margin to the tumor core. Finally, using Gene Ontology analysis of both groups, we confirmed the upregulation of genes associated with transcription and translation, as indicated by the GO terms obtained. Conclusion: In a previous study, the association between PLK4 expression, TP53 mutation and drug resistance was identified by bulk RNA-seq. In the current study, we used single cell RNA-seq to verify the correlation between PLK4 and cell cycle in NSCLC. In addition, we observed that the expression of PLK4 signature genes increased towards the tumor core based on pathological differences. Citation Format: Youngtaek Kim, Joon Yeon Hwang, YoungJoon Park, Dong Kwon Kim, Kwangmin Na, Seul Lee, Sujeong Baek, Seong-san Kang, Seung Min Yang, Mi Hyun Kim, Heekyung Han, Chai Young Lee, Yu Jin Han, Min Hee Hong, Jii Bum Lee, Sun Min Lim, Kyoung-Ho Pyo, Byoung Chul Cho. Single-cell transcriptome analysis reveals that PLK4 signature genes are highly associated with cancer proliferation and hotspot mutations in non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3482.