DGIdb 2.0: mining clinically relevant drug-gene interactions

TFF3 Gene as a Molecular Target Potentially Shared by Idiopathic Pulmonary Fibrosis and Pulmonary Hypertension: Drug Repurposing Prospect with Aminoglutethimide

Idiopathic pulmonary fibrosis (IPF) and pulmonary hypertension (PH) are two chronic conditions that can coexist occasionally, resulting in high morbidity and mortality. Despite their clinical association, the underlying genetic mechanisms and therapeutic targets that might link these two chronic disorders remain poorly understood. The present study used in silico analyses and machine learning to uncover genetic features and potential therapeutic targets shared by IPF and PH. Differentially expressed genes (DEGs) were identified using RNA sequencing data from the Gene Expression Omnibus, which revealed a total of 13 common DEGs between IPF and PH. Importantly, among the identified genes, TFF3 was significantly upregulated in both diseases. TFF3 is targeted by aminoglutethimide as identified through the Drug Gene Interaction Database, and with an interaction score of 3.26. Using the Protein Contact Atlas, PROCHECK, PROSA, and ProtParam tools, the structural model of TFF3 was validated. Finally, molecular docking analysis demonstrated a binding affinity score of -6.1 kcal/mol between TFF3 and aminoglutethimide, indicating a stable and potentially effective interaction between aminoglutethimide and the target protein. Aminoglutethimide displayed favorable ADMET properties as well. In conclusion, this in silico study reports (1) potential overlapping molecular links between IPF and PH, and (2) in silico potential of aminoglutethimide and TFF3 in drug repurposing for therapeutic interventions targeting both IPF and PH. These findings also challenge the traditional paradigm of pharmaceutical innovation that has long relied on the "one drug, one disease" premise, and highlight the potentials of "one drug, polydisease" paradigm of drug discovery and development.

Identification of key genes and diagnostic biomarkers for peripheral atherosclerosis: A multi-omics approach

Peripheral atherosclerosis (PAS), characterized by lipid plaque accumulation in arterial walls, significantly increases cardiovascular risk. This study aimed to identify molecular biomarkers and elucidate underlying mechanisms of PAS progression. We analyzed 2 gene expression omnibus datasets (GSE28829, GSE100927) to identify differentially expressed genes (P < .05, |log2FC| ≥ 0.585). Functional enrichment (Gene Ontology/Kyoto Encyclopedia of Genes and Genomes) and Mendelian randomization analyses were performed using genome-wide association study and expression quantitative trait loci data. Six hub genes were validated through single-cell RNA sequencing and independent datasets. A diagnostic nomogram was developed and evaluated using calibration curves, decision curve analysis, and receiver operating characteristic metrics. Integrated analysis revealed 6 key PAS-associated genes (leukocyte immunoglobulin-like receptor B1, hematopoietic cell-specific lyn substrate 1, plasminogen activator urokinase, C-type lectin domain family 2 member B, phosphatidylinositol-4-phosphate 5-kinase type 1 beta, cofilin 2). The diagnostic model demonstrated exceptional accuracy, achieving area under the receiver operating characteristic curves of 1.0 (training) and 0.975 (validation). Mendelian randomization confirmed causal relationships, with cofilin 2 and phosphatidylinositol-4-phosphate 5-kinase type 1 beta showing protective effects (odds ratio 0.74-0.90), while C-type lectin domain family 2 member B, hematopoietic cell-specific lyn substrate 1, leukocyte immunoglobulin-like receptor B1, and plasminogen activator urokinase emerged as risk factors. This multi-omics study identifies novel molecular signatures of PAS and establishes a robust diagnostic tool. The findings advance our understanding of PAS pathogenesis and pave the way for personalized therapeutic strategies.

Comparative analysis of RNA expression identifies effective targeted drug in myoepithelial carcinoma

Myoepithelial carcinoma is an ultra-rare pediatric solid tumor with no targeted treatments. Clinical implementation of tumor RNA sequencing (RNA-Seq) for identifying therapeutic targets is underexplored in pediatric cancer. We previously published the Comparative Analysis of RNA Expression (CARE), a framework for incorporating RNA-Seq-derived gene expression into the clinic for difficult-to-treat pediatric cancers. Here, we discuss a 4-year-old male diagnosed with myoepithelial carcinoma who was treated at Stanford Medicine Children's Health. A metastatic lung nodule from the patient underwent standard-of-care tumor DNA profiling and CARE analysis, wherein the patient's tumor RNA-Seq profile was compared to over 11,000 uniformly analyzed tumor profiles from public data repositories. DNA profiling yielded no actionable mutations. CARE identified overexpression biomarkers and nominated a treatment that produced a durable clinical response. These findings underscore the utility of data sharing and concurrent analysis of large genomic datasets for clinical benefit, particularly for rare cancers with unknown biological drivers.

Mechanistic role of pyroptosis in Kawasaki disease: An integrative bioinformatics analysis of immune dysregulation, machine learning-based biomarker discovery, WGCNA, and drug repurposing insights

Kawasaki disease (KD) is an acute vasculitis that primarily affects children under five and is a leading cause of acquired heart disease in this age group. Despite the standard treatment with intravenous immunoglobulin (IVIG), approximately 10-20% of patients exhibit IVIG resistance, leading to persistent inflammation and an increased risk of coronary artery aneurysms（CAA）. The underlying molecular mechanisms driving KD, particularly the role of pyroptosis, remain incompletely understood. In this study, we employed integrative bioinformatics approaches to investigate the mechanistic role of pyroptosis in KD. By analyzing transcriptomic datasets, we identified differentially expressed genes (DEGs) associated with pyroptosis and immune dysregulation. Weighted Gene Co-Expression Network Analysis (WGCNA) was utilized to uncover key co-expressed gene modules, followed by functional enrichment analyses to explore the biological significance of these genes. Through machine learning-based biomarker discovery, we identified MYD88 and S100A12 as critical pyroptosis-related genes in KD. Their diagnostic potential was validated using external datasets, and their involvement in immune cell infiltration was assessed through computational deconvolution techniques. Furthermore, drug repurposing analysis and molecular docking simulations suggested that Atogepant, Ubrogepant, and Zanubrutinib could serve as potential therapeutic candidates targeting S100A12 and MYD88. These findings provide novel insights into the molecular pathogenesis of KD and highlight potential biomarkers and therapeutic targets for improving KD diagnosis and treatment strategies.

Integrative bioinformatics and machine learning identify key crosstalk genes and immune interactions in head and neck cancer and Hodgkin lymphoma

Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive malignancy with complex molecular underpinnings. Hodgkin lymphoma (HL), another distinct cancer type, shares several biological characteristics with HNSCC, particularly regarding immune system involvement. However, the molecular crosstalk between HNSCC and HL remains largely unexplored. This study aims to elucidate shared molecular mechanisms, identify potential diagnostic biomarkers, and uncover therapeutic targets through an integrative approach combining bioinformatics and machine learning techniques. Publicly available RNA sequencing datasets were utilized to identify differentially expressed genes (DEGs) in HNSCC, while weighted gene co-expression network analysis (WGCNA) was applied to uncover HL-associated gene modules. The intersection of HNSCC DEGs and HL-related modules was evaluated using protein-protein interaction (PPI) network analysis. Candidate hub genes were selected via machine learning algorithms, including LASSO regression, random forest, and support vector machine-recursive feature elimination (SVM-RFE). Prognostic and diagnostic values were assessed using survival analysis and ROC curves. Furthermore, scRNA-seq data were analyzed to assess gene expression in the tumor microenvironment, and drug sensitivity was evaluated to identify potential therapeutic agents. A total of 150 shared genes were identified at the intersection of HNSCC DEGs and HL-associated gene modules. PPI network analysis highlighted 16 candidate hub genes, among which IL6, CXCL13, and PLAU were prioritized through machine learning methods. Survival analysis revealed that high expression of CXCL13 and PLAU, and low expression of IL6, were significantly associated with poor prognosis in HNSCC patients. ROC curve analysis validated their diagnostic performance. Single-cell RNA-seq data confirmed the expression of these biomarkers in macrophages, epithelial cells, and fibroblasts within the tumor microenvironment. Drug sensitivity analysis identified Andrographolide, Rituximab, and Amiloride as potential therapeutic agents. This study identified IL6, CXCL13, and PLAU as critical biomarkers involved in immune regulation and tumor progression in both HNSCC and HL. These findings provide valuable insights into the shared molecular mechanisms and suggest novel therapeutic strategies for patients affected by these diseases.

Identification of a disulfidptosis-related genes signature for diagnostic and immune infiltration characteristics in cervical cancer

BACKGROUND

Cervical cancer (CC) ranks as the fourth most common malignancy affecting women globally, with research highlighting a rising incidence among younger age groups. Disulfidptosis, a newly identified form of regulated cell death, has been implicated in the pathogenesis of numerous diseases. This study employs bioinformatics analyses to explore the expression profiles and functional roles of disulfidptosis-related genes (DRGs) in the context of cervical cancer.

METHODS

Differential analysis of the gene expression matrix in CC was performed to identify differentially expressed genes. The overlap between these genes and disulfidptosis-related genes was then determined. Key hub genes were identified using multiple machine learning approaches, including LASSO regression, support vector machines (SVM), and random forest (RF). These hub genes were subsequently used to construct a predictive model, which was validated using external datasets to ensure robustness and reliability.

RESULTS

In this study, 11 overlapping genes were identified, among which four hub genes-BRK1, NDUFA11, RAC1, and NDUFS1-were extracted using machine learning techniques. The diagnostic performance of these hub genes was validated with external datasets, and a predictive model was constructed based on their expression. The model demonstrated an exceptionally high area under the curve (AUC) of 0.997. Moreover, AUC values exceeding 0.85 for two independent validation datasets further confirmed the model's accuracy and stability. Notably, NDUFA11 and BRK1 showed significant associations with patient survival, highlighting their prognostic importance in cervical squamous cell carcinoma. Using CMAP and DGIdb databases, Metformin and Coenzyme-I were identified as potential targeted therapies for NDUFS1 and NDUFA11, respectively, offering new therapeutic avenues for patients.

CONCLUSION

This study uncovered a strong association between disulfidptosis and CC and developed a predictive model to assess the risk in CC patients. These findings offer novel insights into identifying biomarkers and potential therapeutic targets for CC, paving the way for improved diagnostic and treatment strategies.

Integrated multiomics analysis identifies potential biomarkers and therapeutic targets for autophagy associated AKI to CKD transition

This study explored the relationship between acute kidney injury (AKI) and chronic kidney disease (CKD), focusing on autophagy-related genes and their immune infiltration during the transition from AKI to CKD. We performed weighted correlation network analysis (WGCNA) using two microarray datasets (GSE139061 and GSE66494) in the GEO database and identified autophagy signatures by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA enrichment analysis. Machine learning algorithms such as LASSO, random forest, and XGBoost were used to construct the diagnostic model, and the diagnostic performance of GSE30718 (AKI) and GSE37171 (CKD) was used as validation cohorts to evaluate its diagnostic performance. The study identified 14 autophagy candidate genes, among which ATP6V1C1 and COPA were identified as key biomarkers that were able to effectively distinguish between AKI and CKD. Immune cell infiltration and GSEA analysis revealed immune dysregulation in AKI, and these genes were associated with inflammation and immune pathways. Single-cell analysis showed that ATP6V1C1 and COPA were specifically expressed in AKI and CKD, which may be related to renal fibrosis. In addition, drug prediction and molecular docking analysis proposed SZ(+)-(S)-202-791 and PDE4 inhibitor 16 as potential therapeutic agents. In summary, this study provides new insights into the relationship between AKI and CKD and lays a foundation for the development of new treatment strategies.

Integrative bioinformatics analysis reveals STAT1, ORC2, and GTF2B as critical biomarkers in lupus nephritis with Monkeypox virus infection

The monkeypox virus (MPXV) is currently spreading rapidly around the world, but the mechanisms by which it interacts with lupus nephritis (LN) are unknown. The aim of this study was to investigate the role and mechanism of lupus nephritis combined with monkeypox virus infection. The data comes from GEO and GeneCards.Through Limma and Weighted Gene Co-expression Network Analysis (WGCNA) analysis, differential expression genes (DEGs) and module genes were identified, and KEGG and GO enrichment analysis was carried out.In addition, a protein-protein interaction (PPI) network was constructed and LASSO regression was used to screen genes related to senescence. The diagnostic effectiveness was evaluated using a Nomogram and the receiver operating characteristic (ROC) curve and verified using GSE99967.Immune infiltration and gene set enrichment analysis (GSEA) Were also included in the study.In the end, miRNet was used to construct a miRNA-mRNA-TF network and screen targeted drugs through DGIdb. 5707 DEGs were identified in the lupus nephritis and 737 in the monkeypox data. WGCNA and Lasso regression analyses screened for three important targets (STAT1, ORC2, and GTF2B) .Predictive modeling and ROC of STAT1, ORC2 and GTF2B by Nomogram showed good diagnostic value .Immune infiltration analysis showed immune cell disorders and related pathway activation.The miRNA-mRNA-TF network covers 516 miRNAs and 15 transcription factors, and enrichment analysis shows that it plays an important role in senescence and inflammation.Potential Target Drugs Screened Include Guttiferone K And Silicon Phthalocyanine 4. This study identifies STAT1, ORC2, and GTF2B as key factors in cellular senescence and immune dysregulation associated with lupus nephritis and monkeypox infection, suggesting they may serve as important predictive targets.

Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning

BACKGROUND

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has significant impacts on patients' quality of life and poses a substantial economic burden on society.

OBJECTIVE

This study aimed to elucidate the molecular mechanisms underlying SLE by analyzing glucocorticoid-related genes (GRGs) expression profiles.

METHODS

We examined the expression profiles of GRGs in SLE and performed consensus clustering analysis to identify stable patient clusters. We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. We conducted Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to investigate biological functional differences, and we also conducted CIBERSORTx to estimate the number of immune cells. Furthermore, we utilized least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF) algorithms to screen for hub genes. We then validated the expression of these hub genes and constructed nomograms for further validation. Moreover, we employed single-sample Gene Set Enrichment Analysis (ssGSEA) to analyze immune infiltration. We also constructed an RNA-binding protein (RBP)-mRNA network and conducted drug sensitivity analysis along with molecular docking studies.

RESULTS

Patients with SLE were divided into two subclusters, revealing a total of 2,681 DEGs. Among these, 1,458 genes were upregulated, while 1,223 were downregulated in cluster_1. GSVA showed significant changes in the pathways associated with cluster_1. Immune infiltration analysis revealed high levels of monocyte in all samples, with greater infiltration of various immune cells in cluster_1. A comparison of SLE patients to control subjects identified 269 DEGs, which were enriched in several pathways. Hub genes, including PTX3, DYSF and F2R, were selected through LASSO and RF methods, resulting in a well-performing diagnostic model. Drug sensitivity and docking studies suggested F2R as a potential new therapeutic target.

CONCLUSION

PTX3, DYSF and F2R are potentially linked to SLE and are proposed as new molecular markers for its onset and progression. Additionally, monocyte infiltration plays a crucial role in advancing SLE.

Comparative analysis of RNA expression in a single institution cohort of pediatric cancer patients

With the low incidence of mutations in pediatric cancers, alternate genomic approaches are needed to identify therapeutic targets. Our study, the Comparative Analysis of RNA Expression to Improve Pediatric and Young Adult Cancer Treatment, was conducted by the UC Santa Cruz Treehouse Childhood Cancer Initiative and Stanford University School of Medicine. RNA sequencing data from 33 children and young adults with a relapsed, refractory or rare cancer underwent CARE analysis to reveal activated cancer driver pathways and nominate treatments. We compare our pipeline to other gene expression outlier detection approaches and discuss challenges for clinical implementation. Of our 33 patients, 31 (94%) had findings of potential clinical significance. Findings were implemented in 5 patients, 3 of which had defined clinical benefit. We demonstrate that comparator cohort composition determines which outliers are detected. This study highlights the clinical utility and challenges of implementing comparative RNA sequencing analysis in the clinic.

Computational drug repurposing: approaches, evaluation of in silico resources and case studies

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for rheumatoid arthritis by integrated bioinformatics analysis and single-cell RNA sequencing analysis

Introduction

Rheumatoid arthritis (RA) is a chronic autoimmune disorder intricately liked with inflammation. Cuproptosis, an emerging type of cell death, has been implicated in the initiation and development of RA. However, the exact alterations in the expression and biological function of cuproptosis-related genes (CRGs) in RA remain poorly understood. Therefore, our study aims to elucidate the potential association between CRGs and RA, with the goal of identifying novel biomarkers for the treatment and prognosis of RA.

Methods

In this study, we identified ten differentially expressed cuproptosis-related genes (DE-CRGs) between patients with RA and controls. Through comprehensive functional enrichment and protein-protein interaction (PPI) network analysis, we explored the functional roles of the DE-CRGs. Additionally, we investigated the correlation between DE-CRGs and immune infiltration, immune factors, diagnostic efficacy, and potential therapeutic drugs.

Results

Leveraging single-cell RNA sequencing data, we conducted a detailed analysis to elucidate alterations in various cell clusters associated with RA. Our study unveiled a significant association between DE-CRGs and diverse biological functions, as well as potential drug candidates.

Discussion

These findings provide crucial insights into the involvement of DE-CRGs in the pathogenesis of RA and shed light on potential therapeutic strategies.

Identification of potential hub genes and drugs in septic kidney injury: a bioinformatic analysis with preliminary experimental validation

Background

Sepsis-associated kidney injury (SAKI) is a prevalent complication in intensive care unit (ICU) patients with sepsis. Diagnosis currently relies on clinical assessment, urine output, and serum creatinine levels, yet effective clinical treatments remain scarce. Our objectives are to explore prospective, targeted medications for the treatment of septic kidney injury and to employ bioinformatics to identify key genes and pathways that may be implicated in the pathogenesis of SAKI.

Methods

We utilized the GEO database for differential gene screening. Related genes of septic kidney injury were identified through Pubmed2Ensembl, followed by annotation and visualization of gene ontology biological processes and KEGG pathways using DAVID. Protein-protein interactions were analyzed with the STRING database, and hub genes were identified using Cytoscape software. Candidate genes were further validated through Metascape. The CTD database was employed to uncover the relationship between hub genes and acute kidney injury (AKI). CIBERSORT was applied to evaluate the infiltration of immune cells and their association with hub genes. Hub genes were experimentally verified through qPCR detection. Lastly, the Drug-Gene Interaction Database (DGIdb) was utilized to identify drug-gene interactions.

Results

Six genes, including TNF, CXCL8, IL-6, IL-1β, IL-2, and IL-10, were associated with three major signaling pathways: the COVID-19 adverse outcome pathway, an overview of pro-inflammatory and pro-fibrotic mediators, and the interleukin-10 signaling pathway. Additionally, 12 targeted drugs were identified as potential therapeutic agents.

A genome-scale drug discovery pipeline uncovers new therapeutic targets and a unique p97 allosteric binding site in Schistosoma mansoni

Schistosomes are parasitic flatworms that infect more than 200 million people globally. However, there is a shortage of molecular tools that enable the discovery of potential drug targets within schistosomes. Thus, praziquantel has remained the frontline treatment for schistosomiasis despite known liabilities. Here, we have conducted a genome-wide study in S. mansoni using the human druggable genome as a bioinformatic template to identify essential genes within schistosomes bearing similarity to catalogued drug targets. Then, we assessed these candidate targets in silico using a set of unbiased criteria to determine which possess ideal characteristics for a ready-made drug discovery campaign. Following this prioritization, we pursued a parasite p97 ortholog as a bona-fide drug target for the development of therapeutics to treat schistosomiasis. From this effort, we identified a covalent inhibitor series that kills schistosomes through an on-target killing mechanism by disrupting the ubiquitin proteasome system. Fascinatingly, these inhibitors induce a conformational change in the conserved D2 domain P-loop of schistosome p97 upon modification of Cys519. This conformational change reveals an allosteric binding site adjacent to the D2 domain active site reminiscent of the 'DFG' flip in protein kinases. This allosteric binding site can potentially be utilized to generate new classes of species-selective p97 inhibitors. Furthermore, these studies provide a resource for the development of alternative therapeutics for schistosomiasis and a workflow to identify potential drug targets in similar systems with few available molecular tools.

Unraveling the molecular landscape of non-small cell lung cancer: Integrating bioinformatics and statistical approaches to identify biomarkers and drug repurposing

Non-small-cell lung cancer (NSCLC) is one of the most malignant tumors and the leading cause of death from cancer worldwide and is increasing at a massive rate every year. Most NSCLC patients are diagnosed at advanced stages, which is associated with a poor prognosis and a very low 5-year survival rate. Therefore, the purpose of this study is to investigate molecular markers for early diagnosis, prognosis and therapy of NSCLC through the integration of bioinformatics and statistical methods. A total of 93 overlapping differentially expressed genes (oDEGs) were identified between NSCLC and normal samples through Linear Models for Microarray (LIMMA) and Significance Analysis of Microarrays (SAM) methods. Six top-degree oDEGs (CCNA2, CDC6, AURKA, CCNB1, MKI67, and PRC1) were identified as key genes (KGs) through the protein-protein interaction (PPI) network. The predictive accuracy analysis of the identified KGs revealed an accuracy of 96.92 %, with a sensitivity of 91.73 % and a specificity of 98.15 %. KGs associated with 3 molecular functions (MFs), 5 cellular components (CCs), 3 biological processes (BPs), and 4 pathways were identified through FunRich software. Analysis of expression levels using the UALCAN database revealed that KGs are significantly associated with potential early diagnostic biomarkers. Survival analysis using the GEPIA database demonstrated that the KGs possessed strong prognostic power for NSCLC. Finally, seven repurposed candidate drugs ENTRECTINIB, SORAFENIB, CHEMBL1765740, TOZASERTIB, NERVIANO, AZD-1152-HQPA, and SELICICLIB were proposed through molecular docking analysis. In conclusion, the findings of this study have the potential to significantly impact the early diagnosis, prognosis, and treatment of NSCLC.

Genetic insights into the shared molecular mechanisms of Crohn's disease and breast cancer: a Mendelian randomization and deep learning approach

The objective of this study was to explore the potential genetic link between Crohn's disease and breast cancer, with a focus on identifying druggable genes that may have therapeutic relevance. We assessed the causal relationship between these diseases through Mendelian randomization and investigated gene-drug interactions using computational predictions. This study sought to identify common genetic pathways possibly involved in immune responses and cancer progression, providing a foundation for future targeted treatment research. The dataset comprises single nucleotide polymorphisms used as instrumental variables for Crohn's disease, analyzed to explore their possible impact on breast cancer risk. Gene ontology and pathway enrichment analyses were conducted to identify genes shared between the two conditions, supported by protein-protein interaction networks, colocalization analyses, and deep learning-based predictions of gene-drug interactions. The identified hub genes and predicted gene-drug interactions offer preliminary insights into possible therapeutic targets for breast cancer and immune-related conditions. This dataset may be valuable for researchers studying genetic links between autoimmune diseases and cancer and for those interested in the early identification of potential drug targets.

Identification and Analysis of Autophagy-Related Genes as Diagnostic Markers and Potential Therapeutic Targets for Tuberculosis Through Bioinformatics

According to the World Health Organization, Mycobacterium tuberculosis infections affect approximately 25% of the world's population. There is mounting evidence linking autophagy and immunological dysregulation to tuberculosis (TB). As a result, this research set out to discover TB-related autophagy-related biomarkers and prospective treatment targets. We used five autophagy databases to get genes linked to autophagy and Gene Expression Omnibus databases to get genes connected to TB. Then, functional modules associated with autophagy were obtained by analyzing them using weighted gene co-expression network analysis. Both Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used to examine the autophagy-related genes (ATGs) of important modules. Limma was used to identify differentially expressed ATGs (DE-ATGs), and the external datasets were used to further confirm their identification. We used DE-ATGs and a protein-protein interaction network to search the hub genes. CIBERSORT was used to estimate the kinds and amounts of immune cells. After that, we built a drug-gene interaction network and a network that included messenger RNA, small RNA, and DNA. At last, the differential expression of hub ATGs was confirmed by RT-qPCR, immunohistochemistry, and western blotting. The diagnostic usefulness of hub ATGs was evaluated using receiver operating characteristic curve analysis. Including 508 ATGs, four of the nine modules strongly linked with TB were deemed essential. Interleukin 1B (IL1B), CAPS1, and signal transducer and activator of transcription 1 (STAT1) were identified by intersection out of 22 DE-ATGs discovered by differential expression analysis. Research into immune cell infiltration found that patients with TB had an increased proportion of plasma cells, CD8 T cells, and M0 macrophages. A competitive endogenous RNA network utilized 10 long non-coding RNAs and 2 miRNAs. Then, the IL1B-targeted drug Cankinumad was assessed using this network. During bioinformatics analysis, three hub genes were validated in mouse and macrophage infection models. We found that IL1B, CASP1, and STAT1 are important biomarkers for TB. As a result, these crucial hub genes may hold promise as TB treatment targets.

MDMNI-DGD: A novel graph neural network approach for druggable gene discovery based on the integration of multi-omics data and the multi-view network

Accurately predicting druggable genes is of paramount importance for enhancing the efficacy of targeted therapies, reducing drug-related toxicities and improving patients' survival rates. Nevertheless, accurately predicting candidate cancer-druggable genes remains a critical challenge in translational medicine due to the high heterogeneity and complexity of cancer data. In this study, we proposed a novel graph neural approach called Druggable Gene Discovery based on the Integration of Multi-omics Data and the Multi-view Network (MDMNI-DGD), aiming to predict and evaluate cancer-druggable genes. MDMNI-DGD integrated a comprehensive set of multi-omics data, including copy number variations, DNA methylation, somatic mutations, and gene expression profiles. Simultaneously, it constructed the multi-view gene association network based on protein-protein interactions (PPI), protein structural domains, gene co-expression, pathway co-occurrence, gene sequence and gene ontology. Compared to other state-of-the-art approaches, MDMNI-DGD exhibits excellent performance in key evaluation metrics such as AUROC and AUPR. Moreover, the case study has also demonstrated the efficacy of our approach in discovering potentially druggable genes. Among more than 20,000 protein-coding genes, MDMNI-DGD successfully identified 872 potentially druggable genes. The findings from this investigation may serve to bolster the assessment of pan-cancer druggable genes, potentially catalyzing the development of more personalized and efficacious therapeutic interventions.

Deep proteomics and network pharmacology reveal sex- and age-shared neuropathic pain signatures in mouse dorsal root ganglia

Our understanding of how sex and age influence chronic pain at the molecular level is still limited with wide-reaching consequences for adolescent patients. Here, we leveraged deep proteome profiling of mouse dorsal root ganglia (DRG) from adolescent (4-week-old) and adult (12-week-old) male and female mice to investigate the establishment of neuropathic pain in the spared nerve injury (SNI)-model in parallel. We quantified over 12,000 proteins, including notable ion channels involved in pain, highlighting the sensitivity of our approach. Differential expression revealed sex- and age-dependent proteome changes upon nerve injury. In contrast to most previous studies, our comprehensive dataset enabled us to determine differentially expressed proteins (DEPs), which were shared between male and female mice of both age groups. Among these, the vast majority (94 %) were also expressed and, in part, altered in human DRG of neuropathic pain patients, indicating evolutionary conservation. Proteome signatures represented numerous targets of FDA-approved drugs comprising both (i) known pain therapeutics (e.g. Pregabalin and opioids) and, importantly, (ii) compounds with high potential for future re-purposing, e.g. Ptprc-modulators and Epoetins. Protein network and multidimensional analysis uncovered distinct hubs of sex- and age-shared biological pathways impacted by neuropathic pain, such as neuronal activity and synaptic function, DNA-damage, and neuroimmune interactions. Taken together, our results capture the complexity of nerve injury-associated DRG alterations in mice at the network level, moving beyond single-candidate studies. Consequently, we provide an innovative resource of the molecular landscape of neuropathic pain, enabling novel opportunities for translational pain research and network-based drug discovery.

Cat-E: A comprehensive web tool for exploring cancer targeting strategies

Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.

RNA-Sequencing Identification of Genes Supporting HepG2 as a Model Cell Line for Hepatocellular Carcinoma or Hepatocytes

Background/Objectives: Cell lines do not faithfully replicate the authentic transcriptomic condition of the disease under study. The HepG2 cell line is widely used for studying hepatocellular carcinoma (HCC), but not all biological processes and genes exhibit congruent expression patterns between cell lines and the actual disease. The objective of this study is to perform a comparative transcriptomic analysis of the HepG2 cell line, HCC, and primary hepatocytes (PH) in order to identify genes suitable for research in HepG2 as a model for PH or HCC research. Methods: We conducted a differential expression analysis between publicly available data from HCC patients, PH, and HepG2. We examined specific overlaps of differentially expressed genes (DEGs) in a pairwise manner between groups in order to obtain a valuable gene list for studying HCC or PH using different parameter filtering. We looked into the function and druggability of these genes. Conclusions: In total, we identified 397 genes for HepG2 as a valuable HCC model and 421 genes for HepG2 as a valuable PH model, and with more stringent criteria, we derived a smaller list of 40 and 21 genes, respectively. The majority of genes identified as a valuable set for the HCC model are involved in DNA repair and protein degradation mechanisms. This research aims to provide detailed guidance on gene selection for studying diseases like hepatocellular carcinoma, primary hepatocytes, or others using cell lines.

Systems Biology and Machine Learning Identify Genetic Overlaps Between Lung Cancer and Gastroesophageal Reflux Disease

One Health and planetary health place emphasis on the common molecular mechanisms that connect several complex human diseases as well as human and planetary ecosystem health. For example, not only lung cancer (LC) and gastroesophageal reflux disease (GERD) pose a significant burden on planetary health, but also the coexistence of GERD in patients with LC is often associated with a poor prognosis. This study reports on the genetic overlaps between these two conditions using systems biology-driven bioinformatics and machine learning-based algorithms. A total of nine hub genes including IGHV1-3, COL3A1, ITGA11, COL1A1, MS4A1, SPP1, MMP9, MMP7, and LOC102723407 were found to be significantly altered in both LC and GERD as compared with controls and with pathway analyses suggesting a significant association with the matrix remodeling pathway. The expression of these genes was validated in two additional datasets. Random forest and K-nearest neighbor, two machine learning-based algorithms, achieved accuracies of 89% and 85% for distinguishing LC and GERD, respectively, from controls using these hub genes. Additionally, potential drug targets were identified, with molecular docking confirming the binding affinity of doxycycline to matrix metalloproteinase 7 (binding affinity: -6.8 kcal/mol). The present study is the first of its kind that combines in silico and machine learning algorithms to identify the gene signatures that relate to both LC and GERD and promising drug candidates that warrant further research in relation to therapeutic innovation in LC and GERD. Finally, this study also suggests upstream regulators, including microRNAs and transcription factors, that can inform future mechanistic research on LC and GERD.

Dynamic chromatin architecture identifies new autoimmune-associated enhancers for IL2 and novel genes regulating CD4+ T cell activation

Genome-wide association studies (GWAS) have identified hundreds of genetic signals associated with autoimmune disease. The majority of these signals are located in non-coding regions and likely impact cis-regulatory elements (cRE). Because cRE function is dynamic across cell types and states, profiling the epigenetic status of cRE across physiological processes is necessary to characterize the molecular mechanisms by which autoimmune variants contribute to disease risk. We localized risk variants from 15 autoimmune GWAS to cRE active during TCR-CD28 co-stimulation of naïve human CD4+ T cells. To characterize how dynamic changes in gene expression correlate with cRE activity, we measured transcript levels, chromatin accessibility, and promoter-cRE contacts across three phases of naive CD4+ T cell activation using RNA-seq, ATAC-seq, and HiC. We identified ~1200 protein-coding genes physically connected to accessible disease-associated variants at 423 GWAS signals, at least one-third of which are dynamically regulated by activation. From these maps, we functionally validated a novel stretch of evolutionarily conserved intergenic enhancers whose activity is required for activation-induced IL2 gene expression in human and mouse, and is influenced by autoimmune-associated genetic variation. The set of genes implicated by this approach are enriched for genes controlling CD4+ T cell function and genes involved in human inborn errors of immunity, and we pharmacologically validated eight implicated genes as novel regulators of T cell activation. These studies directly show how autoimmune variants and the genes they regulate influence processes involved in CD4+ T cell proliferation and activation.

Multiomics integrated analysis and experimental validation identify TLR4 and ALOX5 as oxidative stress-related biomarkers in intracranial aneurysms

BACKGROUND

Intracranial aneurysm (IA) is a severe cerebrovascular disease, and effective gene therapy and drug interventions for its treatment are still lacking. Oxidative stress (OS) is closely associated with the IA, but the key regulatory genes involved are still unclear. Through multiomics analysis and experimental validation, we identified two diagnostic markers for IA associated with OS.

METHODS

In this study, we first analyzed the IA dataset GSE75436 and conducted a joint analysis of oxidative stress-related genes (ORGs). Differential analysis, functional enrichment analysis, immune infiltration, WGCNA, PPI, LASSO, and other methods were used to identify IA diagnostic markers related to OS. Next, the functions of TLR4 and ALOX5 expression in IA and their potential targeted therapeutic drugs were analyzed. We also performed single-cell sequencing of patient IA and control (superficial temporal artery, STA) tissues. 23,342 cells were captured from 2 IA and 3 STA samples obtained from our center. Cell clustering and annotation were conducted using R software to observe the distribution of TLR4 and ALOX5 expression in IAs. Finally, the expression of TLR4 and ALOX5 were validated in IA patients and in an elastase-induced mouse IA model using experiments such as WB and immunofluorescence.

RESULTS

Through bioinformatics analysis, we identified 16 key ORGs associated with IA pathogenesis. Further screening revealed that ALOX5 and TLR4 were highly expressed to activate a series of inflammatory responses and reduce the production of myocytes. Methotrexate (MTX) may be a potential targeted drug. Single-cell analysis revealed a notable increase in immune cells in the IA group, with ALOX5 and TLR4 primarily localized to monocytes/macrophages. Validation through patient samples and mouse models confirmed high expression of ALOX5 and TLR4 in IAs.

CONCLUSIONS

Bioinformatics analysis indicated that ALOX5 and TLR4 are the most significant ORGs associated with the pathogenesis of IA. Single-cell sequencing and experiments revealed that the high expression of ALOX5 and TLR4 are closely related to IA. These two genes are promising new targets for IA therapy.

Alpha-1 antitrypsin targeted neutrophil elastase protects against sepsis-induced inflammation and coagulation in mice via inhibiting neutrophil extracellular trap formation

AIMS

Sepsis pathophysiology is complex and identifying effective treatments for sepsis remains challenging. The study aims to identify effective drugs and targets for sepsis through transcriptomic analysis of sepsis patients, repositioning analysis of compounds, and validation by animal models.

MAIN METHODS

GSE185263 obtained from the GEO database that includes gene expression profiles of 44 healthy controls and 348 sepsis patients categorized by severity. Bioinformatic algorithms revealed the molecular, function, and immune characteristics of the sepsis, and constructed sepsis-related protein-protein interaction networks. Subsequently, Random Walk with Restart analysis was applied to identify candidate drugs for sepsis, which were tested in animal models for survival, inflammation, coagulation, and multi-organ damage.

KEY FINDINGS

Our analysis found 1862 genes linked to sepsis development, enriched in functions like neutrophil extracellular trap formation (NETs) and complement/coagulation cascades. With disease progression, immune activation-associated cells were inhibited, while immune suppression-associated cells were activated. Next, the drug repositioning method identified candidate drugs, such as alpha-1 antitrypsin, that may play a therapeutic role by targeting neutrophil elastase (NE) to inhibit NETs. Animal experiments proved that alpha-1 antitrypsin treatment can improve the survival rate, reduce sepsis score, reduce the levels of inflammation markers in serum, and alleviate muti-organ morphological damage in mice with sepsis. The further results showed that α-1 antitrypsin can inhibit the NETs by suppressing the NE for the treatment of sepsis.

SIGNIFICANCE

Alpha-1 antitrypsin acted on the NE to inhibit NETs thereby protecting mice from sepsis-induced inflammation and coagulation.

Identification of Hub of the Hub-Genes From Different Individual Studies for Early Diagnosis, Prognosis, and Therapies of Breast Cancer

Breast cancer (BC) is a complex disease, which causes of high mortality rate in women. Early diagnosis and therapeutic improvements may reduce the mortality rate. There were more than 74 individual studies that have suggested BC-causing hub-genes (HubGs) in the literature. However, we observed that their HubG sets are not so consistent with each other. It may be happened due to the regional and environmental variations with the sample units. Therefore, it was required to explore hub of the HubG (hHubG) sets that might be more representative for early diagnosis and therapies of BC in different country regions and their environments. In this study, we selected top-ranked 10 HubGs (CCNB1, CDK1, TOP2A, CCNA2, ESR1, EGFR, JUN, ACTB, TP53, and CCND1) as the hHubG set by the protein-protein interaction network analysis based on all of 74 individual HubG sets. The hHubG set enrichment analysis detected some crucial biological processes, molecular functions, and pathways that are significantly associated with BC progressions. The expression analysis of hHubGs by box plots in different stages of BC progression and BC prediction models indicated that the proposed hHubGs can be considered as the early diagnostic and prognostic biomarkers. Finally, we suggested hHubGs-guided top-ranked 10 candidate drug molecules (SORAFENIB, AMG-900, CHEMBL1765740, ENTRECTINIB, MK-6592, YM201636, masitinib, GSK2126458, TG-02, and PAZOPANIB) by molecular docking analysis for the treatment against BC. We investigated the stability of top-ranked 3 drug-target complexes (SORAFENIB vs ESR1, AMG-900 vs TOP2A, and CHEMBL1765740 vs EGFR) by computing their binding free energies based on 100-ns molecular dynamic (MD) simulation based Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach and found their stable performance. The literature review also supported our findings much more for BC compared with the results of individual studies. Therefore, the findings of this study may be useful resources for early diagnosis, prognosis, and therapies of BC.

Identification of Potential Drug Targets for Myopia Through Mendelian Randomization

Purpose

The purpose of this study was to identify potential drug targets for myopia and explore underlying mechanisms.

Methods

Mendelian randomization (MR) was implemented to assess the effect of 2684 pharmacologically targetable genes in the blood and retina on the risk of myopia from a genomewide association study (GWAS) for age-at-onset of spectacle wearing-inferred mean spherical equivalent (MSE; discovery cohort, N = 287,448, European), which was further validated in a GWAS for autorefraction-measured MSE (replication cohort, N = 95,619, European). The reliability of the identified significant potential targets was strengthened by colocalization analysis. Additionally, enrichment analysis, protein-protein interaction network, and molecular docking were performed to explore the functional roles and the druggability of these targets.

Results

This systematic drug target identification has unveiled 6 putative genetically causal targets for myopia-CD34, CD55, Wnt3, LCAT, BTN3A1, and TSSK6-each backed by colocalization evidence in adult blood eQTL datasets. Functional analysis found that dopaminergic neuron differentiation, cell adhesion, Wnt signaling pathway, and plasma lipoprotein-associated pathways may be involved in myopia pathogenesis. Finally, drug prediction and molecular docking corroborated the pharmacological value of these targets with LCAT demonstrating the strongest binding affinity.

Conclusions

Our study not only opens new avenues for the development of therapeutic interventions for myopia but may also help to understand the underlying mechanisms of myopia.

Integrating plasma proteomics with genome-wide association data to identify novel drug targets for inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic disease that includes Crohn's disease (CD) and ulcerative colitis (UC). Although genome-wide association studies (GWASs) have identified many relevant genetic risk loci, the impact of these loci on protein abundance and their potential utility as clinical therapeutic targets remain uncertain. Therefore, this study aimed to investigate the pathogenesis of IBD and identify effective therapeutic targets through a comprehensive and integrated analysis. We systematically integrated GWAS data related to IBD, UC and CD (N = 25,305) by the study of de Lange KM with the human blood proteome (N = 7213) by the Atherosclerosis Risk in Communities (ARIC) study. Proteome-wide association study (PWAS), mendelian randomisation (MR) and Bayesian colocalisation analysis were used to identify proteins contributing to the risk of IBD. Integrative analysis revealed that genetic variations in IBD, UC and CD affected the abundance of five (ERAP2, RIPK2, TALDO1, CADM2 and RHOC), three (VSIR, HGFAC and CADM2) and two (MST1 and FLRT3) cis-regulated plasma proteins, respectively (P < 0.05). Among the proteins identified via Bayesian colocalisation analysis, CADM2 was found to be an important common protein between IBD and UC. A drug and five druggable target genes were identified from DGIdb after Bayesian colocalisation analysis. Our study's findings from genetic and proteomic approaches have identified compelling proteins that may serve as important leads for future functional studies and potential drug targets for IBD (UC and CD).

Thinking Beyond Disease Silos: Dysregulated Genes Common in Tuberculosis and Lung Cancer as Identified by Systems Biology and Machine Learning

The traditional way of thinking about human diseases across clinical and narrow phenomics silos often masks the underlying shared molecular substrates across human diseases. One Health and planetary health fields particularly address such complexities and invite us to think across the conventional disease nosologies. For example, tuberculosis (TB) and lung cancer (LC) are major pulmonary diseases with significant planetary health implications. Despite distinct etiologies, they can coexist in a given community or patient. This is both a challenge and an opportunity for preventive medicine, diagnostics, and therapeutics innovation. This study reports a bioinformatics analysis of publicly available gene expression data, identifying overlapping dysregulated genes, downstream regulators, and pathways in TB and LC. Analysis of NCBI-GEO datasets (GSE83456 and GSE103888) unveiled differential expression of CEACAM6, MUC1, ADM, DYSF, PLOD2, and GAS6 genes in both diseases, with pathway analysis indicating association with lysine degradation pathway. Random forest, a machine-learning-based classification, achieved accuracies of 84% for distinguishing TB from controls and 83% for discriminating LC from controls using these specific genes. Additionally, potential drug targets were identified, with molecular docking confirming the binding affinity of warfarin to GAS6. Taken together, the present study speaks of the pressing need to rethink clinical diagnostic categories of human diseases and that TB and LC might potentially share molecular substrates. Going forward, planetary health and One Health scholarship are poised to cultivate new ways of thinking about diseases not only across medicine and ecology but also across traditional diagnostic conventions.

Identifying drug candidates for pancreatic ductal adenocarcinoma based on integrative multiomics analysis

Background

Due to a lack of early diagnosis methods and effective drugs, pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. DNA methylation, transcriptome expression and gene copy number variation (CNV) have critical relationships with development and progression of various diseases. The purpose of the study was to screen reliable early diagnostic biomarkers and potential drugs based on integrative multiomics analysis.

Methods

We used methylation, transcriptome and CNV profiles to build a diagnostic model for PDAC. The protein expression of three model-related genes were externally validated using PDAC samples. Then, potential therapeutic drugs for PDAC were identified by interaction information related to existing drugs and genes.

Results

Four significant differentially methylated regions (DMRs) were selected from 589 common DMRs to build a high-performance diagnostic model for PDAC. Then, four hub genes, PHF12, FXYD3, PRKCB and ZNF582, were obtained. The external validation results showed that PHF12, FXYD3 and PRKCB protein expression levels were all upregulated in tumor tissues compared with adjacent normal tissues (P<0.05). Promising candidate drugs with activity against PDAC were screened and repurposed through gene expression analysis of online datasets. The five drugs, including topotecan, PD-0325901, panobinostat, paclitaxel and 17-AAG, with the highest activity among 27 PDAC cell lines were filtered.

Conclusions

Overall, the diagnostic model built based on four significant DMRs could accurately distinguish tumor and normal tissues. The five drug candidates might be repurposed as promising therapeutics for particular PDAC patients.

Interferon and B-cell Signatures Inform Precision Medicine in Lupus Nephritis

Introduction

Current therapeutic management of lupus nephritis (LN) fails to induce long-term remission in over 50% of patients, highlighting the urgent need for additional options.

Methods

We analyzed differentially expressed genes (DEGs) in peripheral blood from patients with active LN (n = 41) and active nonrenal lupus (n = 62) versus healthy controls (HCs) (n = 497) from the European PRECISESADS project (NTC02890121), and dysregulated gene modules in a discovery (n = 26) and a replication (n = 15) set of active LN cases.

Results

Replicated gene modules qualified for correlation analyses with serologic markers, and regulatory network and druggability analysis. Unsupervised coexpression network analysis revealed 20 dysregulated gene modules and stratified the active LN population into 3 distinct subgroups. These subgroups were characterized by low, intermediate, and high interferon (IFN) signatures, with differential dysregulation of the "B cell" and "plasma cells/Ig" modules. Drugs annotated to the IFN network included CC-motif chemokine receptor 1 (CCR1) inhibitors, programmed death-ligand 1 (PD-L1) inhibitors, and irinotecan; whereas the anti-CD38 daratumumab and proteasome inhibitor bortezomib showed potential for counteracting the "plasma cells/Ig" signature. In silico analysis demonstrated the low-IFN subgroup to benefit from calcineurin inhibition and the intermediate-IFN subgroup from B-cell targeted therapies. High-IFN patients exhibited greater anticipated response to anifrolumab whereas daratumumab appeared beneficial to the intermediate-IFN and high-IFN subgroups.

Conclusion

IFN upregulation and B and plasma cell gene dysregulation patterns revealed 3 subgroups of LN, which may not necessarily represent distinct disease phenotypes but rather phases of the inflammatory processes during a renal flare, providing a conceptual framework for precision medicine in LN.

AI approaches for the discovery and validation of drug targets

Artificial intelligence (AI) holds immense promise for accelerating and improving all aspects of drug discovery, not least target discovery and validation. By integrating a diverse range of biological data modalities, AI enables the accurate prediction of drug target properties, ultimately illuminating biological mechanisms of disease and guiding drug discovery strategies. Despite the indisputable potential of AI in drug target discovery, there are many challenges and obstacles yet to be overcome, including dealing with data biases, model interpretability and generalisability, and the validation of predicted drug targets, to name a few. By exploring recent advancements in AI, this review showcases current applications of AI for drug target discovery and offers perspectives on the future of AI for the discovery and validation of drug targets, paving the way for the generation of novel and safer pharmaceuticals.

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

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Construction of ferroptosis-related prediction model for pathogenesis, diagnosis and treatment of ruptured abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a dangerous cardiovascular disease, which often brings great psychological burden and economic pressure to patients. If AAA rupture occurs, it is a serious threat to patients' lives. Therefore, it is of clinical value to actively explore the pathogenesis of ruptured AAA and prevent its occurrence. Ferroptosis is a new type of cell death dependent on lipid peroxidation, which plays an important role in many cardiovascular diseases. In this study, we used online data and analysis of ferroptosis-related genes to uncover the formation of ruptured AAA and potential therapeutic targets. We obtained ferroptosis-related differentially expressed genes (Fe-DEGs) from GSE98278 dataset and 259 known ferroptosis-related genes from FerrDb website. Enrichment analysis of differentially expressed genes (DEGs) was performed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG). Receiver Operating characteristic (ROC) curve was employed to evaluate the diagnostic abilities of Fe-DEGs. Transcription factors and miRNAs of Fe-DEGs were identified through PASTAA and miRDB, miRWalk, TargetScan respectively. Single-sample gene set enrichment analysis (ssGSEA) was used to observe immune infiltration between the stable group and the rupture group. DGIdb database was performed to find potential targeted drugs of DEGs. GO and KEGG enrichment analysis found that DEGs mainly enriched in "cellular divalent inorganic cation homeostasis," "cellular zinc ion homeostasis," "divalent inorganic cation homeostasis," "Mineral absorption," "Cytokine - cytokine receptor interaction," "Coronavirus disease - COVID-19." Two up-regulated Fe-DEGs MT1G and DDIT4 were found to further analysis. Both single and combined applications of MT1G and DDIT4 showed good diagnostic efficacy (AUC = 0.8254, 0.8548, 0.8577, respectively). Transcription factors STAT1 and PU1 of MT1G and ARNT and MAX of DDIT4 were identified. Meanwhile, has_miR-548p-MT1G pairs, has_miR-53-3p/has_miR-181b-5p/ has_miR-664a-3p-DDIT4 pairs were found. B cells, NK cells, Th2 cells were high expression in the rupture group compared with the stable group, while DCs, Th1 cells were low expression in the rupture group. Targeted drugs against immunity, GEMCITABINE and INDOMETHACIN were discovered. We preliminarily explored the clinical significance of Fe-DEGs MT1G and DDIT4 in the diagnosis of ruptured AAA, and proposed possible upstream regulatory transcription factors and miRNAs. In addition, we also analyzed the immune infiltration of stable and rupture groups, and found possible targeted drugs for immunotherapy.

Genome-wide CRISPR screens identify PKMYT1 as a therapeutic target in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with an overall 5-year survival rate of <12% due to the lack of effective treatments. Novel treatment strategies are urgently needed. Here, PKMYT1 is identified through genome-wide CRISPR screens as a non-mutant, genetic vulnerability of PDAC. Higher PKMYT1 expression levels indicate poor prognosis in PDAC patients. PKMYT1 ablation inhibits tumor growth and proliferation in vitro and in vivo by regulating cell cycle progression and inducing apoptosis. Moreover, pharmacological inhibition of PKMYT1 shows efficacy in multiple PDAC cell models and effectively induces tumor regression without overt toxicity in PDAC cell line-derived xenograft and in more clinically relevant patient-derived xenograft models. Mechanistically, in addition to its canonical function of phosphorylating CDK1, PKMYT1 functions as an oncogene to promote PDAC tumorigenesis by regulating PLK1 expression and phosphorylation. Finally, TP53 function and PRKDC activation are shown to modulate the sensitivity to PKMYT1 inhibition. These results define PKMYT1 dependency in PDAC and identify potential therapeutic strategies for clinical translation.

Identification and Verification of Ferroptosis-Related Genes in Keratoconus Using Bioinformatics Analysis

Objective

Keratoconus is a commonly progressive and blinding corneal disorder. Iron metabolism and oxidative stress play crucial roles in both keratoconus and ferroptosis. However, the association between keratoconus and ferroptosis is currently unclear. This study aimed to analyze and verify the role of ferroptosis-related genes (FRGs) in the pathogenesis of keratoconus through bioinformatics.

Methods

We first obtained keratoconus-related datasets and FRGs. Then, the differentially expressed FRGs (DE-FRGs) associated with keratoconus were screened through analysis, followed by analysis of their biological functions. Subsequently, the LASSO and SVM-RFE algorithms were used to screen for diagnostic biomarkers. GSEA was performed to explore the potential functions of the marker genes. Finally, the associations between these biomarkers and immune cells were analyzed. qRT‒PCR was used to detect the expression of these biomarkers in corneal tissues.

Results

A total of 39 DE-FRGs were screened, and functional enrichment analysis revealed that the DE-FRGs were closely related to apoptosis, oxidative stress, and the immune response. Then, using multiple algorithms, 6 diagnostic biomarkers were selected, and the ROC curve was used to verify their risk prediction ability. In addition, based on CIBERSORT analysis, alterations in the immune microenvironment of keratoconus patients might be associated with H19, GCH1, CHAC1, and CDKN1A. Finally, qRT‒PCR confirmed that the expression of H19 and CHAC1 was elevated in the keratoconus group.

Conclusion

This study identified 6 DE-FRGs, 4 of which were associated with immune infiltrating cells, and established a diagnostic model with predictive value for keratoconus.

Small molecule drug discovery for glioblastoma treatment based on bioinformatics and cheminformatics approaches

Background: Glioblastoma (GBM) is a common and highly aggressive brain tumor with a poor prognosis for patients. It is urgently needed to identify potential small molecule drugs that specifically target key genes associated with GBM development and prognosis. Methods: Differentially expressed genes (DEGs) between GBM and normal tissues were obtained by data mining the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Gene function annotation was performed to investigate the potential functions of the DEGs. A protein-protein interaction (PPI) network was constructed to explore hub genes associated with GBM. Bioinformatics analysis was used to screen the potential therapeutic and prognostic genes. Finally, potential small molecule drugs were predicted using the DGIdb database and verified using chemical informatics methods including absorption, distribution, metabolism, excretion, toxicity (ADMET), and molecular docking studies. Results: A total of 429 DEGs were identified, of which 19 hub genes were obtained through PPI analysis. The hub genes were confirmed as potential therapeutic targets by functional enrichment and mRNA expression. Survival analysis and protein expression confirmed centromere protein A (CENPA) as a prognostic target in GBM. Four small molecule drugs were predicted for the treatment of GBM. Conclusion: Our study suggests some promising potential therapeutic targets and small molecule drugs for the treatment of GBM, providing new ideas for further research and targeted drug development.

Multiple gene-drug prediction tool reveals Rosiglitazone based treatment pathway for non-segmental vitiligo

Vitiligo is a skin disease characterized by selective loss of melanocytes, which seriously affects the appearance and causes great psychological stress to patients. In this study, we performed a comprehensive analysis of two vitiligo microarray datasets from the GEO database using bioinformatics tools to identify 297 up-regulated mRNAs and 186 down-regulated mRNAs, revealing important roles for pathways related to melanin synthesis, tyrosine metabolism, and inflammatory factors, such as "PPAR signaling pathway", "tyrosine metabolism", "nonalcoholic fatty liver disease (NAFLD) pathway", "melanogenesis", and "IL-17 signaling pathway". Combining the Search Tool for Interacting Chemicals (STITCH) database 5.0 and the drug-gene interaction database 3.0 (DGIdb), we identified that the PPAR-γ agonist rosiglitazone may promote melanin synthesis via EDNRB. Next, we investigated the mechanism of rosiglitazone and PPAR-γ pathway in promoting melanin production. Consistent with the results of bioinformatics analysis, the expression levels of PPAR-γ, EDNRB, and TYR were significantly reduced in human non-segmental vitiligo skin along with the reduction of MITF, a key gene for epidermal melanogenesis. Meanwhile, rosiglitazone increased melanin synthesis capacity in melanocytes and zebrafish by activating PPAR-γ and upregulating TYR, TYRP-1, and TYRP-2. Conversely, treatment of melanocytes with the PPAR-γ antagonist GW resulted in inhibition of melanin synthesis and expression of melanin-related factors. At the same time, simultaneous treatment of rosiglitazone with GW reversed the inhibitory effect of GW on melanin synthesis. In this study, we identified that rosiglitazone, an important insulin sensitizer, promotes melanin synthesis in melanocytes by increasing PPAR-γ activity and upregulating the expression levels of EDNRB and TYR. These findings may provide new ideas for exploring the pathogenesis and potential therapeutic targets of non-segmental vitiligo.

Targeting ERBB2 and PIK3R1 as a therapeutic strategy for dilated cardiomyopathy: A single-cell sequencing and mendelian randomization analysis

Background

Dilated cardiomyopathy (DCM) is widely recognized as a significant contributor to heart failure. Nevertheless, the absence of pharmaceutical interventions capable of reversing disease progression and improving prognosis underscores the imperative for additional research in this area.

Methods

First, we identified and evaluated three gene sets, namely "SC-DCM", "EP-DCM" and "Drug", using big data and multiple bioinformatics analysis methods. Accordingly, drug-treatable ("Hub") genes in DCM were identified. Following this, four microarray expression profile datasets were employed to authenticate the expression levels and discriminatory efficacy of "Hub" genes. Additionally, mendelian randomization analysis was conducted to ascertain the causal association between the "Hub genes" and heart failure. Finally, the "DGIdb" was applied to identify "Hub" genes-targeted drugs. The "ssGSEA" algorithm assessed the level of immune cell infiltration in DCM.

Results

Enrichment analysis showed that the "SC-DCM" and "EP-DCM" gene sets were closely associated with DCM. PIK3R1 and ERBB2 were identified as drug-treatable genes in DCM. Additional analysis using MR supported a causal relationship between ERBB2 and heart failure, but not PIK3R1. Moreover, PIK3R1 was positively correlated with immune activation, while ERBB2 was negatively correlated. We found that everolimus was a pharmacological inhibitor for both PIK3R1 and ERBB2. However, no pharmacological agonist was found for ERBB2.

Conclusion

PIK3R1 and ERBB2 are drug-treatable genes in DCM. ERBB2 has a causal effect on heart failure, and its normal expression may play a role in preventing the progression of DCM to heart failure. In addition, there is a cross-expression of PIK3R1 and ERBB2 genes in both DCM and tumors. The adaptive immune system and PIK3R1 may be involved in DCM disease progression, while ERBB2 exerts a protective effect against DCM.

Bioinformatics analysis and experimental validation of key genes associated with lumbar disc degeneration and biomechanics

Background

Lumbar disc degeneration (LDD) is an important pathological basis for the development of degenerative diseases of the lumbar spine. Most clinical patients have low back pain as their main symptom. The deterioration of the biomechanical environment is an important cause of LDD. Although there is a large amount of basic research on LDD, there are fewer reports that correlate biomechanical mechanisms with basic research. Our research aims to identify 304 key genes involved in LDD due to biomechanical deterioration, using a bioinformatics approach. We focus on SMAD3, CAV1, SMAD7, TGFB1 as hub genes, and screen for 30 potential target drugs, offering novel insights into LDD pathology and treatment options.

Methods

The Gene Cards, GenCLip3, OMIM and Drugbank databases were explored to obtain genes associated with biomechanics and LDD, followed by making veen plots to obtain both co-expressed genes. GO enrichment analysis and KEGG pathway analysis of the co-expressed genes were obtained using the DAVID online platform and visualised via a free online website. Protein interaction networks (PPI) were obtained through the STRING platform and visualised through Cytoscape 3.9.0. These genes were predicted for downstream interaction networks using the STITCH platform. Then, the GSE56081 dataset was used to validate the key genes. RT-PCR was used to detect mRNA expression of core genes in the degenerated nucleus pulposus (NP) samples and western bolt was used for protein expression. Lastly, the obtained hub genes were searched in the drug database (DGIdb) to find relevant drug candidates.

Results

From the perspective of biomechanics-induced LDD, we obtained a total of 304 genes, the GO functional enrichment and KEGG pathway enrichment analysis showed that the functions of these genes are mostly related to inflammation and apoptosis. The PPI network was constructed and four Hub genes were obtained through the plug-in of Cytoscape software, namely SMAD3, CAV1, SMAD7 and TGFB1. The analysis of key genes revealed that biomechanical involvement in LDD may be related to the TGF-β signaling pathway. Validation of the GSE56081 dataset revealed that SMAD3 and TGFB1 were highly expressed in degenerating NP samples. RT-PCR results showed that the mRNA expression of SMAD3 and TGFB1 was significantly increased in the severe degeneration group; Western blot results also showed that the protein expression of TGFB1 and P-SMAD3 was significantly increased. In addition, we identified 30 potential drugs.

Conclusion

This study presented a new approach to investigate the correlation between biomechanical mechanisms and LDD. The deterioration of the biomechanical environment may cause LDD through the TGF-β signaling pathway. TGFB1 and SMAD3 are important core targets. The important genes, pathways and drugs obtained in this study provided a new basis and direction for the study, diagnosis and treatment of LDD.

In Silico Approach to Molecular Profiling of the Transition from Ovarian Epithelial Cells to Low-Grade Serous Ovarian Tumors for Targeted Therapeutic Insights

This paper aims to elucidate the differentially coexpressed genes, their potential mechanisms, and possible drug targets in low-grade invasive serous ovarian carcinoma (LGSC) in terms of the biologic continuity of normal, borderline, and malignant LGSC. We performed a bioinformatics analysis, integrating datasets generated using the GPL570 platform from different studies from the GEO database to identify changes in this transition, gene expression, drug targets, and their relationships with tumor microenvironmental characteristics. In the transition from ovarian epithelial cells to the serous borderline, the FGFR3 gene in the "Estrogen Response Late" pathway, the ITGB2 gene in the "Cell Adhesion Molecule", the CD74 gene in the "Regulation of Cell Migration", and the IGF1 gene in the "Xenobiotic Metabolism" pathway were upregulated in the transition from borderline to LGSC. The ERBB4 gene in "Proteoglycan in Cancer", the AR gene in "Pathways in Cancer" and "Estrogen Response Early" pathways, were upregulated in the transition from ovarian epithelial cells to LGSC. In addition, SPP1 and ITGB2 genes were correlated with macrophage infiltration in the LGSC group. This research provides a valuable framework for the development of personalized therapeutic approaches in the context of LGSC, with the aim of improving patient outcomes and quality of life. Furthermore, the main goal of the current study is a preliminary study designed to generate in silico inferences, and it is also important to note that subsequent in vitro and in vivo studies will be necessary to confirm the results before considering these results as fully reliable.

A network-based drug prioritization and combination analysis for the MEK5/ERK5 pathway in breast cancer

BACKGROUND

Prioritizing candidate drugs based on genome-wide expression data is an emerging approach in systems pharmacology due to its holistic perspective for preclinical drug evaluation. In the current study, a network-based approach was proposed and applied to prioritize plant polyphenols and identify potential drug combinations in breast cancer. We focused on MEK5/ERK5 signalling pathway genes, a recently identified potential drug target in cancer with roles spanning major carcinogenesis processes.

RESULTS

By constructing and identifying perturbed protein-protein interaction networks for luminal A breast cancer, plant polyphenols and drugs from transcriptome data, we first demonstrated their systemic effects on the MEK5/ERK5 signalling pathway. Subsequently, we applied a pathway-specific network pharmacology pipeline to prioritize plant polyphenols and potential drug combinations for use in breast cancer. Our analysis prioritized genistein among plant polyphenols. Drug combination simulations predicted several FDA-approved drugs in breast cancer with well-established pharmacology as candidates for target network synergistic combination with genistein. This study also highlights the concept of target network enhancer drugs, with drugs previously not well characterised in breast cancer being prioritized for use in the MEK5/ERK5 pathway in breast cancer.

CONCLUSION

This study proposes a computational framework for drug prioritization and combination with the MEK5/ERK5 signaling pathway in breast cancer. The method is flexible and provides the scientific community with a robust method that can be applied to other complex diseases.

Osteosarcoma PDX-Derived Cell Line Models for Preclinical Drug Evaluation Demonstrate Metastasis Inhibition by Dinaciclib through a Genome-Targeted Approach

PURPOSE

Models to study metastatic disease in rare cancers are needed to advance preclinical therapeutics and to gain insight into disease biology. Osteosarcoma is a rare cancer with a complex genomic landscape in which outcomes for patients with metastatic disease are poor. As osteosarcoma genomes are highly heterogeneous, multiple models are needed to fully elucidate key aspects of disease biology and to recapitulate clinically relevant phenotypes.

EXPERIMENTAL DESIGN

Matched patient samples, patient-derived xenografts (PDX), and PDX-derived cell lines were comprehensively evaluated using whole-genome sequencing and RNA sequencing. The in vivo metastatic phenotype of the PDX-derived cell lines was characterized in both an intravenous and an orthotopic murine model. As a proof-of-concept study, we tested the preclinical effectiveness of a cyclin-dependent kinase inhibitor on the growth of metastatic tumors in an orthotopic amputation model.

RESULTS

PDXs and PDX-derived cell lines largely maintained the expression profiles of the patient from which they were derived despite the emergence of whole-genome duplication in a subset of cell lines. The cell lines were heterogeneous in their metastatic capacity, and heterogeneous tissue tropism was observed in both intravenous and orthotopic models. Single-agent dinaciclib was effective at dramatically reducing the metastatic burden.

CONCLUSIONS

The variation in metastasis predilection sites between osteosarcoma PDX-derived cell lines demonstrates their ability to recapitulate the spectrum of the disease observed in patients. We describe here a panel of new osteosarcoma PDX-derived cell lines that we believe will be of wide use to the osteosarcoma research community.

Identification of an 8 HPV-related RNA signature as a novel prognostic biomarker for squamous cell carcinoma of the head and neck

Squamous cell carcinoma of the head and neck (SCCHN) is a commonly detected cancer worldwide. Human papillomavirus (HPV) is emerging as an important risk factor affecting SCCHN prognosis. Therefore, identification of HPV status is essential for effective therapies in SCCHN. The aim of this study was to investigate the prognostic value of HPV-associated RNA biomarkers for SCCHN. The clinical data, survival data, and RNA-seq data of SCCHN were downloaded from The Cancer Genome Atlas database. Before the differential expression analysis, the heterogeneity between the 2 groups (HPV+ vs HPV-) of samples was analyzed using principal component analysis. The differentially expressed genes (DEGs) between HPV+ and HPV- SCCHN samples were analyzed using the R edgeR package. The Gene Ontology functional annotations, including biological process, molecular function and cellular component (CC), and Kyoto Encyclopedia of Genes And Genomes pathways enriched by the DEGs were analyzed using DAVID. The obtained matrix was analyzed by weighed gene coexpression network analysis. A total of 350 significant DEGs were identified through differential analysis, and these DEGs were significantly enriched in functions associated with keratinization, and the pathway of neuroactive ligand-receptor interaction. Moreover, 72 hub genes were identified through weighed gene coexpression network analysis. After the hub genes and DEGs were combined, we obtained 422 union genes, including 65 survival-associated genes. After regression analysis, a HPV-related prognostic model was established, which consisted of 8 genes, including Clorf105, CGA, CHRNA2, CRIP3, CTAG2, ENPP6, NEFH, and RNF212. The obtained regression model could be expressed by an equation as follows: risk score = 0.065 × Clorf105 + 0.012 × CGA + 0.01 × CHRNA2 + 0.047 × CRIP3 + 0.043 × CTAG2-0.034 × ENPP6 - 0.003 × NEFH - 0.068 × RNF212. CGA interacted with 3 drugs, and CHRNA2 interacted with 11 drugs. We have identified an 8 HPV-RNA signature associated with the prognosis of SCCHN patients. Such prognostic model might serve as possible candidate biomarker and therapeutic target for SCCHN.

Profiling Kinase Activities for Precision Oncology in Diffuse Gastric Cancer

Gastric cancer (GC) remains a leading cause of cancer-related mortality globally. This is due to the fact that majority of the cases of GC are diagnosed at an advanced stage when the treatment options are limited and prognosis is poor. The diffuse subtype of gastric cancer (DGC) under Lauren's classification is more aggressive and usually occurs in younger patients than the intestinal subtype. The concept of personalized medicine is leading to the identification of multiple biomarkers in a large variety of cancers using different combinations of omics technologies. Proteomic changes including post-translational modifications are crucial in oncogenesis. We analyzed the phosphoproteome of DGC by using paired fresh frozen tumor and adjacent normal tissue from five patients diagnosed with DGC. We found proteins involved in the epithelial-to-mesenchymal transition (EMT), c-MYC pathway, and semaphorin pathways to be differentially phosphorylated in DGC tissues. We identified three kinases, namely, bromodomain adjacent to the zinc finger domain 1B (BAZ1B), WNK lysine-deficient protein kinase 1 (WNK1), and myosin light-chain kinase (MLCK) to be hyperphosphorylated, and one kinase, AP2-associated protein kinase 1 (AAK1), to be hypophosphorylated. LMNA hyperphosphorylation at serine 392 (S392) was demonstrated in DGC using immunohistochemistry. Importantly, we have detected heparin-binding growth factor (HDGF), heat shock protein 90 (HSP90), and FTH1 as potential therapeutic targets in DGC, as drugs targeting these proteins are currently under investigation in clinical trials. Although these new findings need to be replicated in larger study samples, they advance our understanding of signaling alterations in DGC, which could lead to potentially novel actionable targets in GC.

Machine learning-based integration identifies the ferroptosis hub genes in nonalcoholic steatohepatitis

BACKGROUND

Ferroptosis, is characterized by lipid peroxidation of fatty acids in the presence of iron ions, which leads to cell apoptosis. This leads to the disruption of metabolic pathways, ultimately resulting in liver dysfunction. Although ferroptosis is linked to nonalcoholic steatohepatitis (NASH), understanding the key ferroptosis-related genes (FRGs) involved in NASH remains incomplete. NASH may be targeted therapeutically by identifying the genes responsible for ferroptosis.

METHODS

To identify ferroptosis-related genes and develop a ferroptosis-related signature (FeRS), 113 machine-learning algorithm combinations were used.

RESULTS

The FeRS constructed using the Generalized Linear Model Boosting algorithm and Gradient Boosting Machine algorithms exhibited the best prediction performance for NASH. Eight FRGs, with ZFP36 identified by the algorithms as the most crucial, were incorporated into in FeRS. ZFP36 is significantly enriched in various immune cell types and exhibits significant positive correlations with most immune signatures.

CONCLUSION

ZFP36 is a key FRG involved in NASH pathogenesis.

DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms

The Drug-Gene Interaction Database (DGIdb, https://dgidb.org) is a publicly accessible resource that aggregates genes or gene products, drugs and drug-gene interaction records to drive hypothesis generation and discovery for clinicians and researchers. DGIdb 5.0 is the latest release and includes substantial architectural and functional updates to support integration into clinical and drug discovery pipelines. The DGIdb service architecture has been split into separate client and server applications, enabling consistent data access for users of both the application programming interface (API) and web interface. The new interface was developed in ReactJS, and includes dynamic visualizations and consistency in the display of user interface elements. A GraphQL API has been added to support customizable queries for all drugs, genes, annotations and associated data. Updated documentation provides users with example queries and detailed usage instructions for these new features. In addition, six sources have been added and many existing sources have been updated. Newly added sources include ChemIDplus, HemOnc, NCIt (National Cancer Institute Thesaurus), Drugs@FDA, HGNC (HUGO Gene Nomenclature Committee) and RxNorm. These new sources have been incorporated into DGIdb to provide additional records and enhance annotations of regulatory approval status for therapeutics. Methods for grouping drugs and genes have been expanded upon and developed as independent modular normalizers during import. The updates to these sources and grouping methods have resulted in an improvement in FAIR (findability, accessibility, interoperability and reusability) data representation in DGIdb.

Exploring the cuproptosis-related molecular clusters in the peripheral blood of patients with amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a progressive and lethal neurodegenerative disease. Several studies have suggested the involvement of cuproptosis in its pathogenesis. In this research, we intend to explore the cuproptosis-related molecular clusters in ALS and develop a novel cuproptosis-related genes prediction model.

METHODS

The peripheral blood gene expression data was downloaded from the Gene Expression Omnibus (GEO) online database. Based on the GSE112681 dataset, we investigated the critical cuproptosis-related genes (CuRGs) and pathological clustering of ALS. The immune microenvironment features of the peripheral blood in ALS patients were also examined using the CIBERSORT algorithm. Cluster-specific hub genes were determined by the WGCNA. The most accurate prediction model was selected by comparing the performance of four machine learning techniques. ROC curves and two independent datasets were applied to validate the prediction accuracy. The available compounds targeting these hub genes were filtered to investigate their efficacy in treating ALS.

RESULTS

We successfully determined four critical cuproptosis-related genes and two pathological clusters with various immune profiles and biological characteristics in ALS. Functional analysis showed that genes in Cluster1 were primarily enriched in pathways closely associated with immunity. The Support Vector Machine (SVM) model exhibited the best discrimination properties with a large area under the curve (AUC = 0.862). Five hub prediction genes (BAP1, SMG1, BCLAF1, DHX15, EIF4G2) were selected to establish a nomogram model, suggesting significant risk prediction potential for ALS. The accuracy of this model in predicting ALS incidence was also demonstrated through calibration curves, nomograms, and decision curve analysis. Finally, three drugs targeting BAP1 were determined through drug-gene interactions.

CONCLUSION

This study elucidated the complex associations between cuproptosis and ALS and constructed a satisfactory predictive model to explore the pathological characteristics of different clusters in ALS patients.

Epstein-Barr Virus: Human Interactome Reveals New Molecular Insights into Viral Pathogenesis for Potential Therapeutics and Antiviral Drug Discovery

Host-virus Protein-Protein Interactions (PPIs) play pivotal roles in biological processes crucial for viral pathogenesis and by extension, inform antiviral drug discovery and therapeutics innovations. Despite efforts to develop the Epstein-Barr virus (EBV)-host PPI network, there remain significant knowledge gaps and a limited number of interacting human proteins deciphered. Furthermore, understanding the dynamics of the EBV-host PPI network in the distinct lytic and latent viral stages remains elusive. In this study, we report a comprehensive map of the EBV-human protein interactions, encompassing 1752 human and 61 EBV proteins by integrating data from the public repository HPIDB (v3.0) as well as curated high-throughput proteomic data from the literature. To address the stage-specific nature of EBV infection, we generated two detailed subset networks representing the latent and lytic stages, comprising 747 and 481 human proteins, respectively. Functional and pathway enrichment analysis of these subsets uncovered the profound impact of EBV proteins on cancer. The identification of highly connected proteins and the characterization of intrinsically disordered and cancer-related proteins provide valuable insights into potential therapeutic targets. Moreover, the exploration of drug-protein interactions revealed notable associations between hub proteins and anticancer drugs, offering novel perspectives for controlling EBV pathogenesis. This study represents, to the best of our knowledge, the first comprehensive investigation of the two distinct stages of EBV infection using high-throughput datasets. This makes a contribution to our understanding of EBV-host interactions and provides a foundation for future drug discovery and therapeutic interventions.

Multi-omic characterization of ILC and ILC-like cell lines as part of ILC cell line encyclopedia (ICLE) defines new models to study potential biomarkers and explore therapeutic opportunities

Invasive lobular carcinoma (ILC), the most common histological "special type", accounts for ∼10-15% of all BC diagnoses, is characterized by unique features such as E-cadherin loss/deficiency, lower grade, hormone receptor positivity, larger diffuse tumors, and specific metastatic patterns. Despite ILC being acknowledged as a disease with distinct biology that necessitates specialized and precision medicine treatments, the further exploration of its molecular alterations with the goal of discovering new treatments has been hindered due to the scarcity of well-characterized cell line models for studying this disease. To address this, we generated the ILC Cell Line Encyclopedia (ICLE), providing a comprehensive multi-omic characterization of ILC and ILC-like cell lines. Using consensus multi-omic subtyping, we confirmed luminal status of previously established ILC cell lines and uncovered additional ILC/ILC-like cell lines with luminal features for modeling ILC disease. Furthermore, most of these luminal ILC/ILC-like cell lines also showed RNA and copy number similarity to ILC patient tumors. Similarly, ILC/ILC-like cell lines also retained molecular alterations in key ILC genes at similar frequency to both primary and metastatic ILC tumors. Importantly, ILC/ILC-like cell lines recapitulated the CDH1 alteration landscape of ILC patient tumors including enrichment of truncating mutations in and biallelic inactivation of CDH1 gene. Using whole-genome optical mapping, we uncovered novel genomic-rearrangements including novel structural variations in CDH1 and functional gene fusions and characterized breast cancer specific patterns of chromothripsis in chromosomes 8, 11 and 17. In addition, we systematically analyzed aberrant DNAm events and integrative analysis with RNA expression revealed epigenetic activation of TFAP2B - an emerging biomarker of lobular disease that is preferentially expressed in lobular disease. Finally, towards the goal of identifying novel druggable vulnerabilities in ILC, we analyzed publicly available RNAi loss of function breast cancer cell line datasets and revealed numerous putative vulnerabilities cytoskeletal components, focal adhesion and PI3K/AKT pathway in ILC/ILC-like vs NST cell lines. In summary, we addressed the lack of suitable models to study E-cadherin deficient breast cancers by first collecting both established and putative ILC models, then characterizing them comprehensively to show their molecular similarity to patient tumors along with uncovering their novel multi-omic features as well as highlighting putative novel druggable vulnerabilities. Not only we expand the array of suitable E-cadherin deficient cell lines available for modelling human-ILC disease but also employ them for studying epigenetic activation of a putative lobular biomarker as well as identifying potential druggable vulnerabilities for this disease towards enabling precision medicine research for human-ILC.