Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

Conventional and genetic association between migraine and stroke with druggable genome-wide Mendelian randomization

The genetic relationship between migraine and stroke remains underexplored, particularly in the context of druggable targets. Previous studies have been limited by small sample sizes and a lack of focus on genetic-targeted therapies for these conditions. We analyzed the association and causality between migraine and stroke using multivariable logistic regression in the UK Biobank cohort and Mendelian randomization (MR) analyses based on genome-wide association study (GWAS) data. Integrating expression quantitative trait loci (eQTLs) data from blood and brain regions, we explored the phenotypic and genetic links between migraine medications, drug target, and stroke. Additionally, we explored novel druggable genes for migraine and evaluated their effects on migraine signaling molecules and stroke risk. Migraine was significantly associated with stroke, particularly ischemic stroke (IS) and intracerebral hemorrhage (ICH), with MR analysis confirming a causal link to ICH. HTR1A emerged as a potential link between antidepressants (preventive medications for migraine) and stroke. We identified 17 migraine-related druggable genes, with 5 genes (HMGCR, TGFB1, TGFB3, KCNK5, IMPDH2) associated with nine existing drugs. Further MR analysis identified correlation of CELSR3 and IMPDH2 with cGMP pathway marker PRKG1, and identified KCNK5, PLXNB1, and MDK as novel migraine-associated druggable genes significantly linked to the stroke risks. These findings established the phenotypic and genetic link between migraine, its medication and stroke, identifying potential targets for single and dual-purpose therapies for migraine and stoke, and emphasized the need for further research to validate these associations.

Comprehensive interrogation of synthetic lethality in the DNA damage response

The DNA damage response (DDR) is a multifaceted network of pathways that preserves genome stability1,2. Unravelling the complementary interplay between these pathways remains a challenge3,4. Here we used CRISPR interference (CRISPRi) screening to comprehensively map the genetic interactions required for survival during normal human cell homeostasis across all core DDR genes. We captured known interactions and discovered myriad new connections that are available online. We defined the molecular mechanism of two of the strongest interactions. First, we found that WDR48 works with USP1 to restrain PCNA degradation in FEN1/LIG1-deficient cells. Second, we found that SMARCAL1 and FANCM directly unwind TA-rich DNA cruciforms, preventing catastrophic chromosome breakage by the ERCC1-ERCC4 complex. Our data yield fundamental insights into genome maintenance, provide a springboard for mechanistic investigations into new connections between DDR factors and pinpoint synthetic vulnerabilities that could be exploited in cancer therapy.

Transcriptomic signatures and network-based methods uncover new senescent cell anti-apoptotic pathways and senolytics

Cellular senescence is an irreversible cell cycle arrest caused by various stressors that damage cells. Over time, senescent cells accumulate and contribute to the progression of multiple age-related degenerative diseases. It is believed that these cells accumulate partly due to their ability to evade programmed cell death through the development and activation of survival and antiapoptotic resistance mechanisms; however, many aspects of how these survival mechanisms develop and activate are still unknown. By analyzing transcriptomic signature profiles generated by the LINCS L1000 project and using network-based methods, we identified various genes that could represent new senescence-related survival mechanisms. Additionally, employing the same methodology, we identified over 600 molecules with potential senolytic activity. Experimental validation of our computational findings confirmed the senolytic activity of Fluorouracil, whose activity would be mediated by a multitarget mechanism, revealing that its targets AURKA, EGFR, IRS1, SMAD4, and KRAS are new senescent cell antiapoptotic pathways (SCAPs). The development of these pathways could depend on the stimulus that induces cellular senescence. The SCAP development and activation mechanisms proposed in this work offer new insights into how senescent cells survive. Identifying new antiapoptotic resistance targets and drugs with potential senolytic activity paves the way for developing new pharmacological therapies to eliminate senescent cells selectively.

Identification of key hub genes in pancreatic ductal adenocarcinoma: an integrative bioinformatics study

Pancreatic Ductal Adenocarcinoma (PDAC) poses a significant health threat characterized by poor clinical outcomes, largely attributable to late detection, chemotherapy resistance, and the absence of tailored therapies. Despite progress in surgical, radiation, and chemotherapy treatments, 80% of PDAC patients do not benefit optimally from systemic therapy, often due to asymptomatic presentation or disease regression upon diagnosis. The disease's progression is influenced by complex interactions involving immunological, genetic, and environmental factors, among others. However, the precise molecular mechanisms underlying PDAC remain incompletely understood. A major challenge in elucidating PDAC's origins lies in deciphering the genetic variations governing its network. PDAC exhibits heterogeneity, manifesting diverse genetic compositions, cellular attributes, and behaviors across patients and within tumors. This diversity complicates diagnosis, treatment strategies, and prognostication. Identification of "Differentially Expressed Genes" (DEGs) between PDAC and healthy controls is vital for addressing these challenges. These DEGs serve as the foundation for constructing the PDAC protein interaction network, with their network properties being assessed for further insights. Our analysis revealed five key hub genes (KHGs): EGF, SRC, SDC1, ICAM1 and CEACAM5. The KHGs were predominantly enriched in pathways such as: ErbB signaling pathway, Rap1 signaling pathway, etc. Acknowledging the therapeutic promise and biomarker importance of PDAC KHGs, we have also pinpointed approved medications for the identified key genes. Nevertheless, it is crucial to conduct experimental validation on KHGs to confirm their effectiveness within the PDAC context. Overall, this study identified potential key hub genes implicated in the progression of PDAC, offering significant guidance for personalized clinical decision-making and molecular-targeted therapy for PDAC patients.

Evaluation of search-enabled pretrained Large Language Models on retrieval tasks for the PubChem database

Motivation

Databases are indispensable in biological and biomedical research, hosting vast amounts of structured and unstructured data, facilitating the organization, retrieval, and analysis of complex data. Database access, however, remains a manual, tedious, and sometimes overwhelming, task. The availability of Large Language Models (LLMs) has the potential to play a transformative role in accessing databases.

Results

We investigate in this study the current state of using a pretrained, search-enabled LLMs (ChatGPT-4o), for data retrieval from PubChem, a flagship database that plays a critical role in biological and biomedical research. We evaluate eight PubChem access protocols that were previously documented. We develop a methodology for adopting the protocols into an LLM-prompt, where we supplement the prompt with additional context through iterative prompt refinement as needed. To further evaluate the LLM capabilities, we instruct the LLM to perform the retrieval. We quantitatively and qualitatively show that instructing ChatGPT-4o to generate programmatic access is more likely to yield the correct answers. We provide insightful future directions in developing LLMs for database access.

Availability and implementation

All text used to prompt ChatGPT-4o is provided in the manuscript.

Identifying behavior regulatory leverage over mental disorders transcriptomic network hubs toward lifestyle-dependent psychiatric drugs repurposing

BACKGROUND

There is a vast prevalence of mental disorders, but patient responses to psychiatric medication fluctuate. As food choices and daily habits play a fundamental role in this fluctuation, integrating machine learning with network medicine can provide valuable insights into disease systems and the regulatory leverage of lifestyle in mental health.

METHODS

This study analyzed coexpression network modules of MDD and PTSD blood transcriptomic profile using modularity optimization method, the first runner-up of Disease Module Identification DREAM challenge. The top disease genes of both MDD and PTSD modules were detected using random forest model. Afterward, the regulatory signature of two predominant habitual phenotypes, diet-induced obesity and smoking, were identified. These transcription/translation regulating factors (TRFs) signals were transduced toward the two disorders' disease genes. A bipartite network of drugs that target the TRFS together with PTSD or MDD hubs was constructed.

RESULTS

The research revealed one MDD hub, the CENPJ, which is known to influence intellectual ability. This observation paves the way for additional investigations into the potential of CENPJ as a novel target for MDD therapeutic agents development. Additionally, most of the predicted PTSD hubs were associated with multiple carcinomas, of which the most notable was SHCBP1. SHCBP1 is a known risk factor for glioma, suggesting the importance of continuous monitoring of patients with PTSD to mitigate potential cancer comorbidities. The signaling network illustrated that two PTSD and three MDD biomarkers were co-regulated by habitual phenotype TRFs. 6-Prenylnaringenin and Aflibercept were identified as potential candidates for targeting the MDD and PTSD hubs: ATP6V0A1 and PIGF. However, habitual phenotype TRFs have no leverage over ATP6V0A1 and PIGF.

CONCLUSION

Combining machine learning and network biology succeeded in revealing biomarkers for two notoriously spreading disorders, MDD and PTSD. This approach offers a non-invasive diagnostic pipeline and identifies potential drug targets that could be repurposed under further investigation. These findings contribute to our understanding of the complex interplay between mental disorders, daily habits, and psychiatric interventions, thereby facilitating more targeted and personalized treatment strategies.

Potential therapeutic targets for bladder cancer: a proteome-wide Mendelian randomization study

The incidence of bladder cancer (BCa) is increasing worldwide and the development of drug targets for BCa is necessary. We conducted a proteome-wide association study (PWAS) mainly using mendelian randomization (MR) to explore the causal proteins associated with BCa. Protein quantitative trait locis (pQTLs) were derived from two large proteome genome-wide association studies. After validation by multiple sensitivity analysis and two replication analyses, we identified five plasma proteins showed significant causal associations with BCa. Our study indicated that GSTM4 (OR = 0.81 (0.74-0.89), P = 5.14 × 10-6, PPH4 = 0.89) emerged as the most reliable target. Besides, PSCA, LY6D, SLURP1 and GSTM1 also showed clear causal association but only failed in colocalization. We also performed several downstream analyses. Protein-protein interactions analysis found these causal targets came from glutathione S-transferase family or lymphocyte antigen-6 family. Phenome-wide MR analysis revealed PSCA may lead to peptic ulcer and local infections of skin and subcutaneous tissue. We then employed single-cell analysis, protein-protein interactions, and druggability evaluation. Phenome-wide MR analysis was to assess the possible side effects of these drug targets. Finally, the reliability of GSTM4 in BCa was confirmed via colony formation assay.

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.

Patient-derived organoid models of malignant phyllodes tumours for drug sensitivity testing and identification of targeted therapeutic strategies

BACKGROUND

Malignant phyllodes tumours (MPT) of the breast are rare fibroepithelial neoplasms. It exhibits rapid growth, large size, and a high local recurrence rate.

METHODS

In this study, we established novel patient-derived organoid (PDO) models from two primary MPT samples and conducted comprehensive genetic profiling and drug screening.

RESULTS

The PDO models faithfully recapped the histopathological and molecular features of the primary tumours, including stromal overgrowth, leaf-like projections, and the expression of key diagnostic markers. Drug testing revealed significant heterogeneity in response profiles to chemotherapeutic reagents between the two MPT-derived organoids, implying the importance of personalised drug testing. Next-generation sequencing analysis identified recurrent mutations in TP53, RB1, EGFR, ATM, and RECQL4, which correlated with the drug sensitivity profiles observed in the organoid models. Targeted therapeutic drugs, such as Abemaciclib (targeting the RB1 pathway) with an IC50 value of 1.744 µM, and Alflutinib Mesylate (targeting the EGFR pathway) with an IC50 value of 0.9150 µM, exhibited significant cytotoxic effects in the MPT2 organoid models.

CONCLUSIONS

This study highlights the novel application of PDOs for studying the molecular landscape of MPTs and identifying effective therapeutic targets, offering a promising platform for guiding personalised treatment strategies for this rare and challenging cancer.

Identification of Potential Hub Genes Related to Acute Pancreatitis and Chronic Pancreatitis via Integrated Bioinformatics Analysis and In Vitro Analysis

Acute pancreatitis (AP) and chronic pancreatitis (CP) are considered to be two separate pancreatic diseases in most studies, but some clinical retrospective analyses in recent years have found some degree of correlation between the two in actual treatment, however, the exact association is not clear. In this study, bioinformatics analysis was utilized to examine microarray sequencing data in mice, with the aim of elucidating the critical signaling pathways and genes involved in the progression from AP to CP. Differential gene expression analyses on murine transcriptomes were conducted using the R programming language and the R/Bioconductor package. Additionally, gene network analysis was performed using the STRING database to predict correlations among genes in the context of pancreatic diseases. Functional enrichment and gene ontology pathways common to both diseases were identified using Metascape. The hub genes were screened in the cytoscape algorithm, and the mRNA levels of the hub genes were verified in mice pancreatic tissues of AP and CP. Then the drugs corresponding to the hub genes were obtained in the drug-gene relationship. A set of hub genes, including Jun, Cd44, Epcam, Spp1, Anxa2, Hsp90aa1, and Cd9, were identified through analysis, demonstrating their pivotal roles in the progression from AP to CP. Notably, these genes were found to be enriched in the Helper T-cell factor (Th17) signaling pathway. Up-regulation of these genes in both AP and CP mouse models was validated through quantitative real-time polymerase chain reaction (qRT-PCR) results. The significance of the Th17 signaling pathway in the transition from AP to CP was underscored by our findings. Specifically, the essential genes driving this progression were identified as Jun, Cd44, Epcam, Spp1, Anxa2, Hsp90aa1, and Cd9. Crucial insights into the molecular mechanisms underlying pancreatitis progression were provided by this research, offering promising avenues for the development of targeted therapeutic interventions.

Single-cell transcriptome-wide Mendelian randomization and colocalization reveals immune-mediated regulatory mechanisms and drug targets for COVID-19

BACKGROUND

COVID-19 continues to show long-term impacts on our health. Limited effective immune-mediated antiviral drugs have been launched.

METHODS

We conducted a Mendelian randomization (MR) and colocalization analysis using 26,597 single-cell expression quantitative trait loci (sc-eQTL) to proxy effects of expressions of 16,597 genes in 14 peripheral blood immune cells and tested them against four COVID-19 outcomes from COVID-19 Genetic Housing Initiative GWAS meta-analysis Round 7. We also carried out additional validations including colocalization, linkage disequilibrium check and host-pathogen interactome predictions. We integrated MR findings with clinical trial evidence from several drug gene related databases to identify drugs with repurposing potential. Finally, we developed a tier system and identified immune-cell-based prioritized drug targets for COVID-19.

FINDINGS

We identified 132 putative causal genes in 14 immune cells (343 MR associations) for COVID-19, with 58 genes that were not reported previously. 145 (73%) gene-COVID-19 pairs showed effects on COVID-19 in only one immune cell type, which implied widespread immune-cell specific effects. For pathway analyses, we found the putative causal genes were enriched in natural killer (NK) recruiting cells but de-enriched in NK cells. Using a deep learning model, we found 107 (81%) of the putative causal genes (41 novel genes) were predicted to interact with SARS-COV-2 proteins. Integrating the above evidence with drug trial information, we developed a tier system and prioritized 37 drug targets for COVID-19.

INTERPRETATION

Our study showcased the central role of immune-mediated regulatory mechanisms for COVID-19 and prioritized drug targets that might inform interventions for viral infectious diseases.

FUNDING

This work was supported by grants from the National Key Research and Development Program of China (2022YFC2505203).

Causal Inference and Annotation of Phosphoproteomics Data in Multiomics Cancer Studies

Protein phosphorylation plays a crucial role in regulating diverse biological processes. Perturbations in protein phosphorylation are closely associated with downstream pathway dysfunctions, whereas alterations in protein expression could serve as sensitive indicators of pathological status. However, there are currently few methods that can accurately identify the regulatory links between protein phosphorylation and expression, given issues like reverse causation and confounders. Here, we present Phoslink, a causal inference model to infer causal effects between protein phosphorylation and expression, integrating prior evidence and multiomics data. We demonstrated the feasibility and advantages of our method under various simulation scenarios. Phoslink exhibited more robust estimates and lower false discovery rate than commonly used Pearson and Spearman correlations, with better performance than canonical instrumental variable selection methods for Mendelian randomization. Applying this approach, we identified 345 causal links involving 109 phosphosites and 310 proteins in 79 lung adenocarcinoma (LUAD) samples. Based on these links, we constructed a causal regulatory network and identified 26 key regulatory phosphosites as regulators strongly associated with LUAD. Notably, 16 of these regulators were exclusively identified through phosphosite-protein causal regulatory relationships, highlighting the significance of causal inference. We explored potentially druggable phosphoproteins and provided critical clues for drug repurposing in LUAD. We also identified significant mediation between protein phosphorylation and LUAD through protein expression. In summary, our study introduces a new approach for causal inference in phosphoproteomics studies. Phoslink demonstrates its utility in potential drug target identification, thereby accelerating the clinical translation of cancer proteomics and phosphoproteomic data.

Identification of Candidate Lung Function-Related Plasma Proteins to Pinpoint Drug Targets for Common Pulmonary Diseases: A Comprehensive Multi-Omics Integration Analysis

The genome-wide association studies (GWAS) of lung disease and lung function indices suffer from challenges to be transformed into clinical interventions, due to a lack of knowledge on the molecular mechanism underlying the GWAS associations. A proteome-wide association study (PWAS) was first performed to identify candidate proteins by integrating two independent largest protein quantitative trait loci datasets of plasma proteins and four large-scale GWAS summary statistics of lung function indices (forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC and peak expiratory flow (PEF)), followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, with a discovery dataset, we conducted Mendelian randomization (MR) and Bayesian colocalization analyses to select potentially causal proteins, followed by a replicated MR analysis with an independent dataset. Mediation analysis was also performed to explore the possible mediating role of these indices on the association between proteins and two common lung diseases (chronic obstructive pulmonary disease, COPD and Asthma). We finally prioritized the potential drug targets. A total of 210 protein-lung function index associations were identified by PWAS, and were significantly enriched in the pulmonary fibrosis and lung tissue repair. Subsequent MR and colocalization analysis identified 59 causal protein-index pairs, among which 42 pairs were replicated. Further mediation analysis identified 3 potential pathways from proteins to COPD or asthma mediated by FEV1/FVC. The mediated proportion ranges from 68.4% to 82.7%. Notably, 24 proteins were reported as druggable targets in Drug Gene Interaction Database, among which 8 were reported to interact with drugs, including FKBP4, GM2A, COL6A3, MAPK3, SERPING1, XPNPEP1, DNER, and FER. Our study identified the crucial plasma proteins causally associated with lung functions and highlighted potential mediating mechanism underlying the effect of proteins on common lung diseases. These findings may have an important insight into pathogenesis and possible future therapies of lung disorders.

Identification and assessment of hub genes and miRNAs coregulatory associated with immune infiltrations and drug interactions in latent tuberculosis based on MicroarrayData analysis, molecular docking, and dynamic simulation

Tuberculosis (TB) remains a major global health concern, with the transition from latent to active TB still poorly understood. Therefore, enhancing clinical management and prevention strategies for TB is essential. High-throughput sequencing data of genes and miRNAs from individuals at different TB stages were obtained from NCBI. Differential expression analysis was performed using the R package limma, alongside GO and KEGG analyses. The central regulatory network of miRNAs was visualized with Cytoscape, and relevant genes were validated using ROC analysis. The predicted key genes involved in the transition from latent to active TB, including PLEKHG1, CLPB, DOK4, IL1β, and TLR3, are primarily associated with multicellular organism processes, stimulus-response, GPCR ligand binding, and immune functions. Finally, we screened Celastrol and Cefaclor Anhydrous targeting IL1β as potent anti-inflammatory drug to reduce the inflammation due to TB. These findings were further validated with Molecular dynamic simulation MM-GBSA and PCA analysis. Our study advances the understanding of latent tuberculosis and identifies genes and microRNAs as potential biomarkers for diagnosis, monitoring, and treatment, with broader implications for complex disease research.

Identification of Systemic Drug Targets for Anti-cavernous Fibrosis in the Treatment of Erectile Dysfunction, Guided by Genome-Wide Mendelian Randomization

The treatment of erectile dysfunction (ED) remains a significant challenge. Mendelian randomization (MR) is being increasingly utilized to identify novel therapeutic targets. In this study, we carried out a genome-wide MR analysis on druggable targets with the aim of pinpointing latent therapeutic alternatives for ED. We collected data on the druggable genes and filtered out those associated with blood eQTLs, then performed two-sample MR and colocalization analyses using ED genome-wide association data to screen genes significantly linked to the condition. In addition, we carried out phenome-wide studies, enrichment analysis, protein network modeling, drug prediction, and molecular docking. We screened 3,953 druggable genes from the DGIdb and 4,463 from a review. Following data integration, 74 potential druggable genes were found to potentially regulate corpus cavernosum fibrosis. MR analysis of eQTL data uncovered five drug targets (TGFBR2, ABCC6, ABCB4, EGF, and SMAD3) significantly associated with ED risk. Colocalization analysis suggested a shared causal variant between ED susceptibility and TGFBR2, with a posterior probability (PPH4) exceeding 80%. Drug predictions utilizing DSigDB identified nolone phenylpropionate, sorafenib, and NVP-TAE684 as significantly associated with TGFBR2. Finally, molecular docking indicated strong binding affinities between these candidate drugs and the protein encoded by TGFBR2 (Vina score < -50). Through MR and colocalization analyses, the present study identified five potential drug targets for ED, with TGFBR2 showing remarkable relevance in blood. These findings offer valuable insights and potential leads for the development of more effective ED therapies, which may also contribute to cutting down the expenses involved in drug development.

PCR array analysis reveals a novel expression profile of ferroptosis-related genes in idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a chronic, irreversible, and fatal disease characterized by progressive interstitial lung fibrosis. Given its insidious onset and poor outcome, there is an urgent need to elucidate the molecular mechanisms underlying IPF and identify effective therapeutic targets and diagnosis and prognosis biomarkers. Ferroptosis is an iron-dependent form of programmed cell death that occurs as lipid peroxides accumulate. Growing evidence suggests that ferroptosis is important in IPF.

METHODS

Human ferroptosis PCR array was performed on IPF and control lung tissue. The differentially expressed ferroptosis-related genes (DE-FRGs) were identified, underwent functional enrichment analyses, protein-protein interaction network construction, and potential drug target prediction. The DE-FRGs were validated and their value as diagnostic and prognostic blood biomarkers were evaluated using the Gene Expression Omnibus dataset GSE28042.

RESULTS

The array identified 13 DE-FRGs. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that the DE-FRGs were mainly related to iron ion transport, blood microparticles, and oxidoreductase activity, and were involved in porphyrin metabolism, necroptosis, and the p53 signaling pathway in addition to ferroptosis. The 13 DE-FRGs were analyzed using the Drug-Gene Interaction Database to explore novel IPF therapeutic agents, yielding 42 potential drugs. Four DE-FRGs (BBC3, STEAP3, EPRS, SLC39A8) in the peripheral blood of IPF patients from the GSE28042 dataset demonstrated the same expression pattern as that observed in the lung tissue array. The receiver operating characteristic analysis demonstrated that the area under the curve of STEAP3 and EPRS were > 0.75. The survival analysis demonstrated that STEAP3 and EPRS were significantly different between the IPF and control groups.

CONCLUSIONS

The FRG expression profiles in IPF and control lung tissue were characterized. The findings provided valuable ideas to elucidate the role of ferroptosis in IPF and aided the identification of novel IPF therapeutic targets and biomarkers.

Hirschsprung's disease may increase the incidence of inflammatory bowel disease through alterations in CA1

BACKGROUND

The role of Hirschsprung's disease (HSCR) for the development of inflammatory bowel disease (IBD) and the common pathogenesis of the diseases remains unclear. The objective is to investigate the relationship between HSCR and IBD.

METHODS

In our study, the Mendelian randomization approach was employed to analyze the causal relationships. A further search was conducted for differentially expressed genes (DEGs) between disease and control tissues in HSCR and IBD. Subsequently, the potential pathway mechanisms were subjected to an enrichment analysis. Furthermore, the molecular docking was employed to investigate the binding relationship between potential therapeutic targets and drugs.

RESULTS

The results show HSCR have an increased risk of developing IBD (IVW: OR = 1.048, P < 0.05; weighted median: OR = 1.065, P < 0.05). A total of 111 DEGs were identified in IBD, while 471 DEGs were observed in HSCR. CA1 was identified as core gene and exhibited lower expression levels in IBD (P < 0.05). Concomitantly, CA1 exhibited reduced expression levels in inflamed tissues. And the TNF and IL17 signaling pathway were found closely related to CA1 expression.

CONCLUSION

In total, our study shows HSCR promote the occurrence of IBD and reveals pathogenesis. Our results suggest CA1 may provide novel insight for the treatment of HSCR complicated with IBD.

IMPACT

Individuals with HSCR are at a higher risk of developing IBD (IVW: OR = 1.048, P < 0.05; Weighted median: OR = 1.065, P < 0.05). Patients with IBD exhibited lower expression levels of CA1 (P < 0.05). Furthermore, CA1 expression was found to be lower in inflamed tissues (P < 0.05). CA1 may provide novel insight for the treatment of HSCR complicated with IBD.

PSMB4: a potential biomarker and therapeutic target for depression, perspective from integration analysis of depression GWAS data and human plasma proteome

Depression is a common and severe mental disorder that affects more than 300 million people worldwide. While it is known to have a moderate genetic component, identifying specific genes that contribute to the disorder has been challenging. Previous Genome-wide association studies (GWASs) have identified over 100 genomic loci that are significantly associated with depression. But finding useful therapeutic targets and diagnostic biomarkers from this information has proven difficult. To address this challenge, I conducted a plasma protein proteome-wide association study (PWAS) for depression, using human plasma protein QTL (pQTL) and depression GWAS data. I identified four proteins that were significantly associated with depression: BTN3A3 (P value = 6.41 × 10-06), PSMB4 (P value = 1.42 × 10-05), TIMP4 (P value = 3.77 × 10-05), and ITIH1 (P value = 7.86 × 10-05). Specifically, I found that BTN3A3 and PSMB4 play a causal role in depression, as confirmed by colocalization and Mendelian Randomization (MR) analysis. Interestingly, I also discovered that PSMB4 was significantly associated with depression in both the brain proteome studies and the plasma PWAS results, which suggests that it may be a particularly promising candidate for further study. Overall, this work has identified 4 new risk proteins for depression and highlights the potential of plasma proteome data for uncovering novel therapeutic targets and diagnostic biomarkers.

Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

Kidney dysfunction is a major cause of mortality, but its genetic architecture remains elusive. In this study, we conducted a multiancestry genome-wide association study in 2.2 million individuals and identified 1026 (97 previously unknown) independent loci. Ancestry-specific analysis indicated an attenuation of newly identified signals on common variants in European ancestry populations and the power of population diversity for further discoveries. We defined genotype effects on allele-specific gene expression and regulatory circuitries in more than 700 human kidneys and 237,000 cells. We found 1363 coding variants disrupting 782 genes, with 601 genes also targeted by regulatory variants and convergence in 161 genes. Integrating 32 types of genetic information, we present the "Kidney Disease Genetic Scorecard" for prioritizing potentially causal genes, cell types, and druggable targets for kidney disease.

Dysregulated Pathways During Pregnancy Predict Drug Candidates in Neurodevelopmental Disorders

Maternal health during pregnancy has a direct impact on the risk and severity of neurodevelopmental disorders (NDDs) in the offspring, especially in the case of drug exposure. However, little progress has been made to assess the risk of drug exposure during pregnancy due to ethical constraints and drug use factors. We collected and manually curated sub-pathways and pathways (sub-/pathways) and drug information to propose an analytical framework for predicting drug candidates. This framework linked sub-/pathway activity and drug response scores derived from gene transcription data and was applied to human fetal brain development and six NDDs. Further, specific and pleiotropic sub-/pathways/drugs were identified using entropy, and sex bias was analyzed in conjunction with logistic regression and random forest models. We identified 19 disorder-associated and 256 regionally pleiotropic and specific candidate drugs that targeted risk sub-/pathways in NDDs, showing temporal or spatial changes across fetal development. Moreover, 5443 differential drug-sub-/pathways exhibited sex-biased differences after filling in the gender labels. A user-friendly NDDP visualization website ( https://ndd-lab.shinyapps.io/NDDP ) was developed to allow researchers and clinicians to access and retrieve data easily. Our framework overcame data gaps and identified numerous pleiotropic and specific candidates across six disorders and fetal developmental trajectories. This could significantly contribute to drug discovery during pregnancy and can be applied to a wide range of traits.

Integrating HiTOP and RDoC Frameworks Part I: Genetic Architecture of Externalizing and Internalizing Psychopathology

Background

There is considerable comorbidity between externalizing (EXT) and internalizing (INT) psychopathology. Understanding the shared genetic underpinnings of these spectra is crucial for advancing knowledge of their biological bases and informing empirical models like the Research Domain Criteria (RDoC) and Hierarchical Taxonomy of Psychopathology (HiTOP).

Methods

We applied genomic structural equation modeling to summary statistics from 16 EXT and INT traits in European-ancestry individuals (n = 16,400 to 1,074,629). Traits included clinical (e.g., major depressive disorder, alcohol use disorder) and subclinical measures (e.g., risk tolerance, irritability). We tested five confirmatory factor models to identify the best fitting and most parsimonious genetic architecture and then conducted multivariate genome-wide association studies (GWAS) of the resulting latent factors.

Results

A two-factor correlated model, representing EXT and INT spectra, provided the best fit to the data. There was a moderate genetic correlation between EXT and INT (r = 0.37, SE = 0.02), with bivariate causal mixture models showing extensive overlap in causal variants across the two spectra (94.64%, SE = 3.27). Multivariate GWAS identified 409 lead genetic variants for EXT, 85 for INT, and 256 for the shared traits.

Conclusions

The shared genetic liabilities for EXT and INT identified here help to characterize the genetic architecture underlying these frequently comorbid forms of psychopathology. The findings provide a framework for future research aimed at understanding the shared and distinct biological mechanisms underlying psychopathology, which will help to refine psychiatric classification systems and potentially inform treatment approaches.

Integrated bioinformatics analysis of the effects of chronic pain on patients with spinal cord injury

Background

Spinal cord injury (SCI) poses a substantial challenge in contemporary medicine, significantly impacting patients and society. Emerging research highlights a strong association between SCI and chronic pain, yet the molecular mechanisms remain poorly understood. To address this, we conducted bioinformatics and systems biology analyses to identify molecular biomarkers and pathways that link SCI to chronic pain. This study aims to elucidate these mechanisms and identify potential therapeutic targets.

Methods

Through analysis of the GSE151371 and GSE177034 databases, we identified differentially expressed genes (DEGs) linked to SCI and chronic pain. This analysis uncovered shared pathways, proteins, transcription factor networks, hub genes, and potential therapeutic drugs. Regression analysis on the hub genes facilitated the development of a prognostic risk model. Additionally, we conducted an in-depth examination of immune infiltration in SCI to elucidate its correlation with chronic pain.

Results

Analyzing 101 DEGs associated with SCI and chronic pain, we constructed a protein interaction network and identified 15 hub genes. Using bioinformatics tools, we further identified 4 potential candidate genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed a strong correlation between SCI and chronic pain, particularly related to inflammation. Additionally, we examined the relationship between SCI and immune cell infiltration, discovering a significant link between SCI and T cell activation. This is notable as activated T cells can cause persistent inflammation and chronic pain. Lastly, we analyzed the hub genes to explore the transcription factor network, potential therapeutic drugs, and ceRNA networks.

Conclusion

The analysis of 15 hub genes as significant biological markers for SCI and chronic pain has led to the identification of several potential drugs for treatment.

Proteome-Wide Mendelian Randomization Identifies Therapeutic Targets for Abdominal Aortic Aneurysm

BACKGROUND

The proteome is a key source of therapeutic targets. We conducted a comprehensive Mendelian randomization analysis across the proteome to identify potential protein markers and therapeutic targets for abdominal aortic aneurysm (AAA).

METHODS AND RESULTS

Our study used plasma proteomics data from the UK Biobank, comprising 2923 proteins from 54 219 individuals, and from deCODE Genetics, which measured 4907 proteins across 35 559 individuals. Significant proteomic quantitative trait loci were used as instruments for Mendelian randomization. Genetic associations with AAA were sourced from the AAAgen consortium, a large-scale genome-wide association study meta-analysis involving 37 214 cases and 1 086 107 controls, and the FinnGen study, which included 3869 cases and 381 977 controls. Sequential analyses of colocalization and summary-data-based Mendelian randomization were performed to verify the causal roles of candidate proteins. Additionally, single-cell expression analysis, protein-protein interaction network analysis, pathway enrichment analysis, and druggability assessments were conducted to identify cell types with enriched expression and prioritize potential therapeutic targets. The proteome-wide Mendelian randomization analysis identified 34 proteins associated with AAA risk. Among them, 2 proteins, COL6A3 and PRKD2, were highlighted by colocalization analysis, summary-data-based Mendelian randomization, and the heterogeneity in an independent instrument test, providing the most convincing evidence. These protein-coding genes are primarily expressed in macrophages, smooth muscle cells, and mast cells within abdominal aortic aneurysm tissue. Several causal proteins are involved in pathways regulating lipid metabolism, immune responses, and extracellular matrix organization. Nine proteins have already been targeted for drug development in diabetes and other cardiovascular diseases, presenting opportunities for repurposing as AAA therapeutic targets.

CONCLUSIONS

This study identifies causal proteins for AAA, enhancing our understanding of its molecular cause and advancing the development of therapeutics.

Integrative Analyses of Biomarkers and Pathways in Oxidative Stress-Related Genes for Gestational Diabetes Mellitus

PROBLEM

Oxidative stress (OS) plays a key role in the pathogenesis of gestational diabetes mellitus (GDM), but it was not well understood. We aimed to investigate the biomarkers and underlying mechanisms of OS-related genes in GDM.

METHOD OF STUDY

The GSE103552 and GSE70493 datasets of GDM were acquired from the Gene Expression Omnibus (GEO) database. Then, oxidative stress-related differentially expressed genes (OSDEGs) were screened between GDM and normal samples from these two datasets. Further analyses were conducted by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene set enrichment analysis (GSEA) for these OSDEGs. Subsequently, protein-protein interaction (PPI) network analyses of these OSDEGs were carried out to screen the hub genes. Eventually, we used single-sample Gene-set enrichment analysis (ssGSEA) to compare the immune cell infiltration between GDM and normal samples.

RESULTS

We identified 26 OSDEGs. Enrichment analysis suggested that the OSDEGs enriched in OS and diabetes-related pathways. GSEA revealed that these OSDEGs enriched in sensory perception of taste and negative regulation of notch4 signaling pathways. Moreover, PPI analysis showed that 15 OSDEGs were the hub gene in GDM. A total of 14 hub genes were highly expressed in GDM and might be used as diagnosis biomarkers. Moreover, many potential agents might target 10 hub genes for GDM treatment. In addition, immune infiltrate analyses revealed that expression of 14 hub genes was positively correlated to immune infiltrates in GDM.

CONCLUSION

OSDEGs are significant in GDM and may provide potential diagnostic biomarkers and treatment targets for GDM.

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

BACKGROUND

Genome-wide association studies (GWAS) have identified numerous variants associated with psychiatric disorders. However, it remains largely unknown on how GWAS risk variants contribute to psychiatric disorders.

METHODS

Through integrating two largest, publicly available, independent protein quantitative trait loci datasets of plasma protein and nine large-scale GWAS summary statistics of psychiatric disorders, we first performed proteome-wide association study (PWAS) to identify psychiatric disorders-associated plasma proteins, followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we conducted Mendelian randomization (MR) and Bayesian colocalization (COLOC) analyses, with both discovery and parallel replication datasets, to further identify protein-disorder pairs with putatively causal relationships. We finally prioritized the potential drug targets using Drug Gene Interaction Database.

RESULTS

PWAS totally identified 112 proteins, which were significantly enriched in biological processes relevant to immune regulation and response to stimulus including regulation of immune system process (adjusted P = 1.69 × 10-7) and response to external stimulus (adjusted P = 4.13 × 10-7), and viral infection related pathways, including COVID-19 (adjusted P = 2.94 × 10-2). MR and COLOC analysis further identified 26 potentially causal protein-disorder pairs in both discovery and replication analysis. Notably, eight protein-coding genes were immune-related, such as IRF3, CSK, and ACE, five among 16 druggable genes were reported to interact with drugs, including ACE, CSK, PSMB4, XPNPEP1, and MICB.

CONCLUSIONS

Our findings highlighted the immunological hypothesis and identified potentially causal plasma proteins for psychiatric disorders, providing biological insights into the pathogenesis and benefit the development of preventive or therapeutic drugs for psychiatric disorders.

Deciphering the genetic underpinnings of neuroticism: A Mendelian randomization study of druggable gene targets

BACKGROUND

Neuroticism, known for its association with a greater risk of psychiatric conditions such as depression and anxiety, is a critical focus of research.

METHODS

Cis-expression quantitative trait loci (eQTLs) from 31,684 whole blood samples provided by the eQTLGen Consortium, alongside data from a large neuroticism cohort, were analyzed to identify genes causally linked to neuroticism. To further explore the influence of gene expression changes on neuroticism, colocalization analysis was conducted. Identified drug targets were assessed for potential side effects using a phenome-wide association study (PheWAS). Additionally, we utilized multiple databases to explore the interactions between drugs and genes for drug prediction and assess the current medications for drug repurposing.

RESULTS

The analysis involved a total of 4473 druggable genes, with two-sample Mendelian randomization (MR) identifying 186 genes that are causally linked to neuroticism. Colocalization analysis highlighted 11 genes (TLR4, MMRN1, EP300, BRAF, ORM1, ACVR1B, LRRC17, NOS2, ADAMTS6, GPX1, and VCL) with a posterior probability of colocalization (PPH4) >0.8. PheWAS revealed that drugs targeting BRAF, LRRC17, ADAMTS6, and GPX1 were also associated with other traits. Notably, six of these genes (TLR4, MMRN1, BRAF, ACVR1B, NOS2, and GPX1) are already being explored for drug development in psychiatric and other diseases.

CONCLUSION

This study pinpointed six genes as promising therapeutic targets for neuroticism. The repurposing and development of drugs targeting these genes hold potential for managing neuroticism and associated psychiatric disorders.

Systematic Mendelian Randomization Exploring Druggable Genes for Hemorrhagic Strokes

Patients with hemorrhagic stroke have high rates of morbidity and mortality, and drugs for prevention are very limited. Mendelian randomization (MR) analysis can increase the success rate of drug development by providing genetic evidence. Previous MR analyses only analyzed the role of individual drug target genes in hemorrhagic stroke; therefore, we used MR analysis to systematically explore the druggable genes for hemorrhagic stroke. We sequentially performed summary-data-based MR analysis and two-sample MR analysis to assess the associations of all genes within the database with intracranial aneurysm, intracerebral hemorrhage, and their subtypes. Validated genes were further analyzed by colocalization. Only genes that were positive in all three analyses and were druggable were considered desirable genes. We also explored the mediators of genes affecting hemorrhagic stroke incidence. Finally, the associations of druggable genes with other cardiovascular diseases were analyzed to assess potential side effects. We identified 56 genes that significantly affected hemorrhagic stroke incidence. Moreover, TNFSF12, SLC22A4, SPARC, KL, RELT, and ADORA3 were found to be druggable. The inhibition of TNFSF12, SLC22A4, and SPARC can reduce the risk of intracranial aneurysm, subarachnoid hemorrhage, and intracerebral hemorrhage. Gene-induced hypertension may be a potential mechanism by which these genes cause hemorrhagic stroke. We also found that blocking these genes may cause side effects, such as ischemic stroke and its subtypes. Our study revealed that six druggable genes were associated with hemorrhagic stroke, and the inhibition of TNFSF12, SLC22A4, and SPARC had preventive effects against hemorrhagic strokes.

Dihydroergotamine and Bromocriptine: Potential Drugs for the Treatment of Major Depressive Disorder and Alzheimer's Disease Comorbidity

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease that is characterized by memory loss and cognitive impairment. Evidence shows that depression is a common co-occurrence in AD patients, and major depressive disorder (MDD) is considered a risk factor for AD. The crosstalk between the biological procedures related to the two disorders makes it very difficult to treat the comorbid conditions caused by them. Considering the common pathophysiological mechanisms underlying AD and MDD, antidepressant drugs may have beneficial therapeutic effects against their concurrence. In this study, we aimed to explore the potential drug candidates for the prevention and treatment of the comorbidity of AD and MDD. First, we screened the potential drugs for treating MDD by evaluating the distances of drug targets to MDD-related genes on the human protein-protein interaction network (PPIN) via a network-based algorithm. Then, the drugs were further screened to identify those that may be effective for AD treatment by analyzing their affinities with tau protein and Aβ42 peptide via molecular docking. Furthermore, the most stable binding modes were identified via molecular dynamics simulations, and the regulatory effects of drug candidates on genes involved in the pathogenesis of AD and MDD were analyzed. A total of 506 MDD-related genes were retrieved, and 831 drug candidates for MDD treatment were screened via the network-based approach. The results from molecular docking and molecular dynamics simulations indicated dihydroergotamine had the lowest binding affinity with tau protein and bromocriptine could form the most stable binding mode with Aβ42 peptide. Further analyses found that both dihydroergotamine and bromocriptine could regulate the expression of genes involved in the pathogenesis of AD and/or MDD in the brain. The exact mechanisms of the two drugs in treating AD and MDD, as well as their comorbidity, are still unclear, and further exploration is needed to evaluate their roles and mechanisms, both in vitro and in vivo. This study revealed that dihydroergotamine and bromocriptine may be the potential drug candidates for the treatment of the comorbidity of AD and MDD, and the therapeutic effects may be achieved by inhibiting the accumulation and aggregation of Aβ42 and tau protein and regulating the expression of disease-related genes in the brain.

Multi-ancestry genome-wide meta-analysis with 472,819 individuals identifies 32 novel risk loci for psoriasis

BACKGROUND

Psoriasis is a common chronic, recurrent, immune-mediated disease involved in the skin or joints or both. However, deeper insight into the genetic susceptibility of psoriasis is still unclear.

METHODS

Here we performed the largest multi-ancestry meta-analysis of genome-wide association study including 28,869 psoriasis cases and 443,950 healthy controls.

RESULTS

We identified 74 genome-wide significant loci for psoriasis. Of 74 loci, 32 were novel psoriasis risk loci. Across 74 loci, 801 likely causal genes are indicated and 164 causal genes are prioritized. SNP-based heritability analyses demonstrated that common variants explain 15% of genetic risk for psoriasis. Gene-set analyses and the genetic correlation revealed that psoriasis-related genes have the positive correlations with autoimmune diseases such as ulcerative colitis, inflammatory bowel diseases, and Crohn's disease. Gene-drug interaction analysis suggested that psoriasis-associated genes overlapped with targets of current medications for psoriasis. Finally, we used the multi-ancestry meta-analysis to explore drug repurposing and the potential targets for psoriasis.

CONCLUSIONS

We identified 74 genome-wide significant loci for psoriasis. Based on 74 loci, we provided new biological insights to the etiology of psoriasis. Of clinical interest, we gave some hints for 76 potential targets and drug repurposing for psoriasis.

NEK4: prediction of available drug targets and common genetic linkages in bipolar disorder and major depressive disorder

Background

Bipolar disorder (BD) is a mental illness characterized by alternating episodes of elevated mood and depression, while major depressive disorder (MDD) is a debilitating condition that ranks second globally in terms of disease burden. Pharmacotherapy plays a crucial role in managing both BD and MDD. We investigated the genetic differences in populations of individuals with MDD and BD, and from a genetic perspective, we offered new insights into potential drug targets. This will provide clues to potential drug targets.

Methods

This study employed genome-wide association studies (GWAS) and summary-data-based Mendelian randomization (SMR) methods to investigate the genetic underpinnings of patients with bipolar disorder (BD) and major depressive disorder (MDD) and to predict potential drug target genes. Genetic variants associated with BD and MDD were identified through large-scale GWAS datasets. For BD, the study utilized a comprehensive meta-analysis comprising 57 BD cohorts from Europe, North America, and Australia, including 41,917 BD cases and 371,549 controls of European ancestry. This dataset included both type 1 and type 2 BD cases diagnosed based on DSM-IV, ICD-9, or ICD-10 criteria through standardized assessments. For MDD, we used data from a meta-analysis by Howard DM et al., which integrated the largest GWAS studies of MDD, totaling 246,363 cases and 561,190 controls. The SMR approach, combined with expression quantitative trait loci (eQTL) data, was then applied to assess causal associations between these genetic variants and gene expression, aiming to identify genetic markers and potential drug targets associated with BD and MDD. Furthermore, two-sample Mendelian randomization (TSMR) analyses were performed to explore causal links between protein quantitative trait loci (pQTL) and these disorders.

Results

The SMR analysis revealed 41 druggable genes associated with BD, of which five genes appeared in both brain tissue and blood eQTL datasets and were significantly associated with BD risk. Furthermore, 45 druggable genes were found to be associated with MDD by SMR analysis, of which three genes appeared simultaneously in both datasets and were significantly associated with MDD risk. NEK4, a common drug candidate gene for BD and MDD, was also significantly associated with a high risk of both diseases and may help differentiate between type 1 and type 2 BD. Specifically, NEK4 showed a strong association with BD (β brain=0.126, P FDR=0.001; βblood=1.158, P FDR=0.003) and MDD (β brain=0.0316, P FDR=0.022; βblood=0.254, P FDR=0.045). Additionally, NEK4 was notably linked to BD type 1 (βbrain=0.123, P FDR=2.97E-05; βblood=1.018, P FDR=0.002), but showed no significant association with BD type 2.Moreover, TSMR analysis identified four proteins (BMP1, F9, ITIH3, and SIGIRR) affecting the risk of BD, and PSMB4 affecting the risk of MDD.

Conclusion

Our study identified NEK4 as a key gene linked to both bipolar disorder (BD) and major depressive disorder (MDD), suggesting its potential as a drug target and a biomarker for differentiating BD subtypes. Using GWAS, SMR, and TSMR approaches, we revealed multiple druggable genes and protein associations with BD and MDD risk, providing new insights into the genetic basis of these disorders. These findings offer promising directions for precision medicine and novel therapeutic strategies in mental health treatment.

Brain multi-omic Mendelian randomisation to identify novel drug targets for gliomagenesis

BACKGROUND

Genetic variants associated with molecular traits that are also associated with liability to glioma can provide causal evidence for the identification and prioritisation of drug targets.

METHODS

We performed comprehensive two-sample Mendelian randomisation (Wald ratio and/or IVW) and colocalisation analyses of molecular traits on glioma. Instrumentable traits (QTLs P < 5 × 10-8) were identified amongst 11 985 gene expression measures, 13 285 splicing isoforms and 10 198 protein abundance measures, derived from 15 brain regions. Glioma summary-level data was extracted from a genome-wide association meta-analysis of 12 496 cases and 18 190 controls.

RESULTS

We found evidence for causal effect of 22 molecular traits (across 18 genes/proteins) on glioma risk. Thirteen molecular traits have been previously linked with glioma risk and five were novel; HBEGF (5q31.3) expression and all glioma [OR 1.36 (95%CI 1.19-1.55); P = 4.41 × 10-6]; a CEP192 (18p11.21) splice isoform and glioblastoma [OR 4.40 (95%CI 2.28-8.48); P = 9.78 × 10-4]; a FAIM (3q22.3) splice isoform and all glioma [OR 2.72-3.43; P = 1.03 × 10-5 to 1.09 × 10-5]; a SLC8A1 (2p22.1) splice isoform and all glioma [OR 0.37 (95%CI 0.24-0.56; P = 5.72 × 10-6]; D2HGDH (2q37.3) protein and all glioma [OR 0.86 (95%CI 0.80-0.92); P = 5.94 × 10-6)].

CONCLUSIONS

We provide robust causal evidence for prioritising genes and their protein products in glioma research. Our results highlight the importance of alternative splicing as a mechanism in gliomagenesis and as an avenue for exploration of drug targets.

HER2 expression and pathway status in male breast cancer patients: results of an integrated analysis among 6,150 patients

The role of human epidermal growth factor 2 (HER2) in male breast cancer (MBC) is poorly defined. A comprehensive description of HER2 status was conducted. A total of 6,015 MBC patients from 45 studies and 135 MBC patients with sequencing data were identified. HER2 positive rates and hazard ratios (HR) for overall survival (OS) were combined using Metaprop. The prevalence of HER2 + MBC was 10.0% (95% CI: 8.0-13.0%). Subgroup analyses showed that 7% (95% CI: 2.0-14.0%) had HER2 + protein overexpression. 10% of MBC patients had HER2 + overexpression and/or gene amplification. Asian MBC patients had the highest HER2 + incidence of 17% (95% CI: 12.0-22.0%). The prevalence of HER2 positive MBC fluctuated widely from 2001 to 2015 and then stabilized at 10%. HER2 positivity was significantly correlated with worse OS than negative ones (HR = 1.92, 1.47-2.51). The proportion of HER2 + MBC was inconsistent with the results for the intrinsic HER2-enriched subtype. Altered genes in HER2 + MBC, such as ERBB2, AGO2, RECQL4, and CLTC, were not detected in HER2-MBC. Genomic analysis revealed differences between the patients with HER2 + MBC and those with HER2 + FBC. The percentage of HER2 + MBC was slightly lower than that of women. Multiple approaches may be needed to jointly assess HER2 status in MBC.

Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms

Preeclampsia (PE) is a major pregnancy-specific cardiovascular complication posing latent life-threatening risks to mothers and neonates. The contribution of immune dysregulation to PE is not fully understood, highlighting the need to explore molecular markers and their relationship with immune infiltration to potentially inform therapeutic strategies. We used bioinformatics tools to analyze gene expression data from the Gene Expression Omnibus (GEO) database using the GEOquery package in R. Differential expression analysis was performed using the DESeq2 and limma packages, followed by analysis of variance to identify immune-related differentially expressed genes (DEGs). Several machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), bagged trees, and random forest (RF), were used to select immune-related signaling genes closely associated with the occurrence of PE. Our analysis identified 34 immune source-related DEGs. Using the identified PE- and immune source-related genes, we constructed a diagnostic forecasting model employing several ML algorithms. We identified six types of statistically significant immune cells in patients with PE and discovered a strong relationship between biomarkers and immune cells. Moreover, the immune-derived hub genes for PE exhibited strong binding capabilities with drugs, such as alitretinoin, tretinoin, and acitretin. This study presents a robust prediction model for PE that integrates multiple machine learning-derived immune-related biomarkers. Our results indicate that these biomarkers may outperform previously reported molecular signatures in predicting PE and provide insights into the mechanisms underlying immune dysregulation in PE. Further validation in larger cohorts could lead to their clinical application in PE prediction and treatment.

Genomic characterization of Huntington's disease genetic modifiers informs drug target tractability

Huntington's disease is caused by a CAG repeat in the HTT gene. Repeat length correlates inversely with the age of onset but only explains part of the observed clinical variability. Genome-wide association studies highlight DNA repair genes in modifying disease onset, but further research is required to identify causal genes and evaluate their tractability as drug targets. To address these gaps and learn important preclinical information, we analysed genome-wide association study data from a large Huntington's disease age-of-onset study (n = 9064), prioritizing robust candidate Huntington's disease modifier genes using bioinformatic approaches and analysing related information for these genes from large-scale human genetic repositories. We supplemented this information with other Huntington's disease-related screens, including exome studies of Huntington's disease onset and high-throughput assessments of mHTT toxicity. To confirm whether Huntington's disease modifiers are shared across repeat expansion disorders, we also analysed age-of-onset genome-wide association study data from X-linked dystonia-parkinsonism caused by a (CCCTCT)n expansion. We also studied modifier-related associations with rare diseases to inform potential off-target therapeutic effects and conducted comprehensive phenome-wide studies to identify other traits linked to these genes. Finally, we evaluated the aggregated human genetic evidence and theoretical druggability of the prioritized Huntington's disease modifier genes, including characteristics recently associated with clinical trial stoppage due to safety concerns (i.e. human genetic constraint, number of interacting partners and RNA tissue expression specificity). In total, we annotated and assessed nine robust candidate Huntington's disease modifier genes. Notably, we detected a high correlation (R 2 = 0.78) in top age-of-onset genome-wide association study hits across repeat expansion disorders, emphasizing cross-disorder relevance. Clinical genetic repositories analysis showed DNA repair genes, such as MLH1, PMS2 and MSH3, are associated with cancer phenotypes, suggesting potential limitations as drug targets. LIG1 and RRM2B were both associated with neurofibrillary tangles, which may provide a link to a potential role in mHTT aggregates, while MSH3 was associated with several cortical morphology-related traits relevant to Huntington's disease. Finally, human genetic evidence and theoretical druggability analyses prioritized and ranked modifier genes, with PMS1 exhibiting the most favourable profile. Notably, HTT itself ranked poorly as a theoretical drug target, emphasizing the importance of exploring modifier-based alternative targets. In conclusion, our study highlights the importance of human genomic information to prioritize Huntington's disease modifier genes as drug targets, providing a basis for future therapeutic development in Huntington's disease and other repeat expansion disorders.

Identification of Drug-Targetable Genes for Eczema and Dermatitis Using Integrated Genomic and Proteomic Approaches

Background: Eczema and dermatitis are common inflammatory skin conditions with significant morbidity. Identifying drug-targetable genes can facilitate the development of effective treatments. Methods: This study analyzed data obtained by meta-analysis of 2 genome-wide association studies on eczema/dermatitis (57,311 cases and 896,779 controls, European ancestry). We identified drug-targetable genes from the Drug-Gene Interaction Database and Finan et al's findings. Cis-expression quantitative trait loci (eQTL) data from human blood and skin tissues were used for Mendelian randomization (MR) analysis. Bayesian colocalization, proteomic MR, and meta-analysis validated the causal relationships. Finally, protein-protein interactions (PPIs) and correlation analysis of potential drug targets and cytokines were performed. Results: We identified 2532 drug-targetable genes; 3378 Single Nucleotide Polymorphism (SNPs) were associated with 1531 genes in blood cis-eQTLs, 664 SNPs with 667 genes in sun-exposed skin eQTLs, and 572 SNPs with 574 genes in nonsun-exposed skin eQTLs. Five genes (SLC22A5, NOTCH4, AGER, HLA-DRB5, and EHMT2) showed causal relationships with eczema/dermatitis across multiple datasets. Single-variable and multi-variable Mendelian randomization (SMR) and multi-SNP SMR analysis identified 8 genes (PIK3R4, DHODH, CXCR2, Interleukin (IL)18, LGALS9, RPS6KB2, SLC22A5, and AGER) across all tissues. Functional Summary Information for Variants in the Online Network (FUSION) analysis confirmed associations for SLC22A5 and AGER. Bayesian colocalization indicated AGER (PPH4: 0.95) as a shared causal variant. Proteomic MR and meta-analysis showed that increased AGER protein levels were associated with a lower risk of eczema or dermatitis (odds ratio: 0.995, 95% confidence interval: 0.997-0.993, P = 0.0002). A PPI network revealed interactions of AGER with NOTCH4 and multiple cytokines, whereas SLC22A5 showed no cytokine interactions. Conclusions: This study identified potential drug-targetable genes, with AGER showing strong potential as a target for reducing eczema/dermatitis risk. These findings provide a basis for developing targeted therapies.

Identification of crucial pathways and genes linked to endoplasmic reticulum stress in PCOS through combined bioinformatic analysis

Background

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic condition impacting millions of women worldwide. This study sought to identify granulosa cell endoplasmic reticulum stress (GCERS)-related differentially expressed genes (DEGs) between women with PCOS and those without PCOS using bioinformatics and to investigate the related molecular mechanisms.

Methods

Two datasets were downloaded from GEO and analysed using the limma package to identify DEGs in two groups-PCOS and normal granulosa cells. Enrichment analyses, including GO, KEGG, and GSEA, were then conducted on the DEGs. Differential immune infiltration was assessed using CIBERSORT and correlations with immune cell biomarkers were evaluated. Networks for protein-protein interactions, transcription factor-target genes, miRNA-target genes, and drug-target genes were constructed and visualized using Cytoscape to identify key hub gene nodes. Finally, key genes were analysed for differential expression and correlated.

Results

Overall, 127 co-DEGs were identified in the two datasets. Our study revealed that these DEGs were primarily associated with cell cycle arrest, p53-mediated signal transduction, drug response, and gland development, with molecular functions enriched in growth factor binding, collagen binding, and receptor protein kinase activity. GSEA revealed that the co-DEGs were primarily associated with immune and inflammatory pathways. Eleven hub genes-MMP9, SPI1, IGF2R, GPBAR1, PDGFA, BMPR1A, LIFR, PRKAA1, MSH2, CDC25C, and KCNH2-were identified through the PPI, TF target genes, miRNA target genes, and drug target gene networks.

Conclusion

We identified several crucial genes and pathways linked to the onset and development of PCOS. Our findings offer a clear connection between PCOS and GCERS, clarify the molecular mechanisms driving PCOS progression, and offer new perspectives for discovering valuable therapeutic targets and potential biomarkers for the condition.

GoFCards: an integrated database and analytic platform for gain of function variants in humans

Gain-of-function (GOF) variants, which introduce new or amplify protein functions, are essential for understanding disease mechanisms. Despite advances in genomics and functional research, identifying and analyzing pathogenic GOF variants remains challenging owing to fragmented data and database limitations, underscoring the difficulty in accessing critical genetic information. To address this challenge, we manually reviewed the literature, pinpointing 3089 single-nucleotide variants and 72 insertions and deletions in 579 genes associated with 1299 diseases from 2069 studies, and integrated these with the 3.5 million predicted GOF variants. Our approach is complemented by a proprietary scoring system that prioritizes GOF variants on the basis of the evidence supporting their GOF effects and provides predictive scores for variants that lack existing documentation. We then developed a database named GoFCards for general geneticists and clinicians to easily obtain GOF variants in humans (http://www.genemed.tech/gofcards). This database also contains data from >150 sources and offers comprehensive variant-level and gene-level annotations, with the aim of providing users with convenient access to detailed and relevant genetic information. Furthermore, GoFCards empowers users with limited bioinformatic skills to analyze and annotate genetic data, and prioritize GOF variants. GoFCards offers an efficient platform for interpreting GOF variants and thereby advancing genetic research.

ChiTaRS 8.0: the comprehensive database of chimeric transcripts and RNA-seq data with applications in liquid biopsy

Gene fusions are nucleotide sequences formed due to errors in replication and transcription control. These errors, resulting from chromosomal translocation, transcriptional errors or trans-splicing, vary from cell to cell. The identification of fusions has become critical as key biomarkers for disease diagnosis and therapy in various cancers, significantly influencing modern medicine. Chimeric Transcripts and RNA-Sequencing database version 8.0 (ChiTaRS 8.0; http://biosrv.org/chitars) is a specialized repository for human chimeric transcripts, containing 47 445 curated RNA transcripts and over 100 000 chimeric sequences in humans. This updated database provides unique information on 1055 chimeric breakpoints derived from public datasets using chromosome conformation capture techniques (the Hi-C datasets). It also includes an expanded list of gene fusions that are potential drug targets, and chimeric breakpoints across 934 cell lines, positioning ChiTaRS 8.0 as a valuable resource for testing personalized cancer therapies. By utilizing text mining on a curated selection of disease-specific RNA-sequencing data from public datasets, as well as patient blood and plasma samples, we have identified novel chimeras-particularly in diseases such as oral squamous cell carcinoma and glioblastoma-now catalogued in ChiTaRS. Thus, ChiTaRS 8.0 serves as an enhanced fusion transcript repository that incorporates insights into the functional landscape of chimeras in cancers and other complex diseases, based on liquid biopsy results.

Drug discovery and mechanism prediction with explainable graph neural networks

Apprehension of drug action mechanism is paramount for drug response prediction and precision medicine. The unprecedented development of machine learning and deep learning algorithms has expedited the drug response prediction research. However, existing methods mainly focus on forward encoding of drugs, which is to obtain an accurate prediction of the response levels, but omitted to decipher the reaction mechanism between drug molecules and genes. We propose the eXplainable Graph-based Drug response Prediction (XGDP) approach that achieves a precise drug response prediction and reveals the comprehensive mechanism of action between drugs and their targets. XGDP represents drugs with molecular graphs, which naturally preserve the structural information of molecules and a Graph Neural Network module is applied to learn the latent features of molecules. Gene expression data from cancer cell lines are incorporated and processed by a Convolutional Neural Network module. A couple of deep learning attribution algorithms are leveraged to interpret interactions between drug molecular features and genes. We demonstrate that XGDP not only enhances the prediction accuracy compared to pioneering works but is also capable of capturing the salient functional groups of drugs and interactions with significant genes of cancer cells.

Exploring the Diagnostic and Prognostic Predictive Values of Ferroptosis-related Markers in Lung Adenocarcinoma

BACKGROUND

Lung Adenocarcinoma (LUAD), a common and aggressive form of lung cancer, poses significant treatment challenges due to its low survival rates.

AIM

To better understand the role of ferroptosis driver genes in LUAD, this study aimed to explore their diagnostic and prognostic significance, as well as their impact on treatment approaches and tumor immune function in LUAD.

METHODS

To accomplish the defined goals, a comprehensive methodology incorporating both in silico and wet lab experiments was employed. A comprehensive analysis was conducted on a total of 233 ferroptosis driver genes obtained from the FerrDB database. Utilizing various TCGA databases and the RT-qPCR technique, the expression profiles of 233 genes were examined. Among them, TP53, KRAS, PTEN, and HRAS were identified as hub genes with significant differential expression. Notably, TP53, KRAS, and HRAS exhibited substantial up-regulation, while PTEN demonstrated significant down-regulation at both the mRNA and protein levels in LUAD samples. The dysregulation of hub genes was further associated with poor overall survival in LUAD patients. Additionally, targeted bisulfite-sequencing (bisulfite-seq) analysis revealed aberrant promoter methylation patterns linked to the dysregulation of hub genes.

RESULTS & DISCUSSION

Furthermore, hub genes were found to participate in diverse oncogenic pathways, highlighting their involvement in LUAD tumorigenesis. By leveraging the diagnostic and prognostic potential of ferroptosis driver hub genes (TP53, KRAS, PTEN, and HRAS), significant advancements can be made in the understanding and management of LUAD pathogenesis.

CONCLUSION

Therapeutic targeting of these genes using specific drugs holds great promise for revolutionizing drug discovery and improving the overall survival of LUAD patients.

Genetic analyses point to alterations in immune-related pathways underpinning the association between psychiatric disorders and COVID-19

Current literature suggests that people with psychiatric disorders have a higher risk of COVID-19 infection and a worse prognosis of the disease. We aimed to study the genetic contribution to these associations across seven psychiatric disorders as well as a general psychopathology factor (P-factor) and determine whether these are unique or shared across psychiatric disorders using statistical genetic techniques. Using the largest available genome-wide association studies (GWAS), we found a significant genetic overlap between depression, ADHD, PTSD, and the P-factor with both COVID-19 infection and hospitalization, and between anxiety and COVID-19 hospitalization. We used pairwise GWAS to examine this overlap on a fine-grained scale and identified specific regions of the genome shared between several psychiatric disorders, the P-factor, and COVID-19. Gene-based analysis in these genomic regions suggested possible links with immune-related pathways such as thyroid homeostasis, inflammation, and stress response. Finally, we show preliminary evidence for causal associations between depression, ADHD, PTSD, and the P-factor, and higher COVID-19 infection and hospitalization using Mendelian Randomization and Latent Causal Variable methods. Our results support the hypothesis that the relationship between psychiatric disorders and COVID-19 risk is likely due to shared alterations in immune-related pathways and is not a result of environmental factors alone, shedding light on potentially viable therapeutic targets.

Statins as a Potential Treatment for Non-alcoholic Fatty Liver Disease: Target Deconvolution using Protein-protein Interaction Network Analysis

BACKGROUND

The hallmark of non-alcoholic fatty liver disease (NAFLD) is aberrant buildup of triglycerides (TGs) in hepatocytes. Many genes promote NAFLD development. Using bioinformatics tools, we investigated the possible effect of statins on genes involved in NAFLD progression.

METHODS

Protein interactions of statins and NAFLD were searched in gene-drug and gene-disease databases. A Protein-Protein interaction (PPI) network was constructed to find hub genes and Molecular Complex Detection (MCODE) of NAFLD-related genes. Shared protein targets between protein targets of statins and NAFLD-associated genes were identified. Next, targets of each statin were assayed with all modular clusters in the MCODEs related to NAFLD. Biological process and pathway enrichment analysis for shared proteins was performed.

RESULTS

Screening protein targets for conventional statins and curated NAFLD-related genes identified 343 protein targets and 70 genes, respectively. A Venn diagram of NAFLD-related genes and protein targets of statins showed 24 shared proteins. The biological pathways on KEGG enrichment associated with the 24 shared protein sets were evaluated and included cytokine-cytokine receptor interaction, adipocytokine, PPAR, TNF and AMPK signaling pathways. Gene Ontology analysis showed major involvement in lipid metabolic process regulation and inflammatory response. PPI network analysis of 70 protein targets indicated 13 hub genes (PPARA, IL4, CAT, LEP, SREBF1, PRKCA, CYP2E1, NFE2L2, PTEN, NR1H4, ADIPOQ, GSTP1 and TGFB1). Comparing all seven statins with the three MCODE clusterings and 13 hub genes revealed that simvastatin as the most associated statin with NAFLD.

CONCLUSION

Simvastatin has the most impact on NAFLD-related genes versus other statins.

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.

A multiomic atlas identifies a treatment-resistant, bone marrow progenitor-like cell population in T cell acute lymphoblastic leukemia

Refractoriness to initial chemotherapy and relapse after remission are the main obstacles to curing T cell acute lymphoblastic leukemia (T-ALL). While tumor heterogeneity has been implicated in treatment failure, the cellular and genetic factors contributing to resistance and relapse remain unknown. Here we linked tumor subpopulations with clinical outcome, created an atlas of healthy pediatric hematopoiesis and applied single-cell multiomic analysis to a diverse cohort of 40 T-ALL cases. We identified a bone marrow progenitor (BMP)-like leukemia subpopulation associated with treatment failure and poor overall survival. The single-cell-derived molecular signature of BMP-like blasts predicted poor outcome across multiple subtypes of T-ALL and revealed that NOTCH1 mutations additively drive T-ALL blasts away from the BMP-like state. Through in silico and in vitro drug screenings, we identified a therapeutic vulnerability of BMP-like blasts to apoptosis-inducing agents including venetoclax. Collectively, our study establishes multiomic signatures for rapid risk stratification and targeted treatment of high-risk T-ALL.

MicroRNA-guided drug discovery for mitigating persistent pulmonary complications in critical COVID-19 survivors: A longitudinal pilot study

BACKGROUND AND PURPOSE

The post-acute sequelae of SARS-CoV-2 infection pose a significant global challenge, with nearly 50% of critical COVID-19 survivors manifesting persistent lung abnormalities. The lack of understanding about the molecular mechanisms and effective treatments hampers their management. Here, we employed microRNA (miRNA) profiling to decipher the systemic molecular underpinnings of the persistent pulmonary complications.

EXPERIMENTAL APPROACH

We conducted a longitudinal investigation including 119 critical COVID-19 survivors. A comprehensive pulmonary evaluation was performed in the short-term (median = 94.0 days after hospital discharge) and long-term (median = 358 days after hospital discharge). Plasma miRNAs were quantified at the short-term evaluation using the gold-standard technique, RT-qPCR. The analyses combined machine learning feature selection techniques with bioinformatic investigations. Two additional datasets were incorporated for validation.

KEY RESULTS

In the short-term, 84% of the survivors exhibited impaired lung diffusion (DLCO < 80% of predicted). One year post-discharge, 54.4% of this patient subgroup still presented abnormal DLCO. Four feature selection methods identified two specific miRNAs, miR-9-5p and miR-486-5p, linked to persistent lung dysfunction. The downstream experimentally validated targetome included 1473 genes, with heterogeneous enriched pathways associated with inflammation, angiogenesis and cell senescence. Validation studies using RNA-sequencing and proteomic datasets emphasized the pivotal roles of cell migration and tissue repair in persistent lung dysfunction. The repositioning potential of the miRNA targets was limited.

CONCLUSION AND IMPLICATIONS

Our study reveals early mechanistic pathways contributing to persistent lung dysfunction in critical COVID-19 survivors, offering a promising approach for the development of targeted disease-modifying agents.

LINKED ARTICLES

This article is part of a themed issue Non-coding RNA Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.2/issuetoc.

Computational approaches for identifications of altered ion channels in keratoconus

BACKGROUND

Keratoconus is an etiologically complex, degenerative corneal disease that eventually leads to loss of corneal integrity. Cells in corneal epithelium and endothelium express various types of ion channels that play important roles in ocular pathology. This emphasizes the need of understanding alterations of ion channels in keratoconus.

METHOD

Differential gene expression analysis was performed to identify deregulated ion channels in keratoconus patients using transcriptomic data. Thereafter correlation analysis of ion channel expression was performed to obtain the changed correlation between ion channels' expression in keratoconus patients versus control samples. Moreover, Protein-protein interaction networks and a pathway map was constructed to identify cellular processes altered due to the deregulation of ion channels. Furthermore, drugs interacting with deregulated ion channels were identified.

RESULTS

Total 75 ion channels were found to be deregulated in keratoconus, of which 12 were upregulated and 63 were downregulated. Correlations between ion channel expressions found to be different in control and keratoconus samples. Thereafter, protein-protein interactions network was generated to identify hub ion channels in network. Furthermore, the pathway map was constructed to depict calcium signalling, MAPK signalling, synthesis and secretion of cortisol, and cAMP signalling. The 19 FDA- approved drugs that interact with the 5 deregulated ion channels were identified.

CONCLUSION

Down-regulation of voltage-gated calcium channels can be attributed to reduced cell proliferation and differentiation. Additionally, deregulated ion channels in 3',5'- cyclic adenosine monophosphate signalling may be responsible for elevated cortisol level in progressive keratoconus patients.

Diagnostic Power of MicroRNAs in Melanoma: Integrating Machine Learning for Enhanced Accuracy and Pathway Analysis

This study identifies microRNAs (miRNAs) with significant discriminatory power in distinguishing melanoma from nevus, notably hsa-miR-26a and hsa-miR-211, which have exhibited diagnostic potential with accuracy of 81% and 78% respectively. To enhance diagnostic accuracy, we integrated miRNAs into various machine-learning (ML) models. Incorporating miRNAs with AUC scores above 0.70 significantly improved diagnostic accuracy to 94%, with a sensitivity of 91%. These findings underscore the potential of ML models to leverage miRNA data for enhanced melanoma diagnosis. Additionally, using the miRNet tool, we constructed a network of miRNA-miRNA interactions, revealing 170 key genes in melanoma pathophysiology. Protein-protein interaction network analysis via Cytoscape identified hub genes including MYC, BRCA1, JUN, AURKB, CDKN2A, DDX5, MAPK14, DDX3X, DDX6, FOXM1 and GSK3B. The identification of hub genes and their interactions with miRNAs enhances our understanding of the molecular mechanisms driving melanoma. Pathway enrichment analyses highlighted key pathways associated with differentially expressed miRNAs, including the PI3K/AKT, TGF-beta signalling pathway and cell cycle regulation. These pathways are implicated in melanoma development and progression, reinforcing the significance of our findings. The functional enrichment of miRNAs suggests their critical role in modulating essential pathways in melanoma, suggesting their potential as therapeutic targets.

Impact of oral flora in tongue coating and saliva on oral cancer risk and the regulatory role of Interleukin-8

BACKGROUND

Oral flora and inflammatory factors play a crucial role in oral cancer, but the relationship between them and oral cancer has not been clearly established.

METHODS

Oral flora served as exposure factor, oral cancer as outcome factor, and inflammatory factors as mediating factor. Mendelian randomization (MR) analysis was used to analyze the relationship between oral flora and oral cancer, and the potential mediating effect of inflammatory factors was explored through mediation analysis.

RESULTS

29 kinds of oral flora in tongue coating and 22 kinds of oral flora in saliva were associated with increased risk of oral cancer. 18 species of oral flora in tongue coating and 25 species in saliva were associated with a reduced risk of oral cancer. Interleukin-8 (IL8) played a mediating role in the relationship between oral flora and oral cancer, and it was associated with an increased risk of oral cancer. Granulicatella, Streptococcus mitis, Saccharimonadaceae and Haemophilus in oral flora caused oral cancer indirectly through IL8. IL8 expression increased in oral cancer, which has good diagnostic value. IL8-related genes in oral cancer are closely associated with immune cell infiltration. What's more, IL8 has potential medicinal properties.

CONCLUSION

Oral flora of tongue coating and saliva is closely related to oral cancer, and IL8 plays a mediating role in the causal relationship between oral flora and oral cancer.

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

BACKGROUND

Hepatic ischemia-reperfusion injury (HIRI) is a common injury not only during liver transplantation but also during major hepatic surgery. HIRI causes severe complications and affects the prognosis and survival of patients. Cuproptosis, a newly identified form of cell death, plays an important role in a variety of illnesses. However, its role in HIRI remains unknown.

MATERIALS AND METHODS

The GSE151648 dataset was mined from the Gene Expression Omnibus (GEO) database, and differences were analyzed for intersections. Based on the differentially expressed genes (DEGs), functional annotation, differentially expressed cuproptosis-related genes (DE-CRGs) identification and lasso logistic regression were conducted. Correlation analysis of DE-CRGs and immune infiltration was further conducted, and DE-CRGs were applied to construct an HIRI diagnostic model. The hierarchical clustering method was used to classify the specimens of HIRI, and functional annotation was conducted to verify the accuracy of these DE-CRGs in predicting HIRI progression. The GSE14951 microarray dataset and GSE171539 single-cell sequencing dataset were chosen as validation datasets. At the same time, the significance of DE-CRGs was verified using a mouse model of HIRI with cuproptosis inhibitors and inducers. Finally, a network of transcription-factor-DE-CRGs and miRNA-DE-CRGs was constructed to reveal the regulation mechanisms. And potential drugs for DE-CRGs were predicted using Drug-Gene Interaction Database (DGIdb).

RESULTS

Overall, 2390 DEGs and 19 DE-CRGs were identified. Through machine learning algorithms, 8 featured DE-CRGs (GNL3, ALAS1, TSC22D2, KLF5, GTF2B, DNTTIP2, SLFN11 and HNRNPU) were screened, and 2 cuproptosis-related subclusters were defined. Based on the 8 DE-CRGs obtained from the HIRI model [area under the curve (AUC)=0.97], the nomogram model demonstrated accuracy in predicting HIRI. Eight DE-CRGs were highly expressed in HIRI samples and were negatively related to immune cell infiltration. A higher level of immune infiltration and expression of CRG group B was found in the HIRI population. Differences in cell death and immune regulation were found between the 2 groups. The diagnostic value of the 8 DE-CRGs was confirmed in the validation of two datasets. The identification of 7 DE-CRGs (SLFN11 excluded) by HIRI animal model experiments was also confirmed. Using hTFtarget, miRWalk and DGIDB database, we predicted that 17 transcription factors, 192 miRNAs and 10 drugs might interact with the DE-CRGs.

CONCLUSION

This study shows that cuproptosis may occur in HIRI and is correlated with immune infiltration. Additionally, a cuproptosis-related predictive model was constructed for studying the causes of HIRI and developing targeted treatment options for HIRI.

Investigating the Impact of Circulating MicroRNAs on Knee and Hip Osteoarthritis: Causal Links, Biological Mechanisms, and Drug Interactions

Osteoarthritis (OA), particularly in the knee and hip, poses a significant global health challenge due to limited therapeutic options. To elucidate the molecular mechanisms of OA and identify potential biomarkers and therapeutic targets, we utilized genome-wide association studies (GWAS) and cis-miRNA expression quantitative trait loci (cis-miR-eQTL) datasets to identify miRNAs associated with OA, revealing 16 that were linked to knee OA and 21 to hip OA. Among these, hsa-miR-1303 was significantly upregulated in both knee and hip OA (IVW: p = 6.8164×10-36 and 4.7919×10-2 respectively, OR > 1) and identified as a key factor in disease progression. Hsa-miR-1303 potentially regulates 30 genes involved in critical signaling pathways, such as the neurotrophin signaling pathway, and interacts with competing endogenous RNAs (ceRNAs) like circ_0041843 and LINC01338, thereby influencing key regulatory proteins such as SUMO2 and PARP1. Pharmacologically, hsa-miR-1303 targets nine druggable genes, including NRAS, H2AZ1, and RPS3, which have implications for drugs like cantharidin and diindolylmethane, potentially critical for developing novel OA treatments. Conversely, hsa-miR-125a-5p and hsa-miR-125b-5p, which are downregulated in both knee and hip OA, are associated with pathways such as HIF-1 and JAK-STAT, which modulate apoptotic signaling and transcriptional regulation. These miRNAs also interact with ceRNAs such as circ_0000254 and SPACA6P-AS, impacting proteins like STAT3, MCL1, and TRAF6. A drug interaction analysis identified 47 potential treatments, including Resveratrol and Acetaminophen, suggesting new therapeutic possibilities for OA management. This study not only highlights the role of miRNAs like hsa-miR-1303 and hsa-miR-125 in OA but also opens avenues for miRNA-based therapeutic development.