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

Identification of EDNRA as the Key Biomarker for Hypercholesterolemia and Colorectal Cancer

Some studies have investigated the role of cholesterol in the progression of colorectal cancer (CRC). However, the underlying mechanism of action is not clear. In this study, we used bioinformatics tools to elucidate the molecular mechanisms involved. We initially obtained CRC datasets from the Gene Expression Omnibus (GEO) database and hypercholesterolemia data from GeneCards and DisGeNE. Common differentially expressed genes (DEGs) were determined by using Venn diagram web tools. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The hub gene was identified through common expression pattern analysis and survival analysis. Finally, we conducted an immune regulatory point analysis and predicted target drugs based on the hub gene. The results of our analysis revealed 13 common DEGs, with endothelin receptor type A (EDNRA) identified as the hub gene linking hypercholesterolemia and CRC. The results of the GO analysis showed that the common DEGs were primarily associated with the G-protein coupled receptor signaling pathway, extracellular space, and receptor binding. The results of the KEGG pathway enrichment analysis indicated enrichment in pathways related to cancer and the phospholipase D signaling pathway. Additionally, we identified potential target drugs, including Podocarpus montanus, Diospyros kaki, Herba Salviae japoniae, sitaxentan, and ambrisentan. We found that EDNRA might be an underlying biomarker for both hypercholesterolemia and CRC. The predicted target drugs provide new strategies for treating CRC.

Discovering therapeutic possibilities for polycystic ovary syndrome by targeting XIST and its associated ceRNA network through the analysis of transcriptome data

Long non-coding RNA (lncRNA) regulates many physiological processes by acting as competitive endogenous RNA (ceRNA). The dysregulation of lncRNA X-inactive specific transcript (XIST) has been shown in various human disorders. However, its role in the pathogenesis of polycystic ovary syndrome (PCOS) is yet to be explored. This study aimed to explore the underlying mechanism of XIST in the pathogenesis of PCOS, specifically through dataset functional analysis. GEO PCOS datasets including RNA-seq, microarray, and miRNA-seq in granulosa cells (GCs) and blood, were examined and comprehensively analyzed. Enrichment analysis, ROC curve constructions, lncRNA-miRNA-mRNA interaction network analyses, and qRT-PCR validation were performed followed by a series of drug signature screenings. Our results revealed significant dysregulation in the expression of 1131 mRNAs, 30 miRNAs, and XIST in GCs of PCOS patients compared to healthy individuals. Of the120 XIST-correlated upregulated genes, 25 were enriched in inflammation-related pathways. Additionally, 5 miRNAs were identified as negative regulators of XIST-correlated genes. Accordingly, a ceRNA network containing XIST-miRNAs-mRNAs interactions was constructed. Furthermore, 6 genes, including AQP9, ETS2, PLAU, PLEK, SOCS3, and TNFRSF1B served as both GCs and blood-based biomarkers. By analyzing the number of interactions among XIST, miRNAs, and mRNAs, we pinpointed ETS2 as the pivotal gene within the ceRNA network. Our findings reveal a novel XIST- hsa-miR-146a-5p, hsa-miR-144-3p, and hsa-miR-1271-5p-ETS2 axis that comprehensively elucidates the XIST-associated mechanism underlying PCOS onset. qRT-PCR analysis further confirmed the, overexpression of both XIST and ETS2 . Furthermore, our results demonstrated that XIST and ETS2 were correlated with some assisted reproductive technologies outcomes. Finally, we identified two novel compounds including, methotrexate/folate and threonine using drug-gene interaction databases for PCOS management. These findings provide novel insights into the molecular etiology, diagnosis, and potential therapeutic interventions for PCOS.

MicroRNA-centered theranostics for pulmoprotection in critical COVID-19

Elucidating the pathobiological mechanisms underlying post-acute pulmonary sequelae following SARS-CoV-2 infection is essential for early interventions and patient stratification. Here, we investigated the potential of microRNAs (miRNAs) as theranostic agents for pulmoprotection in critical illness survivors. Multicenter study including 172 ICU survivors. Diffusion impairment was defined as a lung-diffusing capacity for carbon monoxide (DLCO) <80% within 12 months postdischarge. A disease-associated 16-miRNA panel was quantified in plasma samples collected at ICU admission. Bioinformatic analyses were conducted using KEGG, Reactome, GTEx, and Drug-Gene Interaction databases. The results were validated using an external RNA-seq dataset. A 3-miRNA signature linked to diffusion impairment (miR-27a-3p, miR-93-5p, and miR-199a-5p) was identified using random forest. Levels of miR-93-5p and miR-199a-5p were independently associated with the outcome, improving patient classification provided by the electronic health record. The experimentally validated targets of these miRNAs exhibited enrichment across diverse pathways, with telomere length quantification in an additional set of samples (n = 83) supporting the role of cell senescence in sequelae. Analysis of an external dataset refined the pathobiological fingerprint of pulmonary sequelae. Gene-drug interaction analysis revealed four FDA-approved drugs. Overall, this study advances our understanding of lung recovery in postacute respiratory infections, highlighting the potential of miRNAs and their targets for pulmoprotection.

Development and validation of a novel biomarker panel for Crohn's disease and rheumatoid arthritis diagnosis and treatment

BACKGROUND

Crohn's disease (CD) and rheumatoid arthritis (RA) are immune-mediated inflammatory diseases. However, the molecular mechanisms linking these two diseases remain unclear.

METHODS

To identify shared core genes between CD and RA, we employed differential gene analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Functional annotation of these core biomarkers was performed using consensus clustering and gene set enrichment analysis. We also constructed a protein-protein network and a miRNA-mRNA network using multiple databases, and potential therapeutic agents targeting the core biomarkers were predicted. Finally, we confirmed the expression of the genes in the biomarker panel in both CD and RA using quantitative PCR.

RESULTS

A total of five shared core genes, namely C-X-C motif chemokine ligand 10 (CXCL10), C-X-C motif chemokine ligand 9 (CXCL9), aquaporin 9 (AQP9), secreted phosphoprotein 1 (SPP1), and metallothionein 1M (MT1M), were identified as core biomarkers. These biomarkers activate classical pro-inflammatory and immune signaling pathways, influencing immune cell aggregation. Additionally, testosterone was identified as a potential therapeutic agent targeting the biomarkers identified in this study. The expression of genes in the biomarker panel in CD and RA was confirmed through quantitative PCR.

CONCLUSION

Our study revealed some core genes shared between CD and RA and established a novel biomarker panel with potential implications for the diagnosis and treatment of these diseases.

A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to genes involved in neurological, thyroidal, bone metabolism, and hematopoietic pathways. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausibility of distinct influences of both sleep duration extremes in cardiovascular health. With several of our loci reflecting specificity towards population background or sex, our discovery sheds light on the importance of embracing granularity when addressing heterogeneity entangled in gene-environment interactions, and in therapeutic design approaches for blood pressure management.

The biomarkers associated with epithelial-mesenchymal transition in human keloids

INTRODUCTION

A keloid is a type of benign fibrotic disease with similar features to malignancies, including anti-apoptosis, over-proliferation, and invasion. Epithelial-mesenchymal transition (EMT) is a crucial mechanism that regulates the metastatic behavior of tumors. Thus, identifying EMT biomarkers is paramount in comprehensively understanding keloid pathogenesis.

METHODS

To identify the differentially expressed genes (DEGs) GSE92566 dataset, with 3 normal skin and 4 keloid tissues, was downloaded from GEO databases to identify the differentially expressed genes (DEGs). Further, EMT-related genes were downloaded from dbEMT 2.0 databases and intersected with GSE92566 DEGs to identify EMT-related-DEGs (ERDEGs). Subsequently, the ERDEGs were used for GO, KEGG, gene set enrichment analysis (GSEA), protein-protein interaction (PPI), and miRNAs-mRNAs network analysis. To predict small molecules for EMT inhibition, the ERDEGs were imported to cMAP databases, whereas hub genes were imported to DGidb databases. Finally, we carried out qRT-PCR and in vitro experiments to validate our findings.

RESULTS

A total of 122 ERDEGs were identified, including 59 upregulated and 63 down-regulated genes. Moreover, enrichment analysis revealed that focal adhesion, AMPK signal pathway, Wnt signal pathway, and EMT biological process were significantly enriched. STRING databases and Cytoscape software were used to construct the PPI network and EMT-related hub genes. Further, 3 modules were explored from the PPI network using the Molecular Complex Detection (MCODE) plugin. In the Cytohubba plugin, 10 hub genes were explored, including FN1, EGF, SOX9, CDH2, PROM1, EPCAM, KRT19, ITGB1, CD24, and KRT18. These genes were then enriched for the focal adhesion pathway. We constructed a microRNA (miRNA)-mRNA network, which predicted hsa-miR-155-5p (8 edges), hsa-miR-124-3p (7 edges), hsa-miR-145-5p (5 edges), hsa-miR-20a-5p (5 edges) and hsa-let-7b-5p (4 edges) as the most connected miRNAs regulating EMT. Based on the ERDEGs and 10 hub genes mentioned above, ribavirin demonstrated high drug-targeting relevance. Subsequently, qRT-PCR confirmed that the expression of FN1, ITGB1, CDH2, and EPCAM corroborated with previous findings. qRT-PCR also showed that the expression levels of hsa-miR-124-3p and hsa-miR-145-5p were significantly lower in keloids and hsa-miR-155-5p was upregulated in keloids. Finally, by treating human keloid fibroblasts (HKFs) with ribavirin in vitro, we confirmed that ribavirin could inhibit HKFs proliferation and EMT.

CONCLUSION

In summary, this work provides novel EMT biomarkers in keloids and predicts new small target molecules for keloid therapy. Our findings improve the understanding of keloid pathogenesis, providing new treatment options.

Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19

Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.

Metformin Therapeutic Targets for Aortic Aneurysms: A Mendelian Randomization and Colocalization Study

Background

Identifying effective pharmacological interventions to prevent the progressive enlargement and rupture of aortic aneurysms (AAs) is critical. Previous studies have suggested links between metformin use and a decreased incidence of AAs. In this study, we employed Mendelian randomization (MR) to investigate causal effects of metformin's targets on AA risk and to explore the underlying mechanisms underlying these effects.

Methods

To examine the relationship between metformin use and AA risk, we implemented both two-sample MR and multivariable MR analyses. Utilizing genetic instrumental variables, we retrieved cis-expression quantitative trait loci (cis-eQTL) data for potential targets of metformin from the Expression Quantitative Trait Loci Genetics Consortium (eQTLGen) Consortium and Genotype-Tissue Expression (GTEx) project. Colocalization analysis was employed to ascertain the probability of shared causal genetic variants between single nucleotide polymorphisms (SNPs) associated with eQTLs and AA.

Results

Our findings reveal that metformin use reduces AA risk, exhibiting a protective effect with an odds ratio (OR) of 4.88 × 10 - 3 (95% confidence interval [CI]: 7.30 × 10 - 5 -0.33, p = 0.01). Furthermore, the protective effect of type 2 diabetes on AA risk appears to be driven by metformin use ( OR MVMR = 1.34 × 10 - 4 , 95% CI: 3.97 × 10 - 8 -0.45, p = 0.03). Significant Mendelian randomization (MR) results were observed for the expression of two metformin-related genes in the bloodstream: NADH:ubiquinone oxidoreductase subunit A6 (NDUFA6) and cytochrome b5 type B (CYB5B), across two independent datasets ( OR CYB5B = 1.35, 95% CI: 1.20-1.51, p = 2.41 × 10 - 7 ; OR NDUFA6 = 1.12; 95% CI: 1.07-1.17, p = 1.69 × 10 - 6 ). The MR analysis of tissue-specific expression also demonstrated a positive correlation between increased NDUFA6 expression and heightened AA risk. Lastly, NDUFA6 exhibited evidence of colocalization with AA.

Conclusions

Our study suggests that metformin may play a significant role in lowering the risk of AA. This protective effect could potentially be linked to the mitigation of mitochondrial and immune dysfunction. Overall, NDUFA6 has emerged as a potential mechanism through which metformin intervention may confer AA protection.

Whole-exome sequencing in UK Biobank reveals rare genetic architecture for depression

Nearly two hundred common-variant depression risk loci have been identified by genome-wide association studies (GWAS). However, the impact of rare coding variants on depression remains poorly understood. Here, we present whole-exome sequencing analyses of depression with seven different definitions based on survey, questionnaire, and electronic health records in 320,356 UK Biobank participants. We showed that the burden of rare damaging coding variants in loss-of-function intolerant genes is significantly associated with risk of depression with various definitions. We compared the rare and common genetic architecture across depression definitions by genetic correlation and showed different genetic relationships between definitions across common and rare variants. In addition, we demonstrated that the effects of rare damaging coding variant burden and polygenic risk score on depression risk are additive. The gene set burden analyses revealed overlapping rare genetic variant components with developmental disorder, autism, and schizophrenia. Our study provides insights into the contribution of rare coding variants, separately and in conjunction with common variants, on depression with various definitions and their genetic relationships with neurodevelopmental disorders.

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

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

Time-series analysis of hematopoietic stem cells

This study aimed to investigate the molecular mechanisms underlying the aging of hematopoietic stem cells (HSCs). Gene expression profile GSE32719 was downloaded from the Gene Expression Omnibus database, including 14 young, 5 middle, and 8 old HSCs. Differential expression analysis, short time-series expression miner analysis, and weighted co-expression network analysis were conducted to screen for hub genes whose expression changed over time during HSC aging. Subsequently, functional enrichment and multiple regulatory network analyses of the hub genes were performed. A total of 124 intersecting time-dependent differentially expressed and module genes were obtained, which were considered hub genes whose expression changed over time during HSC aging. Hub genes were significantly enriched in pathways such as the Hippo and AMP-activated protein kinase (AMPK) signaling pathways. Moreover, AP-1 Transcription Factor Subunit (FOS) and sirtuin 1 (SIRT1) had higher degrees in the protein-protein interaction network, were regulated by more transcription factors (TFs), such as Sp1 transcription factor (SP1) and BRCA1 DNA repair-associated (BRCA1), in the TF-mRNA-miRNA network, were associated with more diseases in the disease-gene network, and could be targeted by more drugs in the drug-gene network. Furthermore, SIRT1 was targeted by miR-9-5p in the TF-mRNA-miRNA network. Hub genes such as FOS and SIRT1 and key pathways such as the Hippo and AMPK signaling pathways may play crucial roles in HSC aging. Moreover, FOS and SIRT1 were regulated by SP1 and BRCA1, respectively, during HSC aging. Furthermore, miR-9-5p may modulate HSC aging by targeting SIRT1. Thus, FOS and SIRT1 may be potential therapeutic targets for age-related hematopoietic dysfunction.

Identification of a novel glioblastoma multiforme molecular subtype with poor prognosis and high immune infiltration based on oxidative stress-related genes

Glioblastoma multiforme (GBM) is a highly malignant primary brain tumor with a poor prognosis. Reactive oxygen species that accumulate during tumorigenesis can cause oxidative stress (OS), which plays a crucial role in cancer cell survival. Clinical and transcriptome data of TCGA-GBM dataset from UCSC Xena database were analyzed. Consensus clustering analysis was conducted to identify OS-related molecular subtypes for GBM. The immune infiltrate level between subtypes were characterized by ESTIMATE algorithm. Differentially expressed genes (DEGs) between subtypes were screened by DESeq2 package. Two OS-related molecular subtypes of GBM were identified, and cluster 2 had poorer overall survival and higher immune infiltration levels than cluster 1. Enrichment analysis showed that 54 DEGs in cluster 2 were significantly enriched in cytokine/chemokine-related functions or pathways. Ten hub genes (CSF2, CSF3, CCL7, LCN2, CXCL6, MMP8, CCR8, TNFSF11, IL22RA2, and ORM1) were identified in GBM subtype 2 through protein-protein interaction network, most of which were positively correlated with immune factors and immune checkpoints. A total of 55 small molecule drugs obtained from drug gene interaction database (DGIdb) may have potential therapeutic effects in GBM subtype 2 patients. Our study identified 10 hub genes as potential therapeutic targets in GBM subtype 2 patients, who have poorer overall survival and higher immune infiltration levels. These findings could pave the way for new treatments for this aggressive form of brain cancer.

Long noncoding RNA LINC00921 serves as a predictive biomarker for lung adenocarcinoma: An observational study

Lung adenocarcinoma (LUAD) is usually diagnosed at advanced stages. Hence, there is an urgent need to seek an effective biomarker to predict LUAD status. Long noncoding RNAs (lncRNAs) play key roles in the development of tumors. However, the relationship between LINC00921 and LUAD remains unclear. The gene expression data of LUAD were downloaded from the Cancer Genome Atlas database to investigate the expression level of LINC00921 in LUAD. Diagnostic ability analysis, survival analysis, tumor mutational burden analysis, and immune cell infiltration analysis of LINC00921 in LUAD patients were performed simultaneously. According to the median expression value of LINC00921, patients were divided into LINC00921 high- and low-expression groups. The function of LINC00921 in LUAD was identified through difference analysis and enrichment analysis. Moreover, drugs that may be relevant to LUAD treatment were screened. Finally, blood samples were collected for real-time polymerase chain reaction. LINC00921 was significantly lower in LUAD tumor tissues. Notably, patients with low expression of LINC00921 had a shorter median survival time. Decreased immune cell infiltration in the tumor microenvironment in the low LINC00921 expression group may contribute to poorer patient outcomes. Tumor mutational burden was significantly different in survival between the LINC00921 high- and low-expression groups. In addition, LINC00921 may exert an influence on cancer development through its regulation of target genes transcription. Glyceraldehyde-3-phosphate dehydrogenase-related drugs may be more likely to be therapeutically effective in LUAD. LINC00921 was able to be used as the potential diagnostic indicator for LUAD.

Potential mechanisms and drug prediction of Rheumatoid Arthritis and primary Sjögren's Syndrome: A public databases-based study

Rheumatoid arthritis (RA) and primary Sjögren's syndrome (pSS) are the most common systemic autoimmune diseases, and they are increasingly being recognized as occurring in the same patient population. These two diseases share several clinical features and laboratory parameters, but the exact mechanism of their co-pathogenesis remains unclear. The intention of this study was to investigate the common molecular mechanisms involved in RA and pSS using integrated bioinformatic analysis. RNA-seq data for RA and pSS were picked up from the Gene Expression Omnibus (GEO) database. Co-expression genes linked with RA and pSS were recognized using weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. Then, we screened two public disease-gene interaction databases (GeneCards and Comparative Toxicogenomics Database) for common targets associated with RA and pSS. The DGIdb database was used to predict therapeutic drugs for RA and pSS. The Human microRNA Disease Database (HMDD) was used to screen out the common microRNAs associated with RA and pSS. Finally, a common miRNA-gene network was created using Cytoscape. Four hub genes (CXCL10, GZMA, ITGA4, and PSMB9) were obtained from the intersection of common genes from WGCNA, differential gene analysis and public databases. Twenty-four drugs corresponding to hub gene targets were predicted in the DGIdb database. Among the 24 drugs, five drugs had already been reported for the treatment of RA and pSS. Other drugs, such as bortezomib, carfilzomib, oprozomib, cyclosporine and zidovudine, may be ideal drugs for the future treatment of RA patients with pSS. According to the miRNA-gene network, hsa-mir-21 may play a significant role in the mechanisms shared by RA and pSS. In conclusion, we identified commom targets as potential biomarkers in RA and pSS from publicly available databases and predicted potential drugs based on the targets. A new understanding of the molecular mechanisms associated with RA and pSS is provided according to the miRNA-gene network.

Protein-protein interaction network analysis for the identification of novel multi-target inhibitors and target miRNAs against Alzheimer's disease

This study presents a strategy for extracting significant gene complexes and then provides prospective therapeutics for AD. In this research, a total of 7905 reports published from 1981 to 2022 were retrieved. Following a review of all those articles, only the genetic association studies on AD were considered. Finally, there is a list of 453 Alzheimer-related genes in our dataset for network analysis. To this end, an experimentally derived protein-protein interaction (PPI) network from the String database was utilized to extract four meaningful gene complexes functionally interconnected using Cytoscape v3.9.1 software. The acquired gene complexes were subjected to an enrichment analysis using the ClueGO v2.5.9 tool to emphasize the most significant biological processes and pathways. Afterward, extracted gene complexes were used to extract the drugs related to AD from DGI v3.0 database and introduce some new drugs which may be helpful for this disease. Finally, a comprehensive network that included every gene connected to each gene complex group as well as the drug targets for each gene has been shown. Moreover, molecular docking studies have been performed with the selected compounds to identify the interaction pattern with the respective targets. Finally, we proposed a list of 62 compounds as multi-targeted directed drug-like compounds with a degree value between 2 and 5 and 30 compounds as target-specific drug-like compounds, which have not been proclaimed as AD-related drugs in prior scientific and medical investigations. Then, new drugs were suggested that can be experimentally examined for future work. In addition to this, four bipartite networks representing each group's genes and target miRNAs were established to introduce target miRNAs by using the miRWalk v3 server.

A method to estimate the contribution of rare coding variants to complex trait heritability

It has been postulated that rare coding variants (RVs; MAF < 0.01) contribute to the "missing" heritability of complex traits. We developed a framework, the Rare variant heritability (RARity) estimator, to assess RV heritability (h2RV) without assuming a particular genetic architecture. We applied RARity to 31 complex traits in the UK Biobank (n = 167,348) and showed that gene-level RV aggregation suffers from 79% (95% CI: 68-93%) loss of h2RV. Using unaggregated variants, 27 traits had h2RV > 5%, with height having the highest h2RV at 21.9% (95% CI: 19.0-24.8%). The total heritability, including common and rare variants, recovered pedigree-based estimates for 11 traits. RARity can estimate gene-level h2RV, enabling the assessment of gene-level characteristics and revealing 11, previously unreported, gene-phenotype relationships. Finally, we demonstrated that in silico pathogenicity prediction (variant-level) and gene-level annotations do not generally enrich for RVs that over-contribute to complex trait variance, and thus, innovative methods are needed to predict RV functionality.

Investigating the impact of Wnt pathway-related genes on biomarker and diagnostic model development for osteoporosis in postmenopausal females

The Wnt signaling pathway is essential for bone development and maintaining skeletal homeostasis, making it particularly relevant in osteoporosis patients. Our study aimed to identify distinct molecular clusters associated with the Wnt pathway and develop a diagnostic model for osteoporosis in postmenopausal Caucasian women. We downloaded three datasets (GSE56814, GSE56815 and GSE2208) related to osteoporosis from the GEO database. Our analysis identified a total of 371 differentially expressed genes (DEGs) between low and high bone mineral density (BMD) groups, with 12 genes associated with the Wnt signaling pathway, referred to as osteoporosis-associated Wnt pathway-related genes. Employing four independent machine learning models, we established a diagnostic model using the 12 osteoporosis-associated Wnt pathway-related genes in the training set. The XGB model showed the most promising discriminative potential. We further validate the predictive capability of our diagnostic model by applying it to three external datasets specifically related to osteoporosis. Subsequently, we constructed a diagnostic nomogram based on the five crucial genes identified from the XGB model. In addition, through the utilization of DGIdb, we identified a total of 30 molecular compounds or medications that exhibit potential as promising therapeutic targets for osteoporosis. In summary, our comprehensive analysis provides valuable insights into the relationship between the osteoporosis and Wnt signaling pathway.

Machine learning and bioinformatic analyses link the cell surface receptor transcript levels to the drug response of breast cancer cells and drug off-target effects

Breast cancer responds variably to anticancer therapies, often leading to significant off-target effects. This study proposes that the variability in tumour responses and drug-induced adverse events is linked to the transcriptional profiles of cell surface receptors (CSRs) in breast tumours and normal tissues. We analysed multiple datasets to compare CSR expression in breast tumours with that in non-cancerous human tissues. Our findings correlate the drug responses of breast cancer cell lines with the expression levels of their targeted CSRs. Notably, we identified distinct differences in CSR expression between primary breast tumour subtypes and corresponding cell lines, which may influence drug response predictions. Additionally, we used clinical trial data to uncover associations between CSR gene expression in healthy tissues and the incidence of adverse drug reactions. This integrative approach facilitates the selection of optimal CSR targets for therapy, leveraging cell line dose-responses, CSR expression in normal tissues, and patient adverse event profiles.

Unraveling the Molecular Complexity of Adenoid Cystic Carcinoma (ACC): A Comprehensive Exploration of Hub Genes, Protein-Protein Interaction (PPI) Networks, microRNA (miRNA) Involvement, and Drug-Gene Interactions (DGIs)

Background Adenoid cystic carcinoma (ACC) poses clinical challenges with its unique histology and potential for perineural invasion, recurrence, and distant metastases. Recent genomic advancements have unveiled key genetic alterations in ACC, offering insights into its pathogenesis. Aim This study aims to unravel the intricate molecular landscape of ACC through a comprehensive analysis of gene expression patterns. By integrating data from multiple microarray datasets, the study explores differentially expressed genes (DEGs), their functional enrichment, protein-protein interactions (PPI), hub genes, microRNA (miRNA) involvement, transcription factors, and potential drug-gene interactions. Methods Three microarray datasets (GSE88804, GSE153002, and GSE36820) related to ACC were selected from the Gene Expression Omnibus (GEO) repository. DEGs were identified using GEO2R and further analyzed for commonalities and differences. Functional enrichment analysis, including Gene Set Enrichment Analysis (GSEA), provided insights into biological processes, cellular components, molecular functions, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with ACC. PPI networks and hub genes were identified using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) (STRING Consortium, Lausanne, Switzerland) database and Cytoscape (Cytoscape Consortium, California, United States). The study also explored miRNAs, transcription factors, and potential drug-gene interactions. Results The integrated analysis revealed 339 common upregulated and 643 downregulated DEGs in ACC. Functional and pathway enrichment analyses unveiled the involvement of these genes in critical cellular processes, signaling cascades, and pathways. The PPI network, comprising 904 nodes and 4139 edges, highlighted the complexity of interactions. Hub genes, including KIF11, BUB1, and DLGAP5, were identified, shedding light on their pivotal roles in cell cycle regulation. The study also identified miRNAs (e.g., hsa-mir-7-5p and hsa-mir-138-5p) and transcription factors (e.g., E2F1 and TP53) associated with ACC. Drug-gene interactions have identified potential therapeutic options, including amsacrine and rucaparib. Conclusions The ACC gene expression highlights a nuanced molecular landscape, identifying pivotal hub genes such as KIF11 and CDK1 as potential therapeutic targets for ACC, given their roles in cell cycle progression. The dysregulation of microRNAs and transcription factors adds complexity to ACC's molecular profile. Exploration of drug-gene interactions reveals promising therapeutic strategies, involving FDA-approved drugs such as amsacrine and rucaparib, providing avenues for personalized interventions.

Profiling Kinase Activities for Precision Oncology in Diffuse Gastric Cancer

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

A novel pipeline for prioritizing cancer type-specific therapeutic vulnerabilities using DepMap identifies PAK2 as a target in head and neck squamous cell carcinomas

There is limited guidance on exploiting the genome-wide loss-of-function CRISPR screens in cancer Dependency Map (DepMap) to identify new targets for individual cancer types. This study integrated multiple tools to filter these data in order to seek new therapeutic targets specific to head and neck squamous cell carcinoma (HNSCC). The resulting pipeline prioritized 143 targetable dependencies that represented both well-studied targets and emerging target classes like mitochondrial carriers and RNA-binding proteins. In total, 14 targets had clinical inhibitors used for other cancers or nonmalignant diseases that hold near-term potential to repurpose for HNSCC therapy. Comparing inhibitor response data that were publicly available for 13 prioritized targets between the cell lines with high vs. low dependency on each target uncovered novel therapeutic potential for the PAK2 serine/threonine kinase. PAK2 gene dependency was found to be associated with wild-type p53, low PAK2 mRNA, and diploid status of the 3q amplicon containing PAK2. These findings establish a generalizable pipeline to prioritize clinically relevant targets for individual cancer types using DepMap. Its application to HNSCC highlights novel relevance for PAK2 inhibition and identifies biomarkers of PAK2 inhibitor response.

Genome-wide meta-analysis, functional genomics and integrative analyses implicate new risk genes and therapeutic targets for anxiety disorders

Anxiety disorders are the most prevalent mental disorders. However, the genetic etiology of anxiety disorders remains largely unknown. Here we conducted a genome-wide meta-analysis on anxiety disorders by including 74,973 (28,392 proxy) cases and 400,243 (146,771 proxy) controls. We identified 14 risk loci, including 10 new associations near CNTNAP5, MAP2, RAB9BP1, BTN1A1, PRR16, PCLO, PTPRD, FARP1, CDH2 and RAB27B. Functional genomics and fine-mapping pinpointed the potential causal variants, and expression quantitative trait loci analysis revealed the potential target genes regulated by the risk variants. Integrative analyses, including transcriptome-wide association study, proteome-wide association study and colocalization analyses, prioritized potential causal genes (including CTNND1 and RAB27B). Evidence from multiple analyses revealed possibly causal genes, including RAB27B, BTN3A2, PCLO and CTNND1. Finally, we showed that Ctnnd1 knockdown affected dendritic spine density and resulted in anxiety-like behaviours in mice, revealing the potential role of CTNND1 in anxiety disorders. Our study identified new risk loci, potential causal variants and genes for anxiety disorders, providing insights into the genetic architecture of anxiety disorders and potential therapeutic targets.

Identification of systemic biomarkers and potential drug targets for age-related macular degeneration

Purpose

Since age-related macular degeneration (AMD) is tightly associated with aging and cellular senescence, objective of this study was to investigate the association between plasma levels of senescence-related proteins (SRPs) and risk of AMD.

Design

The whole study was based on two-sample Mendelian randomization (MR) analysis.

Methods

For MR analysis, the primary approach for MR analysis was the inverse-variance weighted (IVW) method and the heterogeneity and pleiotropy of results were tested. The instrumental single-nucleotide polymorphisms (SNPs) associated with 110 SRPs were filtered and selected from a large genome-wide association study (GWAS) for plasma proteome involving 35,559 participants. The GWAS data of AMD was obtained from FinnGen consortium (6,157 AMD cases and 288,237 controls) and further validated by using data from UK Biobank consortium (3,553 AMD cases and 147,089 controls).

Results

The MR results at both discovery and validation stages supported the causality (IVW-P < 0.00045) between plasma levels of 4 SRPs (C3b, CTNNB1, CCL1, and CCL3L1) and the risk of AMD and supported potential causality (IVW-P < 0.05) between other 10 SRPs and risk of AMD. No heterogeneity or pleiotropy in these results was detected.

Conclusion

Our findings supported that high plasma levels of C3b, CTNNB1, CCL1, and CCL3L1 were associated with increased risk of AMD, thereby highlighting the role of systemic inflammation in AMD pathogenesis and providing the rationale for developing new preventative and therapeutic strategies.

Drug repurposing for neurodegenerative diseases using Zebrafish behavioral profiles

Drug repurposing can accelerate drug development while reducing the cost and risk of toxicity typically associated with de novo drug design. Several disorders lacking pharmacological solutions and exhibiting poor results in clinical trials - such as Alzheimer's disease (AD) - could benefit from a cost-effective approach to finding new therapeutics. We previously developed a neural network model, Z-LaP Tracker, capable of quantifying behaviors in zebrafish larvae relevant to cognitive function, including activity, reactivity, swimming patterns, and optomotor response in the presence of visual and acoustic stimuli. Using this model, we performed a high-throughput screening of FDA-approved drugs to identify compounds that affect zebrafish larval behavior in a manner consistent with the distinct behavior induced by calcineurin inhibitors. Cyclosporine (CsA) and other calcineurin inhibitors have garnered interest for their potential role in the prevention of AD. We generated behavioral profiles suitable for cluster analysis, through which we identified 64 candidate therapeutics for neurodegenerative disorders.

Genes and Pathways Involved in the Progression of Malignant Pleural Mesothelioma: A Meta-analysis of Genome-Wide Expression Studies

Malignant pleural mesothelioma (MPM) is a rare and aggressive neoplasm of the pleural tissue that lines the lungs and is mainly associated with long latency from asbestos exposure. This tumor has no effective therapeutic opportunities nowadays and has a very low five-year survival rate. In this sense, identifying molecular events that trigger the development and progression of this tumor is highly important to establish new and potentially effective treatments. We conducted a meta-analysis of genome-wide expression studies publicly available at the Gene Expression Omnibus (GEO) and ArrayExpress databases. The differentially expressed genes (DEGs) were identified, and we performed functional enrichment analysis and protein-protein interaction networks (PPINs) to gain insight into the biological mechanisms underlying these genes. Additionally, we constructed survival prediction models for selected DEGs and predicted the minimum drug inhibition concentration of anticancer drugs for MPM. In total, 115 MPM tumor transcriptomes and 26 pleural tissue controls were analyzed. We identified 1046 upregulated DEGs in the MPM samples. Cellular signaling categories in tumor samples were associated with the TNF, PI3K-Akt, and AMPK pathways. The inflammatory response, regulation of cell migration, and regulation of angiogenesis were overrepresented biological processes. Expression of SOX17 and TACC1 were associated with reduced survival rates. This meta-analysis identified a list of DEGs in MPM tumors, cancer-related signaling pathways, and biological processes that were overrepresented in MPM samples. Some therapeutic targets to treat MPM are suggested, and the prognostic potential of key genes is shown.

Transcriptome-level discovery of survival-associated biomarkers and therapy targets in non-small-cell lung cancer

BACKGROUND AND PURPOSE

Survival rate of patients with lung cancer has increased by over 60% in the recent two decades. With longer survival, the identification of genes associated with survival has emerged as an issue of utmost importance to uncover the most promising biomarkers and therapeutic targets.

EXPERIMENTAL APPROACH

An integrated database was set up by combining multiple independent datasets with clinical data and transcriptome-level gene expression measurements. Univariate and multivariate survival analyses were performed to identify genes with higher expression levels linked to shorter survival. The strongest genes were filtered to include only those with known druggability.

KEY RESULTS

The entire database includes 2852 tumour specimens from 17 independent cohorts. Of these, 2227 have overall survival data and 1256 samples have progression-free survival time. The most significant genes associated with survival were MIF, UBC and B2M in lung adenocarcinoma and ANXA2, CSNK2A2 and KRT18 in squamous cell carcinoma. We also aimed to reveal the best druggable targets in non-smokers lung cancer. The three most promising hits in this cohort were MDK, THY1 and PADI2. The established lung cancer cohort was added to the Kaplan-Meier plotter (https://www.kmplot.com) enabling the validation of future gene expression-based biomarkers in both the present and yet unexamined subgroups of patients.

CONCLUSIONS AND IMPLICATIONS

In this study, we established a comprehensive database of transcriptome-level data for lung cancer. The database can be utilized to identify and rank the most promising biomarkers and therapeutic targets for different subtypes of lung cancer.

Non-Negative matrix factorization combined with kernel regression for the prediction of adverse drug reaction profiles

Motivation

Post-market unexpected Adverse Drug Reactions (ADRs) are associated with significant costs, in both financial burden and human health. Due to the high cost and time required to run clinical trials, there is significant interest in accurate computational methods that can aid in the prediction of ADRs for new drugs. As a machine learning task, ADR prediction is made more challenging due to a high degree of class imbalance and existing methods do not successfully balance the requirement to detect the minority cases (true positives for ADR), as measured by the Area Under the Precision-Recall (AUPR) curve with the ability to separate true positives from true negatives [as measured by the Area Under the Receiver Operating Characteristic (AUROC) curve]. Surprisingly, the performance of most existing methods is worse than a naïve method that attributes ADRs to drugs according to the frequency with which the ADR has been observed over all other drugs. The existing advanced methods applied do not lead to substantial gains in predictive performance.

Results

We designed a rigorous evaluation to provide an unbiased estimate of the performance of ADR prediction methods: Nested Cross-Validation and a hold-out set were adopted. Among the existing methods, Kernel Regression (KR) performed best in AUPR but had a disadvantage in AUROC, relative to other methods, including the naïve method. We proposed a novel method that combines non-negative matrix factorization with kernel regression, called VKR. This novel approach matched or exceeded the performance of existing methods, overcoming the weakness of the existing methods.

Availability

Code and data are available on https://github.com/YezhaoZhong/VKR.

PANCDR: precise medicine prediction using an adversarial network for cancer drug response

Pharmacogenomics aims to provide personalized therapy to patients based on their genetic variability. However, accurate prediction of cancer drug response (CDR) is challenging due to genetic heterogeneity. Since clinical data are limited, most studies predicting drug response use preclinical data to train models. However, such models might not be generalizable to external clinical data due to differences between the preclinical and clinical datasets. In this study, a Precision Medicine Prediction using an Adversarial Network for Cancer Drug Response (PANCDR) model is proposed. PANCDR consists of two sub-models, an adversarial model and a CDR prediction model. The adversarial model reduces the gap between the preclinical and clinical datasets, while the CDR prediction model extracts features and predicts responses. PANCDR was trained using both preclinical data and unlabeled clinical data. Subsequently, it was tested on external clinical data, including The Cancer Genome Atlas and brain tumor patients. PANCDR outperformed other machine learning models in predicting external test data. Our results demonstrate the robustness of PANCDR and its potential in precision medicine by recommending patient-specific drug candidates. The PANCDR codes and data are available at https://github.com/DMCB-GIST/PANCDR.

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

Genetic architecture distinguishes tinnitus from hearing loss

Tinnitus is a heritable, highly prevalent auditory disorder treated by multiple medical specialties. Previous GWAS indicated high genetic correlations between tinnitus and hearing loss, with little indication of differentiating signals. We present a GWAS meta-analysis, triple previous sample sizes, and expand to non-European ancestries. GWAS in 596,905 Million Veteran Program subjects identified 39 tinnitus loci, and identified genes related to neuronal synapses and cochlear structural support. Applying state-of-the-art analytic tools, we confirm a large number of shared variants, but also a distinct genetic architecture of tinnitus, with higher polygenicity and large proportion of variants not shared with hearing difficulty. Tissue-expression analysis for tinnitus infers broad enrichment across most brain tissues, in contrast to hearing difficulty. Finally, tinnitus is not only correlated with hearing loss, but also with a spectrum of psychiatric disorders, providing potential new avenues for treatment. This study establishes tinnitus as a distinct disorder separate from hearing difficulties.

Molecular insights into sarcopenia: ferroptosis-related genes as diagnostic and therapeutic targets

Ferroptosis, characterized by iron accumulation and lipid peroxidation, leads to cell death. Growing evidence suggests the involvement of ferroptosis in sarcopenia. However, the fundamental ferroptosis-related genes (FRGs) for sarcopenia diagnosis, prognosis, and therapy remain elusive. This study aimed to identify molecular biomarkers of ferroptosis in sarcopenia patients. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between normal and sarcopenia samples were identified using the 'limma' package in R software. FRGs were extracted from GeneCards and FerrDB databases. Functional enrichment analysis determined the roles of DEGs using the 'clusterProfiler' package. A protein-protein network was constructed using Cytoscape software. Immune infiltration analysis and receiver operating characteristic (ROC) analysis were performed. mRNA-miRNA, mRNA-TF, and mRNA-drug interactions were predicted using ENCORI, hTFtarget, and CHIPBase databases. The network was visualized using Cytoscape. We identified 46 FRGs in sarcopenia. Functional enrichment analysis revealed their involvement in critical biological processes, including responses to steroid hormones and glucocorticoids. KEGG enrichment analysis implicated pathways such as carbon metabolism, ferroptosis, and glyoxylate in sarcopenia. Totally, 11 hub genes were identified, and ROC analysis demonstrated their potential as sensitive and specific markers for sarcopenia in both datasets. Additionally, differences in immune cell infiltration were observed between normal and sarcopenia samples. The hub genes identified in this study are closely associated with ferroptosis in sarcopenia and can effectively differentiate sarcopenia from controls. CDKN1A, CS, DLD, FOXO1, HSPB1, LDHA, MDH2, and YWHAZ show high sensitivity and specificity for sarcopenia diagnosis.

Integrated multi-omics profiling to dissect the spatiotemporal evolution of metastatic hepatocellular carcinoma

Comprehensive molecular analyses of metastatic hepatocellular carcinoma (HCC) are lacking. Here, we generate multi-omic profiling of 257 primary and 176 metastatic regions from 182 HCC patients. Primary tumors rich in hypoxia signatures facilitated polyclonal dissemination. Genomic divergence between primary and metastatic HCC is high, and early dissemination is prevalent. The remarkable neoantigen intratumor heterogeneity observed in metastases is associated with decreased T cell reactivity, resulting from disruptions to neoantigen presentation. We identify somatic copy number alterations as highly selected events driving metastasis. Subclones without Wnt mutations show a stronger selective advantage for metastasis than those with Wnt mutations and are characterized by a microenvironment rich in activated fibroblasts favoring a pro-metastatic phenotype. Finally, metastases without Wnt mutations exhibit higher enrichment of immunosuppressive B cells that mediate terminal exhaustion of CD8+ T cells via HLA-E:CD94-NKG2A checkpoint axis. Collectively, our results provide a multi-dimensional dissection of the complex evolutionary process of metastasis.

VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome

VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.

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

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

omicSynth: An open multi-omic community resource for identifying druggable targets across neurodegenerative diseases

Treatments for neurodegenerative disorders remain rare, but recent FDA approvals, such as lecanemab and aducanumab for Alzheimer disease (MIM: 607822), highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use summary-data-based Mendelian randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer disease, 3 amyotrophic lateral sclerosis (MIM: 105400), 5 Lewy body dementia (MIM: 127750), 46 Parkinson disease (MIM: 605909), and 9 progressive supranuclear palsy (MIM: 601104) target genes passing multiple test corrections (pSMR_multi < 2.95 × 10-6 and pHEIDI > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics, classifying 41 novel targets, 3 known targets, and 115 difficult targets (of these, 69.8% are expressed in the disease-relevant cell type from single-nucleus experiments). Our novel class of genes provides a springboard for new opportunities in drug discovery, development, and repurposing in the pre-competitive space. In addition, looking at drug-gene interaction networks, we identify previous trials that may require further follow-up such as riluzole in Alzheimer disease. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community.

Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies.

Identification of potential hub genes and regulatory networks of smoking-related endothelial dysfunction in atherosclerosis using bioinformatics analysis

BACKGROUND

Endothelial dysfunction, the earliest stage of atherosclerosis, can be caused by smoking, but its molecular mechanism requires further investigation.

OBJECTIVE

This study aimed to use bioinformatics analysis to identify potential mechanisms involved in smoking-related atherosclerotic endothelial dysfunction.

METHODS

The transcriptome data used for this bioinformatics analysis were obtained from the Gene Expression Omnibus (GEO) database. The GSE137578 and GSE141136 datasets were used to identify common differentially expressed genes (co-DEGs) in endothelial cells treated with oxidized low-density lipoprotein (ox-LDL) and tobacco. The co-DEGs were annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) databases. Additionally, a protein-protein interaction (PPI) network was constructed to visualize their interactions and screen for hub genes. GSE120521 dataset was used to verify the expression of hub genes in unstable plaques. The miRNA expression profile GSE137580 and online databases (starBase 2.0, TargetScan 8.0 and DGIdb v4.2.0) were used to predict the related non-coding RNAs and drugs.

RESULTS

A total of 232 co-DEGs were identified, including 113 up-regulated genes and 119 down-regulated genes. These DEGs were primarily enriched in detrimental autophagy, cell death, transcription factors, and cytokines, and were implicated in ferroptosis, abnormal lipid metabolism, inflammation, and oxidative stress pathways. Ten hub genes were screened from the constructed PPI network, including up-regulated genes such as FOS, HMOX1, SQSTM1, PTGS2, ATF3, DDIT3, and down-regulated genes MCM4, KIF15, UHRF1, and CCL2. Importantly, HMOX1 was further up-regulated in unstable plaques (p= 0.034). Finally, a regulatory network involving lncRNA/circRNA-miRNA-hub genes and drug-hub genes was established.

CONCLUSION

Atherosclerotic endothelial dysfunction is associated with smoking-induced injury. Through bioinformatics analysis, we identified potential mechanisms and provided potential therapeutic targets.

Identification and validation of cortisol-related hub biomarkers and the related pathogenesis of biomarkers in Ischemic Stroke

BACKGROUND

Ischemic stroke is a disease in which cerebral blood flow is blocked due to various reasons, leading to ischemia, hypoxia, softening, and even necrosis of brain tissues. The level of cortisol is related to the occurrence and progression of ischemic stroke. However, the mechanism governing their interrelationship is still unclear. The main objective of this study was to identify and understand the molecular mechanism between cortisol and IS.

METHODS

The common cortisol-related biological processes were screened by mutual verification of two data sets and the cortisol-related hub biomarkers were identified. Modular analysis of protein interaction networks was performed, and the differential pathway analysis of individual genes was conducted by GSVA and GSEA. Drug and transcription factor regulatory networks of hub genes were excavated, and the diagnostic potential of hub genes was analyzed followed by the construction of a diagnostic model.

RESULTS

By screening the two data sets by GSVA, three biological processes with common differences were obtained. After variation analysis, four cortisol-related hub biomarkers (CYP1B1, CDKN2B, MEN1, and USP8) were selected. Through the modular analysis of the protein-protein interaction network and double verification of GSVA and GSEA, a series of potential molecular mechanisms of hub genes were discovered followed by a series of drug regulatory networks and transcription factor regulatory networks. The hub biomarkers were found to have a high diagnostic value by ROC; thus, a diagnostic model with high diagnostic efficiency was constructed. The diagnostic value was mutually confirmed in the two data sets.

CONCLUSION

Four cortisol-related hub biomarkers are identified in this study, which provides new ideas for the key changes of cortisol during the occurrence of IS.

B2M is a Biomarker Associated With Immune Infiltration In High Altitude Pulmonary Edema

BACKGROUND

High altitude pulmonary edema (HAPE) is a serious mountain sickness with certain mortality. Its early diagnosis is very important. However, the mechanism of its onset and progression is still controversial.

AIM

This study aimed to analyze the HAPE occurrence and development mechanism and search for prospective biomarkers in peripheral blood.

METHODS

The difference genes (DEGs) of the Control group and the HAPE group were enriched by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and then GSEA analysis was performed. After identifying the immune-related hub genes, QPCR was used to verify and analyze the hub gene function and diagnostic value with single-gene GSEA and ROC curves, and the drugs that acted on the hub gene was found in the CTD database. Immune infiltration and its association with the hub genes were analyzed using CIBERSORT. Finally, WGCNA was employed to investigate immune invasion cells' significantly related gene modules, following enrichment analysis of their GO and KEGG.

RESULTS

The dataset enrichment analysis, immune invasion analysis and WGCNA analysis showed that the occurrence and early progression of HAPE were unrelated to inflammation. The hub genes associated with immunity obtained with MCODE algorithm of Cytoscape were JAK2 and B2M.. RT-qPCR and ROC curves confirmed that the hub gene B2M was a specific biomarker of HAPE and had diagnostic value, and single-gene GSEA analysis confirmed that it participated in MHC I molecule-mediated antigen presentation ability decreased, resulting in reduced immunity.

CONCLUSION

Occurrence and early progression of high altitude pulmonary edema may not be related to inflammation. B2M may be a new clinical potential biomarker for HAPE for early diagnosis and therapeutic evaluation as well as therapeutic targets, and its decrease may be related to reduced immunity due to reduced ability of MCH I to participate in antigen submission.

Strategies for translating proteomics discoveries into drug discovery for dementia

Tauopathies, diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of frontotemporal dementia, make up the vast majority of dementia cases. Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments, ongoing progress is required to ensure these are effective, economical, and accessible for the globally ageing population. As such, continued identification of new potential drug targets and biomarkers is critical. "Big data" studies, such as proteomics, can generate information on thousands of possible new targets for dementia diagnostics and therapeutics, but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development. In this review, we discuss current tauopathy biomarkers and therapeutics, and highlight areas in need of improvement, particularly when addressing the needs of frail, comorbid and cognitively impaired populations. We highlight biomarkers which have been developed from proteomic data, and outline possible future directions in this field. We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development, and demonstrate its application to our group's recent tau interactome dataset as an example.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

Integrative analysis of an endoplasmic reticulum stress-related signature in multiple myeloma

BACKGROUND

Multiple myeloma (MM) is a malignancy in which plasma cells proliferate abnormally, and it remains incurable. The cells are characterized by high levels of endoplasmic reticulum stress (ERS) and depend on the ERS response for survival. Thus, we aim to find an ERS-related signature of MM and assess its diagnostic value.

METHODS

We downloaded three datasets of MM from the Gene Expression Omnibus database. After identifying ERS-related differentially expressed genes (ERDEGs), we analyzed them using Gene Ontology enrichment analysis. A protein-protein interaction network, a transcription factor-mRNA network, a miRNA-mRNA network and a drug-mRNA network were constructed to explore the ERDEGs. The clinical application of these genes was identified by calculating the infiltration of immune cells and using receiver operating characteistic analyses. Finally, qPCR was performed to further confirm the roles of ERDEGs.

RESULTS

We obtained nine ERDEGs of MM. Gene Ontology enrichment indicated that the ERDEGs played a role in the endoplasmic reticulum membrane. Additionally, the protein-protein interaction network showed interaction among the ERDEGs, and there were 20 proteins, 107 transcription factors, 42 drugs or molecular compounds and 51 miRNAs which were likely to interact with the nine genes. In addition, immune cell infiltration analyses showed that there was a strong correlation between the nine genes and immune cells, and these potential biomarkers exhibited good diagnostic values. Finally, the expression of ERDEGs in MM cells was different from that in healthy donor samples.

CONCLUSION

The nine ERS-related genes, CR2, DHCR7, DNAJC3, KDELR2, LPL, OSBPL3, PINK1, VCAM1 and XBP1 are potential biomarkers of MM, and this supports further clinical development of the diagnosis and treatment of MM.

Bioinformatics analysis of carotid vulnerable plaques associated with the SARS-CoV-2 pattern

The rupture of carotid artery vulnerable plaque plays a critical role in ischemic stroke, and the widely spread new coronavirus in recent years plays a certain role in the development of human carotid artery vulnerable plaque, we screened out 27 differential expression genes (DEGs) of stable plaque and vulnerable plaque associated with the new coronavirus. Through the construction of the protein-protein interaction (PPI) network, the Cathepsin B (CTSB) and Niemann-Pick Disease Type 2 (NPC2) were identified as crucial expression genes, and further, we confirmed the validity of core gene expression in two validation sets. Additionally, we discovered a significant connection between CTSB, NPC2 and 28 different kinds of immune cells in carotid plaque tissue. We screened out 65 target interacting drugs based on 10 differentially expressed genes through online tools and finally verified the high expression of 2 core genes in fragile plaques through clinical sample experiments. These findings imply that two core genes may be novel targets for molecular diagnostics and immunotherapy of vulnerable plaques.

Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

BACKGROUND

The pharmaceutical field faces a significant challenge in validating drug target interactions (DTIs) due to the time and cost involved, leading to only a fraction being experimentally verified. To expedite drug discovery, accurate computational methods are essential for predicting potential interactions. Recently, machine learning techniques, particularly graph-based methods, have gained prominence. These methods utilize networks of drugs and targets, employing knowledge graph embedding (KGE) to represent structured information from knowledge graphs in a continuous vector space. This phenomenon highlights the growing inclination to utilize graph topologies as a means to improve the precision of predicting DTIs, hence addressing the pressing requirement for effective computational methodologies in the field of drug discovery.

RESULTS

The present study presents a novel approach called DTIOG for the prediction of DTIs. The methodology employed in this study involves the utilization of a KGE strategy, together with the incorporation of contextual information obtained from protein sequences. More specifically, the study makes use of Protein Bidirectional Encoder Representations from Transformers (ProtBERT) for this purpose. DTIOG utilizes a two-step process to compute embedding vectors using KGE techniques. Additionally, it employs ProtBERT to determine target-target similarity. Different similarity measures, such as Cosine similarity or Euclidean distance, are utilized in the prediction procedure. In addition to the contextual embedding, the proposed unique approach incorporates local representations obtained from the Simplified Molecular Input Line Entry Specification (SMILES) of drugs and the amino acid sequences of protein targets.

CONCLUSIONS

The effectiveness of the proposed approach was assessed through extensive experimentation on datasets pertaining to Enzymes, Ion Channels, and G-protein-coupled Receptors. The remarkable efficacy of DTIOG was showcased through the utilization of diverse similarity measures in order to calculate the similarities between drugs and targets. The combination of these factors, along with the incorporation of various classifiers, enabled the model to outperform existing algorithms in its ability to predict DTIs. The consistent observation of this advantage across all datasets underlines the robustness and accuracy of DTIOG in the domain of DTIs. Additionally, our case study suggests that the DTIOG can serve as a valuable tool for discovering new DTIs.

The clinical trials puzzle: How network effects limit drug discovery

The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model to enhance drug discovery in clinical trials.

Potential roles of FGF5 as a candidate therapeutic target in prostate cancer

Fibroblast growth factor (FGF) is a secreted ligand that is widely expressed in embryonic tissues but its expression decreases with age. In the developing prostate, FGF5 has been proposed to interact with the Hedgehog (Hh) signaling pathway to guide mitogenic processes. In the adult prostate, the FGF/FGFR signaling axis has been implicated in prostate carcinogenesis, but focused studies on FGF5 functions in the prostate are limited. Functional studies completed in other cancer models point towards FGF5 overexpression as an oncogenic driver associated with stemness, metastatic potential, proliferative capacity, and increased tumor grade. In this review, we explore the significance of FGF5 as a therapeutic target in prostate cancer (PCa) and other malignancies; and we introduce a potential route of investigation to link FGF5 to benign prostatic hyperplasia (BPH). PCa and BPH are two primary contributors to the disease burden of the aging male population and have severe implications on quality of life, psychological wellbeing, and survival. The development of new FGF5 inhibitors could potentially alleviate the health burden of PCa and BPH in the aging male population.

Inflammatory protein signatures in individuals with obesity and metabolic syndrome

There is variability in the metabolic health status among individuals presenting with obesity; some may be metabolically healthy, while others may have developed the metabolic syndrome, a cluster including insulin resistance, hypertension, dyslipidemia, and increased risk of cardiovascular disease and type 2 diabetes. The mechanisms contributing to this metabolic heterogeneity are not fully understood. To address this question, plasma samples from 48 individuals with BMI ≥ 35 kg/m2 were examined (27 with and 21 without metabolic syndrome). Fasting plasma samples were subjected to Olink proteomics analysis for 184 cardiometabolic and inflammation-enriched proteins. Data analysis showed a clear differentiation between the two groups with distinct plasma protein expression profiles. Twenty-four proteins were differentially expressed (DEPs) between the two groups. Pathways related to immune cell migration, leukocyte chemotaxis, chemokine signaling, mucosal inflammatory response, tissue repair and remodeling were enriched in the group with metabolic syndrome. Functional analysis of DEPs revealed upregulation of 15 immunological pathways. The study identifies some of the pathways that are altered and reflect metabolic health in individuals with obesity. This provides valuable insights into some of the underlying mechanisms and can lead to identification of therapeutic targets to improve metabolic health in individuals with obesity.

Alterations in DNA methylation associate with reduced migraine and headache days after medication withdrawal treatment in chronic migraine patients: a longitudinal study

BACKGROUND

Chronic migraine, a highly disabling migraine subtype, affects nearly 2% of the general population. Understanding migraine chronification is vital for developing better treatment and prevention strategies. An important factor in the chronification of migraine is the overuse of acute headache medication. However, the mechanisms behind the transformation of episodic migraine to chronic migraine and vice versa have not yet been elucidated. We performed a longitudinal epigenome-wide association study to identify DNA methylation (DNAm) changes associated with treatment response in patients with chronic migraine and medication overuse as part of the Chronification and Reversibility of Migraine clinical trial. Blood was taken from patients with chronic migraine (n = 98) at baseline and after a 12-week medication withdrawal period. Treatment responders, patients with ≥ 50% reduction in monthly headache days (MHD), were compared with non-responders to identify DNAm changes associated with treatment response. Similarly, patients with ≥ 50% versus < 50% reduction in monthly migraine days (MMD) were compared.

RESULTS

At the epigenome-wide significant level (p < 9.42 × 10-8), a longitudinal reduction in DNAm at an intronic CpG site (cg14377273) within the HDAC4 gene was associated with MHD response following the withdrawal of acute medication. HDAC4 is highly expressed in the brain, plays a major role in synaptic plasticity, and modulates the expression and release of several neuroinflammation markers which have been implicated in migraine pathophysiology. Investigating whether baseline DNAm associated with treatment response, we identified lower baseline DNAm at a CpG site (cg15205829) within MARK3 that was significantly associated with MMD response at 12 weeks.

CONCLUSIONS

Our findings of a longitudinal reduction in HDAC4 DNAm status associated with treatment response and baseline MARK3 DNAm status as an early biomarker for treatment response, provide support for a role of pathways related to chromatin structure and synaptic plasticity in headache chronification and introduce HDAC4 and MARK3 as novel therapeutic targets.

A Bioinformatics Toolkit for Next-Generation Sequencing in Clinical Oncology

Next-generation sequencing (NGS) has taken on major importance in clinical oncology practice. With the advent of targeted therapies capable of effectively targeting specific genomic alterations in cancer patients, the development of bioinformatics processes has become crucial. Thus, bioinformatics pipelines play an essential role not only in the detection and in identification of molecular alterations obtained from NGS data but also in the analysis and interpretation of variants, making it possible to transform raw sequencing data into meaningful and clinically useful information. In this review, we aim to examine the multiple steps of a bioinformatics pipeline as used in current clinical practice, and we also provide an updated list of the necessary bioinformatics tools. This resource is intended to assist researchers and clinicians in their genetic data analyses, improving the precision and efficiency of these processes in clinical research and patient care.

Normalization of drug and therapeutic concepts with Thera-Py

Objective

The diversity of nomenclature and naming strategies makes therapeutic terminology difficult to manage and harmonize. As the number and complexity of available therapeutic ontologies continues to increase, the need for harmonized cross-resource mappings is becoming increasingly apparent. This study creates harmonized concept mappings that enable the linking together of like-concepts despite source-dependent differences in data structure or semantic representation.

Materials and Methods

For this study, we created Thera-Py, a Python package and web API that constructs searchable concepts for drugs and therapeutic terminologies using 9 public resources and thesauri. By using a directed graph approach, Thera-Py captures commonly used aliases, trade names, annotations, and associations for any given therapeutic and combines them under a single concept record.

Results

We highlight the creation of 16 069 unique merged therapeutic concepts from 9 distinct sources using Thera-Py and observe an increase in overlap of therapeutic concepts in 2 or more knowledge bases after harmonization using Thera-Py (9.8%-41.8%).

Conclusion

We observe that Thera-Py tends to normalize therapeutic concepts to their underlying active ingredients (excluding nondrug therapeutics, eg, radiation therapy, biologics), and unifies all available descriptors regardless of ontological origin.