Drug-Gene Interaction Database Research Articles

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

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

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

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

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

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

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

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

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.

Searching for Essential Genes and Targeted Drugs Common to Breast Cancer and Osteoarthritis.

It is documented that osteoarthritis can promote the progression of breast cancer (BC). This study aims to search for the essential genes associated with breast cancer (BC) and osteoarthritis (OA), explore the relationship between epithelial-mesenchymal transition (EMT)-related genes and the two diseases, and identify the candidate drugs. The genes related to both BC and OA were determined by text mining. Protein-protein Interaction (PPI) analysis was carried out, and as a result, the exported genes were found to be related to EMT. PPI and the correlation of mRNA of these genes were also analyzed. Different kinds of enrichment analyses were performed on these genes. A prognostic analysis was performed on these genes for examining their expression levels at different pathological stages, in different tissues, and in different immune cells. Drug-gene interaction database was employed for potential drug discovery. A total number of 1422 genes were identified as common to BC and OA and 58 genes were found to be related to EMT. We found that HDAC2 and TGFBR1 were significantly poor in overall survival. High expression of HDAC2 plays a vital role in the increase of pathological stages. Four immune cells might play a role in this process. Fifty-seven drugs were identified that could potentially have therapeutic effects. EMT may be one of the mechanisms by which OA affects BC. Using the drugs can have potential therapeutic effects, which may benefit patients with both diseases and broaden the indications for drug use.

Abstract A001: Transcriptomic and microRNA profiling revealed novel therapeutic targets for breast cancer subtypes

Abstract Numerous studies suggested breast cancer (BC) from the Middle East and North Africa (MENA) to be presented at younger age with advanced tumor grade, suggesting underlying biological differences. Given the limited transcriptomic profiling data on BC from the MENA region, we performed mRNA and microRNA (miRNA) transcriptomic profiling on a local cohort of BC (n=96) from Qatar. Our data revealed the differentially expressed genes and miRNAs in relation to BC molecular subtypes (HR+, HER2+, HER2+HR+, and TNBC), tumor grade (high vs low), patients’ age (young (≤ 40) vs old (&amp;gt; 40)), and ethnicity (MENA vs non-MENA). Our data revealed close similarity between TNBC and HER2+, while the transcriptome of HER2+HR+ tumor was similar to that from HR+ tumors. Integrated network analysis identified complex miRNA-mRNA regulatory networks in each BC molecular subtype, in high vs low grade tumors, in tumors from young vs old, and in tumors from MENA vs non-MENA, thus implicating miRNA-mediated gene regulation as an key mechanism in shaping the transcriptome of BC. Integration of our transcriptomic data with CRISPR-Cas9 functional screen data and the Drug-Gene Interaction Database identified numerous dependencies and therapeutic vulnerabilities for each BC molecular subtype. Among those, CDC123 was functionally validated as potential therapeutic target for TNBC. Survival analyses identified mRNA and miRNA-based signatures predicative of relapse free survival (RFS), which were validated in larger BC cohorts. Our data provides comprehensive transcriptomic profiling and unraveled the miRNA-mRNA regulatory networks in BC patients from the region and identified numerous actionable gene targets, employing integrated approach. Findings from the current study have potential implications to improve the current standard-of-care for BC from the MENA as well as patients from other ethnicities. Citation Format: Ramesh Elango, Radhakrishnan Vishnubalaji, Sameera Rashid, Nehad Alajez. Transcriptomic and microRNA profiling revealed novel therapeutic targets for breast cancer subtypes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A001.

Transcriptomic Analysis of Metastatic Colorectal Tumor with Low Mutational Burden

Objective: To identify potential drug targets for metastasis colorectal cancer (CRC) patients with low mutational burden by examining differences in immune-related gene expression. Methods: For this study, 623 samples were collected from The Cancer Genome Atlas (TCGA) database, comprising tumor mutational burden (TMB), RNA sequencing (RNA-Seq), and clinical data. Differential gene expression analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the identified genes were conducted using the R package. Additionally, a comparative analysis of immune infiltrating cell composition in metastatic and non-metastatic groups was performed. Hub genes, exhibiting high levels of interaction, were selected using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The Drug Gene Interaction Database (DGIdb) was then utilized to estimate drugs targeting the identified hub genes. Results: The transcriptome data of 326 colorectal cancer patients with low TMB were analyzed, comprising 58 patients with metastasis and 268 patients without metastasis. Among the differential expression in 1,111 genes for patients with metastasis compared to those without metastasis, 733 genes were upregulated, and 378 genes were downregulated. KEGG and GO enrichment analysis indicated significant differences in gene expression in CRC metastatic patients with low TMB compared to non-metastasis patients with low TMB. Enriched pathways included humoral immuneresponse, immunoglobulin production, and regulation of AMPA receptor activity. Two genes related to interleukin-12 were identified through secondary enrichment for immune-related genes. Analysis of tumor-infiltrating immune cell data revealed significant differences in memory-activated T cell CD4 and T cell CD8. Conclusions: This analysis of RNA sequencing data and immune-filtrating cell data revealed significant differences between metastatic colorectal cancer patients with low TMB and their non-metastatic counterparts. These distinctions suggest the possibility of identifying more effective drugs or therapies for metastatic colorectal cancer patients with low TMB.

Identification of CCL20 as a prognostic predictor for severe fever with thrombocytopenia syndrome based on plasma proteomics.

Severe fever with thrombocytopenia syndrome (SFTS), a lethal tick-borne hemorrhagic fever, prompted our investigation into prognostic predictors and potential drug targets using plasma Olink Proteomics. Employing the Olink assay, we analyzed 184 plasma proteins in 30 survivors and 8 non-survivors of SFTS. Validation was performed in a cohort of 154 SFTS patients using enzyme-linked immunosorbent assay. We utilized the Drug Gene Interaction database to identify protein-drug interactions. Non-survivors exhibited 110 differentially expressed proteins (DEPs) compared to survivors, with functional enrichment in the cell chemotaxis-related pathway. Thirteen DEPs, including C-C motif chemokine 20 (CCL20), calcitonin gene-related peptide alpha and Pleiotrophin, were associated with multiple organ dysfunction syndrome. CCL20 emerged as the top predictor of death, demonstrating an area under the curve of 1 (P = .0004) and 0.9033 (P < .0001) in the discovery and validation cohort, respectively. Patients with CCL20 levels exceeding 45.74 pg/mL exhibited a fatality rate of 45.65%, while no deaths occurred in those with lower CCL20 levels. Furthermore, we identified 202 FDA-approved drugs targeting 37 death-related plasma proteins. Distinct plasma proteomic profiles characterize SFTS patients with different outcomes, with CCL20 emerging as a novel, sensitive, accurate, and specific biomarker for predicting SFTS prognosis.

Whole transcriptome sequencing identifies key lncRNAs,circRNAs, and mRNAs for exploring the pathogenesis and therapeutic target of mouse pneumoconiosis

BackgroundPneumoconiosis is a kind of lung dysfunction caused by the inhalation of mineral dust. However, the potential molecular mechanism of pneumoconiosis have not been fully elucidated. MethodsIn this study, the silica-treated pneumoconiosis mice model was constructed and the transcriptome sequencing data including lncRNA, circRNA, and mRNA were obtained. Firstly, differentially expressed lncRNA, circRNA, and mRNA (DElncRNA, DEcircRNA, DEGs) between control and pneumoconiosis/silicosis samples were screened, the target miRNAs (co-pre-miRNAs) were obtained by intersecting the miRNAs predicted by DElncRNA and DEcircRNA, respectively, and the target mRNAs (co-mRNA) were obtained by intersecting the mRNAs predicted by target miRNA and DEGs. Then, the lncRNA/circRNA-miRNA-mRNA networks were constructed by Cytoscape. Next, the key mRNAs were obtained by protein–protein interaction (PPI) analysis, and the key lncRNAs/circRNAs were selected by correlation analysis. Moreover, the expression of the key lncRNAs, circRNAs and mRNAs on chromosome were studied by the “circlize” package. Furthermore, the TFs-miRNA-mRNA network was constructed and the function of DEGs were explored by Ingenuity Pathway Analysis (IPA). To demonstrate the feasibility and value of the constructed ceRNA networks, we validated key genes and mmu-miR-682 pathway. Finally, We used the Drug-Gene Interaction database to predict potential drugs that could interfere with key genes,which may help to find promising treatment. ResultsThere were 427 DElncRNAs, 107 DEcircRNAs and 1,597 DEGs between silicosis and control groups. Totals of 77 co-pre-miRNAs and 96 co-mRNA were screened, and the lncRNA/circRNA-miRNA-mRNA networks were constructed with 27 lncRNA/25 circRNAs, 74 miRNAs and 96 mRNAs. Then, 6 key mRNAs including Igf1, Klf4, Ptgs2, Epas1, Gnao1, and Il1a were obtained by PPI, and all of these key mRNAs and 10 key lncRNAs and 8 circRNAs were significantly different between the pneumoconiosis and normal groups, in which 10 lncRNAs and 9 circRNA that have not been previously studied in pneumoconiosis/silicosis can be used as new potential therapeutic targets. Moreover, the TFs-miRNA-mRNA network were constructed with 11 TFs, 1 key miRNA (mmu-miR-682) and 3 key mRNAs (Igf1, Epas1, Ptgs2). And the validation of key genes revealing by RNA-seq through experimental approaches shows the the predictive power of this study. Finally, IPA results indicated that 41 pathways were activated and 2 pathways were suppressed in pneumoconiosis/silicosis groups, and Pathogen Induced Cytokine Storm Signaling Pathway was the most significant pathway affected by pneumoconiosis/silicosis. In addition, 93 drugs were screened out by Drug-Gene Interaction database. Among them, Hydroxychloroquine was a kind of drug which associated with Il1a and Ptgs2, may be a promising treatment. ConclusionThis study constructed the lncRNA/circRNA-miRNA-mRNA and TFs-miRNA-mRNA networks, which could deepen the potential molecular regulatory mechanism of pneumoconiosis/silicosis.

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

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

Gene-network based analysis of human placental trophoblast subtypes identifies critical genes as potential targets of therapeutic drugs.

During early pregnancy, extravillous trophoblasts (EVTs) play a crucial role in modifying the maternal uterine environment. Failures in EVT lineage formation and differentiation can lead to pregnancy complications such as preeclampsia, fetal growth restriction, and pregnancy loss. Despite recent advances, our knowledge on molecular and external factors that control and affect EVT development remains incomplete. Using trophoblast organoid in vitro models, we recently discovered that coordinated manipulation of the transforming growth factor beta (TGFβ) signaling is essential for EVT development. To further investigate gene networks involved in EVT function and development, we performed weighted gene co-expression network analysis (WGCNA) on our RNA-Seq data. We identified 10 modules with a median module membership of over 0.8 and sizes ranging from 1005 (M1) to 72 (M27) network genes associated with TGFβ activation status or in vitro culturing, the latter being indicative for yet undiscovered factors that shape the EVT phenotypes. Lastly, we hypothesized that certain therapeutic drugs might unintentionally interfere with placentation by affecting EVT-specific gene expression. We used the STRING database to map correlations and the Drug-Gene Interaction database to identify drug targets. Our comprehensive dataset of drug-gene interactions provides insights into potential risks associated with certain drugs in early gestation.

Identification of novel biomarkers in obstructive sleep apnea via integrated bioinformatics analysis and experimental validation

Background Obstructive sleep apnea (OSA) is a complex and multi-gene inherited disease caused by both genetic and environmental factors. However, due to the high cost of diagnosis and complex operation, its clinical application is limited. This study aims to explore potential target genes associated with OSA and establish a corresponding diagnostic model. Methods This study used microarray datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) related to OSA and perform functional annotation and pathway analysis. The study employed multi-scale embedded gene co-expression network analysis (MEGENA) combined with least absolute shrinkage and selection operator (LASSO) regression analysis to select hub genes and construct a diagnostic model for OSA. In addition, the study conducted correlation analysis between hub genes and OSA-related genes, immunoinfiltration, gene set enrichment analysis (GSEA), miRNA network analysis, and identified potential transcription factors (TFs) and targeted drugs for hub genes. Finally, the study used chronic intermittent hypoxia (CIH) mouse model to simulate OSA hypoxic conditions and verify the expression of hub genes in CIH mice. Results In this study, a total of 401 upregulated genes and 275 downregulated genes were identified, and enrichment analysis revealed that these differentially expressed genes may be associated with pathways such as vasculature development, cellular response to cytokine stimulus, and negative regulation of cell population proliferation. Through MEGENA combined with LASSO regression, seven OSA hub genes were identified, including C12orf54, FOS, GPR1, OR9A4, MYO5B, RAB39B, and KLHL4. The diagnostic model constructed based on these genes showed strong stability. The expression levels of hub genes were significantly correlated with the expression levels of OSA-related genes and mainly acted on pathways such as the JAK/STAT signaling pathway and the cytosolic DNA-sensing pathway. Drug-target predictions for hub genes were made using the Connectivity Map (CMap) database and the Drug-Gene Interaction database (Dgidb), which identified targeted therapeutic drugs for the hub genes. In vivo experiments showed that the hub genes were all decreasing in the OSA mouse model. Conclusions This study identified novel biomarkers for OSA and established a reliable diagnostic model. The transcriptional changes identified may help to reveal the pathogenesis, mechanisms, and sequelae of OSA.

Abstract IA047: A multiomic approach towards identifying actionable targets to combat prostate cancer health disparities

Abstract Prostate cancer (PC) is the most commonly diagnosed cancer among African American men (AA) and continues to be the largest difference in absolute incidence and cancer specific deaths between AA and Caucasian men (CA). AA have a 70% higher incidence of PC than CA and over a 2 times higher mortality rate. Among men undergoing prostate biopsy In an equal access setting (i.e. the VA), AA are ~50% more likely to have PC and ~80% more likely to have high-grade PC than CA. The frequency of PC associated genetic alterations and differences in oncogene expression in PCs is altered from CA and AA. While social economic status and environmental factors may play a role in PC health disparities, there are inherent genetic/molecular differences that account for the differences in PC outcomes between CA and AA. We sought to test our  hypothesis that there is subset of actionable gene products that are essential for enhanced PC progression in AA. A focused and clinically informed CRISPR knockout screen was conducted to  identify and functionally validate race specific actionable genes that contribute to the increased risks of fatal PC in AA. The CRISPR screen was designed leveraging a radical prostatectomy trancscriptomic dataset from the Durham VA Medical Center that was linked with long-term clinical outcome data. This unique dataset consists of ~600-profiled radical prostatectomy samples of which over 50% were from AA patients. A cox hazard analysis revealed differentially expressed genes (DEGs) which positively associated with increased risk of metastatic disease unique to AA PC patients. To identify drug targetable genes, these DEGs were filtered against the drug gene interaction database (DGIdb).This approach yielded 89 genes positively associated with increased risk of metastatic outcome in AA and 236 genes in CA only. The CRISPR KO screen resulted in 19 genes significantly depleted genes. Of these 19 hits, 11 genes were genes associated with metastasis in AA, while 8 hits were CA genes, with 5 of 11 AA hits have documented small molecule inhibitors. To further prioritize our hits, we have conducted spatial transcriptomic analysis of radical prostatectomy tissues from AA and CA PCs. By standard H and E staining these tumors looking quite similar. But spatial analysis of AA compared to CA tissues revealed differences in immune cells and cells associated with tumor microenvironment. These integative multiomic approach reveals new cell identities that may contribute to the aggressive pathogenicity of PC in AA compared to CA. CRISPR models and small molecule inhibitors of our top candidates will be used in investigate druggability of these DEGs. This study lays the groundwork for potential therapeutics targeted towards PC in AA men. Citation Format: Felipe Dezem, Aiza Akhtar, Sungyong You, So Young Kim, Jasmine Plummer, Everardo Macias. A multiomic approach towards identifying actionable targets to combat prostate cancer health disparities [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr IA047.

Abstract B025: A multi-omic, spatial approach using patient prostatectomies to identify actionable drug targets for combatting prostate cancer health disparities

Abstract Prostate cancer (PC) is the most commonly diagnosed cancer among African American men (AA) and continues to be the largest difference in absolute incidence and cancer-specific deaths between AA and Caucasian men (CA). AA have a 70% higher incidence of PC than CA and over a 2 times higher mortality rate. Among men undergoing prostate biopsy In an equal access setting (i.e. the VA), AA are ~50% more likely to have PC and ~80% more likely to have high-grade PC than CA. The frequency of PC-associated genetic alterations and differences in oncogene expression in PCs is altered from CA and AA. While social economic status and environmental factors may play a role in PC health disparities, there are inherent genetic/molecular differences that account for the differences in PC outcomes between CA and AA. We sought to test our hypothesis that there is a subset of actionable gene products that are essential for enhanced PC progression in AA. A focused and clinically informed CRISPR knockout screen was conducted to identify and functionally validate race-specific actionable genes that contribute to the increased risks of fatal PC in AA. The CRISPR screen was designed leveraging a radical prostatectomy transcriptomic dataset from the Durham VA Medical Center that was linked with long-term clinical outcome data. This unique dataset consists of ~600-profiled radical prostatectomy samples of which over 50% were from AA patients. A Cox hazard analysis revealed differentially expressed genes (DEGs) which are positively associated with an increased risk of metastatic disease unique to AA PC patients. To identify drug-targetable genes, these DEGs were filtered against the drug-gene interaction database (DGIdb). This approach yielded 89 genes positively associated with an increased risk of metastatic outcome in AA and 236 genes in CA only. The CRISPR KO screen resulted in 19 genes significantly depleted genes. Of these 19 hits, 11 genes were genes associated with metastasis in AA, while 8 hits were CA genes, with 5 of 11 AA hits having documented small molecule inhibitors. To further prioritize our hits, we have conducted a spatial transcriptomic analysis of radical prostatectomy tissues from AA and CA PCs. By standard H and E staining these tumors look quite similar. But spatial analysis of AA compared to CA tissues revealed differences in immune cells and cells associated with the tumor microenvironment. This integrative multi-omic approach reveals new cell identities that may contribute to the aggressive pathogenicity of PC in AA compared to CA. CRISPR models and small molecule inhibitors of our top candidates will be used to investigate the druggability of these DEGs. This study lays the groundwork for potential therapeutics targeted toward PC in AA men. Citation Format: Felipe Segato Dezem, Aiza Akhtar, Arjumand Wanj, Sunyoung You, Hannah Chasteen, So Young Kim, Jasmine Plummer, Everardo Macias. A multi-omic, spatial approach using patient prostatectomies to identify actionable drug targets for combatting prostate cancer health disparities [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B025.

Decoding the role of m6A Regulators in identifying and characterizing molecular subtypes of rosacea

Rosacea is a common skin disease that predominantly affects individuals aged between 30 and 50 years. While the exact cause of the disease remains unclear, various factors have been shown to trigger or exacerbate its symptoms. N6-methyladenosine (m6A) modification is one of the most abundant epigenetic methylation modification in messenger RNA (mRNA) and non-coding RNA (ncRNA), plays a crucial role in RNA splicing, export, stability, and translation. In this study, we aimed to characterize m6A genes in rosacea, identify molecular subtypes based on m6A gene expression, characterize the immune features among subtypes, explore key molecules based on co-expression analysis, and identify potential targets and drugs. To achieve our objectives, we first compared the expression pattern and immune regulation of m6A genes between healthy and diseased groups. Then, we performed clustering to stratify disease samples into different subtypes and analyzed immune regulation and functional enrichment among the subtypes. Then, we conducted differential analysis between subtypes and applied weighted gene co-expression network analysis (WGCNA) in three subtypes. We found hub differential expression analysis (DEG) genes and their potential drug based on the WGCNA results and the drug-gene interaction database (DGIdb). Finally, in vivo and in vitro studies showed significant differences in m6A methyltransferase METTL3 levels in rosacea mice and control mice, as well as in the skin of rosacea patients and healthy people, while reducing METTL3 significantly inhibited the inflammatory response of human fibroblasts (HDFs) stimulated by LL37, suggesting that METTL3 may be associated with changes in overall m6A levels in rosacea. Taken together, our findings provide valuable insights into therapeutic targets and drug predictions for rosacea.

Impact of curcumin on ferroptosis-related genes in colorectal cancer: Insights from in-silico and in-vitro studies.

Colorectal cancer (CRC)is responsible for a significant number of cancer-related fatalities worldwide. Researchers are investigating the therapeutic potential of ferroptosis, a type of iron-dependent controlled cell death, in the context of CRC. Curcumin, a natural compound found in turmeric, exhibits anticancer properties. This study explores the effects of curcumin on genes related to ferroptosis (FRGs)in CRC. To gather CRC data, we used the Gene Expression Profiling Interactive Analysis (GEPIA)and Gene Expression Omnibus (GEO)databases, while FRGs were obtained from the FerrDb database and PubMed. We identified 739 CRC differentially expressed genes (DEGs)in CRC and discovered 39 genes that were common genes between FRGs and CRC DEGs. The DEGs related to ferroptosis were enriched with various biological processes and molecular functions, including the regulation of signal transduction and glucose metabolism. Using the Drug Gene Interaction Database (DGIdb),we predicted drugs targeting CRC-DEGsand identified 17 potential drug targets. Additionally, we identified eight essential proteins related to ferroptosis in CRC, including MYC, IL1B, and SLC1A5. Survival analysis revealed that alterations in gene expression of CDC25A, DDR2, FABP4, IL1B, SNCA, and TFAM were associated with prognosis in CRC patients. In SW480 human CRC cells, treatment with curcumin decreased the expression of MYC, IL1B, and EZH2 mRNA, while simultaneously increasing the expression of SLCA5 and CAV1. The findings of this study suggest that curcumin could regulate FRGs in CRC and have the potential to be utilized as a therapeutic agent for treating CRC.

XL1R-Net: Explainable AI-driven improved [formula omitted]-regularized deep neural architecture for NSCLC biomarker identification

Background and Objective:Non-small cell lung cancer (NSCLC) exhibits intrinsic molecular heterogeneity, primarily driven by the mutation of specific biomarkers. Identification of these biomarkers would assist not only in distinguishing NSCLC into its major subtypes — Adenocarcinoma and Squamous Cell Carcinoma, but also in developing targeted therapy. Medical practitioners use one or more types of omic data to identify these biomarkers, copy number variation (CNV) being one such type. CNV provides a measure of genomic instability, which is considered a hallmark of carcinoma. However, the CNV data has not received much attention for biomarker identification. This paper aims to identify biomarkers for NSCLC using CNV data. Methods:An eXplainable AI (XAI)-driven L1-regularized deep learning architecture, XL1R-Net, is proposed that introduces a novel modification of the standard L1-regularized gradient descent algorithm to arrive at an improved deep neural classifier for NSCLC subtyping. Further, XAI-based feature identification has been used to leverage the trained classifier to uncover a set of twenty NCSLC-relevant biomarkers. Results:The identified biomarkers are evaluated based on their classification performance and clinical relevance. Using Multilayer Perceptron (MLP)-based model, a classification accuracy of 84.95% using 10-fold cross-validation is achieved. Moreover, the statistical significance test on the classification performance also revealed the superiority of the MLP model over the competitive machine learning models. Further, the publicly available Drug–Gene Interaction Database reveals twelve of the identified biomarkers as potentially druggable. The K–M Plotter tool was used to verify eighteen of the identified biomarkers with a high probability of predicting NSCLC patients’ likelihood of survival. While nine of the identified biomarkers confirm the recent literature, five find mention in the OncoKB Gene List. Conclusion:A set of seven novel biomarkers that have not been reported in the literature could be investigated for their potential contribution towards NSCLC therapy. Given NSCLC’s genetic diversity, using only one omics data type may not adequately capture the tumor’s complexity. Multiomics data and its integration with other sources will be examined in the future to better understand NSCLC heterogeneity.

Unveiling Key Biomarkers and Therapeutic Drugs in Polycystic Ovary Syndrome (PCOS) Through Pathway Enrichment Analysis and Hub Gene-miRNA Networks.

Polycystic ovary syndrome (PCOS) affects women of reproductive age globally with an incidence rate of 5% - 26%. Growing evidence reports important roles for microRNAs (miRNAs) in the pathophysiology of granulosa cells (GCs) in PCOS. The objectives of this study were to identify the top differentially expressed miRNAs (DE-miRNAs) and their corresponding targets in hub gene-miRNA networks, as well as identify novel DE-miRNAs by analyzing three distinct microarray datasets. Additionally, functional enrichment analysis was performed using bioinformatics approaches. Finally, interactions between the 5 top-ranked hub genes and drugs were investigated. Using bioinformatics approaches, three GC profiles from the gene expression omnibus (GEO), namely gene expression omnibus series (GSE)-34526, GSE114419, and GSE137684, were analyzed. Targets of the top DE-miRNAs were predicted using the multiMiR R package, and only miRNAs with validated results were retrieved. Genes that were common between the "DE-miRNA prediction results" and the "existing tissue DE-mRNAs" were designated as differentially expressed genes (DEGs). Gene ontology (GO) and pathway enrichment analyses were implemented for DEGs. In order to identify hub genes and hub DE-miRNAs, the protein-protein interaction (PPI) network and miRNA-mRNA interaction network were constructed using Cytoscape software. The drug-gene interaction database (DGIdb) database was utilized to identify interactions between the top-ranked hub genes and drugs. Out of the top 20 DE-miRNAs that were retrieved from the GSE114419 and GSE34526 microarray datasets, only 13 of them had "validated results" through the multiMiR prediction method. Among the 13 DE-miRNAs investigated, only 5, namely hsa-miR-8085, hsa-miR-548w, hsa-miR-612, hsa-miR-1470, and hsa-miR-644a, demonstrated interactions with the 10 hub genes in the hub gene-miRNA networks in our study. Except for hsa-miR-612, the other 4 DE-miRNAs, including hsa-miR-8085, hsa-miR-548w, hsa-miR-1470, and hsa-miR-644a, are novel and had not been reported in PCOS pathogenesis before. Also, GO and pathway enrichment analyses identified "pathogenic E. coli infection" in the Kyoto encyclopedia of genes and genomes (KEGG) and "regulation of Rac1 activity" in FunRich as the top pathways. The drug-hub gene interaction network identified ACTB, JUN, PTEN, KRAS, and MAPK1 as potential targets to treat PCOS with therapeutic drugs. The findings from this study might assist researchers in uncovering new biomarkers and potential therapeutic drug targets in PCOS treatment.

Bioinformatics Analysis Identifies Potential Hub Genes, Therapeutic Agents and Crucial Pathways in the Pathogenesis of Refsum Disease

Refsum Disease is a infrequent, inherited, autosomal recessive condition. The purpose of this study was to investigate prospective therapeutic drugs to cure Refsum disease and to determine the critical genes/pathways through bioinformatics analysis. Using the text mining method, Refsum disease-related genes were discovered and MCODE plugin, GO &amp; KEGG analyses was used to examine modules. A PPI network was created by STRING and visualized in Cytoscape. Drug-gene interactions were investigated using the drug-gene interaction database. Using the text mining of gene (TMG) method, 64 distinct genes in Homo sapiens linked to Refsum illness. There were 71 edges and 48 nodes in a PPI network. 13 hub node genes were discovered using a cluster analysis of filtering nodes. The genes were then significantly elevated in protein trans-membrane transport, protein import to the peroxisome matrix and fatty acid beta-oxidation, according to the enrichment analysis. We discovered that two already-approved medications could target two of the 13 chosen genes. The findings revealed the hub genes for Refsum illness to be SLC27A2, AGXT, GNPAT, CRAT, HSD17B4, AMACR, CAT, SCP2, PEX13, PEX10, PEX1, PEX7, and PEX2. Additionally, Ibuprofen and alcohol were named as prospective therapeutic agents for the management and therapy of Refsum’s illness. Only bioinformatics tools were used in this study further in-vitro and in-vivo studies are required because phenotypic differences will vary from person to person depending on the expressivity of the gene.