Reactome Pathway Enrichment Analysis Research Articles

Integrating Metabolomic Analysis, Network Pharmacology, and Molecular Docking to Underlying Pharmacological Mechanism and Ethnobotanical Rationalization for Diabetes Mellitus: Study on Medicinal Plant Fibraurea tinctoria Lour.

Fibraurea tinctoria Lour. has long been used in traditional medicine to treat diabetes mellitus (DM). However, a comprehensive scientific understanding of its potential active compounds and underlying pharmacological mechanisms still needs to be unveiled. This study, therefore, presents a novel approach by integrating metabolomic profiling, pharmacological network, and molecular docking analysis to investigate the potential of F. tinctoria as antidiabetes mellitus. Active compounds were obtained through analysis using ultrahigh-performance liquid chromatography-quadrupole-orbital ion trap-high resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) and screening of active compounds using Lipinski rule of five and ADMET parameters. Potential targets of F. tinctoria compounds and DM-related targets were retrieved from public databases, such as DisGeNET, GeneCards, OMIM, PharmaGKB, and TTD. The targets' gene ontology (GO) was created using DAVID and protein-protein interactions using STRING. The plant-organ-compound-target-disease network was constructed using Cytoscape. Then, molecular docking analysis predicted and verified the interactions of essential bioactive compounds of F. tinctoria and DM core targets. The network pharmacology approach identified 35 active compounds, 565 compound-related targets, and 17,289 DM-related targets. EGFR, HSP90AA1, ESR1, HSP90AB1, and GSK3B were the core targets, whereas isolariciresinol, cubebin, corypalmine, (-)-8-oxocanadine, and (+)-N-methylcoclaurine were the most active compounds of F. tinctoria with DM potential. GO functional enrichment analysis revealed 483 biological processes, 485 cellular components, and 463 molecular functions. REACTOME pathway enrichment analysis yielded 463 significantly enriched signaling pathways. Of these pathways, the cytokine signaling in the immune system pathway may play a key role in treating DM. The results of molecular docking analysis showed that the core targets of DM, such as 5gnk, 3o0i, 6psj, 5ucj, and 1q5k, bind stably to the analyzed bioactive compounds of F. tinctoria. This study provides significant insights into the potential mechanism of F. tinctoria in treating DM. The main active compounds of F. tinctoria were found to interact with the core targets (EGFR, HSP90AA1, ESR1, HSP90AB1, and GSK3B) through the cytokine signaling pathway in the immune system, suggesting a potential therapeutic pathway for DM. However, it is essential to note that these findings are preliminary, and further research is necessary to validate them. Those research studies could involve invitro and invivo studies to confirm the bioactivity of the identified compounds and their interactions with the core targets. When the findings are confirmed, they could have significant clinical implications, potentially leading to developing new therapeutic strategies for DM.

Adunctin E from Conamomum rubidum Induces Apoptosis in Lung Cancer via HSP90AA1 Modulation: A Network Pharmacology and In Vitro Study.

Lung cancer stands out as a leading cause of death among various cancer types, highlighting the urgent need for effective anticancer drugs and the discovery of new compounds with potent therapeutic properties. Natural sources, such as the Conamomum genus, offer various bioactive compounds. Adunctin E (AE), a dihydrochalcone derived from Conamomum rubidum, exhibited several pharmacological activities, and its potential as an anticancer agent remains largely unexplored. Thus, this study aimed to elucidate its apoptotic-inducing effect and identify its molecular targets. The network pharmacology analysis led to the identification of 71 potential targets of AE against lung cancer. Subsequent gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathway enrichment analyses revealed the involvement of these targets in cancer-associated signaling pathways. Notably, HSP90AA1, MAPK1, and PIK3CA emerged as key players in apoptosis. In silico molecular docking and dynamic simulations suggested a strong and stable interaction between AE and HSP90AA1. In vitro experiments further confirmed a significant apoptotic-inducing effect of AE on lung cancer cell lines A549 and H460. Furthermore, immunoblot analysis exhibited a substantial decrease in HSP90AA1 levels in response to AE treatment. These findings support the potential anticancer activity of AE through the HSP90AA1 mechanism, underscoring its promise as a novel compound worthy of further research and development for anti-lung cancer therapy.

Identification and interaction analysis of molecular markers in myocardial infarction by bioinformatics and next-generation sequencing data analysis

BackgroundCardiovascular diseases are prevalent worldwide with any age, and it is characterized by sudden blockage of blood flow to heart and permanent damage to the heart muscle, whose cause and underlying molecular mechanisms are not fully understood. This investigation aimed to explore and identify essential genes and signaling pathways that contribute to the progression of MI.MethodsThe aim of this investigation was to use bioinformatics and next-generation sequencing (NGS) data analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in MI. NGS dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database. DEGs between MI and normal control samples were identified using the DESeq2 R bioconductor tool. The gene ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed using g:Profiler. Next, four kinds of algorithms in the protein–protein interaction (PPI) were performed to identify potential novel biomarkers. Next, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network were constructed by miRNet and NetworkAnalyst database, and Cytoscape software. Finally, the diagnostic effectiveness of hub genes was predicted by receiver operator characteristic curve (ROC) analysis and AUC more than 0.800 was considered as having the capability to diagnose MI with excellent specificity and sensitivity.ResultsA total of 958 DEGs were identified, consisting of 480 up-regulated genes and 478 down-regulated genes. The enriched GO terms and pathways of the DEGs include immune system, neuronal system, response to stimulus and multicellular organismal process. Ten hub genes (namely cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1) were obtained via protein–protein interaction analysis results. MiRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-409-3p, hsa-mir-3200-3p, creb1 and tp63 might play an important role in the MI.ConclusionsAnalysis of next-generation sequencing dataset combined with global network information and validation presents a successful approach to uncover the risk hub genes and prognostic markers of MI. Our investigation identified four risk- and prognostic-related gene signatures, including cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of MI.

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

BackgroundEndometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis.MethodsNext-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes.ResultsA total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network.ConclusionsThis investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

In Vitro evaluation of the anti-pancreatic cancer activity of epimedium herb.

Introduction: Pancreatic cancer (PC) is a particularly aggressive malignancy with limited therapeutic options. The search for innovative treatments has focused on traditional Chinese medicine, specifically epimedium. This research investigates epimedium's active ingredients, potential targets, and underlying mechanisms in treating PC. Methods: High-performance liquid chromatography (HPLC) was used to quantify the active components of epimedium and HPLC-Q-TOF-MS was employed for qualitative identification. Potential targets of epimedium's active ingredients were identified using the TCMSP, ETCM, CTD, and Swiss Target Prediction databases. Potential PC-related targets were sourced from DisGeNET, GeneCards, and OMIM databases. A Venn diagram was utilized to identify overlapping PC-related and epimedium targets. Core targets and pathways were elucidated through protein-protein interaction (PPI) network analysis, Gene Ontology (GO) assessments, and Reactome pathway enrichment analyses. Molecular docking techniques investigated interactions between active compounds and these targets. The expression and prognostic implications of target genes were evaluated using GEPIA2 and the Human Protein Atlas (HPA) databases. In vitro studies assessed the impact of epimedium extract (EPE) on Panc-1 cell viability, and Western blot analysis examined the expression levels of key targets. Results: Network pharmacological indicate that epimedium econtains active components such as baohuoside I, icariin, hyperoside, and epimedin B, which have potential therapeutic effects against PC. In vitro assays confirmed that EPE significantly reduced the viability of Panc-1 cells. Western blot analysis revealed a considerable decrease in the expression of key targets in EPE-treated cells, including AKT1, EGFR, p-EGFR, JUN, BCL2, IL6, and SRC. The R-HSA-1280215: Interleukin-4 and Interleukin-13 signaling pathways involving these genes were identified as potential therapeutic targets. Discussion: Epimedium holds promise as a candidate for treating PC. The modulation of interleukin-4 and interleukin-13 signaling pathways could be a pivotal mechanism by which epimedium impedes tumor development. Further research is warranted to validate these findings and explore the clinical applicability of epimedium in PC treatment.

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

Cannflavins isolated from Cannabis sativa impede Caenorhabditis elegans response to noxious heat.

Cannflavins, flavonoids abundantly present in Cannabis sativa, possess a distinct chemical structure comprising a vanillyl group. Notably, the capsaicin structure also contains a vanillyl group, which is considered essential for interacting with the vanilloid receptor. The vanilloid receptor plays a crucial role in the perception of pain, heat, and inflammation and mediates the analgesic effects of capsaicin. Therefore, we postulated that prolonged exposure to cannflavin A (Can A) and cannflavin B (Can B) would provoke vanilloid receptor desensitization and hinder nocifensive responses to noxious thermal stimuli. C. elegans wild-type (N2) and mutants were exposed to Can A and Can B solutions for 60min and then aliquoted on Petri dishes divided into quadrants for thermal stimulation. We then determined the thermal avoidance index for each C. elegans experimental group. Proteomics was performed to identify proteins and pathways associated with Can A or B treatment. Prolonged exposure to Can A and Can B hindered heat avoidance (32-35°C) in C. elegans. No antinociceptive effect was observed 6h post Can A or B exposure. Proteomics and Reactome pathway enrichment analyses identified hierarchical differences between Can A- and B-treated nematodes. However, both treatments were related to eukaryotic translation initiation (R-CEL-72613) and metabolic processes strongly associated with pain development. Our study aids in characterizing the pharmacological activity of cannflavins isolated from Cannabis sativa and outlines a possible application as pain therapy.

#4458 SINGLE CELL RNA SEQUENCING OF CIRCULATING LEUKOCYTES FROM IGA NEPHROPATHY PATIENTS IDENTIFIES AN IMPORTANT ROLE FOR INNATE IMMUNE CELLS

Abstract Background and Aims IgA-nephropathy (IgAN) is a slowly progressive disease characterized by mesangial deposition of IgA1 that causes kidney injury and can eventually lead to end stage renal disease. A key factor underlying the development of IgAN is the presence of galactose-deficient IgA1 (Gd-IgA1) in the circulation. The formation of IgA and IgG antibodies against Gd-IgA1 results in the formation of immune complexes which deposit in the kidney mesangium. Production of Gd-IgA1 hints to defects in B cell regulation. However, the exact immunological dysregulation leading to the formation of Gd-IgA1, and the subsequent antibody formation against it, remains elusive. Here we use single cell RNA sequencing (scRNA-seq) of circulating leukocytes for in depth analysis of immune cell populations in patients with active IgAN to gain a better understanding of the pathogenesis of this disease. Method Peripheral blood mononuclear cells were isolated for scRNA-seq from the blood of four patients with active IgAN, and four controls, matched for age and estimated glomerular filtration rate (eGFR). Cell hashing (Biolegend) of individual samples was performed before pooling samples for scRNA-seq. scRNA expression libraries were performed using the chromium X machine (10xgenomics) according to 10XGenomics single cell 5’ V2 user guide. Libraries were sequenced on an Illumina Nextseq500 and reads were mapped to GrCh38 using CellRanger. Differential gene expression analysis was performed for each immune cell type (FDR < 0.05). Reactome pathway enrichment analysis was performed on the differentially expressed genes. Results Identification of immune cell subtypes in IgAN patients and matched controls did not show a significant difference in cell numbers or ratios. B cells showed only minor changes in gene expression between IgAN patients and controls, and T cell populations showed changes in a limited set of genes, whereas interestingly, most differentially expressed genes were found in monocytes and NK cells (Figure 1A). Analysis of the differentially expressed genes showed enrichment for genes in interferon signaling pathways, mostly in monocytes and NK cells (Figure 1B). Gene enrichment analysis showed differential expression of type I IFN genes and antigen presentation genes, also mainly in monocytes and NK cells. Conclusion Our data shows the specific endotype of circulating immune cells from patients with active IgAN. While gene expression changes in T and B cells are discrete, changes in monocytes and NK cells appear especially prominent. In these cell types, we observed an important role for interferon signaling. Further analysis of scRNA-seq data in a larger patient cohort will follow, as well as analysis of T and B cell receptor clones in IgAN patients, with which we aim to gain new insights into immune dysregulation in IgAN.

Bioinformatic analysis of gene expression data reveals Src family protein tyrosine kinases as key players in androgenetic alopecia.

Androgenetic alopecia (AGA) is a common progressive scalp hair loss disorder that leads to baldness. This study aimed to identify core genes and pathways involved in premature AGA through an in-silico approach. Gene expression data (GSE90594) from vertex scalps of men with premature AGA and men without pattern hair loss was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the bald and haired samples were identified using the limma package in R. Gene ontology and Reactome pathway enrichment analyses were conducted separately for the up-regulated and down-regulated genes. The DEGs were annotated with the AGA risk loci, and motif analysis in the promoters of the DEGs was also carried out. STRING Protein-protein interaction (PPI) and Reactome Functional Interaction (FI) networks were constructed using the DEGs, and the networks were analyzed to identify hub genes that play could play crucial roles in AGA pathogenesis. The in-silico study revealed that genes involved in the structural makeup of the skin epidermis, hair follicle development, and hair cycle are down-regulated, while genes associated with the innate and adaptive immune systems, cytokine signaling, and interferon signaling pathways are up-regulated in the balding scalps of AGA. The PPI and FI network analyses identified 25 hub genes namely CTNNB1, EGF, GNAI3, NRAS, BTK, ESR1, HCK, ITGB7, LCK, LCP2, LYN, PDGFRB, PIK3CD, PTPN6, RAC2, SPI1, STAT3, STAT5A, VAV1, PSMB8, HLA-A, HLA-F, HLA-E, IRF4, and ITGAM that play crucial roles in AGA pathogenesis. The study also implicates that Src family tyrosine kinase genes such as LCK, and LYN in the up-regulation of the inflammatory process in the balding scalps of AGA highlighting their potential as therapeutic targets for future investigations.

URINARY PROTEOMIC SIGNATURE OF THE RETINAL MICROVASCULATURE

Objective: The retinal vasculature provides a window into the body's microcirculation. Variations in retinal vessel diameters have been associated with adverse cardiovascular outcomes, such as stroke and coronary heart disease. As the pathogenesis of retinal microvascular diameter changes remain poorly understood, this study aimed to provide novel insights by characterizing the urinary proteomic signature of the retinal microvasculature. Design and method: The central retinal arteriolar equivalent (CRAE), central retinal venular equivalent (CRVE), and arteriole-to venule-ratio (AVR) were measured from digitized retinal photographs of 562 randomly recruited Flemish participants (mean age, 48.9 years; 50.9% women). The urinary proteome was analyzed using capillary electrophoresis coupled with mass spectrometry. Multivariable linear regression analysis identified significant urinary peptides with adjustment for age, sex, body mass index, smoking, hypertension, plasma glucose, total and HDL cholesterol, eGFR, and history of cardiovascular disease. Reactome pathway enrichment analysis was performed to interpret the proteomic signature. Results: Table 1 shows participant characteristics. Of the 359 peptides analyzed, 19 were significantly associated with CRAE after adjusting for potential confounders and multiple testing (Table 2). In the proteomic profile, 14 peptides (74%) were fragments of collagen I, III, and XI proteins, while the remaining peptides were fragments of the fibrinogen alpha chain (11%), xylosyltransferase 1 (5%), S100 calcium-binding protein A9 (5%), and hemoglobin subunit alpha (5%) proteins. Pathway analysis showed that these proteins were predominantly involved in endothelial cell proliferation and migration, collagen formation and degradation, and cell-extracellular matrix interactions (Figure 1). No peptides were significantly associated with CRVE and AVR following adjustment. Conclusions: This study revealed a novel urinary proteomic signature as a potential biomarker of retinal arteriolar narrowing. The signature suggests fibrosis and neovascularization may be involved in retinal microvascular diameter changes. Further investigation may help elucidate the pathophysiologic mechanisms underlying the relationship between microvascular and macrovascular disease.

Studies on the active constituents and the related mechanisms of Jinhong Tablet in the treatment of chronic gastritis based on network pharmacology and in vivo experimental validation

ObjectiveChronic gastritis (CG), the common disease in the digestion department at present, is a chronic inflammation of gastric mucosa. In the absence of an effective prevention and treatment, chronic inflammation of the stomach can lead to gastric cancer. Jinhong tablet (JHT), originated from “Jinlingzi San” in the Yuan Dynasty of Chinese history (1271 AD-1368AD), is one of the most effective medicines used to treat CG in clinical practice with the effects of dispersing stagnated liver qi to relieve depression and promoting blood circulation and relieving pain. However, the active ingredients and mechanisms of JHT in the treatment of CG have not been clarified. Therefore, this study aimed to elucidate the active constituents and mechanisms based on network pharmacology. MethodsFirst of all, the chemical components of JHT were identified using an UPLC-QTOF-MS, and then the obtained components were used to establish the active and potential ingredient-target networks. A protein-protein interaction (PPI) network was then constructed, and reactome pathway enrichment analysis was performed to determine the key targets in the treatment on CG of JHT. In addition, the further pathway analysis of JHT against CG was carried out on the platform of Bio Informatics. In the end, the predicted targets were validated by ELISA. ResultsA total of 98 chemical constituents were identified in JHT, and under the screening criteria of oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18, 27 active ingredients were determined. Further network pharmacology analysis revealed that quercetin, kaempferol, morin, naringenin and tetrahydroberberine are vital components for the anti-CG efficacy of JHT, and JUN, TP53, TNF, VEGFA and AKT1 with highest average scores were considered as the key central genes. Additionally, the subsequent validation studies confirmed JUN was a key target and the therapeutic effect of JHT for CG may be related to the decrease of inflammatory cytokine such as IL-1β and IL-8. ConclusionIn summary, our results indicate that JHT can treat CG by reducing the inflammatory cytokines directly or indirectly to play anti-inflammatory activity and flavonoids may be the key constituents.

Abstract 3021: Epigenetic signatures of virus-associated cervical cancer in women living with HIV

Abstract Background: Low- and middle-income countries are facing a high health burden of cervical cancer (CC). The situation is worsened by a high prevalence of human immunodeficiency virus (HIV). We aim to identify epigenetic signatures to help understand the virus-associated pathways underlying CC development, which is fundamental to developing effective CC screening tools or therapeutic approaches for women living with HIV. Methods: We recruited a total of 365 Nigerian women with mean age of 52 (239 HIV+ and 126 HIV-; 98 without CC, 106 with cervical lesions, and 161 with CC). DNA methylation profiling in cervical tissue samples was performed using Illumina EPIC array covering over 860K methylation sites. The epigenetic signatures were identified among HIV+ women through epigenome-wide association study comparing CC vs. CC-free (Bonferroni adjusted p <0.05), which were further validated by comparing: 1) pairs of tumor and adjacent normal samples; and 2) pairs of cervical lesions and adjacent normal samples. The comparisons were adjusted for age, BMI, education, employment, parity, study site, and technical batch variables. We then compared the identified signatures between HIV+ and HIV- among CC women to delineate the role of HIV. We constructed a methylation risk score (MRS) using the signatures, and we performed receiver operating characteristic (ROC) analysis to evaluate the performance of distinguishing across cancer statuses (CC, cervical lesions, CC-free). Results: We identified 46 differentially methylated markers (p-value ranging from 5.7e-8 to 2.4e-19) in HIV+ CC women. The effect sizes among the 46 markers in the paired tumor-normal analysis and the paired lesion-normal analysis were highly correlated with the CC vs. CC-free analysis (r=0.99 and r=0.95, respectively) with smaller magnitudes (1.8 and 4.3 times smaller, respectively). Gene ontology and Reactome pathway enrichment analysis revealed that these 46 markers were enriched in genes involving activation of PI3K/AKT/mTOR signaling network (e.g., RPTOR, HDAC3, MAPKAP1), which plays a crucial role in virus/host crosstalk and virus-induced carcinogenesis. The PI3K/AKT/mTOR signaling cascade suppressors, including gene PRDM8, were silenced by promoter hypermethylation among HIV+ CC women (p=4.3e-12). These epigenetic changes, however, were not observed in CC women without HIV. In an independent dataset, MRS was the lowest in women without CC (2.3±2.1), followed by women with cervical lesions (4.9±1.1), and the highest in CC women (9.6±3.9) (p-trend < 2e-16). The MRS achieved areas under the ROC curve = 0.93 and 0.88 in distinguishing CC and cervical lesions from CC-free among HIV+ women, respectively. Conclusion: HIV may communicate with cervical cells and promote CC through epigenetic activation of PI3K/AKT/mTOR signaling pathway. Our epigenetic signatures may serve as novel biomarkers for CC early detection and treatment for women living with HIV. Citation Format: Yinan Zheng, Olugbenga Silas, Jonah Musa, Yishu Qu, Tao Gao, Kyeezu Kim, Brian Joyce, Jun Wang, Demirkan Gursel, Abdulkareem Fatimah, Godwin Imade, Alani Akanmu, Jian-Jun Wei, Masha Kocherginsky, Kwang-Youn Kim, Firas Wehbe, Chad Achenbach, Rose Anorlu, Melissa Simon, Atiene Sagay, Folasade Ogunsola, Robert Murphy, Lifang Hou. Epigenetic signatures of virus-associated cervical cancer in women living with HIV [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3021.

Shared miRNA landscapes of COVID-19 and neurodegeneration confirm neuroinflammation as an important overlapping feature.

Development and worsening of most common neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis, have been associated with COVID-19 However, the mechanisms associated with neurological symptoms in COVID-19 patients and neurodegenerative sequelae are not clear. The interplay between gene expression and metabolite production in CNS is driven by miRNAs. These small non-coding molecules are dysregulated in most common neurodegenerative diseases and COVID-19. We have performed a thorough literature screening and database mining to search for shared miRNA landscapes of SARS-CoV-2 infection and neurodegeneration. Differentially expressed miRNAs in COVID-19 patients were searched using PubMed, while differentially expressed miRNAs in patients with five most common neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, and multiple sclerosis) were searched using the Human microRNA Disease Database. Target genes of the overlapping miRNAs, identified with the miRTarBase, were used for the pathway enrichment analysis performed with Kyoto Encyclopedia of Genes and Genomes and Reactome. In total, 98 common miRNAs were found. Additionally, two of them (hsa-miR-34a and hsa-miR-132) were highlighted as promising biomarkers of neurodegeneration, as they are dysregulated in all five most common neurodegenerative diseases and COVID-19. Additionally, hsa-miR-155 was upregulated in four COVID-19 studies and found to be dysregulated in neurodegeneration processes as well. Screening for miRNA targets identified 746 unique genes with strong evidence for interaction. Target enrichment analysis highlighted most significant KEGG and Reactome pathways being involved in signaling, cancer, transcription and infection. However, the more specific identified pathways confirmed neuroinflammation as being the most important shared feature. Our pathway based approach has identified overlapping miRNAs in COVID-19 and neurodegenerative diseases that may have a valuable potential for neurodegeneration prediction in COVID-19 patients. Additionally, identified miRNAs can be further explored as potential drug targets or agents to modify signaling in shared pathways. Graphical AbstractShared miRNA molecules among the five investigated neurodegenerative diseases and COVID-19 were identified. The two overlapping miRNAs, hsa-miR-34a and has-miR-132, present potential biomarkers of neurodegenerative sequelae after COVID-19. Furthermore, 98 common miRNAs between all five neurodegenerative diseases together and COVID-19 were identified. A KEGG and Reactome pathway enrichment analyses was performed on the list of shared miRNA target genes and finally top 20 pathways were evaluated for their potential for identification of new drug targets. A common feature of identified overlapping miRNAs and pathways is neuroinflammation. AD, Alzheimer's disease; ALS, amyotrophic lateral sclerosis; COVID-19, coronavirus disease 2019; HD, Huntington's disease; KEGG, Kyoto Encyclopedia of Genes and Genomes; MS, multiple sclerosis; PD, Parkinson's disease.

Antinociceptive Activity of Vanilloids in Caenorhabditis elegans is Mediated by the Desensitization of the TRPV Channel OCR-2 and Specific Signal Transduction Pathways.

Vanilloids, including capsaicin and eugenol, are ligands of transient receptor potential channel vanilloid subfamily member 1 (TRPV1). Prolonged treatment with vanilloids triggered the desensitization of TRPV1, leading to analgesic or antinociceptive effects. Caenorhabditis elegans (C. elegans) is a model organism expressing vanilloid receptor orthologs (e.g., OSM-9 and OCR-2) that are associated with behavioral and physiological processes, including sensory transduction. We have shown that capsaicin and eugenol hamper the nocifensive response to noxious heat in C. elegans. The objective of this study was to perform proteomics to identify proteins and pathways responsible for the induced phenotype and to identify capsaicin and eugenol targets using a thermal proteome profiling (TPP) strategy. The results indicated hierarchical differences following Reactome Pathway enrichment analyses between capsaicin- and eugenol-treated nematodes. However, both treated groups were associated mainly with signal transduction pathways, energy generation, biosynthesis and structural processes. Wnt signaling, a specific signal transduction pathway, is involved following treatment with both molecules. Wnt signaling pathway is noticeably associated with pain. The TPP results show that capsaicin and eugenol target OCR-2 but not OSM-9. Further protein-protein interaction (PPI) analyses showed other targets associated with enzymatic catalysis and calcium ion binding activity. The resulting data help to better understand the broad-spectrum pharmacological activity of vanilloids.

Severe COVID-19 in pregnancy has a distinct serum profile, including greater complement activation and dysregulation of serum lipids.

Pregnancies complicated by Coronavirus Disease 2019 (COVID-19) are at an increased risk of severe morbidity due to physiologic changes in immunologic, cardiovascular, and respiratory function. There is little is known about how severity of COVID-19 changes protein and metabolite expression in pregnancy. This study aims to investigate the pathophysiology behind various clinical trajectories in pregnant patients diagnosed with COVID-19 using multi-omics profiling. This is a prospective cohort study of 30 pregnant patients at a single tertiary care center. Participants were categorized by severity of COVID-19 disease (control, asymptomatic, mild/moderate, or severe). Maternal serum samples underwent LC-MS-based multiomics analysis for profiling of proteins, lipids, electrolytes, and metabolites. Linear regression models were used to assess how disease severity related to analyte levels. Reactome pathway enrichment analysis was conducted on differential analytes. Of 30 participants, 25 had confirmed diagnosis of COVID-19 (6 asymptomatic (one post-infection), 13 mild/moderate (all post-infection), 6 severe), and 5 participants were controls. Severe COVID-19 was associated with distinct profiles demonstrating significant proteomic and lipidomic signatures which were enriched for annotations related to complement and antibody activity. (FDR < 0.05). Downregulated analytes were not significantly enriched but consisted of annotation terms related to lipoprotein activity (FDR > 0.2). Post-infection mild/moderate COVID-19 did not have significantly altered serum protein, metabolite, or lipid metabolite levels compared to controls. Pregnancies with severe COVID-19 demonstrate greater inflammation and complement activation and dysregulation of serum lipids. This altered multiomic expression provides insight into the pathophysiology of severe COVID-19 in pregnancy and may serve as potential indicators for adverse pregnancy outcomes.

A Breast Cancer Prediction Model Based on a Panel from Circulating Exosomal miRNAs.

Breast cancer (BC) has been a serious threat to women's health. Exosomes contain a variety of biomolecules, which is an excellent choice as disease diagnostic markers, but whether it could be applied as a noninvasive biomarker for BC diagnosis demands to be additional studied. In this study, we aimed at creating a predictive model and reveal the value of plasma exosomal miRNA (exo-miRNA) in early diagnosis of BC. Firstly, exosomes isolated from plasma were identified by Nanoparticle Tracking Analysis (NTA), Transmission Electron Microscope (TEM), and Western Blot. miRNA expression in plasma samples from 56 BC patients and 40 normal controls was analyzed by high-throughput sequencing. miRNAs with strong correlation characteristics were selected by Lasso logistic regression. Then, we built the training set and test set, evaluated the Lasso regression accuracy, and evaluated the performance of different models in the training set and test set. Finally, GO analysis, KEGG, and Reactome pathway enrichment analysis were used to understand the biological significance of 16 characteristic miRNAs. The successful separation of exosomes in serum was identified by NTA, TEM, and Western Blot. The training set data matrix containing 1962 miRNAs was obtained by sequencing for model construction, and 16 strongly correlated miRNAs were selected by Lasso logistic regression. The accuracy of Lasso regression in training set and test set were 97.22% and 95.83%, respectively. We built different models and evaluated the performance of each model in the training set and test set. The results showed that the AUC values of Lasso, SVM, GBDT, and Random Forest model in the training set were 1, and the AUC values in the test set were 0.979, 0.936, 0.971, and 0.979, respectively. Bioinformatics analysis showed that 16 signature miRNAs were significantly enriched in cancer-related pathways such as herpes simplex virus 1 infection, TGF-β signaling, and Toll-like receptor family. The results of this study suggest that the 16 characteristic miRNAs screened from plasma exosomes can be used as a group of biomarkers, and the prediction model constructed based on this set of markers is expected to be used in the early diagnosis of BC.

34004 Topical ruxolitinib blocks interferon gamma signaling in cutaneous lichen planus

Lichen planus (LP) is a chronic inflammatory condition characterized by well-marginated, violaceous, polygonal, pruritic papules and plaques. In the pathogenesis of LP, interferon gamma (IFNG) primes keratinocytes for CD8+ T-cell-mediated cytotoxicity through Janus kinase (JAK)-2/STAT1 signaling. Ruxolitinib selectively inhibits JAK1 and JAK2. We conducted whole transcriptomic analysis on lesional LP (n = 19) and control (n = 10) tissue at baseline, and on responsive (n = 13) and refractory (n = 5) lesional tissue after treatment with ruxolitinib. Our transcriptomic data confirms type I and II IFNs as critical signaling molecules in cutaneous LP. Reactome pathway enrichment analysis identified additional upregulation of cellular regulators of immune response, and phagocytosis. IFNG was elevated at baseline in disease samples and did not change following treatment. Fifty-two interferon stimulated genes (ISGs) were elevated in disease compared with control tissue. Treatment responsive tissue showed reversal of expression patterns of 12 ISGs (TRIM45, IRF9, TRIM14, HLA-DQB1, OAS2, OAS1, GBP1, OAS3, PTAFR, IRF7, OASL, and TRIM17). STAT1, a critical signaling protein in LP and target of topical ruxolitinib, was downregulated in responsive disease post-treatment. Type I MHCs (HLA-A, B, C), whose expression is regulated by pSTAT1 on keratinocytes, were upregulated at pre-treatment and were downregulated in treatment responsive tissue. Taken together, these findings suggest that ruxolitinib abrogated cytotoxic T-cell responses and augmented anti-apoptotic signaling, resulting in the high response rate and rapid improvement that we observed clinically.

Identification of key genes and pathways associated with diabetes of the exocrine pancreas.

This study aimed to identify potential essential genes and pathways in diabetes of the exocrine pancreas (DEP) and explore possible molecular mechanisms. The array dataset GSE76895 was downloaded from the Gene Expression Omnibus database. Pancreatic tissue samples from 20 Diabetes of the exocrine pancreas and 32 nondiabetic individuals were selected for analysis. GEO2R analyzed differentially expressed genes (DEGs) in the 2 groups. Gene ontology annotation, Kyoto Encyclopedia of Genes Genomes and Reactome pathway enrichment analyses and Gene Set Enrichment Analysis were performed in this study. Protein-protein interaction (PPI) networks were constructed using Cytoscape software, and core networks were identified using MCODE plugins. A total of 62 genes, including 59 up-regulated and 3 down-regulated genes, were differentially expressed in DEP samples compared with nondiabetic patients. PPI network with 53 nodes and 138 edges was established. HLA-DRA is identified as the central gene of the PPI network and maybe a marker gene for DEP. Furthermore, up-regulated DEGs are mainly enriched in pathways related to the immune system and infection. The results of this study suggest that HLA-DRA and immune system pathways may play essential roles in DEP.

Construction, bioinformatics analysis, and validation of competitive endogenous RNA networks in ulcerative colitis.

Background: Ulcerative colitis (UC) is a common chronic disease of the digestive system. Recently, competitive endogenous RNAs (ceRNAs) have been increasingly used to reveal key mechanisms for the pathogenesis and treatment of UC. However, the role of ceRNA in UC pathogenesis has not been fully clarified. This study aimed to explore the mechanism of the lncRNA-miRNA-mRNA ceRNA network in UC and identify potential biomarkers and therapeutic targets. Materials and Methods: An integrative analysis of mRNA, microRNA (miRNA), and long non-coding RNA (lncRNA) files downloaded from the Gene Expression Omnibus (GEO) was performed. Differentially expressed mRNA (DE-mRNAs), miRNA (DE-miRNAs), and lncRNA (DE-lncRNAs) were investigated between the normal and UC groups by the limma package. A weighted correlation network analysis (WGCNA) was used to identify the relative model for constructing the ceRNA network, and, concurrently, miRWalk and DIANA-LncBase databases were used for target prediction. Consecutively, the Gene Ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) pathway, and Reactome pathway enrichment analyses, protein-protein interaction (PPI) network, Cytohubba, and ClueGO were performed to identify hub genes. Additionally, we examined the immune infiltration characteristics of UC and the correlation between hub genes and immune cells using the immuCellAI database. Finally, the expression of potential biomarkers of ceRNA was validated via qRT-PCR in an experimental UC model induced by dextran sulfate sodium (DSS). Result: The ceRNA network was constructed by combining four mRNAs, two miRNAs, and two lncRNAs, and the receiver operating characteristic (ROC) analysis showed that two mRNAs (CTLA4 and STAT1) had high diagnostic accuracy (area under the curve [AUC] > 0.9). Furthermore, CTLA4 up-regulation was positively correlated with the infiltration of immune cells. Finally, as a result of this DSS-induced experimental UC model, CTLA4, MIAT, and several associate genes expression were consistent with the results of previous bioinformatics analysis, which proved our hypothesis. Conclusion: The investigation of the ceRNA network in this study could provide insight into UC pathogenesis. CTLA4, which has immune-related properties, can be a potential biomarker in UC, and MIAT/miR-422a/CTLA4 ceRNA networks may play important roles in UC.

Identifying potential prognosis markers in hepatocellular carcinoma via integrated bioinformatics analysis and biological experiments.

Background: Hepatocellular carcinoma is one kind of clinical common malignant tumor with a poor prognosis, and its pathogenesis remains to be clarified urgently. This study was performed to elucidate key genes involving HCC by bioinformatics analysis and experimental evaluation. Methods: We identified common differentially expressed genes (DEGs) based on gene expression profile data of GSE60502 and GSE84402 from the Gene Expression Omnibus (GEO) database. Gene Ontology enrichment analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, REACTOME pathway enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were used to analyze functions of these genes. The protein-protein interaction (PPI) network was constructed using Cytoscape software based on the STRING database, and Molecular Complex Detection (MCODE) was used to pick out two significant modules. Hub genes, screened by the CytoHubba plug-in, were validated by Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas (HPA) database. Then, the correlation between hub genes expression and immune cell infiltration was evaluated by Tumor IMmune Estimation Resource (TIMER) database, and the prognostic values were analyzed by Kaplan-Meier plotter. Finally, biological experiments were performed to illustrate the functions of RRM2. Results: Through integrated bioinformatics analysis, we found that the upregulated DEGs were related to cell cycle and cell division, while the downregulated DEGs were associated with various metabolic processes and complement cascade. RRM2, MAD2L1, MELK, NCAPG, and ASPM, selected as hub genes, were all correlated with poor overall prognosis in HCC. The novel RRM2 inhibitor osalmid had anti-tumor activity, including inhibiting proliferation and migration, promoting cell apoptosis, blocking cell cycle, and inducing DNA damage of HCC cells. Conclusion: The critical pathways and hub genes in HCC progression were screened out, and targeting RRM2 contributed to developing new therapeutic strategies for HCC.