Gene Expression Omnibus Datasets Research Articles

Identification of qualitative characteristics of immunosuppression in sepsis based on immune-related genes and immune infiltration features

ObjectiveSepsis is linked to high morbidity and mortality rates. Consequently, early diagnosis is crucial for proper treatment, reducing hospitalization, and mortality rates. Additionally, over one-fifth of sepsis patients still face a risk of death. Hence, early diagnosis, and effective treatment play pivotal roles in enhancing the prognosis of patients with sepsis. MethodThe study analyzed whole blood data obtained from patients with sepsis and control samples sourced from three datasets (GSE57065, GSE69528, and GSE28750). Commonly dysregulated immune-related genes (IRGs) among these three datasets were identified. The differential characteristics of these common IRGs in the sepsis and control samples were assessed using the REO-based algorithm. Based on these differential characteristics, samples from eight Gene Expression Omnibus (GEO) databases (GSE57065, GSE69528, GSE28750, GSE65682, GSE69063, GSE95233, GSE131761, and GSE154918), along with three ArrayExpress databases (E-MTAB-4421, E-MTAB-4451, and E-MTAB-7581), were categorized and scored. The effectiveness of these differential characteristics in distinguishing sepsis samples from control samples was evaluated using the AUC value derived from the receiver operating characteristic curve (ROC) curve. Furthermore, the expression of IRGs was validated in peripheral blood samples obtained from patients with sepsis through qRT-PCR. ResultsAmong the three training datasets, a total of 84 common dysregulated immune-related genes (IRGs) were identified. Utilizing a within-sample relative expression ordering (REOs)-based algorithm to analyze these common IRGs, differential characteristics were observed in three reverse stable pairs (ELANE-RORA, IL18RAP-CD247, and IL1R1-CD28). In the eight GEO datasets, the expression of ELANE, IL18RAP, and IL1R1 demonstrated significant upregulation, while RORA, CD247, and CD28 expression exhibited notable downregulation during sepsis. These three pairs of immune-related marker genes displayed accuracies of 95.89% and 97.99% in distinguishing sepsis samples among the eight GEO datasets and the three independent ArrayExpress datasets, respectively. The area under the receiver operating characteristic curve ranged from 0.81 to 1.0. Additionally, among these three immune-related marker gene pairs, mRNA expression levels of ELANE and IL1R1 were upregulated, whereas the levels of CD247 and CD28 mRNA were downregulated in blood samples from patients with sepsis compared to normal controls. ConclusionThese three immune-related marker gene pairs exhibit high predictive performance for blood samples from patients with sepsis. They hold potential as valuable auxiliary clinical blood screening tools for sepsis.

Identification of key genes and molecular mechanisms of chronic urticaria based on bioinformatics.

Chronic urticaria (CU) is characterized by persistent skin hives, redness, and itching, enhanced by immune dysregulation and inflammation. Our main objective is identifying key genes and molecular mechanisms of chronic urticaria based on bioinformatics. We used the Gene Expression Omnibus (GEO) database and retrieved two GEO datasets, GSE57178 and GSE72540. The raw data were extracted, pre-processed, and analyzed using the GEO2R tool to identify the differentially expressed genes (DEGs). The samples were divided into two groups: healthy samples and CU samples. We defined cut-off values of log2 fold change ≥1 and p<.05. Analyses were performed in the Kyoto Encyclopaedia of Genes and Genomes (KEGG), theDatabasefor Annotation, Visualization and Integrated Discovery (DAVID), Metascape, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and CIBERSOFT databases. We obtained 1613 differentially expressed genes. There were 114 overlapping genes in both datasets, out of which 102 genes were up-regulated while 12 were down-regulated. The biological processes included activation of myeloid leukocytes, response to inflammations, and response to organic substances. Moreover, the KEGG pathways of CU were enriched in the Nuclear Factor-Kappa B (NF-kB) signaling pathway, Tumor Necrosis Factor (TNF) signaling pathway, and Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway. We identified 27 hub genes that were implicated in the pathogenesis of CU, such as interleukin-6 (IL-6), Prostaglandin-endoperoxide synthase 2 (PTGS2), and intercellular adhesion molecule-1 (ICAM1). The complex interplay between immune responses, inflammatory pathways, cytokine networks, and specific genes enhances CU. Understanding these mechanisms paves the way for potential interventions to mitigate symptoms and improve the quality of life of CU patients.

Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets

BackgroundLupus nephritis (LN) is a complication of SLE characterised by immune dysfunction and oxidative stress (OS). Limited options exist for LN. We aimed to identify LN-related OS, highlighting the need...

DNMT3A promotes glioma growth and malignancy via TNF-α/NF-κB signaling pathway.

DNMT3A is the main molecule responsible for DNA methylation in cells. DNMT3A affects the progression of inflammation, degenerative diseases, and malignant tumors, and exhibits significant aberrantly expression in tumor tissues. Transcriptome data and relevant clinical information were downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) datasets. Differential expression analysis and prognostic analysis were conducted based on above statistics. We constructed a clinical prognostic model and identified DNMT3A as an independent prognostic factor to accurately predict patient prognosis. Differential gene enrichment analysis revealed that DNMT3A affects the progression of glioma through multiple pathways, among which the tumor necrosis factor-α (TNF-α)/nuclear factor-kappa B (NF-κB) pathway shows a strong correlation. Immunological analysis also revealed a certain correlation between DNMT3A and tumor immunity. We demonstrated through gene editing that DNMT3A can affect the release of TNF-α in cells, thereby affecting the progression of glioma. Functional experiments have also demonstrated that DNMT3A plays a crucial role in tumors. RNA-sequencing and survival analyses of lower-grade glioma (LGG) patients in TCGA, CGGA, and GEO cohorts showed that high DNMT3A expression correlated with poor prognosis of LGG patients. Univariate and multivariate Cox regression analyses showed that DNMT3A expression was an independent prognostic indicator in LGG. The prognosis prediction nomogram with age, World Health Organization (WHO) grading, and DNMT3A expression showed reliable performance in predicting the 1-, 3-, and 5-year overall survival (OS) of LGG patients. Functional enrichment analysis, gene set enrichment analysis (GSEA), and ESTIMATE algorithm analyses showed that DNMT3A expression was associated with the tumor infiltration of immune cells and predicted response to immunotherapy in two immunotherapy cohorts of pan-cancer patients. Furthermore, short hairpin RNA (shRNA)-mediated knockdown of DNMT3A in the LGG cell lines suppressed proliferation, migration, and invasion of LGG cells by downregulating the TNF-α/NF-κB signaling pathway. Our data showed that DNMT3A was a potential prognostic biomarker in glioma. DNMT3A promoted proliferation and malignancy of LGG cells through the TNF-α/NF-κB signaling pathway. DNMT3A is a promising therapeutic target for treating patients with LGG.

Integrating transcriptomics, glycomics and glycoproteomics to characterize hepatitis B virus-associated hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) ranks as the third most common cause of cancer related death globally, representing a substantial challenge to global healthcare systems. In China, the primary risk factor for HCC is the hepatitis B virus (HBV). Aberrant serum glycoconjugate levels have long been linked to the progression of HBV-associated HCC (HBV-HCC). Nevertheless, few study systematically explored the dysregulation of glycoconjugates in the progression of HBV-associated HCC and their potency as the diagnostic and prognostic biomarker.MethodsAn integrated strategy that combined transcriptomics, glycomics, and glycoproteomics was employed to comprehensively investigate the dynamic alterations in glyco-genes, N-glycans, and glycoproteins in the progression of HBV- HCC.ResultsBioinformatic analysis of Gene Expression Omnibus (GEO) datasets uncovered dysregulation of fucosyltransferases (FUTs) in liver tissues from HCC patients compared to adjacent tissues. Glycomic analysis indicated an elevated level of fucosylated N-glycans, especially a progressive increase in fucosylation levels on IgA1 and IgG2 determined by glycoproteomic analysis.ConclusionsThe findings indicate that the abnormal fucosylation plays a pivotal role in the progression of HBV-HCC. Systematic and integrative multi-omic analysis is anticipated to facilitate the discovery of aberrant glycoconjugates in tumor progression.

Multicausal disruption of complement system activity in schizophrenia: abnormal transcription of C4, complement control proteins and microglia specific genes in brain and blood

IntroductionThe synaptic pruning process is based on the joint action of the complement system and microglia. In schizophrenia, accumulating evidence support that abnormal synaptic pruning during adolescence may be due to an altered Complement system activity. While this hypothesis is supported by C4 overexpression in various brain regions of individuals with schizophrenia, such alterations should be replicated and extended to other brain regions. Moreover, transcriptional studies of genes encoding regulators of the complement system activity (complement control proteins, CCP) and microglia-specific genes are lacking. Furthermore, it remains unknown whether brain and peripheral expression of such genes are related.ObjectivesTo explore expression of C4 as well as 4 CCP encoding genes and 10 microglia-specific genes at the brain and peripheral levels in individuals with schizophrenia as compared to healthy controls.MethodsWe analyzed candidate gene expression from 9 Gene Expression Omnibus datasets obtained from 333 individuals with schizophrenia and 306 healthy controls (HC). We first compared expression of the candidate genes between individuals with schizophrenia and HC in postmortem brain samples from 7 different brain regions. Then, the same comparison was made in 4 different peripheral tissues.ResultsRegarding the complement system, we observed C4 overexpression in the DLPFC, parietal, temporal cortex and associative striatum of individuals with schizophrenia. We report distinct altered expression patterns of CCP genes in the DLPFC, hippocampus and cerebellum of individuals with schizophrenia. Only CD46 expression was altered in the blood of individuals with schizophrenia. Regarding microglia, we report an underexpression of several microglia-specific genes in the cerebellum, associative striatum, hippocampus and parietal cortex of individuals with schizophrenia vs. HC. At the peripheral level, we observed a mixed altered expression pattern in the whole blood of individuals with schizophrenia.ConclusionsFirstly, our results suggest that the CCP-mediated regulatory mechanisms of the Complement system are impaired in the brain of individuals with schizophrenia, potentially contributing to an excessive Complement system activity (CSA). Secondly, our results support the hypothesis of a widespread underexpression of microglia-specific genes in brain tissues of individuals with schizophrenia. Functionally, the observed transcriptional alterations may be related to the synaptic pruning impairment. Alternatively, they may translate a compensatory mechanism for neuroinflammation. In the whole blood, the altered transcriptional pattern may represent a potential peripheral signature of SZ.Disclosure of InterestNone Declared

Mechanistic insights into FEN1-mediated drug sensitivity and risk signature in colon cancer: An integrative bioinformatics study.

The overexpression of Flap endonuclease 1 (FEN1) has been implicated in drug resistance and prognosis across various cancer types. However, the precise role of FEN1 in colon cancer remains to be fully elucidated. In this study, we employed comprehensive datasets from The Cancer Genome Atlas, Gene Expression Omnibus, and Human Protein Atlas to examine FEN1 expression and assess its correlation with clinical pathology and prognosis in colon cancer. We utilized the pRRophetic algorithm to evaluate drug sensitivity and performed differential expression analysis to identify genes associated with FEN1-mediated drug sensitivity. Gene set enrichment analysis was conducted to further investigate these genes. Additionally, single-cell sequencing analysis was employed to explore the relationship between FEN1 expression and functional states. Cox regression analysis was implemented to construct a prognostic model, and a nomogram for prognosis was developed. Our analysis of The Cancer Genome Atlas and Gene Expression Omnibus datasets revealed a significant upregulation of FEN1 in colon cancer. However, while FEN1 expression showed no notable correlation with prognosis, it displayed associations with metastasis. Single-cell sequencing analysis further confirmed a positive correlation between FEN1 expression and colon cancer metastasis. Furthermore, we detected marked discrepancies in drug responsiveness between the High_FEN1 and Low_FEN1 groups, identifying 342 differentially expressed genes. Enrichment analysis showed significant suppression in processes related to DNA replication, spliceosome, and cell cycle pathways in the Low_FEN1 group, while the calcium signaling pathway, cAMP signaling pathway, and other pathways were activated. Of the 197 genes differentially expressed and strongly linked to FEN1 expression, 39 were significantly implicated in colon cancer prognosis. Finally, we constructed a risk signature consisting of 5 genes, which, when combined with drug treatment and pathological staging, significantly improved the prediction of colon cancer prognosis. This study offers novel insights into the interplay among FEN1 expression levels, colon cancer metastatic potential, and sensitivity to therapeutic agents. Furthermore, we successfully developed a multi-gene prognostic risk signature derived from FEN1.

GEO dataset mining analysis reveals novel Staphylococcus aureus virulence gene regulatory networks and diagnostic targets in mice.

Staphylococcus (S.) aureus infection is a serious, worldwide health concern, particularly in many communities and hospitals. Understanding the S. aureus pathogenetic regulatory network will provide significant insights into diagnostic target screening to improve clinical treatment of diseases caused by S. aureus. We screened differentially expressed genes between normal mice and S. aureus-infected mice. We used the Gene Expression Omnibus (GEO) DataSets database for functional analysis (GO-analysis) and the DAVID and KEGG databases for signaling pathway analyses. We next integrated the gene and pathway analyses with Transcriptional Regulatory Element Database (TRED) to build an antimicrobial resistance gene regulatory network of S. aureus. We performed association analysis of network genes and diseases using DAVID online annotation tools. We identified a total of 437 virulence genes and 15 transcription factors (TFs), as well as 444 corresponding target genes, in the S. aureus TF regulatory network. We screened seven key network nodes (Met, Mmp13, Il12b, Il4, Tnf, Ptgs2, and Ctsl), four key transcription factors (Jun, C3, Spil, and Il6) and an important signaling pathway (TNF). We hypothesized that the cytokine activity and growth factor activity of S. aureus are combinatorically cross-regulated by Met, Mmp13, Il12b, Il4, Tnf, Ptgs2, and Ctsl genes, the TFs Jun, C3, Spi1, and Il6, as well as the immune response, cellular response to lipopolysaccharide, and inflammatory response. Our study provides information and reference values for the molecular understanding of the S. aureus pathogenetic gene regulatory network.

Potential diagnostic markers shared between non-alcoholic fatty liver disease and atherosclerosis determined by machine learning and bioinformatic analysis.

Evidence indicates that chronic non-alcoholic fatty liver disease (NAFLD) can increase the risk of atherosclerosis (AS), but the underlying mechanism remains unclear. This study is intended for confirming key genes shared between NAFLD and AS, and their clinical diagnostic value to establish a foundation for searching novel therapeutic targets. We downloaded the Gene Expression Omnibus (GEO) datasets, GSE48452 and GSE89632 for NAFLD and GSE100927, GSE40231 and GSE28829 for AS. The progression of NAFLD co-expression gene modules were recognized via weighted gene co-expression network analysis (WGCNA). We screened for differentially expressed genes (DEGs) associated with AS and identified common genes associated with NAFLD and AS using Venn diagrams. We investigated the most significant core genes between NAFLD and AS using machine learning algorithms. We then constructed a diagnostic model by creating a nomogram and evaluating its performance using ROC curves. Furthermore, the CIBERSORT algorithm was utilized to explore the immune cell infiltration between the two diseases, and evaluate the relationship between diagnostic genes and immune cells. The WGCNA findings associated 1,129 key genes with NAFLD, and the difference analysis results identified 625 DEGs in AS, and 47 genes that were common to both diseases. We screened the core RPS6KA1 and SERPINA3 genes associated with NAFLD and AS using three machine learning algorithms. A nomogram and ROC curves demonstrated that these genes had great clinical meaning. We found differential expression of RPS6KA1 in patients with steatosis and NASH, and of SERPINA3 only in those with NASH compared with normal individuals. Immune infiltration findings revealed that macrophage and mast cell infiltration play important roles in the development of NAFLD and AS. Notably, SERPINA3 correlated negatively, whereas RPS6KA1 correlated positively with macrophages and mast cells. We identified RPS6KA1 and SERPINA3 as potential diagnostic markers for NAFLD and AS. The most promising marker for a diagnosis of NAFLD and AS might be RPS6KA1, whereas SERPINA3 is the most closely related gene for NASH and AS. We believe that further exploration of these core genes will reveal the etiology and a pathological relationship between NAFLD and AS.

IFIT1 modulates the proliferation, migration and invasion of pancreatic cancer cells via Wnt/β-catenin signaling.

Previously, Interferon-induced Protein with Tetratricopeptide Repeats 1 (IFIT1) has been shown to promote cancer development. Here, we aimed to explore the role of IFIT1 in the development and progression of pancreatic cancer, including the underlying mechanisms. We explored IFIT1 expression in pancreatic cancer samples using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cell Counting Kit-8 (CCK8), colony formation, scratch wound-healing and Transwell assays were performed to assess the proliferation, migration and invasion abilities of pancreatic cancer cells. Gene Set Enrichment Analysis (GSEA) and Western blotting were performed to assess the regulatory effect of IFIT1 on the Wnt/β-catenin pathway. We found that upregulation of IFIT1 expression is common in pancreatic cancer and is negatively associated with overall patient survival. Knockdown of IFIT1 expression led to decreased proliferation, migration and invasion of pancreatic cancer cells. We also found that IFIT1 could regulate Wnt/β-catenin signaling, and that a Wnt/β-catenin agonist could reverse this effect. In addition, we found that IFIT1 can promote epithelial-mesenchymal transition (EMT) of pancreatic cancer cells. Our data indicate that IFIT1 increases pancreatic cancer cell proliferation, migration and invasion by activating the Wnt/β-catenin pathway. In addition, we found that EMT could be regulated by IFIT1. IFIT1 may serve as a potential therapeutic target for pancreatic cancer.

Construction of the underlying circRNA-miRNA-mRNA regulatory network and a new diagnostic model in ulcerative colitis by bioinformatics analysis.

Circular RNAs (circRNAs) are involved in the pathogenesis of many diseases through competing endogenous RNA (ceRNA) regulatory mechanisms. To investigate a circRNA-related ceRNA regulatory network and a new predictive model by circRNA to understand the diagnostic mechanism of circRNAs in ulcerative colitis (UC). We obtained gene expression profiles of circRNAs, miRNAs, and mRNAs in UC from the Gene Expression Omnibus dataset. The circRNA-miRNA-mRNA network was constructed based on circRNA-miRNA and miRNA-mRNA interactions. Functional enrichment analysis was performed to identify the biological mechanisms involved in circRNAs. We identified the most relevant differential circRNAs for diagnosing UC and constructed a new predictive nomogram, whose efficacy was tested with the C-index, receiver operating characteristic curve (ROC), and decision curve analysis (DCA). A circRNA-miRNA-mRNA regulatory network was obtained, containing 12 circRNAs, three miRNAs, and 38 mRNAs. Two optimal prognostic-related differentially expressed circRNAs, hsa_circ_0085323 and hsa_circ_0036906, were included to construct a predictive nomogram. The model showed good discrimination, with a C-index of 1(> 0.9, high accuracy). ROC and DCA suggested that the nomogram had a beneficial diagnostic ability. This novel predictive nomogram incorporating hsa_circ_0085323 and hsa_circ_0036906 can be conveniently used to predict the risk of UC. The circRNa-miRNA-mRNA network in UC could be more clinically significant.

Low-density lipoprotein receptor-related protein 5/6 promotes endometrial cancer progression and cancer cell immune escape.

The study investigated the potential association of the low-density lipoprotein (LDL) genome with endometrial cancer progression based on the Gene Expression Omnibus data set and The Cancer Genome Atlas data set. Differential and weighted gene coexpression network analysis was performed on endometrial cancer transcriptome datasets GSE9750 and GSE106191. The protein-protein interaction network was built using LDL-receptor proteins and the top 50 tumor-associated genes. Low-density lipoprotein-related receptors 5/6 (LRP5/6) in endometrial cancer tissues were correlated with oncogenes, cell cycle-related genes, and immunological checkpoints using Spearman correlation. MethPrimer predicted the LRP5/6 promoter CpG island. LRP2, LRP6, LRP8, LRP12, low-density lipoprotein receptor-related protein-associated protein, and LRP5 were major LDL-receptor-related genes associated with endometrial cancer. LRP5/6 was enriched in various cancer-related pathways and may be a key LDL-receptor-related gene in cancer progression. LRP5/6 may be involved in the proliferation process of endometrial cancer cells by promoting the expression of cell cycle-related genes. LRP5/6 may be involved in the proliferation of endometrial cancer cells by promoting the expression of cell cycle-related genes. LRP5/6 may promote the immune escape of cancer cells by promoting the expression of immune checkpoints, promoting endometrial cancer progression. The MethPrimer database predicted that the LRP5/6 promoter region contained many CpG islands, suggesting that DNA methylation can occur in the LRP5/6 promoter region. LRP5/6 may aggravate endometrial cancer by activating the phosphoinositide 3-kinase/protein kinase B pathway.

NDRG2 acts as a negative regulator of the progression of small-cell lung cancer through the modulation of the PTEN-AKT-mTOR signalling cascade

N-myc downstream-regulated gene 2 (NDRG2) has been recognised as a negative regulator of the progression of numerous tumours, yet its specific role in small-cell lung carcinoma (SCLC) is not fully understood. The purpose of the current study was to investigate the biological role and mechanism of NDRG2 in SCLC. Initial investigation using the Gene Expression Omnibus (GEO) dataset revealed marked downregulation of NDRG2 transcripts in SCLC. The decreased abundance of NDRG2 in SCLC was verified by examining clinical specimens. Increasing NDRG2 expression in SCLC cell lines caused significant changes in cell proliferation, cell cycle progression, colony formation, and chemosensitivity. NDRG2 overexpression decreased the levels of phosphorylated PTEN, AKT and mTOR. In PTEN-depleted SCLC cells, the upregulation of NDRG2 did not result in any noticeable impact on AKT or mTOR activation. Additionally, the reactivation of AKT reversed the antitumour effects of NDRG2 in SCLC cells. Notably, increasing NDRG2 expression retarded the growth of SCLC cell-derived xenografts in vivo. In conclusion, NDRG2 serves as an inhibitor of SCLC, and its cancer-inhibiting effects are achieved through the suppression of AKT/mTOR via the activation of PTEN. This work suggests that NDRG2 is a potential druggable target for SCLC treatment.

Determination of target genes for classified molecular subtypes of triple-negative breast cancer form microarray gene expression profiling: An integrative in silico approach.

Highly heterogeneous triple-negative breast cancer (TNBC) has tough clinical features, which were gradually solving and improving in diagnosis by the molecular subtyping of TNBC. Presently, this study was focused on analyzing the genetic makeup of TNBC subtypes. This study explored the MicroArray expression profiling of differentially expressed genes in molecular subtypes BL1, BL2, IM, luminal androgen receptor, M, and mesenchymal stem-like of TNBC by analyzing the Gene Expression Omnibus dataset GSE167213. Various gene ontologies-based protein-protein interaction (PPI) networks were subtyped TNBC genes. The effect of genetic alteration on TNBC cases was also interpreted. The MicroArray gene expression profiling was done through R programming and subjected to functional annotation through the database for annotation, visualization, and integrated discovery. The PPI networking of functionally associated genes was interpreted by STRING. The survival analysis was done through cBioPortal. The t -test was used through R programming to generate the P values for a test of the significance of expressed genes. A total of 54,613 significant probes were analyzed in the TNBC MicroArray dataset. The functional PPI networks of BL1, BL2, and IM upregulated genes showed significant associations. The survival analysis of differentially expressed genes showed the significant prognostic effect of 32 upregulated genes of different subtypes on TNBC cases with genetic alterations, whereas the remaining genes showed no significant effects. The output of the present study provided significant target gene panels for different TNBC subtypes, which would add an informative genetic value to TNBC diagnosis.

Abstract 2436: In-depth analysis of microRNA signature in breast cancer derived extracellular vesicles: A potential biomarker repository for breast cancer diagnosis

Abstract Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. With the rising incidence of breast cancer, early and accurate diagnosis is pivotal in improving survival rates and patient outcomes. Our study addresses to identify vesicles associated with breast cancer in scope of miRNA signature. We focused on isolating breast cancer-derived extracellular vesicles (BEVs) from the bloodstream with immune affinity capture techniques. Then, we analyzed the miRNA profiles of BEVs in plasma samples from 120 breast cancer patients, 46 individuals with benign tumors, and 45 healthy controls. In this study, we analyzed to discover miRNA candidates for diagnosis of breast cancer with The Gene Expression Omnibus (GEO) database and found 379 down-regulated and 584 up-regulated differentially expressed miRNAs (DEMs) in tissue samples across 5 GEO datasets, and 28 down-regulated and 80 up-regulated DEMS in blood samples. Subsequently, we measured the expression levels of these 10 candidate miRNAs in BEVs from breast cancer cells and compared them with EVs from human mammary cells (MCF10a), using our BEV isolation method. The expression profiles of these miRNAs in BEVs were significantly elevated, supporting their potential as surrogate markers for breast cancer diagnosis. When comparing the AUC values of 10 candidate miRNAs through ROC analysis, 5 EV miRNAs (miR-21, miR-106b, miR-181a, miR-484, and miR-1260b) showed a high AUC values above 0.8. Subsequently, we verified a distinct signature of 5 EV miRNAs that effectively differentiate breast cancer patients from normal controls with 57.14% of sensitivity and 95% of specificity. Overall, our study not only complements existing diagnostic methods with high accuracy but also provides a deeper understanding of the molecular aspects of breast cancer, heralding a significant advancement in precision medicine and personalized cancer care. Citation Format: Jee Ye Kim, Min Woo Kim, Young Kim, Sol Moon, Suji Lee, Hyojung Lee, Joon Ye Kim, Seung Il Kim. In-depth analysis of microRNA signature in breast cancer derived extracellular vesicles: A potential biomarker repository for breast cancer diagnosis [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 2436.

ENO1 contributes to the gemcitabine resistance of pancreatic cancer through the YAP1 signaling pathway.

Pancreatic cancer (PC), a leading cause of cancer-related deaths, has a 5-year survival rate of approximately 10%. α-Enolase (ENO1) is a junction channel protein involved in tumor cell apoptosis and chemoresistance. However, the role of ENO1 in PC remains unclear. The expression and prognosis of ENO1 levels were determined in PC using public databases based on The Cancer Genome Atlas (TCGA) data sets. Cell viability, half maximal inhibitory concentration (IC50), autophagy, apoptosis, and autophagy markers were examined using cell counting kit-8 (CCK-8), transmission electron microscope, flow cytometry assays, and immunoblot, respectively. Using the Gene Expression Omnibus (GEO) and TCGA data sets, we found that ENO1 was significantly enriched in PC tumor tissues, and high expression levels of ENO1 were associated with an unfavorable prognosis. Whereas ENO1 silencing suppressed proliferation, autophagy, and induced cell apoptosis in PC cells, and inhibited tumor growth in vivo. Mechanistically, knockdown of ENO1 enhanced cellular cytotoxicity of gemcitabine (GEM), as well as reducing the expression of yes-associated protein 1 (YAP1), a major downstream effector of the Hippo pathway in vitro. YAP1 promoted autophagy and protected PC cells from GEM-induced apoptotic cell death. Furthermore, YAP1 overexpression attenuated the inhibition effects of ENO1 silencing. Our results suggest that ENO1 overexpression promotes cell growth and tumor progression by increasing the expression of YAP1 in PC. Further studies are required to understand the detailed mechanisms between ENO1 and YAP1 in PC.

Excessive fatty acids activate PRMT5/MDM2/Drosha pathway to regulate miRNA biogenesis and lipid metabolism.

Excessive fatty acids in the liver lead to the accumulation of lipotoxic lipids and then cellular stress to further evoke the related disease, like non-alcoholic fatty liver disease (NAFLD). As reported, fatty acid stimulation can cause some specific miRNA dysregulation, which caused us to investigate the relationship between miRNA biogenesis and fatty acid overload. Gene expression omnibus (GEO) dataset analysis, miRNA-seq, miRNA cleavage assay, RT-qPCR, western blotting, immunofluorescence and co-immunoprecipitation (co-IP) were used to reveal the change of miRNAs under pathological status and explore the relevant mechanism. High fat, high fructose, high cholesterol (HFHFrHC) diet-fed mice transfected with AAV2/8-shDrosha or AAV2/8-shPRMT5 were established to investigate the invivo effects of Drosha or PRMT5 on NAFLD phenotype. We discovered that the cleavage of miRNAs was inhibited by analysing miRNA contents and detecting some representative pri-miRNAs in multiple mouse and cell models, which was further verified by the reduction of the Microprocessor activity in the presence of palmitic acid (PA). Invitro, PA could induce Drosha, the core RNase III in the Microprocessor complex, degrading through the proteasome-mediated pathway, while invivo, knockdown of Drosha significantly promoted NAFLD to develop to a more serious stage. Mechanistically, our results demonstrated that PA can increase the methyltransferase activity of PRMT5 to degrade Drosha through MDM2, a ubiquitin E3 ligase for Drosha. The above results indicated that PRMT5 may be a critical regulator in lipid metabolism during NAFLD, which was confirmed by the knocking down of PRMT5 improved aberrant lipid metabolism invitro and invivo. We first demonstrated the relationship between miRNA dosage and NAFLD and proved that PA can activate the PRMT5-MDM2-Drosha signalling pathway to regulate miRNA biogenesis.

Abstract 3656: Target enrichment of methylated circulating tumor DNA for colorectal cancer detection

Abstract Aberrant DNA methylation changes are one of the earliest signatures of cancer development. Methylation profiling of circulating tumor DNA (ctDNA) from plasma has emerged as a promising approach for early cancer detection (ECD). However, the set of methylation targets that can successfully distinguish cancer through liquid biopsy will vary depending on cancer type and subtype. Here, we explored a methylation target enrichment workflow and developed a methylation-based classification model to distinguish between patients with colorectal cancer (CRC) and healthy individuals. First, CRC-specific CpG targets were identified by comparing the methylation landscape of CRC and normal samples (tissue and blood) available in The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. This analysis resulted in &amp;gt;800 CRC-specific CpG targets for initial evaluation. To develop and test the classification model, CRC patients and healthy individuals with plasma samples were included. A machine learning model was used to evaluate the highest performing CpG targets that effectively discriminated between CRC patients with detected ctDNA (“CRC ctDNA-positive”) and healthy individuals. Correlation between methylation levels of the high performing CpG targets and the variant allele frequency (VAF) among CRC ctDNA-positive patients was calculated. In total, 86 patients were included in this proof of concept study (mean age, 62 ±12 years; male, 55%). 50 CRC ctDNA-positive patients (24% stage I, 40% stage II, 24% stage III, and 12% stage IV) and 36 healthy normals were included in the analysis. We investigated this cohort to identify the highest performing CpG targets among the &amp;gt;800 potential CpG targets that provided effective discrimination between CRC patients and healthy individuals. Using the highest performing CpG targets, observed methylation level was correlated with VAF of single nucleotide variants detected by ctDNA testing (R2: 0.8). In summary, we developed a cost-effective methylation target enrichment workflow and utilized machine-learning algorithms to demonstrate the potential use of ctDNA methylation markers in early detection. Additional studies are needed to confirm that these and other markers can accurately distinguish between patients with CRC and healthy individuals. Citation Format: Hsiao-Yun Huang, Ravi Vijaya Satya, Wen-Ching Chan, Esha Atolia, Joshua Babiarz, Bernhard Zimmermann, Trupti Kawli. Target enrichment of methylated circulating tumor DNA for colorectal cancer detection [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 3656.

Identification and validation of mutual hub genes in idiopathic pulmonary fibrosis and rheumatoid arthritis-associated usual interstitial pneumonia

ObjectivesThe study aims at exploring common hub genes and pathways in idiopathic pulmonary fibrosis (IPF) and rheumatoid arthritis-associated usual interstitial pneumonia (RA-UIP) through integrated bioinformatics analyses. MethodsThe GSE199152 dataset containing lung tissue samples from IPF and RA-UIP patients was acquired from the Gene Expression Omnibus (GEO) database. The identification of overlapping differentially expressed genes (DEGs) in IPF and RA-UIP was carried out through R language. Protein–protein interaction (PPI) network analysis and module analysis were applied to filter mutual hub genes in the two diseases. Enrichment analyses were also conducted to analyze the possible biological functions and pathways of the overlapped DEGs and hub genes. The diagnostic value of key genes was assessed with R language, and the expressions of these genes in pulmonary cells of IPF and rheumatoid arthritis-associated interstitial lung disease (RA-ILD) patients were analyzed with single cell RNA-sequencing (scRNA-seq) datasets. The expression levels of hub genes were validated in blood samples from patients, specimens of human lung fibroblasts, lung tissue samples from mice, as well as external GEO datasets. ResultsFour common hub genes (THBS2, TIMP1, POSTN, and CD19) were screened. Enrichment analyses showed that the abnormal expressions of DEGs and hub genes may be connected with the onset of IPF and RA-UIP by regulating the progression of fibrosis. ScRNA-seq analyses illustrated that for both IPF and RA-ILD patients, THBS2, TIMP1, and POSTN were mainly expressed in lung fibroblasts, while CD19 was uniquely high-expressed in B cells. The qRT-PCR and immunohistochemistry (IHC) results verified that the expression levels of hub genes were mostly in accordance with the findings obtained from the bioinformatics analyses. ConclusionThough IPF and RA-UIP are distinct diseases, they may to some extent have mutual pathogenesis in the development of fibrosis. THBS2, TIMP1, POSTN, and CD19 may be the potential biomarkers of IPF and RA-UIP, and intervention on related pathways of these genes could offer new strategies for the precision treatment of IPF and RA-UIP.

Profiling of immune infiltration landscape of ruptured intracranial aneurysm.

Previous research has indicated that the rupture of intracranial aneurysm (IA) is a significant contributor to mortality from stroke. The objective of this present study was to examine the infiltration patterns in ruptured intracranial aneurysm (RIA), with the aim of generating insights that could inform the development of effective immunotherapeutic approaches. To achieve this, we obtained Gene Expression Omnibus datasets pertaining to ruptured aneurysms, encompassing a total of 19 unruptured intracranial aneurysms (UIA) and 27 RIA. Subsequently, we conducted differential gene analysis and immune cell analysis specifically for the RIA. According to the conducted studies, the analysis has identified 10 hub genes within key modules. Through the utilization of Kyoto Encyclopedia of Genes and Genomes pathway and gene ontology terms analyses, it has been established that genes exhibiting differential expression are associated with immune cell infiltration in the aneurysm wall. Furthermore, the implementation of the CIBERSORT algorithm has revealed that there are 22 distinct immune cells between RIA and tissues of UIA. IA samples contained a higher proportion of macrophages M1, mast cells resting, and CD4 naive T cells, while macrophages M0 and neutrophils were relatively lower in RIA compared with those in UIA. The current study initially identified highly conservative hub genes and immune cell infiltration patterns in IA. Data presented in the current study improved understanding of immune genes that drive IA which can be exploited in development of effective immunotherapies.