Gene Set Enrichment Research Articles

MRI radiomics and biological correlations for predicting axillary lymph node burden in early-stage breast cancer

Background and aimsPreoperative prediction of axillary lymph node (ALN) burden in patients with early-stage breast cancer is pivotal for individualised treatment. This study aimed to develop a MRI radiomics model for evaluating the ALN burden in early-stage breast cancer and to provide biological interpretability to predictions by integrating radiogenomic data.MethodsThis study retrospectively analyzed 1211 patients with early-stage breast cancer from four centers, supplemented by data from The Cancer Imaging Archive (TCIA) and Duke University (DUKE). MRI radiomic features were extracted from dynamic contrast-enhanced MRI images and an ALN burden-related radscore was constructed by the backpropagation neural network algorithm. Clinical and combined models were developed, integrating ALN-related clinical variables and radscore. The Kaplan–Meier curve and log-rank test were used to assess the prognostic differences between the predicted high- and low-ALN burden groups in both Center I and DUKE cohorts. Gene set enrichment and immune infiltration analyses based on transcriptomic TCIA and TCIA Breast Cancer dataset were used to investigate the biological significance of the ALN-related radscore.ResultsThe MRI radiomics model demonstrated an area under the curve of 0.781–0.809 in three validation cohorts. The predicted high-risk population demonstrated a poorer prognosis (log-rank P < .05 in both cohorts). Radiogenomic analysis revealed migration pathway upregulation and cell differentiation pathway downregulation in the high radscore groups. Immune infiltration analysis confirmed the ability of radiological features to reflect the heterogeneity of the tumor microenvironment.ConclusionsThe MRI radiomics model effectively predicted the ALN burden and prognosis of early-stage breast cancer. Moreover, radiogenomic analysis revealed key cellular and immune patterns associated with the radscore.

Plasma Proteomics to Identify Drug Targets and Potential Drugs for Retinal Artery Occlusion: An Integrated Analysis in the UK Biobank.

Retinal artery occlusion (RAO), which is positively correlated with acute ischemic stroke (IS) and results in severe visual impairment, lacks effective intervention drugs. This study aims to perform integrated analysis using UK Biobank plasma proteome data of RAO and IS to identify potential targets and preventive drugs. A total of 7191 participants (22 RAO patients, 1457 IS patients, 8 individuals with both RAO and IS, and 5704 healthy age-gender-matched controls) were included in this study. Unique 1461 protein expression profiles of RAO, IS, and the combined data set, extracted from UK Biobank Plasma proteomics projects, were analyzed using both differential expression analysis and elastic network regression (Enet) methods to identify shared key proteins. Subsequent analyses, including single cell type expression assessment, pathway enrichment, and druggability analysis, were conducted for verifying shared key proteins and discovery of new drugs. Five proteins were found to be shared among the samples, with all of them showing upregulation. Notably, adhesion G-protein coupled receptor G1 (ADGRG1) exhibited high expression in glial cells of the brain and eye tissues. Gene set enrichment analysis revealed pathways associated with lipid metabolism and vascular regulation and inflammation. Druggability analysis unveiled 15 drug candidates targeting ADGRG1, which demonstrated protective effects against RAO, especially troglitazone (-8.5 kcal/mol). Our study identified novel risk proteins and therapeutic drugs associated with the rare disease RAO, providing valuable insights into potential intervention strategies.

Transcriptomic landscape of quiescent and proliferating human corneal stromal fibroblasts

This study analyzed the transcriptional changes in primary human corneal stromal fibroblasts (hCSFs) grown under quiescent (serum-free) and proliferating (serum-supplemented) culture conditions to identify genes, pathways, and protein‒protein interaction networks influencing corneal repair and regeneration. Primary hCSFs were isolated from donor human corneas and maintained in serum-free or serum-laden conditions. RNA was extracted from confluent cultures using Qiagen kit and subjected to RNA sequencing (RNAseq) analysis. Differential gene expression (DGE) and pathway enrichment analyses were conducted using DESeq2 and Gene Set Enrichment Analysis (GSEA), respectively. Protein‒protein interaction (PPI) networks were created exploiting the STRING database and analyzed with Cytoscape and the cytoHubba plugin. RNA-seq revealed 5,181 genes that were significantly differentially expressed/changed among the 18,812 annotated genes (p value ˂0.05). A cutoff value of a log2-fold change of ±1.5 or greater was used to identify 674 significantly upregulated and 771 downregulated genes between quiescent and proliferating hCSFs. Pathway enrichment analysis revealed significant changes in genes linked to cell cycle regulation, inflammatory, and oxidative stress response pathways, such as E2F Targets, G2M Checkpoint, and MYC Targets, TNFA signaling via NF-kB, and oxidative phosphorylation. Protein-protein interaction network analysis highlighted critical hub genes. The FGF22, CD34, ASPN, DPT, LUM, FGF10, PDGFRB, ECM2, DCN, VEGFD, OMD, OGN, ANGPT1, CDH5, and PRELP were upregulated, whereas genes linked to cell cycle regulation and mitotic progression, such as BUB1, TTK, KIF23, KIF11, BUB1B, DLGAP5, NUSAP1, CCNA2, CCNB1, BIRC5, CDK1, KIF20A, AURKB, KIF2C, and CDCA8, were downregulated. The RNA sequences and gene count files have been submitted to the Gene Expression Omnibus (accession # GSE260476). Our study provides a comprehensive information on the transcriptional and molecular changes in hCSFs under quiescent and proliferative conditions and highlights key pathways and hub genes.

Identification of HDAC10 as a candidate oncogene in clear cell renal carcinoma that facilitates tumor proliferation and metastasis

BackgroundClear cell renal cell carcinoma (ccRCC) remains one of the most lethal urological malignancies even though a great number of improvements in diagnosis and management have achieved over the past few decades. Accumulated evidence revealed that histone deacetylases (HDACs) play vital role in cell proliferation, differentiation and apoptosis. Nevertheless, the biological functions of histone deacetylation modification related genes in ccRCC remains poorly understood.MethodBulk transcriptomic data and clinical information of ccRCC patients were obtained from the TCGA database and collected from the Chinese PLA General Hospital. A total of 36 histone deacetylation genes were selected and studied in our research. Univariate cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, random forest (RF) analysis, and protein-protein interaction (PPI) network analysis were applied to identify key genes affecting the prognosis of ccRCC. The ‘oncoPredict’ algorithm was utilized for drug-sensitive analysis. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to explore the potential biological function. The ssGSEA algorithm was used for tumor immune microenvironment analysis. The expression levels of HDAC10 were validated by RT-PCR and immunohistochemistry (IHC). 5-ethynyl-2′-deoxyuridine (EdU assay), CCK-8 assay, cell transwell migration and invasion assay and colony formation assay were performed to detect the proliferation and invasion ability of ccRCC cells. A nomogram incorporating HDAC10 and clinicopathological characteristics was established to predict the prognosis of ccRCC patients.ResultTwo machine learning algorithms and PPI analysis identified four histone deacetylation genes that have a significant association with the prognosis of ccRCC, with HDAC10 being the key gene among them. HDAC10 is highly expressed in ccRCC and its high expression is associated with poor prognosis for ccRCC patients. Pathway enrichment and the experiments of EdU staining, CCK-8 assay, cell transwell migration and invasion assay and colony formation assay demonstrated that HDAC10 mediated the proliferation and metastasis of ccRCC cells and involved in reshaping the tumor microenvironment (TME) of ccRCC. A clinically reliable prognostic predictive model was established by incorporating HDAC10 and other clinicopathological characteristics ( https://nomogramhdac10.shinyapps.io/HDAC10_Nomogram/ ).ConclusionOur study found the increased expression of HDAC10 was closely associated with poor prognosis of ccRCC patients. HDAC10 showed a pro-tumorigenic effect on ccRCC and promote the proliferation and metastasis of ccRCC, which may provide new light on targeted therapy for ccRCC.

Abstract A062 Therapeutic targeting of the SAGA KAT module impairs MYCN-amplified neuroblastoma growth through reduction of the MYCN oncogenic gene expression program

Abstract Pediatric cancers are frequently driven by genomic alterations that result in aberrant transcription factor activity. While direct targeting of transcription factors is difficult, one method to circumvent this problem is to target co-factors and chromatin complexes that are necessary for their oncogenic function. To evaluate protein complex dependencies enriched in MYCN-amplified neuroblastoma, a disease of dysregulated development driven by aberrant expression of an oncogenic expression program, we curated a list of protein complexes using the CORUM database and mined the Dependency Map using single sample gene set enrichment analysis. This analysis identified the KAT module of the transcriptional coactivator Spt-Ada-Gcn5-acetyltransferase (SAGA) complex as a selective dependency in MYCN-amplified neuroblastoma. Loss of SAGA complex activity in neuroblastoma through both genetic knockout and induced protein degradation of the lysine acetyltransferase (KAT) module subunit TADA2B resulted in a global reduction of histone lysine acetylation and impaired neuroblastoma cell growth. Genetic knockout of TADA2B did not impair the growth of pediatric sarcoma cell lines, suggesting that SAGA loss-of-function is not broadly deleterious. Furthermore, SAGA loss-of-function reduced MYCN binding to chromatin, resulting in suppression of the MYCN-driven gene expression program and impaired cell cycle progression. Importantly, the SAGA complex is pharmacologically targetable in vitro and in vivo with GSK-699, a PROTAC that induces proteolysis of both KAT2A and KAT2B paralogs. As observed with genetic knockout studies, loss of SAGA activity through KAT2A/B degradation reduced the MYCN-driven transcriptional signature and impaired cell cycle progression. Our findings expand our understanding of the chromatin complexes that maintain the oncogenic transcriptional state in MYCN-amplified neuroblastoma and suggest therapeutic potential for inhibitors of SAGA KAT activity in MYCN-amplified neuroblastoma. Citation Format: Nathaniel W. Mabe, Clare F. Malone, Alexandra B. Forman, Gabriela Alexe, Kathleen L. Engel, Ying-Jiun C. Chen, Melinda Soeung, Silvi Salhotra, Allen Basanthakumar, Bin Liu, Sharon YR Dent, Kimberly Stegmaier. Therapeutic targeting of the SAGA KAT module impairs MYCN-amplified neuroblastoma growth through reduction of the MYCN oncogenic gene expression program [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A062.

Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restriction.

Fetal growth restriction (FGR) occurs in 10% of pregnancies worldwide. Placenta dysfunction, as one of the most common causes of FGR, is associated with various poor perinatal outcomes. The main objectives of this study were to screen potential diagnostic biomarkers for FGR and to evaluate the function of immune cell infiltration in the process of FGR. Firstly, differential expression genes (DEGs) were identified in two Gene Expression Omnibus (GEO) datasets, and gene set enrichment analysis was performed. Diagnosis-related key genes were identified by using three machine learning algorithms (least absolute shrinkage and selection operator, random forest, and support vector machine model), and the nomogram was then developed. The receiver operating characteristic curve, calibration curve, and decision curve analysis curve were used to verify the validity of the diagnostic model. Using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), the characteristics of immune cell infiltration in placental tissue of FGR were evaluated and the candidate key immune cells of FGR were screened. In addition, this study also validated the diagnostic efficacy of TREM1 in the real world and explored associations between TREM1 and various clinical features. By overlapping the genes selected by three machine learning algorithms, four key genes were identified from 290 DEGs, and the diagnostic model based on the key genes showed good predictive performance (AUC = 0.971). The analysis of immune cell infiltration indicated that a variety of immune cells may be involved in the development of FGR, and nine candidate key immune cells of FGR were screened. Results from real-world data further validated TREM1 as an effective diagnostic biomarker (AUC = 0.894) and TREM1 expression was associated with increased uterine artery PI (UtA-PI) (p-value = 0.029). Four candidate hub genes (SCD, SPINK1, TREM1, and HIST1H2BB) were identified, and the nomogram was constructed for FGR diagnosis. TREM1 was not only associated with a variety of key immune cells but also correlated with increased UtA-PI. The results of this study could provide some new clues for future research on the prediction and treatment of FGR.

The Use of Identified Hypoxia-related Genes to Generate Models for Predicting the Prognosis of Cerebral Ischemia‒reperfusion Injury and Developing Treatment Strategies.

Cerebral ischemia‒reperfusion injury (CIRI) is a type of secondary brain damage caused by reperfusion after ischemic stroke due to vascular obstruction. In this study, a CIRI diagnostic model was established by identifying hypoxia-related differentially expressed genes (HRDEGs) in patients with CIRI. The ischemia‒reperfusion injury (IRI)-related datasets were downloaded from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ), and hypoxia-related genes in the Gene Cards database were identified. After the datasets were combined, hypoxia-related differentially expressed genes (HRDEGs) expressed in CIRI patients were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the HRDEGs were performed using online tools. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed with the combined gene dataset. CIRI diagnostic models based on HRDEGs were constructed via least absolute shrinkage and selection operator (LASSO) regression analysis and a support vector machine (SVM) algorithm. The efficacy of the 9 identified hub genes for CIRI diagnosis was evaluated via mRNA‒microRNA (miRNA) interaction, mRNA-RNA-binding protein (RBP) network interaction, immune cell infiltration, and receiver operating characteristic (ROC) curve analyses. We then performed logistic regression analysis and constructed logistic regression models based on the expression of the 9 HRDEGs. We next established a nomogram and calibrated the prediction data. Finally, the clinical utility of the constructed logistic regression model was evaluated via decision curve analysis (DCA). This study revealed 9 critical genes with high diagnostic value, offering new insights into the diagnosis and selection of therapeutic targets for patients with CIRI. : Not applicable.

Anti-Müllerian hormone type II receptor protein expression in non-small cell lung cancer and the effect of AMH/AMHR2 signaling on cancer cell proliferation.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths worldwide despite advances in cancer therapeutics. In several gynecological cancers, anti-Müllerian hormone receptor type 2 (AMHR2) mediates AMH-induced growth inhibition and is expressed at high levels. Furthermore, 5%-8% of NSCLCs exhibit high AMHR2 expression, suggesting that AMH may inhibit the progression of some lung cancers. However, the clinical relevance of AMHR2 expression and its role in lung cancer is not fully clarified. Immunostaining was performed on 79 surgical specimens of NSCLC. The Cancer Genome Atlas RNA-seq data for lung adenocarcinoma were analyzed, and gene ontology and gene set enrichment analyses were performed. In cellular experiments, AMHR2-overexpressing NSCLC cell lines were established, and the role of the AMH-AMHR2 pathway in cell proliferation with recombinant human AMH protein treatment was examined. A total of 13 cases (16.5%) were positive for immunostaining in lung adenocarcinoma tissues; no positive signals were detected in lung squamous carcinoma tissues. Gene expression variation analysis using The Cancer Genome Atlas data showed that the expression of genes related to the cell cycle was downregulated in the AMHR2-high group. Cellular experiments showed that activation of the AMH-AMHR2 pathway suppressed cell proliferation. In lung adenocarcinoma tissues with high expression of AMHR2, activation of the AMH-AMHR2 pathway may suppress cell proliferation.

A combination analysis based on bioinformatics tools reveals new signature genes related to maternal obesity and fetal programming.

Maternal obesity significantly influences fetal development and health later in life; however, the molecular mechanisms behind it remain unclear. This study aims to investigate signature genes related to maternal obesity and fetal programming based on a genomic-wide transcriptional placental study using a combination of different bioinformatics tools. The dataset (GSE128381) was obtained from Gene Expression Omnibus (GEO). The data of 100 normal body mass index (BMI) and 27 obese mothers were included in the analysis. Differentially expressed genes (DEGs) were evaluated by limma package. Thereafter, functional enrichment analysis was implemented. Then, weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) analysis were used to further screening of signature genes. Simple linear regression analysis was used to assess the correlation between signature genes and newborn birth weight. Gene set enrichment analysis (GSEA) was implemented to study signaling pathways related to signature genes. The expression of the signature genes was also explored in 48 overweight mothers in the same dataset. A total of 167 DEGs were obtained, of which 122 were up-regulated while 45 were down-regulated. The dataset was then clustered into 11 modules by WGCNA, and the MEbrown was found as the most significant module related to maternal obesity and fetal programming (cor = 0.2, p = 0.03). The LASSO analysis showed that PTX3, NCF2, HOXB5, ABCA6, and C1orf162 are signature genes related to maternal obesity and fetal programming, which were increased in the placenta of obese mothers compared to those with normal BMI. The area under the curve (AUC) of the signature genes in the receiver operating characteristic curve (ROC) was 0.709, 0.660, 0.674, 0.667, and 0.717, respectively. Simple linear regression analysis showed that HOXB5 was associated with newborn birth weight. GSEA analysis revealed that these signature genes positively participate in various signaling pathways/functions in the placenta. PTX3, NCF2, HOXB5, ABCA6, and C1orf162 are novel signature genes related to maternal obesity and fetal programming, of which HOXB5 is implicated in newborn birth weight.

A best-match approach for gene set analyses in embedding spaces.

Embedding methods have emerged as a valuable class of approaches for distilling essential information from complex high-dimensional data into more accessible lower-dimensional spaces. Applications of embedding methods to biological data have demonstrated that gene embeddings can effectively capture physical, structural, and functional relationships between genes. However, this utility has been primarily realized by using gene embeddings for downstream machine-learning tasks. Much less has been done to examine the embeddings directly, especially analyses of gene sets in embedding spaces. Here, we propose an Algorithm for Network Data Embedding and Similarity (ANDES), a novel best-match approach that can be used with existing gene embeddings to compare gene sets while reconciling gene set diversity. This intuitive method has important downstream implications for improving the utility of embedding spaces for various tasks. Specifically, we show how ANDES, when applied to different gene embeddings encoding protein-protein interactions, can be used as a novel overrepresentation- and rank-based gene set enrichment analysis method that achieves state-of-the-art performance. Additionally, ANDES can use multiorganism joint gene embeddings to facilitate functional knowledge transfer across organisms, allowing for phenotype mapping across model systems. Our flexible, straightforward best-match methodology can be extended to other embedding spaces with diverse community structures between set elements.

Olfr2-positive macrophages originate from monocytes proliferate in situ and present a pro-inflammatory foamy-like phenotype.

Olfactory receptor 2 (Olfr2) has been identified in a minimum of 30% of vascular macrophages, and its depletion was shown to reduce atherosclerosis progression. Mononuclear phagocytes, including monocytes and macrophages within the vessel wall, are major players in atherosclerosis. Single-cell RNA sequencing studies revealed that atherosclerotic artery walls encompass several monocytes and vascular macrophages, defining at least nine distinct subsets potentially serving diverse functions in disease progression. This study investigates the functional phenotype and ontogeny of Olfr2-expressing vascular macrophages in atherosclerosis. Olfr2+ macrophages rapidly increase in Apoe-/- mice's aorta when fed a Western diet (WD). Mass cytometry showed that Olfr2+ cells are clustered within the CD64 high population and enriched for CD11c and Ccr2 markers. Olfr2+ macrophages express many pro-inflammatory cytokines, including Il1b, Il6, Il12, and Il23, and chemokines, including Ccl5, Cx3cl1, Cxcl9, and Ccl22. By extracting differentially expressed genes from bulk RNA sequencing (RNA-seq) of Olfr2+ vs. Olfr2- macrophages, we defined a signature that significantly mapped to single-cell data of plaque myeloid cells, including monocytes, subendothelial MacAir, and Trem2Gpnmb foamy macrophages. By adoptive transfer experiments, we identified that Olfr2 competent monocytes from CD45.1Apoe-/-Olfr2+/+ mice transferred into CD45.2Apoe-/-Olfr2-/- recipient mice fed WD for 12 weeks, accumulate in the atherosclerotic aorta wall already at 72 h, and differentiate in macrophages. Olfr2+ macrophages showed significantly increased BrdU incorporation compared to Olfr2- macrophages. Flow cytometry confirmed that at least 50% of aortic Olfr2+ macrophages are positive for BODIPY staining and have increased expression of both tumour necrosis factor and interleukin 6 compared to Olfr2- macrophages. Gene set enrichment analysis of the Olfr2+ macrophage signature revealed a similar enrichment pattern in human atherosclerotic plaques, particularly within foamy/TREM2hi-Mφ and monocytes. In summary, we conclude that Olfr2+ macrophages in the aorta originate from monocytes and can accumulate at the early stages of disease progression. These cells can undergo differentiation into MacAir and Trem2Gpnmb foamy macrophages, exhibiting proliferative and pro-inflammatory potentials. This dynamic behaviour positions them as key influencers in shaping the myeloid landscape within the atherosclerotic plaque.

Identification and validation of diagnostic genes associated with neutrophil extracellular traps of type 2 diabetes mellitus.

Neutrophil extracellular traps (NETs) cause delayed wound closed up in type 2 diabetes mellitus (T2DM), but the specific regulatory mechanism of NETs-related genes (NETs-RGs) in T2DM is unclear. We acquired GSE21321 and GSE15932 datasets from gene expression omnibus (GEO) database. First, differentially expressed genes (DEGs) between T2DM and control samples of GSE21321 dataset were sifted out by differential expression analysis. NETs scores were calculated for all samples in GSE21321 dataset, and key module genes associated with NETs scores were screened by constructing co-expression network. Then, DEGs and key module genes were intersected to yield intersection genes, and candidate genes were identified by constructing a protein protein interaction (PPI) network. Least absolute shrinkage and selection operator (LASSO) regression analysis was implemented on candidate genes to screen out diagnostic genes, and they were subjected to single sample gene set enrichment analysis (ssGSEA). Finally, immune characteristic analysis was carried out, and we constructed the gene-drug and transcription factor (TF)-miRNA-mRNA networks. Besides, we validated the expression of diagnostic genes by quantitative real-time polymerase chain reaction (qRT-PCR). In total, 23 candidate genes were gained by PPI analysis. The 5 diagnostic genes, namely, inter-trypsin inhibitor heavy chain 3 (ITIH3), fibroblast growth factor 1 (FGF1), neuron cell adhesion molecule (NRCAM), advanced glycosylation end-product-specific receptor (AGER), and calcium voltage-gated channel subunit alpha1 C (CACNA1C), were identified via LASSO analysis, and they were involved in carboxylic acid transport, axonogenesis, etc. M2 Macrophage, Monocyte, Natural killer (NK) cell, and Myeloid dendritic cells (DC) were remarkably different between T2DM and control samples. Diagnostic genes had the strongest and the most significant positive correlation with B cells. The gene-drug network included CACNA1C-Isradipine, CACNA1C-Benidipine and other relationship pairs. Totally 76 nodes and 44 edges constituted the TF-miRNA-mRNA network, including signal transducer and activator of transcription 1(STAT1) -hsa-miR-3170-AGER, CCCTC-binding factor (CTCF)-hsa-miR-455-5p-CACNA1C, etc. Moreover, qRT-PCR suggested that the expression trends of FGF1 and AGER were in keeping with the results of bioinformatic analysis. FGF1 and AGER were markedly regulated downwards in the T2DM group. Through bioinformatic analysis, we identified NETs-related diagnostic genes (ITIH3, FGF1, NRCAM, AGER, CACNA1C) in T2DM, and explored their mechanism of action from different aspects, providing new ideas for the studies related to diagnosis and treatment of T2DM.

Beyond Glycolysis: Aldolase A Is a Novel Effector in Reelin-Mediated Dendritic Development.

Reelin, a secreted glycoprotein, plays a crucial role in guiding neocortical neuronal migration, dendritic outgrowth and arborization, and synaptic plasticity in the adult brain. Reelin primarily operates through the canonical lipoprotein receptors apolipoprotein E receptor 2 (Apoer2) and very low-density lipoprotein receptor (Vldlr). Reelin also engages with noncanonical receptors and unidentified coreceptors; however, the effects of which are less understood. Using high-throughput tandem mass tag (TMT) liquid chromatography tandem mass spectrometry (LC-MS/MS)-based proteomics and gene set enrichment analysis (GSEA), we identified both shared and unique intracellular pathways activated by Reelin through its canonical and noncanonical signaling in primary murine neurons of either sex during dendritic growth and arborization. We observed pathway cross talk related to regulation of cytoskeleton, neuron projection development, protein transport, and actin filament-based process. We also found enriched gene sets exclusively by the noncanonical Reelin pathway including protein translation, mRNA metabolic process, and ribonucleoprotein complex biogenesis suggesting Reelin fine-tunes neuronal structure through distinct signaling pathways. A key discovery is the identification of aldolase A, a glycolytic enzyme and actin-binding protein, as a novel effector of Reelin signaling. Reelin induced de novo translation and mobilization of aldolase A from the actin cytoskeleton. We demonstrated that aldolase A is necessary for Reelin-mediated dendrite growth and arborization in primary murine neurons and mouse brain cortical neurons. Interestingly, the function of aldolase A in dendrite development is independent of its known role in glycolysis. Altogether, our findings provide new insights into the Reelin-dependent signaling pathways and effector proteins that are crucial for dendritic development.

Identification of gene co-expression modules from zebrafish brain data: Applications in psychiatry illustrated through alcohol-related traits

Cumulative evidence suggests that zebrafish is a useful model in psychiatric research. Weighted Gene Co-expression Network Analysis (WGCNA) enables the reduction of genome-wide expression data to modules of highly co-expressed genes, which are hypothesized to interact within molecular networks. In this study, we first applied WGCNA to zebrafish brain expression data across different experimental conditions. Then, we characterized the different co-expression modules by gene-set enrichment analysis and hub gene-phenotype association. Finally, we analyzed association of polygenic risk scores (PRSs) based on genes of some interesting co-expression modules with alcohol dependence in 524 patients and 729 controls from Galicia, using competitive tests. Our approach revealed 34 co-expression modules in the zebrafish brain, with some showing enrichment in human synaptic genes, brain tissues, or brain developmental stages. Moreover, certain co-expression modules were enriched in psychiatry-related GWAS and comprised hub genes associated with psychiatry-related traits in both human GWAS and zebrafish models. Expression patterns of some co-expression modules were associated with the tested experimental conditions, mainly with substance withdrawal and cold stress. Notably, a PRS based on genes from co-expression modules exclusively associated with substance withdrawal in zebrafish showed a stronger association with human alcohol dependence than PRSs based on randomly selected brain-expressed genes. In conclusion, our analysis led to the identification of co-expressed gene modules that may model human brain gene networks involved in psychiatry-related traits. Specifically, we detected a cluster of co-expressed genes whose expression was exclusively associated with substance withdrawal in zebrafish, which significantly contributed to alcohol dependence susceptibility in humans.

Exploring the transcriptomic landscape of moyamoya disease and systemic lupus erythematosus: insights into crosstalk genes and immune relationships.

Systemic Lupus Erythematosus (SLE) is acknowledged for its significant influence on systemic health. This study sought to explore potential crosstalk genes, pathways, and immune cells in the relationship between SLE and moyamoya disease (MMD). We obtained data on SLE and MMD from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify common genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on these shared genes. Hub genes were further selected through the least absolute shrinkage and selection operator (LASSO) regression, and a receiver operating characteristic (ROC) curve was generated based on the results of this selection. Finally, single-sample Gene Set Enrichment Analysis (ssGSEA) was utilized to assess the infiltration levels of 28 immune cells in the expression profile and their association with the identified hub genes. By intersecting the important module genes from WGCNA with the DEGs, the study highlighted CAMP, CFD, MYO1F, CTSS, DEFA3, NLRP12, MAN2B1, NMI, QPCT, KCNJ2, JAML, MPZL3, NDC80, FRAT2, THEMIS2, CCL4, FCER1A, EVI2B, CD74, HLA-DRB5, TOR4A, GAPT, CXCR1, LAG3, CD68, NCKAP1L, TMEM33, and S100P as key crosstalk genes linking SLE and MMD. GO analysis indicated that these shared genes were predominantly enriched in immune system process and immune response. LASSO analysis identified MPZL3 as the optimal shared diagnostic biomarkers for both SLE and MMD. Additionally, the analysis of immune cell infiltration revealed the significant involvement of activation of T and monocytes cells in the pathogenesis of SLE and MMD. This study is pioneering in its use of bioinformatics tools to explore the close genetic relationship between MMD and SLE. The genes CAMP, CFD, MYO1F, CTSS, DEFA3, NLRP12, MAN2B1, NMI, QPCT, KCNJ2, JAML, MPZL3, NDC80, FRAT2, THEMIS2, CCL4, FCER1A, EVI2B, CD74, HLA-DRB5, TOR4A, GAPT, CXCR1, LAG3, CD68, NCKAP1L, TMEM33, and S100P have been identified as key crosstalk genes that connect MMD and SLE. Activation of T and monocytes cells-mediated immune responses are proposed to play a significant role in the association between MMD and SLE.

LMNA-Related Dilated Cardiomyopathy: Single-Cell Transcriptomics during Patient-Derived iPSC Differentiation Support Cell Type and Lineage-Specific Dysregulation of Gene Expression and Development for Cardiomyocytes and Epicardium-Derived Cells with Lamin A/C Haploinsufficiency.

LMNA-related dilated cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C (LMNA) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. The molecular mechanisms of the disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA-related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA-mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (four from Patients and eight from Controls) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for cardiac progenitors to cardiomyocytes (CMs) and epicardium-derived cells (EPDCs). Data integration and comparative analyses of Patient and Control cells found cell type and lineage-specific differentially expressed genes (DEGs) with enrichment, supporting pathway dysregulation. Top DEGs and enriched pathways included 10 ZNF genes and RNA polymerase II transcription in pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CMs; LMNA and epigenetic regulation, as well as DDIT4 and mTORC1 signaling in EPDCs. Top DEGs also included XIST and other X-linked genes, six imprinted genes (SNRPN, PWAR6, NDN, PEG10, MEG3, MEG8), and enriched gene sets related to metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs, as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model.

Prognostic signature and immune landscape of 5-methylcytosine-related long non-coding RNAs in gastric cancer

BackgroundLong non-coding RNAs (lncRNAs) have been demonstrated to be useful in assessing the prognosis of cancer patients. However, few studies have focused on 5-methylcytosine-related lncRNAs (m5C-lncRNAs) in gastric cancer (GC). In this study, we aimed to establish a m5C-lncRNAs prognostic signature (m5C-LPS) and explore its potential impact on guiding clinical practice for GC. MethodsRNA-sequence and clinicopathological data were retrieved from The Cancer Genome Atlas (TCGA) database, while the coexpression of long non-coding RNAs (lncRNAs) was determined using Pearson's correlation analysis. A m5C-LPS model was constructed using univariate and Lasso Cox regression, and its prognostic value and accuracy were subsequently validated. Subsequently, the expression of 11 m5C-lncRNAs was verified via quantitative real-time PCR (qRT-PCR) in gastric cancer (GC) cell lines. The potential biological mechanism of this signature was elucidated using Gene Set Enrichment Analysis (GSEA). Based on the GSEA findings, CIBERSORT and ESTIMATE algorithms were utilized to conduct a comprehensive investigation of the tumor immune microenvironment (TIME) in GC. Additionally, pRRophetic and TIDE algorithms were employed to predict drug sensitivity and the efficacy of immunotherapy for GC patients. Results280 lncRNAs were identified as m5C-lncRNAs, including RHPN1-AS1, AC093752.3, TSC22D1-AS1, AL391152.1, MAGI2-AS3, AC048382.2, AL033527.3, AC007405.2, AC036103.1, CCDC183-AS1, and ADORA2A-AS1. Their prognostic value was validated, and the expression of these 11 lncRNAs was confirmed in four gastric cancer cell lines using quantitative reverse transcription PCR (qRT-PCR). A nomogram incorporating a risk score was developed to provide more precise clinical decision-making. Gene Set Enrichment Analysis (GSEA) showed that many classical signaling pathways related to tumor progression were enriched in this signature. Analyses related to immunity and drug sensitivity demonstrated distinct differences in features between high-risk and low-risk subgroups. ConclusionThe m5C-LPS can predict the survival of gastric cancer (GC) patients, provide novel therapeutic targets, and thus offer more thoughtful perspectives for future clinical decisions regarding GC.

Identification of key regulatory genes involved in myelination after spinal cord injury by GSEA analysis

Multilayer dense myelin tissue provides insulating space and nutritional support for axons in healthy spinal cord tissue. Oligodendrocyte precursor cells (OPCs) are the main glial cells that complement myelin loss in the central nervous system and play an important role in the repair of spinal cord injury (SCI). However, the regulation of axonal remyelination after SCI is still insufficient. In this study, we focused on the changes in genes related to myelin repair after rat hemisection SCI by gene set enrichment analysis (GSEA). Key genes proteolipid protein 1 (Plp1), hexosaminidase subunit alpha (Hexa), and hexosaminidase subunit beta (Hexb) during remyelination after SCI were found. Through quantitative real-time polymerase chain reaction (qPCR) experiments, we confirmed that within 28 days after rat hemisection SCI, the mRNA expression of gene Plp1 gradually decreased, while the expressions of gene Hexa and Hexb gradually increased, which was consistent with RNA sequencing results. In vitro, we performed EdU proliferation assays on OPC cell line OLN-93 and primary rat OPCs. We found that interference of Plp1 promoted OPC proliferation, while interference of Hexa and Hexb inhibited OPC proliferation. In addition, we performed in vitro differentiation experiments on primary rat OPCs. By measuring myelin sheath branch outgrowth and the fluorescence intensity of the mature myelin sheath marker myelin basic protein (MBP), we found that interference of Hexa or Hexb promoted OPC differentiation and maturation, but interference of Plp1 inhibited this process. Finally, we injected Hexb siRNA in vivo and found that interfering Hexb could improve motor movements and myelin regeneration after SCI in rats. Our results provide new target genes that can selectively regulate the proliferation and differentiation of endogenous OPCs, providing new ideas for promoting remyelination and functional recovery after SCI.

Targeting RAC1 reactivates pyroptosis to reverse paclitaxel resistance in ovarian cancer by suppressing P21-activated kinase 4.

Pyroptosis may play an important role in the resistance of ovarian cancer (OC) to chemotherapy. However, the mechanism by which pyroptosis modulation can attenuate chemotherapy resistance has not been comprehensively studied in OC. Here, we demonstrated that RAS-associated C3 botulinum toxin substrate 1 (RAC1) is highly expressed in OC and is negatively correlated with patient outcomes. Through cell function tests and in vivo tumor formation tests, we found that RAC1 can promote tumor growth by mediating paclitaxel (PTX) resistance. RAC1 can mediate OC progression by inhibiting pyroptosis, as evidenced by high-throughput automated confocal imaging, the release of lactate dehydrogenase (LDH), the expression of the inflammatory cytokines IL-1β/IL-18 and the nucleotide oligomerization domain-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome. Mechanically, RNA-seq, gene set enrichment analysis (GSEA), coimmunoprecipitation (Co-IP), mass spectrometry (MS), and ubiquitination tests further confirmed that RAC1 inhibits caspase-1/gasdermin D (GSDMD)-mediated canonical pyroptosis through the P21-activated kinase 4 (PAK4)/mitogen-activated protein kinase (MAPK) pathway, thereby promoting PTX resistance in OC cells. Finally, the whole molecular pathway was verified by the results of in vivo drug combination tests, clinical specimen detection and the prognosis. In summary, our results suggest that the combination of RAC1 inhibitors with PTX can reverse PTX resistance by inducing pyroptosis through the PAK4/MAPK pathway.

540. INFILTRATION OF RESECTED OESOPHAGEAL ADENOCARCINOMA WITH ANTIGEN-EXPERIENCED PD-1 AND CD39 POSITIVE CD8+ TISSUE RESIDENT MEMORY CELLS CORRELATE WITH IMPROVED SURVIVAL

Abstract Background The development of novel treatments for oesophageal adenocarcinoma (OAC) represents an area of significant unmet need. Despite the application of immune checkpoint blockade alongside chemotherapy as standard of care for some patients with metastatic OAC, responses to immunotherapies are modest and 5-year survival is 17%. The reasons for this remains unclear and while tumour infiltration with CD8+ T lymphocytes (TILs) at the time of resection is associated with improved outcomes, this heterogeneous population remains incompletely characterised in OAC. Tissue resident memory cells (TRM) are increasingly understood as key mediators of immunotherapy response but their role in OAC is poorly understood. Methods Tumour samples were accessed by punch biopsy from the central portion of cancers resected from 44 patients undergoing curative surgery for OAC. Samples were assessed by multi-parametric flow cytometry for the presence of antigen-experienced TILs and markers of activation and exhaustion. Populations of antigen-experienced PD-1 and CD39 positive CD8+ OAC TILs were sorted, and bulk RNA sequencing (RNAseq) undertaken using a modified SmartSeq2 protocol allowing differential gene expression analysis and gene set enrichment analysis. Flow cytometric assessment of functionality was completed. Results Resected OAC are often highly infiltrated with antigen experienced CD8+ TILs expressing high levels of PD-1 and CD39 and an abundance of this population correlates with improved survival (%PD-1/CD39+ &amp;gt;Median, OS HR 0.12). RNAseq of sorted TILs identified this PD-1+ and CD39+ lymphocyte population as enriched for precursor exhausted-like CD8+ TRM (NES 2.01 p=0.00). This is correlated by their CD103+ and TIM3- predominance on flow cytometry in keeping with TRMsassociated with immunotherapy response in other cancers. This population demonstrated a maintained proliferative potential, an ability to degranulate and produce the key effector molecules IFN-γ, TNF-α and Granzyme B. Conclusions An abundance of a PD-1 and CD39 positive CD8+ TIL population, consistent with a precursor exhausted-like TRM population with maintained proliferative and functional capacity, is observed to correlate with improved outcomes following curative surgery for OAC. This TRM population is consistent with analogous populations observed to be responsible for immunotherapy response in other cancers and their identification may predict benefit from such approaches in OAC and allow better patient selection for treatment with immune checkpoint inhibitors.