Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes
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ABSTRACT:
SUBMITTER: Pei F
PROVIDER: S-EPMC10870152 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:Endometriosis (EMS) is a chronic disease that can cause dysmenorrhea, chronic pelvic pain, and infertility, among other symptoms. EMS diagnosis is often delayed compared to other chronic diseases, and there are currently no accurate, easily accessible, and non-invasive diagnostic tools. Therefore, it is important to elucidate the mechanism of EMS and explore potential biomarkers and diagnostic tools for its accurate diagnosis and treatment. In the present study, we comprehensively analyzed the differential expression, immune infiltration, and interactions of EMS-related genes in three Homo sapiens datasets. Our results identified 332 differentially expressed genes (DEGs) associated with EMS. Gene ontology analysis showed that these changes mainly focused on the positive regulation of endometrial cell proliferation, cell metabolism, and extracellular space, and EMS involved the integrin, complement activation, folic acid metabolism, interleukin, and lipid signaling pathways. The LASSO regression model was established using immune DEGs with an area under the curve of 0.783 for the internal dataset and 0.656 for the external dataset. Five genes with diagnostic value, ACKR1, LMNB1, MFAP4, NMU, and SEMA3C, were screened from M1 and M2 macrophages, activated mast cells, neutrophils, natural killer cells, follicular T helper cells, CD8+, and CD4+ cells. A protein-protein interaction network based on the immune DEGs was constructed, and ten hub genes with the highest scores were identified. Our results may provide a framework for the development of pathological molecular networks in EMS.
| S-EPMC9582281 | biostudies-literature
Project description:BackgroundEndometriosis (EMS) occurs when normal uterine tissue grows outside the uterus and causes chronic pelvic pain and infertility. Endometriosis-associated infertility is thought to be caused by unknown mechanisms. In this study, using necroptosis-related genes, we developed and validated multigene joint signatures to diagnose EMS and explored their biological roles.MethodsWe downloaded two databases (GSE7305 and GSE1169) from the Gene Expression Omnibus (GEO) database and 630 necroptosis-related genes from the GeneCards and GSEA databases. The limma package in Rsoftware was used to identify differentially expressed genes (DEGs). We interleaved common differentially expressed genes (co-DEGs) and necroptosis-related genes (NRDEGs) in the endometriosis dataset. The DEGs functions were reflected by gene ontology analysis (GO), pathway enrichment analysis, and gene set enrichment analysis (GSEA). We used CIBERSORT to analyze the immune microenvironment differences between EMS patients and controls. Furthermore, a correlation was found between necroptosis-related differentially expressed genes and infiltrating immune cells to better understand the molecular immune mechanism.ResultsCompared with the control group, this study revealed that 10 NRDEGs were identified in EMS. There were two types of immune cell infiltration abundance (activated NK cells and M2 macrophages) in these two datasets, and the correlation between different groups of samples was statistically significant (P < 0.05). MYO6 consistently correlated with activated NK cells in the two datasets. HOOK1 consistently demonstrated a high correlation with M2 Macrophages in two datasets. The immunohistochemical result indicated that the protein levels of MYO6 and HOOK1 were increased in patients with endometriosis, further suggesting that MYO6 and HOOK1 can be used as potential biomarkers for endometriosis.ConclusionsWe identified ten necroptosis-related genes in EMS and assessed their relationship with the immune microenvironment. MYO6 and HOOK1 may serve as novel biomarkers and treatment targets in the future.
| S-EPMC10566087 | biostudies-literature
Project description:ObjectiveTo determine the potential diagnostic markers and extent of immune cell infiltration in endometriosis (EMS).MethodsTwo published profiles (GSE7305 and GSE25628 datasets) were downloaded, and the candidate biomarkers were identified by support vector machine recursive feature elimination analysis and a Lasso regression model. The diagnostic value and expression levels of biomarkers in EMS were verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blotting, then further validated in the GSE5108 dataset. CIBERSORT was used to estimate the composition pattern of immune cell components in EMS.ResultsOne hundred and fifty-three differential expression genes (DEGs) were identified between EMS and endometrial with 83 upregulated and 51 downregulated genes. Gene sets related to arachidonic acid metabolism, cytokine-cytokine receptor interactions, complement and coagulation cascades, chemokine signaling pathways, and systemic lupus erythematosus were differentially activated in EMS compared with endometrial samples. Aquaporin 1 (AQP1) and ZW10 binding protein (ZWINT) were identified as diagnostic markers of EMS, which were verified using qRT-PCR and western blotting and validated in the GSE5108 dataset. Immune cell infiltrate analysis showed that AQP1 and ZWINT were correlated with M2 macrophages, NK cells, activated dendritic cells, T follicular helper cells, regulatory T cells, memory B cells, activated mast cells, and plasma cells.ConclusionAQP1 and ZWINT could be regarded as diagnostic markers of EMS and may provide a new direction for the study of EMS pathogenesis in the future.
| S-EPMC9118874 | biostudies-literature
Project description:Introduction: Endometriosis is a prevalent and recurrent medical condition associated with symptoms such as pelvic discomfort, dysmenorrhea, and reproductive challenges. Furthermore, it has the potential to progress into a malignant state, significantly impacting the quality of life for affected individuals. Despite its significance, there is currently a lack of precise and non-invasive diagnostic techniques for this condition. Methods: In this study, we leveraged microarray datasets and employed a multifaceted approach. We conducted differential gene analysis, implemented weighted gene co-expression network analysis (WGCNA), and utilized machine learning algorithms, including random forest, support vector machine, and LASSO analysis, to comprehensively explore senescence-related genes (SRGs) associated with endometriosis. Discussion: Our comprehensive analysis, which also encompassed profiling of immune cell infiltration and single-cell analysis, highlights the therapeutic potential of this gene assemblage as promising targets for alleviating endometriosis. Furthermore, the integration of these biomarkers into diagnostic protocols promises to enhance diagnostic precision, offering a more effective diagnostic journey for future endometriosis patients in clinical settings. Results: Our meticulous investigation led to the identification of a cluster of genes, namely BAK1, LMNA, and FLT1, which emerged as potential discerning biomarkers for endometriosis. These biomarkers were subsequently utilized to construct an artificial neural network classifier model and were graphically represented in the form of a Nomogram.
| S-EPMC10583561 | biostudies-literature
Project description:Osteoarthritis (OA) is a chronic degenerative disease of the bone and joints. Immune-related genes and immune cell infiltration are important in OA development. We analyzed immune-related genes and immune infiltrates to identify OA diagnostic markers. The datasets GSE51588, GSE55235, GSE55457, GSE82107, and GSE114007 were downloaded from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and differentially expressed immune-related genes (DEIRGs), and functional correlation analysis was conducted. Second, CIBERSORT was used to evaluate infiltration of immune cells in OA tissue. Finally, the least absolute shrinkage and selection operator logistic regression algorithm and support vector machine-recurrent feature elimination algorithm were used to screen and verify diagnostic markers of OA. A total of 711 DEGs and 270 DEIRGs were identified in this study. Functional enrichment analysis showed that the DEGs and DEIRGs are closely related to cellular calcium ion homeostasis, ion channel complexes, chemokine signaling pathways, and JAK-STAT signaling pathways. Differential analysis of immune cell infiltration showed that M1 macrophage infiltration was increased but that mast cell and neutrophil infiltration were decreased in OA samples. The machine learning algorithm cross-identified 15 biomarkers (BTC, PSMD8, TLR3, IL7, APOD, CIITA, IFIH1, CDC42, FGF9, TNFAIP3, CX3CR1, ERAP2, SEMA3D, MPO, and plasma cells). According to pass validation, all 15 biomarkers had high diagnostic efficacy (AUC > 0.7), and the diagnostic efficiency was higher when the 15 biomarkers were fitted into one variable (AUC = 0.758). We developed 15 biomarkers for OA diagnosis. The findings provide a new understanding of the molecular mechanism of OA from the perspective of immunology.
| S-EPMC8007625 | biostudies-literature
Project description:Endometriosis (EMT) is an aggressive disease of the reproductive system, also called "benign cancer". However, effective treatments for EMT are still lacking in clinical practice. Interestingly, immune infiltration is significantly involved in EMT pathogenesis. Currently, no studies have shown the involvement of cuproptosis-related genes (CRGs) in regulating immune infiltration in EMT. This study identified three CRGs such as GLS, NFE2L2, and PDHA1, associated with EMT using machine learning algorithms. These three CRGs were upregulated in the endometrium of patients with moderate/severe EMT and downregulated in patients with infertility. Single sample genomic enrichment analysis (ssGSEA) revealed that these CRGs were closely correlated with autoimmune diseases such as systemic lupus erythematosus. Furthermore, these CRGs were correlated with immune cells such as eosinophils, natural killer cells, and macrophages. Therefore, profiling patients based on these genes aid in a more accurate diagnosis of EMT progression. The mRNA and protein expression levels of GLS, NFE2L2 and PDHA1 were validated by qRT-PCR and WB studies in EMT samples. These findings provide a new idea for the pathology and treatment of endometriosis, suggesting that CRGs such as GLS, NFE2L2 and PDHA1 may play a key role in the occurrence and development of endometriosis.
| S-EPMC10703883 | biostudies-literature
Project description:Pediatric sepsis is a serious disease characterized by multiple organ failure. Due to its unique pathogenesis, its clinical mortality rate is very high. This study systematically evaluated the value of efferocytosis related genes in the diagnosis of sepsis in children. We downloaded gene expression profiles related to pediatric sepsis from the gene expression omnibus database, identify differentially expressed genes (DEGs) by limma R package, and retrieve adult sepsis gene expression profiles to determine the specificity of pediatric sepsis biomarkers. Selected pediatric sepsis specific genes from these profiles and used clusterProfiler for Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology, and Reactome databases for functional enrichment. Genesets related to Efferocytosis was searched in the KEGG database, and the intersection with pediatric sepsis specific genes was considered as pediatric sepsis-efferocytosis genes. Immune infiltration analysis was performed using the CIBERSORT package. Constructed a protein-protein interaction (PPI) network and screened for hub genes in pediatric sepsis-efferocytosis genes. Further select diagnostic markers through gene expression and receiver operating characteristic (ROC) curve. We identified a total of 281 DEGs specific to pediatric sepsis, which are enriched in pathways such as phagosome, autophagy and efferocytosis. We found that the efferocytosis pathway is significantly up-regulated in pediatric sepsis, while this pathway is not significant in adult sepsis. We noticed that 12 types of immune cells infiltration levels including macrophages in pediatric sepsis patients. We selected the top 20 hub genes with PPI network. By overlapping hub genes with pediatric sepsis-efferocytosis genes, we obtained 2 hub pediatric sepsis-efferocytosis genes (ALOX5, CD36). The ROC curve suggested that these genes may be potential diagnostic markers for pediatric sepsis. We have identified ALOX5 and CD36 as efferocytosis related genes associated with pediatric sepsis, which can reliably identify pediatric sepsis and provide prospective clinical references for the pathogenesis of pediatric sepsis.
| S-EPMC11813005 | biostudies-literature
Project description:BackgroundEfferocytosis is a biological process in which phagocytes remove apoptotic cells and vesicles from tissues. This process is initiated by the release of inflammatory mediators from apoptotic cells and plays a crucial role in resolving inflammation. The signals associated with efferocytosis have been found to regulate the inflammatory response and the tumor microenvironment (TME), which promotes the immune escape of tumor cells. However, the role of efferocytosis in glioblastoma multiforme (GBM) is not well understood and requires further investigation.MethodsIn this study, we conducted a comprehensive analysis of 22 efferocytosis-related genes (ERGs) by searching for studies related to efferocytosis. Using bulk RNA-Seq and single-cell sequencing data, we analyzed the expression and mutational characteristics of these ERGs. By using an unsupervised clustering algorithm, we obtained ERG clusters from 549 GBM patients and evaluated the immune infiltration characteristics of each cluster. We then identified differential genes (DEGs) in the two ERG clusters and classified GBM patients into different gene clusters using univariate cox analysis and unsupervised clustering algorithms. Finally, we utilized the Boruta algorithm to screen for prognostic genes and reduce dimensionality, and the PCA algorithm was applied to create a novel efferocytosis-related scoring system.ResultsDifferential expression of ERGs in glioma cell lines and normal cells was analyzed by rt-PCR. Cell function experiments, on the other hand, validated TIMD4 as a tumor risk factor in GBM. We found that different ERG clusters and gene clusters have distinct prognostic and immune infiltration profiles. The ERG signature we developed provides insight into the tumor microenvironment of GBM. Patients with lower ERG scores have a better survival rate and a higher likelihood of benefiting from immunotherapy.ConclusionsOur novel efferocytosis-related signature has the potential to be used in clinical practice for risk stratification of GBM patients and for selecting individuals who are likely to respond to immunotherapy. This can help clinicians design appropriate targeted therapies before initiating clinical treatment.
| S-EPMC10583324 | biostudies-literature
Project description:BackgroundOsteoarthritis (OA) is a leading cause of disability globally, affecting over 500 million individuals worldwide. However, accurate and early diagnosis of OA is challenging to achieve. Immune-related genes play an essential role in OA development. Therefore, the objective of this study was to develop a diagnostic model for OA based on immune-related genes identified in synovial membrane.MethodsThe gene expression profile of OA were downloaded based on four datasets. The significantly differentially expressed genes (DEGs) between OA and control groups were selected. The differential immune cells were analyzed, followed by immune-related DEGs screening. WGCNA was used to screen module genes and these genes were further selected through optimization algorithm. Then, nomogram model was constructed. Chemical drug small molecule related to OA was predicted. Finally, expression levels of several key genes were validated by qRT-PCR through construction of OA rat models.ResultsThe total 656 DEGs were obtained. Eight immune cells were significantly differential between two groups, and 317 immune-related DEGs were obtained. WGCNA identified three modules. The genes in modules were significantly involved in 15 pathways, involving in 65 genes. Then 12 DEGs were screened as the final optimal combination of DEGs, such as CEBPB, CXCL1, JUND, GABARAPL2 and PDGFC. The Nomogram model was also constructed. Furthermore, the chemical small molecules, such as acetaminophen, aspirin, and caffeine were predicted. The expression levels of CEBPB, CXCL1, GABARAPL2 and PDGFC were validated in OA rat models.ConclusionA diagnostic model based on twelve immune related genes was constructed. These model genes, such as CEBPB, CXCL1, GABARAPL2, and PDGFC, may serve as diagnostic biomarkers and immunotherapeutic targets.
| S-EPMC10770511 | biostudies-literature
Project description:ObjectiveColon adenocarcinoma (COAD) is one of the most prevalent cancers worldwide. However, the pyroptosis-related lncRNAs of COAD have not been deeply examined and validated. Here, we constructed and validated a risk model on pyroptosis-related lncRNAs in COAD.MethodsThe RNA sequencing transcriptome and clinical data of COAD patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed pyroptosis-related mRNAs and mRNA-lncRNA coexpression network were identified. After univariate and multifactorial cox analyses of prognosis-related lncRNAs, a risk model was constructed. Next, we analyzed the differences in immune infiltration, immune checkpoint blockade-, immune checkpoint-, and N6-methyladenosine-related gene expressions between the high- and low-risk groups. RT-qPCR was used to validate the expression of lncRNAs.ResultA risk model was constructed based on 9 pyroptosis-related lncRNAs and separated COAD patients into the high- and low-risk groups. Immune infiltration analysis and immune checkpoint blockade-, immune checkpoint-, and N6-methyladenosine-related genes showed significant differences between the two subgroups. RT-qPCR showed that the 9 pyroptosis-related lncRNAs could be used as prognostic indicators.ConclusionA novel risk model based on pyroptosis-related lncRNAs was constructed and demonstrated that these lncRNAs might be used as independent prognostic biomarkers. This will also assist shed light on the COAD prognosis and therapy.
| S-EPMC9509522 | biostudies-literature