Dataset Information

Machine learning algorithms for a novel cuproptosis-related gene signature of diagnostic and immune infiltration in endometriosis.

ABSTRACT: 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.

SUBMITTER: Wang J

PROVIDER: S-EPMC10703883 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Machine learning algorithms for a novel cuproptosis-related gene signature of diagnostic and immune infiltration in endometriosis.

Wang Jiajia J Lu Yiming Y Sun Guangyu G Fang Zhihao Z Xing Zhiyong Z Nong Weihua W Wei Yunbao Y Wang Shan S Shi Guiling G Dong Mingyou M Wang Junli J

Scientific reports 20231207 1

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 alg ...[more]

PMID: 38062233

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Project description:Background and aimsCuproptosis has been identified as a key player in the development of several diseases. In this study, we investigate the potential role of cuproptosis-related genes in the pathogenesis of nonalcoholic fatty liver disease (NAFLD).MethodThe gene expression profiles of NAFLD were obtained from the Gene Expression Omnibus database. Differential expression of cuproptosis-related genes (CRGs) were determined between NAFLD and normal tissues. Protein-protein interaction, correlation, and function enrichment analyses were performed. Machine learning was used to identify hub genes. Immune infiltration was analyzed in both NAFLD patients and controls. Quantitative real-time PCR was employed to validate the expression of hub genes.ResultsFour datasets containing 115 NAFLD and 106 control samples were included for bioinformatics analysis. Three hub CRGs (NFE2L2, DLD, and POLD1) were identified through the intersection of three machine learning algorithms. The receiver operating characteristic curve was plotted based on these three marker genes, and the area under the curve (AUC) value was 0.704. In the external GSE135251 dataset, the AUC value of the three key genes was as high as 0.970. Further nomogram, decision curve, calibration curve analyses also confirmed the diagnostic predictive efficacy. Gene set enrichment analysis and gene set variation analysis showed these three marker genes involved in multiple pathways that are related to the progression of NAFLD. CIBERSORT and single-sample gene set enrichment analysis indicated that their expression levels in macrophages, mast cells, NK cells, Treg cells, resting dendritic cells, and tumor-infiltrating lymphocytes were higher in NAFLD compared with control liver samples. The ceRNA network demonstrated a complex regulatory relationship between the three hub genes. The mRNA level of these hub genes were further confirmed in a mouse NAFLD liver samples.ConclusionOur study comprehensively demonstrated the relationship between NAFLD and cuproptosis, developed a promising diagnostic model, and provided potential targets for NAFLD treatment and new insights for exploring the mechanism for NAFLD.

Project description:BackgroundA novel form of cell death termed cuproptosis was proposed recently. miRNAs play important roles in colorectal cancer (CRC). However, their relationships have not been reported.MethodsmiRNAs that negatively regulate 16 cuproptosis regulators were predicted using Targetscan database. The univariate Cox, LASSO, and multivariate Cox regression analyses were performed to select cuproptosis-related miRNAs. GSEA and ssGSEA analysis was carried out for functional enrichment analysis. The immune cell proportion score (IPS) and the efficiencies of multiple chemotherapy drugs were compared between different risk groups. The CCK8, cell colony, edu, and flow cytometry assays were performed to validate the roles of miRNA. Luciferase reporter assay confirmed the regulatory mechanism of miRNA on cuproptosis.ResultsSix cuproptosis-related miRNAs (hsa-miR-653, hsa-miR-216a, hsa-miR-3684, hsa-miR-4437, hsa-miR-641, and hsa-miR-552) were screened out for model construction. The risk score could act as an independent prognostic indicator in CRC (p < 0.001, 95% HR = 1.243 (1.129-1.369)). The nomogram could efficiently predict the overall survival rate (AUC = 0.836). Then, the level of immunosuppressive pathways, immunosuppressive cells, stromal-activated genes, and stromal score was higher in the high-risk group. The IPS analysis showed a better response to immunotherapy in the low-risk group. Also, the risk score was closely correlated with efficiencies of multiple chemotherapy drugs. Furthermore, miR-653 was highly expressed in CRC tissues (p < 0.001), closely correlated with T stage (p < 0.001), metastasis (p < 0.001), and tumor stage (p < 0.001). High expression of miR-653 predicted a shorter overall survival (p = 0.0282) and disease-free survival (p = 0.0056). In addition, miR-653 promoted cell proliferation, inhibited apoptosis, and negatively regulated the expression of DLD through directly binding to the 3'-UTR of DLD mRNA.ConclusionWe constructed a cuproptosis-related miRNA signature for the prediction of CRC patient survival and immunotherapy sensitivity. miR-653 was highly expressed in CRC tissues, promoted cell proliferation, and inhibited apoptosis by negatively regulating the expression of DLD.

Project description:BackgroundCuproptosis is a newly identified form of cell death that is dependent on copper (Cu) ions, termed Cu-dependent cytotoxicity. This process is distinct from other forms of cell death such as apoptosis, necrosis, and ferroptosis. The accumulation of copper is known to play a significant role in various biological processes, including angiogenesis (the formation of new blood vessels) and metastasis (the spread of cancer cells to different parts of the body). These processes are crucial for tumor growth and progression, indicating that copper and the cuproptosis-related genes (CPRGs) might be indispensable in the context of cancer development and progression. Given this background, we aimed to explore the relationship between CPRGs and both prognostic predictions and tumor microenvironment (TME) infiltration in bladder cancer (BLCA).MethodsFor this study, we utilized data from The Cancer Genome Atlas (TCGA) to identify CPRGs and subsequently divided BLCA patients into three distinct molecular clusters based on these genes. To assess the proportions of various immune cell types within the TME, we employed single-sample gene set enrichment analysis (ssGSEA) and the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) method. These computational techniques allowed us to quantify the infiltration of different immune cells, providing insights into the immune landscape of the tumors. Furthermore, we developed a risk score model using CPRGs to predict the survival prospects of BLCA patients.ResultsOur analysis identified three molecular clusters of BLCA patients, each exhibiting unique clinical features and patterns of TME infiltration. Among these clusters, cluster 1 was associated with a poor prognosis. Interestingly, this cluster also showed significant infiltration of activated CD4+ (ssGSEA P<0.001) and CD8+ T (ssGSEA P<0.05) cells, which are crucial components of the immune response against tumors. This finding suggests a complex interaction between the immune system and the tumor, where a high presence of T cells does not necessarily correlate with better outcomes. Additionally, our risk score model revealed that the high-risk group, characterized by a specific expression pattern of CPRGs, also had enhanced infiltration of CD4+ and CD8+ T cells. This indicates that the cuproptosis-based risk model has a robust ability to predict patient prognosis and can guide immunotherapy decisions.ConclusionsOur study sheds light on the biological functions of CPRGs within the TME of BLCA and their correlations with clinical parameters and patient prognosis. The identification of distinct molecular clusters with varying prognoses and immune cell infiltrations highlights the heterogeneity of BLCA and underscores the potential of CPRGs as biomarkers for prognosis and therapeutic targets. These findings offer new perspectives for the development of immunotherapeutic strategies in the treatment of BLCA patients, potentially leading to more personalized and effective cancer therapies.

Project description:Osteosarcoma (OS) is one of the most prevalent primary bone tumors at all ages of human development. The objective of our study was to develop a model of Cuproptosis-Related Genes (CRGs) for predicting prognosis in OS patients. All datasets of OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and Gene Expression Omnibus (GEO) database. We obtained the gene set (81 CRGs) related to cuproptosis by accessing the database and previous literature. All the CRGs were analyzed by univariate COX regression, least absolute shrinkage and selection operator (LASSO) COX regression analysis to screen for CRGs associated with prognosis in OS patients. Then these CRGs were used to construct a prognostic signature, which was further verified by independent cohort (GSE21257) and clinical correlation analysis. Afterward, to identify underlying mechanisms, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for the high-risk group by using the GSEA method. The association between the prognostic signature and 28 types of immune infiltrating cells in the tumor microenvironment was assessed. Ultimately, Lipoic Acid Synthetase (LIAS) (HR=0.632, P=0.004), Lipoyltransferase 1 (LIPT1) (HR=0.524, P=0.011), BCL2 Like 1 (BCL2L1/BCL-XL) (HR=0.593, P=0.022), and Pyruvate Dehydrogenase Kinase 1 (PDK1) (HR=0.662, P=0.025) were identified. Subsequently, they were used to calculate the risk score and build a prognostic model. In the training cohort, risk score (HR=1.878, P=0.003) could be considered as an independent prognostic factor, and OS patients with high-risk scores showed lower survival rates. Biological pathways related to substance metabolism and transport were enriched. There were significant differences in immune infiltrating cells in the tumor microenvironment. All in all, The CRGs signature is related to the tumor immune microenvironment and could be used as a credible predictor of the prognostic status in OS patients.

Dataset Information

Machine learning algorithms for a novel cuproptosis-related gene signature of diagnostic and immune infiltration in endometriosis.

Publications

Machine learning algorithms for a novel cuproptosis-related gene signature of diagnostic and immune infiltration in endometriosis.

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