Abstract Background: Polycystic ovary syndrome (PCOS) is a complex reproductive endocrine disorder，with an increased risk of type 2 diabetes mellitus and cardiovascular disease, of which metabolic syndrome (MS) is an indispensable springboard to communicate PCOS and various comlications. Our aim was to study the potential metabolic characteristics of PCOS-MS, and identify sensitive biomarkers so as to provide targets for clinical screening, diagnosis and treatment. Methods: 44 PCOS patients with MS, 34 PCOS patients without MS and 32 healthy controls were studied. Plasma samples of subjects were tested by ultra performance liquid chromatography (UPLC) system combined with LTQ-orbi-trap mass spectrometry. The changes of metabolic characteristics from PCOS to PCOS-MS were systematically analyzed. Correlations between differential metabolites and clinical characteristics of PCOS-MS were assessed. Differential metabolites with high correlation were further evaluated by the receiver operating characteristic (ROC) curve to identify their sensitivity as screening indicators. Results: There were significant difference in general characteristics, reproductive hormone and metabolic parameters in PCOS-MS group when compared with PCOS group and healthy controls. We found 30 differential metabolites which were involved in 23 pathways when compared with PCOS group. The metabolic network further reflects the metabolic environment, including the interaction between metabolic pathways, modules, enzymes, reactions and metabolites.In the correlation analysis, 17 pairs of correlation coefficient between differential metabolites and clincal parameters were greater than 0.4, involving 11 metabolites that has the potential to be a marker for clinical diagnosis. They were assessed by ROC whose area under curve (AUC) were all greater than 0.7, with a good sensitivity. Furthermore, combinational metabolic biomarkers, such as glutamic acid+leucine+phenylalanine and carnitine C 4: 0+carnitine C18:1+carnitine C5:0 are expected to be sensitive combinational biomarkers in clinical practice. Conclusion: Our study provides a new insight to understand the pathogenesis mechanism, and the discriminating metabolites may help screen high-risk of MS in patients with PCOS and provide sensitive biomarkers for clinical diagnosis.
Abstract Background Endometriosis (EMs) is an enigmatic disease of yet-unknown pathogenesis. Disulfidptosis, a novel identified form of programmed cell death resulting from disulfide stress, stands a chance of treating diverse ailments. However, the potential roles of disulfidptosis-related genes (DRGs) in EMs remain elusive. This study aims to thoroughly explore the key disulfidptosis genes involved in EMs, and probe novel diagnostic markers and candidate therapeutic compounds from the aspect of disulfidptosis based on bioinformatics analysis, machine learning, and animal experiments. Results Enrichment analysis on key module genes and differentially expressed genes (DEGs) of eutopic and ectopic endometrial tissues in EMs suggested that EMs was closely related to disulfidptosis. And then, we obtained 20 and 16 disulfidptosis-related DEGs in eutopic and ectopic endometrial tissue, respectively. The protein-protein interaction (PPI) network revealed complex interactions between genes, and screened nine and ten hub genes in eutopic and ectopic endometrial tissue, respectively. Furthermore, immune infiltration analysis uncovered distinct differences in the immunocyte, human leukocyte antigen (HLA) gene set, and immune checkpoints in the eutopic and ectopic endometrial tissues when compared with health control. Besides, the hub genes mentioned above showed a close correlation with the immune microenvironment of EMs. Furthermore, four machine learning algorithms were applied to screen signature genes in eutopic and ectopic endometrial tissue, including the binary logistic regression (BLR), the least absolute shrinkage and selection operator (LASSO), the support vector machine-recursive feature elimination (SVM-RFE), and the extreme gradient boosting (XGBoost). Model training and hyperparameter tuning were implemented on 80% of the data using a ten-fold cross-validation method, and tested in the testing sets which determined the excellent diagnostic performance of these models by six indicators (Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, Accuracy, and Area Under Curve). And seven eutopic signature genes (ACTB, GYS1, IQGAP1, MYH10, NUBPL, SLC7A11, TLN1) and five ectopic signature genes (CAPZB, CD2AP, MYH10, OXSM, PDLIM1) were finally identified based on machine learning. The independent validation dataset also showed high accuracy of the signature genes (IQGAP1, SLC7A11, CD2AP, MYH10, PDLIM1) in predicting EMs. Moreover, we screened 12 specific compounds for EMs based on ectopic signature genes and the pharmacological impact of tretinoin on signature genes was further verified in the ectopic lesion in the EMs murine model. Conclusion This study verified a close association between disulfidptosis and EMs based on bioinformatics analysis, machine learning, and animal experiments. Further investigation on the biological mechanism of disulfidptosis in EMs is anticipated to yield novel advancements for searching for potential diagnostic biomarkers and revolutionary therapeutic approaches in EMs.
Background Polycystic ovary syndrome (PCOS) is a complex reproductive endocrine disorder. And metabolic syndrome (MS) is an important bridge for PCOS patients to develop other diseases, such as diabetes and coronary heart disease. Our aim was to study the potential metabolic characteristics of PCOS-MS and identify sensitive biomarkers so as to provide targets for clinical screening, diagnosis, and treatment. Methods In this study, 44 PCOS patients with MS, 34 PCOS patients without MS, and 32 healthy controls were studied. Plasma samples of subjects were tested by ultraperformance liquid chromatography (UPLC) system combined with LTQ-orbi-trap mass spectrometry. The changes of metabolic characteristics from PCOS to PCOS-MS were systematically analyzed. Correlations between differential metabolites and clinical characteristics of PCOS-MS were assessed. Differential metabolites with high correlation were further evaluated by the receiver operating characteristic (ROC) curve to identify their sensitivity as screening indicators. Results There were significant differences in general characteristics, reproductive hormone, and metabolic parameters in the PCOS-MS group when compared with the PCOS group and healthy controls. We found 40 differential metabolites which were involved in 23 pathways when compared with the PCOS group. The metabolic network further reflected the metabolic environment, including the interaction between metabolic pathways, modules, enzymes, reactions, and metabolites. In the correlation analysis, there were 11 differential metabolites whose correlation coefficient with clinical parameters was greater than 0.4, which were expected to be taken as biomarkers for clinical diagnosis. Besides, these 11 differential metabolites were assessed by ROC, and the areas under curve (AUCs) were all greater than 0.7, with a good sensitivity. Furthermore, combinational metabolic biomarkers, such as glutamic acid + leucine + phenylalanine and carnitine C 4: 0 + carnitine C18:1 + carnitine C5:0 were expected to be sensitive combinational biomarkers in clinical practice. Conclusion Our study provides a new insight to understand the pathogenesis mechanism, and the discriminating metabolites may help screen high-risk of MS in patients with PCOS and provide sensitive biomarkers for clinical diagnosis.
Impaired decidualization was recognized as one of the crucial pathomechanisms accounting for unexplained recurrent spontaneous abortion (URSA). Currently, the exact molecular mechanism and targeted clinical decision are still in exploration. Bushen Huoxue decoction (BSHXD) has previously been proved effective in treating URSA, but its mechanism remains to be elucidated. This study aimed to explore the regulation mechanism of BSHXD in decidualization from its intervention in autophagy so as to rationalize its potential as a novel therapeutic regime for URSA. Decidua tissues were collected from patients with URSA and healthy pregnant women who underwent legal terminations for non-medical reasons at the first trimester. Besides, cell line T-hESCs was utilized to establish induced decidualization model, and were randomly divided into ESC group, DSC group, 3-MA group, AMPK siRNA group, scrambled siRNA group and AMPK siRNA + BSHXD group. Transmission electron microscopy, Monodansylcadaverine (MDC) assay, qRT-PCR, immunohistochemistry, immunofluorescence and Western blotting were used to evaluate the level of decidualization, autophagy and activation of AMPK signaling pathway in decidua tissues and cell experiments. Experiments on decidua tissues showed that decidualization was impaired in URSA with inhibited autophagy. Besides, pAMPK T172 and pULK1 S556 were decreased, and pmTOR S2448 and pULK1 S757 were increased. Cell experiments showed that the level of autophagy increased during induced decidualization, but when autophagy was inhibited, decidualization was impaired. In addition, AMPK/mTOR/ULK1 affected decidualization by mediating autophagy, and BSHXD improved decidualization through this mechanism. In conclusion, this study clarified that the inhibition of autophagy mediated by AMPK/mTOR/ULK1 was associated with impaired decidualization, and the intervention of BSHXD on this pathological process may be a vital mechanism for its treatment of URSA. This study laid the foundation for further research and application of BSHXD.
Lupus nephritis (LN) remains a predominant cause of morbidity and mortality in SLE. Here we performed a meta-analysis to evaluate the efficacy and safety of the induction treatment with mycophenolate mofetil (MMF) and cyclophosphamide (CYC) for LN.Relevant literature was searched by computer from the establishment of the database to November 2019. A meta-analysis was conducted to analysis the efficacy and safety between mycophenolate mofetil and cyclophosphamide as induction therapy in LN patients. The primary end-point was response to urine protein, serum creatinine (Scr) and serum complement C3, and the secondary end-points were complete remission and adverse reactions.Eighteen articles were selected for the final meta-analysis, involving 1989 patients with LN, of which the renal biopsy result could be classified into class III-V according to the standards of WHO/ISN. The results revealed that MMF was superior to CYC in increasing the level of serum complement C3 [SMD = 0.475, 95%CI (0.230-0.719)] and complete remission [RR = 1.231, 95%CI (1.055-1.437)]. Furthermore, the subgroup analysis showed that it was in Asian patients, rather than in Caucasian patients, that CYC exerted a better effect on lowering the level of urine protein (UPRO) than MMF [SMD = 0.405, 95%CI (0.081-0.730)]. Besides, when the initial UPRO level was less than 4 g/day, the effect of CYC was better than MMF [SMD = 0.303, 95%CI (0.014-0.591)]. There was no significant difference between MMF and CYC in improving Scr [SMD = 0.090, 95%CI (-0.060-0.239)]. When it came to the comparison of safety between MMF and CYC, the meta-analysis showed that MMF was superior to CYC in decreasing infection in Caucasian patients [RR = 0.727, 95%CI (0.532-0.993)], reducing the risk of leukopenia and menstrual abnormalities in Asian patients and lowering the frequency of gastrointestinal symptoms [RR = 0.639, 95%CI (0.564-0.724)], independent of race.MMF precedes CYC in improving serum complement C3 and complete remission regardless of race, as well as shows fewer adverse drug reactions in the induction treatment of LN belonging to type III-V. But for Asian patients or those initial UPRO levels are less than 4 g/day, CYC may be superior to MMF.
Unexplained recurrent spontaneous abortion (URSA) is a severe challenge to reproductive females worldwide, and its etiology and pathogenesis have not yet been fully clarified. Abnormal intercellular communication between macrophages (Mφ) and decidual stromal cells (DSCs) or trophoblasts has been supposed to be the key to URSA. However, the exact molecular mechanisms in the crosstalk are not yet well understood. This study aimed to explore the potential molecule mechanism that may be involved in the communication between Mφ and DSC or trophoblast cells and determine their diagnostic characteristics by using the integrated research strategy of bioinformatics analysis, machine learning and experiments. First, microarrays of decidual tissue (GSE26787, GSE165004) and placenta tissue (GSE22490) in patients with URSA, as well as microarrays involving induced decidualization (GSE94644) and macrophage polarization in vitro (GSE30595) were derived from the gene expression omnibus (GEO) database. And 721 decidua-differentially expressed genes (DEGs), 613 placenta-DEGs, 510 Mφ polarization DEGs were obtained in URSA by differential expression analysis. Then, the protein-protein interaction (PPI) network was constructed, and the hub genes were identified by CytoHubba in Cytoscape software and validated by real-time PCR assay. Subsequently, immune enrichment analysis on decidua-DEGs and placenta-DEGs by ClueGO verified their regulation effects on Mφ. Besides, functional enrichment analysis was performed on Mφ polarization DEGs and the essential module genes derived from the weighted gene co-expression network analysis (WGCNA) to uncover the biological function that were related to abnormal polarization of Mφ. Furthermore, we screened out 29, 43 and 22 secreted protein-encoding genes from DSC-DEGs, placenta-DEGs and Mφ polarization DEGs, respectively. Besides, the hub secreted-protein-encoding genes were screened by CytoHubba. Moreover, we conducted functional enrichment analysis on these genes. And spearman correlation analysis between hub secreted-protein-encoding genes from donor cells and hub genes in recipient cells was performed to further understand the molecular mechanism of intercellular communication further. Moreover, signature genes with diagnostic value were screened from secreted protein-encoding genes by machine learning and validated by immunofluorescence co-localization analysis with clinical samples. Finally, three biomarkers of DSCs (FGF9, IL1R2, NID2) and three biomarkers of Mφ (CFB, NID2, CXCL11) were obtained. In conclusion, this project provides new ideas for understanding the mechanism regulatory network of intercellular communication involving macrophages at the maternal-fetal interface of URSA. Also, it provides innovative insights for the diagnosis and treatment of URSA.
Prenatal tobacco exposure (PTE) correlates significantly with a surge in adverse pregnancy outcomes, yet its pathological mechanisms remain partially unexplored. This study aims to meticulously examine the repercussions of PTE on placental immune landscapes, employing a coordinated research methodology encompassing bioinformatics, machine learning and animal studies. Concurrently, it aims to screen biomarkers and potential compounds that could sensitively indicate and mitigate placental immune disorders. In the course of this research, two gene expression omnibus (GEO) microarrays, namely GSE27272 and GSE7434, were included. Gene set enrichment analysis (GSEA) and immune enrichment investigations on differentially expressed genes (DEGs) indicated that PTE might perturb numerous innate or adaptive immune-related biological processes. A cohort of 52 immune-associated DEGs was acquired by cross-referencing the DEGs with gene sets derived from the ImmPort database. A protein-protein interaction (PPI) network was subsequently established, from which 10 hub genes were extracted using the maximal clique centrality (MCC) algorithm (JUN, NPY, SST, FLT4, FGF13, HBEGF, NR0B2, AREG, NR1I2, SEMA5B). Moreover, we substantiated the elevated affinity of tobacco reproductive toxicants, specifically nicotine and nitrosamine, with hub genes through molecular docking (JUN, FGF13 and NR1I2). This suggested that these genes could potentially serve as crucial loci for tobacco's influence on the placental immune microenvironment. To further elucidate the immune microenvironment landscape, consistent clustering analysis was conducted, yielding three subtypes, where the abundance of follicular helper T cells (p < 0.05) in subtype A, M2 macrophages (p < 0.01), neutrophils (p < 0.05) in subtype B and CD8+ T cells (p < 0.05), resting NK cells (p < 0.05), M2 macrophages (p < 0.05) in subtype C were significantly different from the control group. Additionally, three pivotal modules, designated as red, blue and green, were identified, each bearing a close association with differentially infiltrated immunocytes, as discerned by the weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis was subsequently conducted on these modules. To further probe into the mechanisms by which immune-associated DEGs are implicated in intercellular communication, 20 genes serving as ligands or receptors and connected to differentially infiltrating immunocytes were isolated. Employing a variety of machine learning techniques, including one-way logistic regression, LASSO regression, random forest and artificial neural networks, we screened 11 signature genes from the intersection of immune-associated DEGs and secretory protein-encoding genes derived from the Human Protein Atlas. Notably, CCL18 and IFNA4 emerged as prospective peripheral blood markers capable of identifying PTE-induced immune disorders. These markers demonstrated impressive predictive power, as indicated by the area under the curve (AUC) of 0.713 (0.548-0.857) and 0.780 (0.618-0.914), respectively. Furthermore, we predicted 34 potential compounds, including cyclosporine, oestrogen and so on, which may engage with hub genes and attenuate immune disorders instigated by PTE. The diagnostic performance of these biomarkers, alongside the interventional effect of cyclosporine, was further corroborated in animal studies via ELISA, Western blot and immunofluorescence assays. In summary, this study identifies a disturbance in the placental immune landscape, a secondary effect of PTE, which may underlie multiple pregnancy complications. Importantly, our research contributes to the noninvasive and timely detection of PTE-induced placental immune disorders, while also offering innovative therapeutic strategies for their treatment.
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