The role of the gut microbiome has been a hot topic in recent years. One aim of this review is to shed light on the crosstalk between sex hormones and the gut microbiome. Researchers have observed a sex bias of the composition of the gut microbiome in mice and have proved that sex differences influence the composition of the gut microbiome, although the influence is usually obscured by genetic variations. Via cell studies, animal studies and some observational studies in humans, researchers have confirmed that the gut microbiome can be shaped by the hormonal environment. On other hand, some theories suggest that the gut microbiota regulates the levels of sex hormones via interactions among its metabolites, the immune system, chronic inflammation and some nerve-endocrine axes, such as the gut-brain axis. In addition, bidirectional interactions between the microbiome and the hormonal system have also been observed, and the mechanisms of these interactions are being explored. We further describe the role of the gut microbiome in sex hormone-related diseases, such as ovarian cancer, postmenopausal osteoporosis (PMOP), polycystic ovary syndrome and type 1 diabetes. Among these diseases, PMOP is described in detail. Finally, we discuss the treatments of these diseases and the application prospects of microbial intervention.

In solid tumors, regulatory T cell (Treg) and mast cell perform different roles depending on the microenvironment. Nevertheless, mast cell and Treg-mediated interactions in gastric cancer (GC) are unclear, as are their regulation, function, and clinical significance. The present study demonstrated the mechanism of tumor-infiltrating mast cells stimulating ICOS⁺ regulatory T cells via the IL-33/IL-2 axis to promote the growth of gastric cancer. Analyses of 98 patients with GC were conducted to examine mast cell counts, ICOS⁺ Tregs, and the levels of IL-33 or IL-2. Isolated ICOS⁺ Treg and CD8⁺ T cell were stimulated, cultured and tested for their functional abilities in vitro and in vivo. GC patients exhibited a significantly more production of IL-33 in tumors. Mast cell stimulated by tumor-derived IL-33 exhibited a prolonged lifespan through IL-33 mediated inhibition of apoptosis. Moreover, mast cells stimulated by tumor-derived IL-33 secreted IL-2, which induced Treg expansion. These inducible Tregs displayed an activated immunosuppressive phenotype with positive expression for the inducible T cell co-stimulator (ICOS). In vitro, IL-2 from IL to 33-stimulated mast cells induced increased numbers of ICOS⁺ Tregs with increased immunosuppressive activity against proliferation and effector function of CD8⁺ T cell. In vivo, ICOS⁺ Tregs were treated with anti-IL-2 neutralizing antibody followed by co-injection with CD8⁺ T cells in GC mouse model, which showed an increased CD8⁺ T cell infiltration and effector molecules production, meanwhile tumor growth and progression were inhibited. Besides, reduction in GC patient survival was associated with tumor-derived ICOS⁺ Tregs. Our results highlight a crosstalk between GC-infiltrating mast cells and ICOS⁺ Tregs and provide a novel mechanism describing ICOS⁺ Treg expansion and induction by an IL-33/mast cell/IL-2 signaling axis in GC, and also provide functional evidence that the modulation of this immunosuppressive pathway can attenuate GC-mediated immune tolerance.

Numerous types of molecular mechanisms mediate the development of cancer. Non‑coding RNAs (ncRNAs) are being increasingly recognized to play important role in mediating the development of diseases, including cancer. Long non‑coding RNAs (lncRNAs) and microRNAs (miRNAs) are the two most widely studied ncRNAs. Thus far, lncRNAs are known to have biological roles through a variety of mechanisms, including genetic imprinting, chromatin remodeling, cell cycle control, splicing regulation, mRNA decay and translational regulation, and miRNAs regulate gene expression through the degradation of mRNAs and lncRNAs. Although ncRNAs account for a major proportion of the total RNA, the mechanisms underlying the physiological or pathological processes mediated by various types of ncRNAs, and the specific interaction mechanisms between miRNAs and lncRNAs in various physiological and pathological processes, remain largely unknown. Thus, further research in this field is required. In general, the interaction mechanisms between miRNAs and lncRNAs in human cancer have become important research topics, and the study thereof has led to the recent development of related technologies. By providing examples and descriptions, and performing chart analysis, the present study aimed to review the interaction mechanisms and research approaches for these two types of ncRNAs, as well as their roles in the occurrence and development of cancer. These details have far‑reaching significance for the utilization of these molecules in the diagnosis and treatment of cancer.

Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim of this study was to develop an interpretable machine learning model for early prediction of 28-day mortality in patients with SALI. Data from the Medical Information Mart for Intensive Care (MIMIC-IV, v2.2, MIMIC-III, v1.4) were used in this study. The study cohort from MIMIC-IV was randomized to the training set (0.7) and the internal validation set (0.3), with MIMIC-III (2001 to 2008) as external validation. The features with more than 20% missing values were deleted and the remaining features were multiple interpolated. Lasso-CV that lasso linear model with iterative fitting along a regularization path in which the best model is selected by cross-validation was used to select important features for model development. Eight machine learning models including Random Forest (RF), Logistic Regression, Decision Tree, Extreme Gradient Boost (XGBoost), K Nearest Neighbor, Support Vector Machine, Generalized Linear Models in which the best model is selected by cross-validation (CV_glmnet), and Linear Discriminant Analysis (LDA) were developed. Shapley additive interpretation (SHAP) was used to improve the interpretability of the optimal model. At last, a total of 1043 patients were included, of whom 710 were from MIMIC-IV and 333 from MIMIC-III. Twenty-four clinically relevant parameters were selected for model construction. For the prediction of 28-day mortality of SALI in the internal validation set, the area under the curve (AUC (95% CI)) of RF was 0.79 (95% CI: 0.73–0.86), and which performed the best. Compared with the traditional disease severity scores including Oxford Acute Severity of Illness Score (OASIS), Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score II (SAPS II), Logistic Organ Dysfunction Score (LODS), Systemic Inflammatory Response Syndrome (SIRS), and Acute Physiology Score III (APS III), RF also had the best performance. SHAP analysis found that Urine output, Charlson Comorbidity Index (CCI), minimal Glasgow Coma Scale (GCS_min), blood urea nitrogen (BUN) and admission_age were the five most important features affecting RF model. Therefore, RF has good predictive ability for 28-day mortality prediction in SALI. Urine output, CCI, GCS_min, BUN and age at admission(admission_age) within 24 h after intensive care unit(ICU) admission contribute significantly to model prediction.

Pentagalloyl glucose (PGG) is a natural hydrolyzable gallotannin abundant in various plants and herbs. It has a broad range of biological activities, specifically anticancer activities, and numerous molecular targets. Despite multiple studies available on the pharmacological action of PGG, the molecular mechanisms underlying the anticancer effects of PGG are unclear. Here, we have critically reviewed the natural sources of PGG, its anticancer properties, and underlying mechanisms of action. We found that multiple natural sources of PGG are available, and the existing production technology is sufficient to produce large quantities of the required product. Three plants (or their parts) with maximum PGG content were Rhus chinensis Mill, Bouea macrophylla seed, and Mangifera indica kernel. PGG acts on multiple molecular targets and signaling pathways associated with the hallmarks of cancer to inhibit growth, angiogenesis, and metastasis of several cancers. Moreover, PGG can enhance the efficacy of chemotherapy and radiotherapy by modulating various cancer-associated pathways. Therefore, PGG can be used for treating different human cancers; nevertheless, the data on the pharmacokinetics and safety profile of PGG are limited, and further studies are essential to define the clinical use of PGG in cancer therapies.