PGD: Shared gene linking polycystic ovary syndrome and endometrial cancer, influencing proliferation and migration through glycometabolism.

The relationship among polycystic ovary syndrome (PCOS), endometrial cancer (EC), and glycometabolism remains unclear. We explored shared genes between PCOS and EC, using bioinformatics to unveil their pathogenic connection and influence on EC prognosis. Gene Expression Omnibus datasets GSE226146 (PCOS) and GSE196033 (EC) were used. A protein-protein interaction (PPI) network was constructed to identify the central genes. Candidate markers were screened using dataset GSE54250. Differences in marker expression were confirmed in mouse PCOS and human EC tissues using RT-PCR and immunohistochemistry. The effect of PGD on EC proliferation and migration was explored using Ki-67 and Transwell assays. PGD's impact on the glycometabolic pathway within carbon metabolism was assessed by quantifying glucose content and lactic acid production. R software identified 31 common genes in GSE226146 and GSE196033. Gene Ontology functional classification revealed enrichment in the "purine nucleoside triphosphate metabolism process," with key Kyoto Encyclopedia of Genes and Genomes pathways related to "carbon metabolism." The PPI network identified 15 hub genes. HK2, NDUFS8, PHGDH, PGD, and SMAD3 were confirmed as candidate markers. The RT-PCR analysis validated distinct HK2 and PGD expression patterns in mouse PCOS ovarian tissue and human EC tissue, as well as in normal and EC cells. Transfection experiments with Ishikawa cells further confirmed PGD's influence on cell proliferation and migration. Suppression of PGD expression impeded glycometabolism within the carbon metabolism of EC cells, suggesting PGD as a significant PCOS risk factor impacting EC proliferation and migration through modulation of single carbon metabolism. These findings highlight PGD's pivotal role in EC onset and prognosis.

A novel immune-related risk-scoring system associated with the prognosis and response of cervical cancer patients treated with radiation therapy.

Objective: The tumor microenvironment plays a critical role in the radiotherapy and immunotherapy response of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Radioresistance is a key factor in treatment failure among patients who receive radical radiotherapy. Thus, new immune-related biomarkers associated with radiotherapy response in CESC are needed. Methods: In this study, the CIBERSORT and ESTIMATE methods were applied to determine the percentage of tumor-infiltrating cells and the number of immune components in 103 CESCs treated with radiotherapy from The Cancer Genome Atlas (TCGA) database. The main dysregulated genes were subjected to multivariate and univariate analyses. The prognostic value of this system was studied via receiver operating characteristic curve and survival analysis. For further confirmation, the biomarkers' expression levels and predictive value were validated by immunohistochemistry (IHC) and qRT-PCR. The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in cervical cancer patients treated with radiation therapy. Results: Data for 17 radioresistant and 86 radiosensitive tumors were obtained from the The Cancer Genome Atlas database. 53 immune-related DEGs were identified. GO and KEGG analyses revealed that the DEGs were enriched in protein kinase B signaling, growth factors in cytokines, the MAPK pathway and the PI3K-Akt pathway. Then, 14 key immune-related genes built a risk scoring model were deemed prognostic in CESC with radiotherapy. The area under the curve (AUC) of the model was 0.723, and the high-risk group presented worse outcomes than the low-risk group. In addition, the high-risk group tended to have persistent tumors (p = 0.001). The high expression of WT1 and SPOUYT4 were associated with relapse, the high expression of Angiotensinogen and MIEN1 were associated with nonrelapse. Analysis of the immune microenvironment indicated that M0 macrophages, M2 macrophages, activated mast cells and resting memory CD4+ T cells were positively correlated with the risk score (p < 0.05). Conclusion: The novel immune-related risk scoring system has some advantages in predicting the prognosis and treatment response of cervical cancer patients treated with radiotherapy. Moreover, it might provide novel clues for providing targeted immune therapy to these patients.

The Role of Gluten in Food Products and Dietary Restriction: Exploring the Potential for Restoring Immune Tolerance.

Wheat is extensively utilized in various processed foods due to unique proteins forming from the gluten network. The gluten network in food undergoes morphological and molecular structural changes during food processing, affecting the final quality and digestibility of the food. The present review introduces the formation of the gluten network and the role of gluten in the key steps of the production of several typical food products such as bread, pasta, and beer. Also, it summarizes the factors that affect the digestibility of gluten, considering that different processing conditions probably affect its structure and properties, contributing to an in-depth understanding of the digestion of gluten by the human body under various circumstances. Nevertheless, consumption of gluten protein may lead to the development of celiac disease (CD). The best way is theoretically proposed to prevent and treat CD by the inducement of oral tolerance, an immune non-response system formed by the interaction of oral food antigens with the intestinal immune system. This review proposes the restoration of oral tolerance in CD patients through adjunctive dietary therapy via gluten-encapsulated/modified dietary polyphenols. It will reduce the dietary restriction of gluten and help patients achieve a comprehensive dietary intake by better understanding the interactions between gluten and food-derived active products like polyphenols.

Association of the LEP gene with immune infiltration as a diagnostic biomarker in preeclampsia.

Objective: Preeclampsia (PE) is a serious condition in pregnant women and hence an important topic in obstetrics. The current research aimed to recognize the potential and significant immune-related diagnostic biomarkers for PE. Methods: From the Gene Expression Omnibus (GEO) data sets, three public gene expression profiles (GSE24129, GSE54618, and GSE60438) from the placental samples of PE and normotensive pregnancy were downloaded. Differentially expressed genes (DEGs) were selected and determined among 73 PE and 85 normotensive control pregnancy samples. The DEGs were used for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA). The candidate biomarkers were identified by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analysis. The receiver operating characteristic curve (ROC) was applied to evaluate diagnostic ability. For further confirmation, the expression levels and diagnostic value of biomarkers in PE were verified in the GSE75010 data set (80 PE and 77 controls) and validated by qRT-RCR, Western blot, and immunohistochemistry (IHC). The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in PE. Results: In total, 15 DEGs were recognized. The GO and KEGG analyses revealed that the DEGs were enriched in the steroid metabolic process, receptor ligand activity, GnRH secretion, and neuroactive ligand-receptor interaction. The recognized DEGs were primarily implicated in cell-type benign neoplasm, kidney failure, infertility, and PE. Gene sets related to hormone activity, glycosylation, multicellular organism process, and response to BMP were activated in PE. The LEP gene was distinguished as a diagnostic biomarker of PE (AUC = 0.712) and further certified in the GSE75010 data set (AUC = 0.850). The high expression of LEP was associated with PE in clinical samples. In addition, the analysis of the immune microenvironment showed that gamma delta T cells, memory B cells, M0 macrophages, and regulatory T cells were positively correlated with LEP expression (P < 0.05). Conclusion: LEP expression can be considered to be a diagnostic biomarker of PE and can offer a novel perspective for future studies regarding the occurrence and molecular mechanisms of PE.

DPP6 and MFAP5 are associated with immune infiltration as diagnostic biomarkers in distinguishing uterine leiomyosarcoma from leiomyoma.

Objective: Uterine leiomyosarcoma (ULMS) is the most common subtype of uterine sarcoma and is difficult to discern from uterine leiomyoma (ULM) preoperatively. The aim of the study was to determine the potential and significance of immune-related diagnostic biomarkers in distinguishing ULMS from ULM. Methods: Two public gene expression profiles (GSE36610 and GSE64763) from the GEO datasets containing ULMS and ULM samples were downloaded. Differentially expressed genes (DEGs) were selected and determined among 37 ULMS and 25 ULM control samples. The DEGs were used for Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Disease Ontology (DO) enrichment analyses as well as gene set enrichment analysis (GSEA). The candidate biomarkers were identified by least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analyses. The receiver operating characteristic curve (ROC) was applied to evaluate diagnostic ability. For further confirmation, the biomarker expression levels and diagnostic value in ULMS were verified in the GSE9511 and GSE68295 datasets (12 ULMS and 10 ULM), and validated by immunohistochemistry (IHC). The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in ULMS. Result: In total, 55 DEGs were recognized via GO analysis, and KEGG analyses revealed that the DEGs were enriched in nuclear division, and cell cycle. The recognized DEGs were primarily implicated in non-small cell lung carcinoma and breast carcinoma. Gene sets related to the cell cycle and DNA replication were activated in ULMS. DPP6 and MFAP5 were distinguished as diagnostic biomarkers of ULMS (AUC = 0.957, AUC = 0.899, respectively), and they were verified in the GSE9511 and GSE68295 datasets (AUC = 0.983, AUC = 0.942, respectively). The low expression of DPP6 and MFAP5 were associated with ULMS. In addition, the analysis of the immune microenvironment indicated that resting mast cells were positively correlated with DPP6 and MFAP5 expression and that eosinophils and M0 macrophages were negatively correlated with DPP6 expression (P<0.05). Conclusion: These findings indicated that DPP6 and MFAP5 are diagnostic biomarkers of ULMS, thereby offering a novel perspective for future studies on the occurrence, function and molecular mechanisms of ULMS.