Endometrial cancer(EC)is a malignant tumour that occurs in the epithelial cells of the endometrium and represents one of the most common malignancies involving the female reproductive system,with endometrioid adenocar...Endometrial cancer(EC)is a malignant tumour that occurs in the epithelial cells of the endometrium and represents one of the most common malignancies involving the female reproductive system,with endometrioid adenocarcinoma as the most common type.In recent years,with an increasingly aging society and the growing number of obese people,the incidence of EC is constantly rising,posing a serious threat to women’s health.Some studies have reported that the interruption of digestion and absorption caused by imbalance in intestinal microbiota may lead to conditions such as obesity,hypertension,diabetes,and hormone imbalance,which are all risk factors for EC.Meanwhile,intestinal bacteria produce a series of metabolites during colonization and reproduction,which can rapidly respond to changes in the microenvironment of the body.Changes in their types and quantities can serve as sensitive indicators of physiological and pathological changes in the body.Patients with EC often suffer from metabolic diseases,which can lead to metabolic disorders involving carbohydrates,fats,and amino acid in their bodies.展开更多
Objective: The mortality and morbidity rates associated with pancreatic cancer (PaCa) are extremely high. Various studies have demonstrated that pancreatic cancer will be the fourth cancer-related death by 2030, raisi...Objective: The mortality and morbidity rates associated with pancreatic cancer (PaCa) are extremely high. Various studies have demonstrated that pancreatic cancer will be the fourth cancer-related death by 2030, raising more concern for scholars to find effective methods to prevent and treat in order to improve the pancreatic cancer outcome. Using bioinformatic analysis, this study aims to pinpoint key genes that could impact PaCa patients’ prognosis and could be used as therapeutic targets. Methods: The TCGA and GEO datasets were integratively analyzed to identify prognosis-related differentially expressed genes. Next, the STRING database was used to develop PPI networks, and the MCODE and CytoNCA Cytoscape in Cytoscape were used to screen for critical genes. Through CytoNCA, three kinds of topology analysis were considered (degree, betweenness, and eigenvector). Essential genes were confirmed as potential target treatment through Go function and pathways enrichment analysis, a developed predictive risk model based on multivariate analysis, and the establishment of nomograms using the clinical information. Results: Overall, the GSE183795 and TCGA datasets associated 1311 and 2244 genes with pancreatic cancer prognosis, respectively. We identified 132 genes that were present in both datasets. The PPI network analysis using, the centrality analysis approach with the CytoNCA plug-in, showed that CDK2, PLK1, CCNB1, and TOP2A ranked in the top 5% across all three metrics. The independent analysis of a risk model revealed that the four key genes had a Hazard Ratio (HR) > 1. The monogram showed the predictive risk model and individual patient survival predictions were accurate. The results indicate that the effect of the selected vital genes was significant and that they could be used as biomarkers to predict a patient’s outcome and as possible target therapy in patients with pancreatic cancer. GO function and pathway analysis demonstrated that crucial genes might affect the P53 signaling pathway and FoxO signaling pathway, through which Meiotic nuclear division and cell cycle may have a significant function in essential genes affecting the outcome of patients who have pancreatic cancer. Conclusions: This study suggests that CDK2, CCNB1, PLK1 and TOP2A are four key genes that have a significant influence on PaCa migration and proliferation. CDK2, CCNB1, PLK1, and TOP2A can be used as potential PaCa prognostic biomarkers and therapeutic targets. However, experimental validation is necessary to confirm these predictions. Our study comes into contributions to the development of personalized target therapy for pancreatic cancer patients.展开更多
基金funded by the Fundamental Research Program of Shanxi Province(Grant number 202103021224394).
文摘Endometrial cancer(EC)is a malignant tumour that occurs in the epithelial cells of the endometrium and represents one of the most common malignancies involving the female reproductive system,with endometrioid adenocarcinoma as the most common type.In recent years,with an increasingly aging society and the growing number of obese people,the incidence of EC is constantly rising,posing a serious threat to women’s health.Some studies have reported that the interruption of digestion and absorption caused by imbalance in intestinal microbiota may lead to conditions such as obesity,hypertension,diabetes,and hormone imbalance,which are all risk factors for EC.Meanwhile,intestinal bacteria produce a series of metabolites during colonization and reproduction,which can rapidly respond to changes in the microenvironment of the body.Changes in their types and quantities can serve as sensitive indicators of physiological and pathological changes in the body.Patients with EC often suffer from metabolic diseases,which can lead to metabolic disorders involving carbohydrates,fats,and amino acid in their bodies.
文摘Objective: The mortality and morbidity rates associated with pancreatic cancer (PaCa) are extremely high. Various studies have demonstrated that pancreatic cancer will be the fourth cancer-related death by 2030, raising more concern for scholars to find effective methods to prevent and treat in order to improve the pancreatic cancer outcome. Using bioinformatic analysis, this study aims to pinpoint key genes that could impact PaCa patients’ prognosis and could be used as therapeutic targets. Methods: The TCGA and GEO datasets were integratively analyzed to identify prognosis-related differentially expressed genes. Next, the STRING database was used to develop PPI networks, and the MCODE and CytoNCA Cytoscape in Cytoscape were used to screen for critical genes. Through CytoNCA, three kinds of topology analysis were considered (degree, betweenness, and eigenvector). Essential genes were confirmed as potential target treatment through Go function and pathways enrichment analysis, a developed predictive risk model based on multivariate analysis, and the establishment of nomograms using the clinical information. Results: Overall, the GSE183795 and TCGA datasets associated 1311 and 2244 genes with pancreatic cancer prognosis, respectively. We identified 132 genes that were present in both datasets. The PPI network analysis using, the centrality analysis approach with the CytoNCA plug-in, showed that CDK2, PLK1, CCNB1, and TOP2A ranked in the top 5% across all three metrics. The independent analysis of a risk model revealed that the four key genes had a Hazard Ratio (HR) > 1. The monogram showed the predictive risk model and individual patient survival predictions were accurate. The results indicate that the effect of the selected vital genes was significant and that they could be used as biomarkers to predict a patient’s outcome and as possible target therapy in patients with pancreatic cancer. GO function and pathway analysis demonstrated that crucial genes might affect the P53 signaling pathway and FoxO signaling pathway, through which Meiotic nuclear division and cell cycle may have a significant function in essential genes affecting the outcome of patients who have pancreatic cancer. Conclusions: This study suggests that CDK2, CCNB1, PLK1 and TOP2A are four key genes that have a significant influence on PaCa migration and proliferation. CDK2, CCNB1, PLK1, and TOP2A can be used as potential PaCa prognostic biomarkers and therapeutic targets. However, experimental validation is necessary to confirm these predictions. Our study comes into contributions to the development of personalized target therapy for pancreatic cancer patients.
