Analysis of the different characteristics between omental preadipocytes and differentiated white adipocytes using bioinformatics methods

Omental preadipocytes stimulate matrix remodeling and IGF signaling to support ovarian cancer metastasis

Ovarian cancer can metastasize to the omentum, which is associated with a complex tumor microenvironment. Omental stromal cells facilitate ovarian cancer colonization by secreting cytokines and growth factors. Improved understanding of the tumor supportive functions of specific cell populations in the omentum could identify strategies to prevent and treat ovarian cancer metastasis. Here, we showed that omental preadipocytes enhance the tumor initiation capacity of ovarian cancer cells. Secreted factors from preadipocytes supported cancer cell viability during nutrient and isolation stress and enabled prolonged proliferation. Co-culturing with pre-adipocytes led to upregulation of genes involved in extracellular matrix (ECM) organization, cellular response to stress, and regulation of insulin-like growth factor (IGF) signaling in ovarian cancer cells. IGF-1 induced ECM genes and increased alternative NF-κB signaling by activating RelB. Inhibiting the IGF-1 receptor (IGF1R) initially increased tumor omental adhesion but decreased growth of established preadipocyte-induced subcutaneous tumors as well as established intraperitoneal tumors. Together, this study shows that omental preadipocytes support ovarian cancer progression, which has implications for targeting metastasis.

Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm

Objective

Energy metabolism plays a crucial role in the improvement of heart dysfunction as well as the development of heart failure (HF). The current study is designed to identify energy metabolism-related diagnostic biomarkers for predicting the risk of HF due to myocardial infarction.

Methods

Transcriptome sequencing data of HF patients and non-heart failure (NF) people (GSE66360 and GSE59867) were obtained from gene expression omnibus (GEO) database. Energy metabolism-related differentially expressed genes (DEGs) were screened between HF and NF samples. The subtyping consistency analysis was performed to enable the samples to be grouped. The immune infiltration level among subtypes was assessed by single sample gene set enrichment analysis (ssGSEA). Random forest algorithm (RF) and support vector machine (SVM) were applied to identify diagnostic biomarkers, and the receiver operating characteristic curves (ROC) was plotted to validate the accuracy. Predictive nomogram was constructed and validated based on the result of the RF. Drug screening and gene-miRNA network were analyzed to predict the energy metabolism-related drugs and potential molecular mechanism.

Results

A total of 22 energy metabolism-related DEGs were identified between HF and NF patients. The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. Random-forest and support vector machine algorithm eventually identified ten diagnostic markers (MEF2D, RXRA, PPARA, FOXO1, PPARD, PPP3CB, MAPK14, CREB1, MEF2A, PRMT1) for risk prediction of HF patients, and the proposed nomogram resulted in good predictive performance (GSE66360, AUC = 0.91; GSE59867, AUC = 0.84) and the clinical usefulness in HF patients. More importantly, 10 drugs and 15 miRNA were predicted as drug target and hub miRNA that associated with energy metabolism-related genes, providing further information on clinical HF treatment.

Conclusion

This study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.

Resveratrol Supplementation in Obese Pregnant Rats Improves Maternal Metabolism and Prevents Increased Placental Oxidative Stress

Maternal obesity (MO) causes maternal and fetal oxidative stress (OS) and metabolic dysfunction. We investigated whether supplementing obese mothers with resveratrol improves maternal metabolic alterations and reduces OS in the placenta and maternal and fetal liver. From weaning through pregnancy female Wistar rats ate chow (C) or a high-fat diet (MO). One month before mating until 19 days’ gestation (dG), half the rats received 20 mg resveratrol/kg/d orally (Cres and MOres). At 19dG, maternal body weight, retroperitoneal fat adipocyte size, metabolic parameters, and OS biomarkers in the placenta and liver were determined. MO mothers showed higher body weight, triglycerides and leptin serum concentrations, insulin resistance (IR), decreased small and increased large adipocytes, liver fat accumulation, and hepatic upregulation of genes related to IR and inflammatory processes. Placenta, maternal and fetal liver OS biomarkers were augmented in MO. MOres mothers showed more small and fewer large adipocytes, lower triglycerides serum concentrations, IR and liver fat accumulation, downregulation of genes related to IR and inflammatory processes, and lowered OS in mothers, placentas, and female fetal liver. Maternal resveratrol supplementation in obese rats improves maternal metabolism and reduces placental and liver OS of mothers and fetuses in a sex-dependent manner.

Zeaxanthin Remodels Cytoplasmic Lipid Droplets via β3-Adrenergic Receptor Signaling and Enhances Perilipin 5-Mediated Lipid Droplet–Mitochondria Interactions in Adipocytes

Cytoplasmic lipid droplets (LDs), which are remarkably dynamic, neutral lipid storage organelles, play fundamental roles in lipid metabolism and energy homeostasis. Both the dynamic remodeling of LDs and LD–mitochondria interactions...