Cancer-associated fibroblasts (CAFs) are a major contributor to tumor stromal crosstalk in the tumor microenvironment (TME) and boost tumor progression by promoting angiogenesis and lymphangiogenesis. This study aimed to identify prognostic genes associated with CAFs that lead to high morbidity and mortality in ovarian cancer (OC) patients. We performed bioinformatics analysis in 16 multicenter studies (2,742 patients) and identified CAF-associated hub genes using the weighted gene co-expression network analysis (WGCNA). A machine learning methodology was used to identify COL16A1, COL5A2, GREM1, LUM, SRPX, and TIMP3 and construct a prognostic signature. Subsequently, a series of bioinformatics algorithms indicated risk stratification based on the above signature, suggesting that high-risk patients have a worse prognosis, weaker immune response, and lower tumor mutational burden (TMB) status but may be more sensitive to routine chemotherapeutic agents. Finally, we characterized prognostic markers using cell lines, immunohistochemistry, and single-cell sequencing. In conclusion, these results suggest that the CAF-related signature may be a novel pretreatment guide for anti-CAFs, and prognostic markers in CAFs may be potential therapeutic targets to inhibit OC progression.

Background: The impact of androgens on metabolic diseases, cardiovascular diseases (CVD), and long-term mortality in the general female population remains poorly understood. This study, utilizing data from the National Health and Nutrition Examination Survey (NHANES) database managed by the Centers for Disease Control and Prevention, seeks to elucidate the relationship between androgen levels and metabolic syndrome (MS), CVD, and mortality in adult women. Methods: After excluding ineligible individuals, descriptive analyses were conducted on demographic characteristics, metabolic-related indicators, and disease prevalence, based on the presence of high androgenemia and androgen quartile grouping. Logistic regression models were developed to assess the associations of androgen markers, including total testosterone (TT), Free Androgen Index (FAI), with MS, CVD, and cox regression models were used to explore the relationships with mortality. Results: Our results show that, even without adjustment for age, age at menarche, marital status, and smoking status, both in patients with hyperandrogenemia and across the general population stratified by quartiles of FAI, higher androgen levels are associated with increased waist circumference, weight, Body Mass Index, fasting insulin, and the monocyte/high-density lipoprotein cholesterol ratio. In adjusted correlational analysis, MS remained positively correlated with FAI, even after controlling for age, tobacco use, and alcohol consumption. As FAI quartiles increased, the correlation strengthened, achieving an odds ratio (OR) of 1.45 (95% CI 1.04 to 2.02, P=0.03) in the highest quartile. This indicates that androgen levels are strongly associated with metabolic syndrome, with FAI proving more sensitive than TT. Conclusion: The greater sensitivity of FAI may be attributed to its ability to reflect bioavailable testosterone more accurately than TT, underscoring its potential utility in clinical assessments of metabolic risk. This study found no significant correlation between androgen levels and CVD or mortality.

To investigate the effect of phenolic environmental estrogens on uterine leiomyoma from the perspective of clinical epidemiology. Urine and blood samples were collected from Han women with uterine leiomyoma and women without uterine leiomyoma, living in Nanjing, China, between September 2011 and February 2013. A total of 156 urine samples and 214 blood samples were collected from the uterine leiomyoma group and 106 urine samples and 126 blood plasma samples from the control group. Bisphenol A (BPA), nonylphenol (NP) and octylphenol (OP) concentrations were determined by solid-phase extraction (SPE) coupled with liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Phenolic environmental estrogens in the uterine leiomyoma and control groups were compared based on: gravida>3 and gravida ≤ 3. In participants with gravida>3, urine OP concentration was significantly (P<0.05) higher in the uterine leiomyoma group than in the control group. In participants with gravida ≤ 3, urine NP concentration was significantly (P<0.05) higher in the uterine leiomyoma group compared to controls. Despite obstetric history, urine BPA mean exposure concentration was significantly (P0.05) different between gravida>3 and gravida ≤ 3 patients. There was no significant (P>0.05) difference in plasma concentrations of BPA, OP and NP between the leiomyoma group and control group. Mean exposure concentration and range of distribution of BPA, OP and NP plasma concentration differed between the uterine leiomyoma and control group. Exposure level of phenolic environmental estrogens in human was related with leiomyoma tumorigenesis.

Background: Ferroptosis and iron-metabolism are regulated by Long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of ferroptosis and iron-metabolism related lncRNAs (FIRLs) in OC is crucial for proposing therapeutic strategies and survival prediction.Methods: In multi-omics data from OC patients, FIRLs were identified by calculating Pearson correlation coefficients with ferroptosis and iron-metabolism related genes (FIRGs). Cox-Lasso regression analysis was performed on the FIRLs to screen further the lncRNAs participating in FIRLs signature. In addition, all patients were divided into two robust risk subtypes using the FIRLs signature. Receiver operator characteristic (ROC) curve, Kaplan–Meier analysis, decision curve analysis (DCA), Cox regression analysis and calibration curve were used to confirm the clinical benefits of FIRLs signature. Meanwhile, two nomograms were constructed to facilitate clinical application. Moreover, the potential biological functions of the signature were investigated by genes function annotation. Finally, immune microenvironment, chemotherapeutic sensitivity, and the response of PARP inhibitors were compared in different risk groups using diversiform bioinformatics algorithms.Results: The raw data were randomized into a training set (n = 264) and a testing set (n = 110). According to Pearson coefficients between FIRGs and lncRNAs, 1075 FIRLs were screened for univariate Cox regression analysis, and then LASSO regression analysis was used to construct 8-FIRLs signature. It is worth mentioning that a variety of analytical methods indicated excellent predictive performance for overall survival (OS) of FIRLs signature (p < 0.05). The multivariate Cox regression analysis showed that FIRLs signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the abundance of immune cells, immune-related pathways, and drug response were excavated in different risk subtypes (p < 0.05).Conclusion: The FIRLs signature can independently predict overall survival and therapeutic effect in OC patients.