We designed the present study in order to evaluate the eventual role of polymorphisms in the genes encoding cytochrome P450c17alpha (CYP17) and the progesterone receptor (PROGINS) as risk factors for endometriosis development. Eligible cases consisted of 121 women with surgically confirmed endometriosis who underwent treatment in a hospital in São Paulo, Brazil during the period from September 2003 to September 2005. The 281 controls were participants with normal gynecological as well as pelvic ultrasound evaluation, who did not have any gynecological conditions during their reproductive lives such as pelvic pain and/or dyspareunia nor infertility history. Genomic DNA was obtained from buccal cells and processed for DNA extraction using the GFX DNA extraction kit (GE Healthcare). The CYP17 (-34T-->C) polymerase chain reaction-restriction fragment length polymorphism assay has been described previously, as has the progesterone receptor polymorphism (PROGINS) detection assay. PROGINS heterozygosis genotype frequencies were shown to be statistically higher in endometriosis cases compared with controls. On the other hand, differences in the CYP17 polymorphism (-34T-->C) frequencies were not even close to significance (p = 0.278) according to our findings.

Presently, little is understood about how endometriosis is established or maintained, or how genetic factors can predispose women to the disease. Because of the crucial role that the progesterone receptor polymorphism PROGINS plays in predisposing women to the development of endometriosis, we hypothesized that this variant may influence critical steps during endometrial cell metabolism that are involved in the pathogenesis of endometriosis. Eutopic endometria were collected from three sources: women with endometriosis who had a single PROGINS allele (from the progesterone receptor gene); women with endometriosis who had the wild-type progesterone receptor allele; and women without endometriosis who had the wild-type allele. Cells prepared from the eutopic endometria of these women were stimulated with both estradiol and progesterone, and then examined for cell proliferation, viability, and apoptosis.

This study investigated the association between COVID-19 infection and host metabolic signatures as prognostic markers for disease severity and mortality. We enrolled 82 patients with RT-PCR confirmed COVID-19 infection who were classified as mild, moderate, or severe/critical based upon their WHO clinical severity score and compared their results with 31 healthy volunteers. Data on demographics, comorbidities and clinical/laboratory characteristics were obtained from medical records. Peripheral blood samples were collected at the time of clinical evaluation or admission and tested by quantitative mass spectrometry to characterize metabolic profiles using selected metabolites. The findings in COVID-19 (+) patients reveal changes in the concentrations of glutamate, valeryl-carnitine, and the ratios of Kynurenine/Tryptophan (Kyn/Trp) to Citrulline/Ornithine (Cit/Orn). The observed changes may serve as predictors of disease severity with a (Kyn/Trp)/(Cit/Orn) Receiver Operator Curve (ROC) AUC = 0.95. Additional metabolite measures further characterized those likely to develop severe complications of their disease, suggesting that underlying immune signatures (Kyn/Trp), glutaminolysis (Glutamate), urea cycle abnormalities (Cit/Orn) and alterations in organic acid metabolism (C5) can be applied to identify individuals at the highest risk of morbidity and mortality from COVID-19 infection. We conclude that host metabolic factors, measured by plasma based biochemical signatures, could prove to be important determinants of Covid-19 severity with implications for prognosis, risk stratification and clinical management.

Breast cancer remains a leading cause of morbidity and mortality worldwide yet methods for early detection remain elusive. We describe the discovery and validation of biochemical signatures measured by mass spectrometry, performed upon blood samples from patients and controls that accurately identify (>95%) the presence of clinical breast cancer. Targeted quantitative MS/MS conducted upon 1225 individuals, including patients with breast and other cancers, normal controls as well as individuals with a variety of metabolic disorders provide a biochemical phenotype that accurately identifies the presence of breast cancer and predicts response and survival following the administration of neoadjuvant chemotherapy. The metabolic changes identified are consistent with inborn-like errors of metabolism and define a continuum from normal controls to elevated risk to invasive breast cancer. Similar results were observed in other adenocarcinomas but were not found in squamous cell cancers or hematologic neoplasms. The findings describe a new early detection platform for breast cancer and support a role for pre-existing, inborn-like errors of metabolism in the process of breast carcinogenesis that may also extend to other glandular malignancies.Statement of Significance: Findings provide a powerful tool for early detection and the assessment of prognosis in breast cancer and define a novel concept of breast carcinogenesis that characterizes malignant transformation as the clinical manifestation of underlying metabolic insufficiencies.