Among gynecological cancers, endometrial cancer is the most common in developed countries. Extracellular vesicles (EVs) are cell-derived membrane-surrounded vesicles that contain proteins involved in immune response and apoptosis. A deep proteomic approach can help to identify dysregulated extracellular matrix (ECM) proteins in EVs correlated to key pathways for tumor development. In this study, we used a proteomics approach correlating the two acquisitions—data-dependent acquisition (DDA) and data-independent acquisition (DIA)—on EVs from the conditioned medium of four cell lines identifying 428 ECM proteins. After protein quantification and statistical analysis, we found significant changes in the abundance (p < 0.05) of 67 proteins. Our bioinformatic analysis identified 26 pathways associated with the ECM. Western blotting analysis on 13 patients with type 1 and type 2 EC and 13 endometrial samples confirmed an altered abundance of MMP2. Our proteomics analysis identified the dysregulated ECM proteins involved in cancer growth. Our data can open the path to other studies for understanding the interaction among cancer cells and the rearrangement of the ECM.

Early detection of recurrences in gynecological cancers is crucial for women’s health. Circulating tumor DNA (ctDNA) analysis through liquid biopsy offers a promising approach for monitoring disease progression and identifying relapses. This study investigated the utility of digital Polymerase Chain Reaction (dPCR) for ctDNA detection in three gynecological cancer patients with clinically confirmed relapses during a two-year post-surgical follow-up. Patient-specific tumor mutations were identified through whole-exome sequencing (WES) and confirmed via Sanger sequencing. dPCR probes targeting these mutations were used to quantify the ctDNA levels in plasma samples collected throughout the follow-up period, and the findings were compared with standard serum biochemical markers. In two patients, persistent positive dPCR signals for the selected mutations were detected after tumor removal, with ctDNA levels progressively increasing even after post-surgical chemotherapy. Notably, dPCR identified elevated ctDNA levels before an increase in the cancer antigen 125 (CA125) biochemical marker was observed. In the third patient, no ctDNA signals from the two selected mutations were detected despite clinical evidence of recurrence, suggesting the emergence of new mutations. While this study highlights the promise of dPCR for early recurrence detection in gynecological cancers, it also underscores the critical need for comprehensive mutation panels to overcome the inherent challenges posed by tumor heterogeneity and the emergence of new mutations during disease progression.

Background/Objectives: Endometrial cancer (EC) is the second most frequent gynecological malignant tumor in postmenopausal women. Pathogenic mechanisms related to the onset and development of the disease are still unknown. To identify dysregulated proteins associated with EC we exploited a combined in vitro/in silico approach analyzing the proteome of exosomes with advanced MS techniques and annotating their results by using Chymeris1 AI tools. Methods: To this aim in this pilot study, we performed a deep proteomics analysis with high resolution MS (HRMS), advanced computational tools and western blotting for proteomics data validation. Results: That allowed us to identify 3628 proteins in serum albumin-depleted exosomes from 10 patients with EC compared to 10 healthy controls. This is the largest number of proteins identified in EC serum EVs. After quantification and statistical analysis, we identified 373 significantly (p < 0.05) dysregulated proteins involved in neutrophil and platelet degranulation pathways. A more detailed bioinformatics analysis revealed 61 dysregulated enzymes related to metabolic and catabolic pathways linked to tumor invasion. Through this analysis, we identified 49 metabolic and catabolic pathways related to tumor growth. Conclusions: Altogether, data shed light on the metabolic pathways involved in tumors. This is very important for understanding the metabolism of EC and for the development of new therapies.

Recent evidence has highlighted the role of the gut–brain axis in the progression of autism spectrum disorder (ASD), with significant changes in the gut microbiome of individuals with this condition. This report investigates the effects of probiotics and human milk oligosaccharide (HMO) supplements on the gut microbiome, inflammatory cytokine profile, and clinical outcomes in an ASD adolescent with chronic gastrointestinal dysfunction and cognitive impairment. Following treatment, we observed a decrease in proinflammatory cytokines’ concentration alongside Sutterella relative abundance, a bacterium reported to be linked with gastrointestinal diseases. Also, we reported a notable increase in mood stability. The study aims to evaluate the use of gut microbiome-based therapy in selected ASD patients, highlighting its potential to improve related clinical symptoms.

The HNF1A transcription factor, implicated in the regulation of pancreatic beta cells, as well as in glucose and lipid metabolism, is responsible for type 3 maturity-onset diabetes of the young (MODY3). HNF1A is also involved in increased susceptibility to polygenic forms of diabetes, such as type 2 diabetes (T2D) and gestational diabetes (GD), while its possible role in type 1 diabetes (T1D) is not known. In this study, 277 children and adolescents with T1D and 140 healthy controls were recruited. The following SNPs in HNF1A gene were selected: rs1169286, rs1169288, rs7979478, and rs2259816. Through linear or logistic regression analysis, we analyzed their association with T1D susceptibility and related clinical traits, such as insulin dose-adjusted glycated hemoglobin A1c (IDAA1c) and glycated hemoglobin (HbA1c). We found that rs1169286 was associated with IDAA1c and HbA1c values (p-value = 0.0027 and p-value = 0.0075, respectively), while rs1169288 was associated with IDAA1c (p-value = 0.0081). No association between HNF1A SNPs and T1D development emerged. In conclusion, our findings suggest for the first time that HNF1A variants may be a risk factor for beta cell function and glycaemic control in T1D individuals.

Endometrial cancers (ECs) are mainly adenocarcinomas arising from the uterine endometrium. In this work, we employed data-independent acquisition (DIA) mass spectrometry (MS)-based label-free quantification (LFQ-MS) proteomics to analyze the proteome of tissue washings collected from 25 control (CTRL) subjects, 25 patients with low-grade type 1 endometrial cancer (EC), and 24 patients with high-grade type 1 EC. Following quantification and statistical analysis, we identified 42 proteins able to discriminate CTRL from EC patients, and 151 proteins differentiating high-grade EC cases from low-grade EC cases. Notably, PRRC2A and SYDE2 effectively distinguished both EC patients from controls and advanced EC cases from low-grade EC cases. Validation by Western blot analysis in an independent cohort comprising 19 CTRL patients, 19 patients with low-grade EC, and 19 patients with high-grade EC confirmed the upregulation of PRRC2A and SYDE2. These proteins are implicated in the translocation of SLC2A4, the regulation of MECP2, and extracellular matrix (ECM) proteoglycan pathways, all of which are associated with tumor growth. Our results demonstrate that DIA-based proteomic analysis of tissue washings enables the identification of potential biomarkers for endometrial cancer (EC). Moreover, this study highlights tissue washings as a promising biological fluid for biomarker discovery in EC.