Endometrial cancer is one of the most frequently diagnosed gynecological cancers worldwide, and its prevalence has increased by more than 50% over the last two decades. Despite the understanding of the major signaling pathways driving the growth and metastasis of endometrial cancer, clinical trials targeting these signals have reported poor outcomes. The heterogeneous nature of endometrial cancer is suspected to be one of the key reasons for the failure of targeted therapies. In this study, we perform a sequential window acquisition of all theoretical fragment ion spectra (SWATH)-based comparative proteomic analysis of 63 tumor biopsies collected from 20 patients and define differences in protein signature in multiple regions of the same tumor. We develop organoids from multiple biopsies collected from the same tumor and show that organoids capture heterogeneity in endometrial cancer growth. Overall, using quantitative proteomics and patient-derived organoids, we define the heterogeneous nature of endometrial cancer within a patient's tumor.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512672 | PMC |
http://dx.doi.org/10.1016/j.xcrm.2022.100738 | DOI Listing |
BMJ Open
January 2025
Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany.
Objectives: Sedentary behaviour (SB) is associated with increased risks of breast, colorectal, endometrial, ovarian and rectal cancers. However, the number of cancer cases attributable to SB in Germany and the associated costs are unknown.
Setting: Numbers and proportions (population-attributable fractions, PAF) of new cancer cases attributable to SB with published risk estimates for Germany for the years 2024, 2030 and 2040.
Medicine (Baltimore)
November 2024
Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
Consensus remains elusive regarding the relationship between C-reactive protein (CRP) levels and endometrial cancer (EC). Our study sought to elucidate the causal association between CRP and EC, aiming to contribute to the understanding of this complex interplay. We primarily utilized the random-effects inverse variance-weighted method.
View Article and Find Full Text PDFOncol Lett
March 2025
Department of Obstetrics and Gynecology, Mie University School of Medicine, Tsu, Mie 514-8507, Japan.
Ovarian cancer has a poor prognosis, and screening methods have not been established. Biomarkers based on molecular genetic characteristics must be identified to develop diagnostic and therapeutic strategies for all cancer types, particularly ovarian cancer. The present study aimed to evaluate the usefulness of genetic analysis of cervical and endometrial liquid-based cytology (LBC) specimens for detecting somatic mutations in patients with ovarian cancer.
View Article and Find Full Text PDFBMJ Open
December 2024
University College London Hospitals NHS Foundation Trust, London, UK
Introduction: Adaptive ChemoTherapy for Ovarian cancer (ACTOv) is a phase II, multicentre, randomised controlled trial, evaluating an adaptive therapy (AT) regimen with carboplatin in women with relapsed, platinum-sensitive high-grade serous or high-grade endometrioid cancer of the ovary, fallopian tube and peritoneum whose disease has progressed at least 6 months after day 1 of the last cycle of platinum-based chemotherapy. AT is a novel, evolutionarily informed approach to cancer treatment, which aims to exploit intratumoral competition between drug-sensitive and drug-resistant tumour subpopulations by modulating drug dose according to a patient's own response to the last round of treatment. ACTOv is the first clinical trial of AT in this disease setting.
View Article and Find Full Text PDFJ Transl Med
January 2025
School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, Guizhou, 550000, China.
Background: Human kinesin family member 11 (KIF11) plays a vital role in regulating the cell cycle and is implicated in the tumorigenesis and progression of various cancers, but its role in endometrial cancer (EC) is still unclear. Our current research explored the prognostic value, biological function and targeting strategy of KIF11 in EC through approaches including bioinformatics, machine learning and experimental studies.
Methods: The GSE17025 dataset from the GEO database was analyzed via the limma package to identify differentially expressed genes (DEGs) in EC.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!