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Objective: Epidemiological and in vitro studies of epithelial ovarian cancer (OC) strongly suggest a link between hormone receptor (HR) expression, tumorigenesis, and survival. Antihormonal therapies have shown antitumor activity in OC, both alone and combined with other treatments. The primary objective of this study is to examine the expression patterns of estrogen- and progesterone receptors (ER and PR) in OC across different histological subtypes and assess their prognostic value in disease progression.
Design: Retrospective analysis of data from 164 patients who received primary treatment at University Hospital Frankfurt between January 1999 and December 2019.
Materials, Setting, Methods: The expression of both hormone receptors was determined through immunostaining of tissue samples and evaluated using the immunoreactive score (IRS) according to Remmele and Stegner. Correlation and survival analyses evaluated the prognostic and predictive significance of HR expression.
Results: The correlation between ER and PR expression with histological subtypes was significant (p=0.002 and p=0.013, respectively). Strong ER and PR expression was more common in HGSC, LGSC, and EC, while low PR expression was linked to higher tumor grading (p=0.032). Notably, CCC patients with weak PR expression had better survival rates than those with strong PR expression (p=0.025). The difference in OS between ER-positive and ER-negative patients was minimal (55 vs. 51 months; p=0.906). Median PFS and OS were slightly better in cases with weak PR expression (24 and 58 months) compared to strong PR expression (19 and 53 months; p=0.797 and p=0.45, respectively). In cases with strong ER expression and suboptimal debulking (TR >1 cm), disease recurrence was delayed (median PFS: 8 vs. 14 months; p=0.038), a difference not seen after optimal debulking or in overall OS.
Limitations: This single-center, retrospective study limits generalizability. We could not distinguish PR isoforms or assess ER/PR ratios or interactions, limiting molecular insight.
Conclusion: ER and PR expression did not demonstrate a significant overall impact on survival in the entire cohort. However, the expression patterns and associated prognosis of ER and PR differed significantly depending on histological subtypes and clinical factors.
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http://dx.doi.org/10.1159/000547773 | DOI Listing |
Ann Surg Oncol
September 2025
Orthopaedic Oncology Service, Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, USA.
Background: Undifferentiated pleomorphic sarcoma (UPS) is a prevalent soft tissue sarcoma subtype associated with poor prognosis. Current prognostic tools lack the ability to incorporate personalized data for predicting survival. Machine learning (ML) offers a potential solution to enhance survival prediction accuracy.
View Article and Find Full Text PDFZhonghua Bing Li Xue Za Zhi
September 2025
Department of Pathology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
To explore the clinicopathological and molecular genetic characteristics of anaplastic lymphoma kinase (ALK)-rearranged renal cell carcinoma (RCC), including a rare case with the TPM1-ALK gene subtype. Three cases of ALK-rearranged RCC diagnosed in the Department of Pathology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China from January 2020 to December 2024 were collected. Their clinical pathological and next-generation sequencing (NGS) data were analyzed.
View Article and Find Full Text PDFJ Neurosurg Case Lessons
September 2025
Department of Neurosurgery, Fleming Neuroscience Institute, Allentown, Pennsylvania.
Background: High-grade astrocytoma with piloid features (HGAP) was recently added to the WHO 2021 CNS classification system among the group of circumscribed astrocytic gliomas. These tumors present with high-grade piloid histology with similarities to glioblastoma. HGAPs in the pineal region become particularly challenging due to its deep location and proximity to deep venous structures, the midbrain, and the thalamus.
View Article and Find Full Text PDFFront Oncol
August 2025
Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Objective: To develop a deep learning radiomics(DLR)model integrating PET/CT radiomics, deep learning features, and clinical parameters for early prediction of bone oligometastases (≤5 lesions) in breast cancer.
Methods: We retrospectively analyzed 207 breast cancer patients with 312 bone lesions, comprising 107 benign and 205 malignant lesions, including 89 lesions with confirmed bone metastases. Radiomic features were extracted from computed tomography (CT), positron emission tomography (PET), and fused PET/CT images using PyRadiomics embedded in the uAI Research Portal.
Front Immunol
September 2025
Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Intrinsic genetic alterations and dynamic transcriptional changes contribute to the heterogeneity of solid tumors. Lung adenocarcinoma (LUAD) is characterized by its significant histological, cellular and molecular heterogeneity. The present study aimed to study the spatial transcriptomics of primary LUAD with initial hopes to decipher molecular characteristics of subtype transitions in LUAD progression, offering new insights for novel therapeutic strategies.
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