Evidence map›Paper›PMID 41748948›Full record

ArticleVirchows Archiv : an international journal of pathology2026

Breast carcinoma with osteoclast-like giant cells: clinicopathologic analysis and RANKL-related biological implications.

Huayan Ren, Xin He, Meng Zhang, Xinyan Chen, Huifen Huang, Na Wei, Yuqiong Liu, Huixiang Li

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Article in Virchows Archiv : an international journal of pathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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8 authors.

Huayan RenDepartment of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China. renhuayanhappy@163.com.ORCID http://orcid.org/0000-0002-5194-7996
Xin HeDepartment of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Meng ZhangDepartment of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Xinyan ChenUniversity of Pittsburgh, Pittsburgh, PA, USA.
Huifen HuangDepartment of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Na WeiDepartment of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Yuqiong LiuDepartment of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Huixiang LiDepartment of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast carcinoma with osteoclast-like giant cells (BC-OGC) is a rare morphological variant of invasive breast cancer characterized by scattered benign-appearing giant cells in the stroma. Although these cells are believed to be macrophage-derived, their biological role, polarization state, and clinical significance remain unclear. Recent data suggest that tumor-derived RANKL may drive giant cell formation and influence tumor aggressiveness. A total of 12 BC-OGC cases diagnosed between 2012 and 2024 were retrospectively analyzed, along with 36 matched breast cancer cases without osteoclast-like giant cells (non-BC-OGC). Clinicopathological, immunophenotypic, and stromal features were compared. Immunohistochemistry assessed the expression of macrophage markers (CD68, CD163, CD206) and RANKL. Associations with metastatic outcomes were evaluated. In vitro, the effects of RANKL overexpression and knockdown on breast cancer cell proliferation, migration, and invasion were evaluated using MCF7 cell lines. BC-OGC cases predominantly exhibited the Luminal A-like molecular subtype but showed distinctive loose, vascular-rich stroma compared to controls (p = 0.018). Osteoclast-like giant cells expressed macrophage markers, with widespread CD68 positivity and partial M2 polarization (CD163, CD206). RANKL immunostaining was significantly stronger and more diffuse in BC-OGC than in controls (p = 0.00386), with expression highest in metastatic cases. Metastatic events occurred more frequently in BC-OGC (33.3% vs. 2.8%, CMH p = 0.0126), and within the BC-OGC group, higher RANKL expression was significantly associated with metastasis (p = 0.0123). In vitro, RANKL overexpression promoted, and knockdown suppressed, MCF7 cell proliferation, migration, and invasion. BC-OGC represents a rare but biologically distinctive breast cancer variant with a macrophage-rich, angiogenic stroma and high RANKL expression. RANKL may contribute to both macrophage fusion into giant cells and increased tumor aggressiveness. These findings highlight the potential value of RANKL as a biomarker for metastatic risk and as a therapeutic target in high-risk BC-OGC, warranting further validation in larger cohorts and mechanistic studies.

Indexed as

Breast cancerImmunohistochemistryMetastasisOsteoclast-like giant cellsRANKL

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.