Evidence map›Paper›PMID 41491484›Full record

ArticleBMC musculoskeletal disorders2026

PD-L1 mRNA expression correlates with tumor growth rate in giant cell tumor of bone: a volumetric MRI analysis.

Youngkeun Lee, Ji-Yoon Choi, Min-Jeong Kim, Sung Wook Seo

Abstract read
In one paragraph

Article in BMC musculoskeletal disorders, 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Youngkeun LeeDepartment of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea.ORCID http://orcid.org/0000-0003-2409-6097
Ji-Yoon ChoiDepartment of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea.ORCID http://orcid.org/0009-0007-4766-273X
Min-Jeong KimDepartment of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea.ORCID http://orcid.org/0009-0009-4535-5044
Sung Wook SeoDepartment of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea. sungwseo@skku.edu.ORCID http://orcid.org/0000-0001-8643-7825

Funding

National Research Foundation of Korea RS-2021-NR057970
6 · The paper itself

Abstract

backgroundGiant cell tumor of bone (GCTB) is a locally aggressive neoplasm with variable biological behavior. Programmed death-ligand 1 (PD-L1) has been implicated in tumor progression across multiple malignancies, but its relationship to growth kinetics in GCTB remains unclear. This study investigated whether PD-L1 mRNA expression correlates with tumor growth rate in extremity GCTB.

methodsThis single-institution retrospective cohort study included 36 patients with GCTB treated surgically between January 2020 and December 2024. Of these, 18 provided consent for research use of tissue; four were excluded due to lack of serial MRI data, yielding a final cohort of 14 patients. PD-L1 mRNA levels were quantified using real-time PCR. Tumor volumes were segmented from MRI using 3D Slicer, and growth rate (cc/month) was calculated. Patients were stratified into high- and low-PD-L1 groups using the cohort median fold change (2.62) as an exploratory cutoff. Group comparisons used Mann–Whitney U and Fisher’s exact tests, and Spearman’s rank correlation analysis was performed to assess the relationship between continuous PD-L1 expression and tumor growth rate. Exploratory linear regressions were performed adjusting for age, sex, tumor site, and preoperative denosumab use.

resultsMedian age was 32 years; 57% were male. Tumors with high PD-L1 expression showed faster growth than those with low expression (5.0 [1.3–7.1] vs. 0.8 [0.2–2.1] cc/month; p = 0.038). This relationship was further supported by a significant positive correlation between PD-L1 mRNA levels and tumor growth rate (ρ = 0.6, p = 0.026). The association remained significant in exploratory multivariable and robust regression analyses adjusting for covariates.

conclusionsHigher PD-L1 mRNA expression correlated with accelerated tumor growth in extremity GCTB. These exploratory findings suggest PD-L1 may serve as a potential biomarker of tumor aggressiveness, warranting validation in larger cohorts.

Indexed as

B7-H1 AntigenBone NeoplasmsGiant Cell Tumor of BoneRNA, MessengerAdolescentAdultBiomarkers, TumorFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesTumor BurdenYoung AdultB7-H1 AntigenBiomarkers, TumorCD274 protein, humanRNA, MessengerDenosumabGiant cell tumor of bonePD-L1Real-time PCRTumor growth rate

Identifiers

PMID41491484
PMCPMC12930590

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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.