Evidence map›Paper›PMID 42137987›Full record

ArticleCancer cytopathology2026

Prediction of lymphocyte-predominant breast cancer using microRNA expression in cytology specimens.

Azusa Kai, Koji Arihiro, Hideo Shigematsu, Yukari Ohue-Uchihata, Momoko Nakamura, Ai Amioka, Shinsuke Sasada, Morihito Okada

Abstract read
In one paragraph

Article in Cancer cytopathology, 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

8 authors.

Azusa KaiDepartment of Surgical Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan.ORCID 0009-0002-7155-0627
Koji ArihiroDepartment of Anatomical Pathology, Hiroshima University Hospital, Hiroshima, Japan.
Hideo ShigematsuDepartment of Surgical Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan.
Yukari Ohue-UchihataDepartment of Anatomical Pathology, Hiroshima University Hospital, Hiroshima, Japan.
Momoko NakamuraDepartment of Anatomical Pathology, Hiroshima University Hospital, Hiroshima, Japan.
Ai AmiokaDepartment of Surgical Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan.
Shinsuke SasadaDepartment of Surgical Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan.
Morihito OkadaDepartment of Surgical Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTumor-infiltrating lymphocytes are prognostic and predictive biomarkers of breast cancer; however, conventional assessment is hindered by invasiveness and subjective evaluation, limiting clinical reproducibility. This study investigated the utility of microRNA (miRNA) profiles in predicting lymphocyte-predominant breast cancer (LPBC).

methodsThis study included 56 patients with breast cancer who underwent fine-needle aspiration cytology at the time of diagnosis (28 LPBC and 28 non-LPBC cases). Total RNA was extracted from cytology smear specimens, and the expression levels of six candidate miRNAs (miR-30a-3p, miR-187, miR-196b, miR-1247, miR-4485, and miR-6510) were quantified using reverse-transcriptase polymerase chain reaction. The performance in identifying LPBC was evaluated using receiver operating characteristic curve analysis and logistic regression models.

resultsAll six miRNAs showed significantly lower expression levels in the LPBC group than in the non-LPBC group (all p < .05). Receiver operating characteristic curve analysis demonstrated moderate to high diagnostic accuracy, with an area under the curve of 0.708 for miR-30a-3p, 0.739 for miR-187, 0.783 for miR-196b, 0.777 for miR-1247, 0.790 for miR-4485, and 0.673 for miR-6510. Multivariate analysis further identified each miRNA as an independent diagnostic predictor for LPBC.

conclusionsmiRNA profiling of cytologic samples enabled the objective identification of LPBC. These findings suggest that specific miRNA expression patterns reflect the tumor immune microenvironment and can be used to evaluate the status of tumor-infiltrating lymphocytes in breast cancer.

Indexed as

Biomarkers, TumorBreast NeoplasmsLymphocytesLymphocytes, Tumor-InfiltratingMicroRNAsAdultAgedBiopsy, Fine-NeedleCytodiagnosisFemaleHumansMiddle AgedPrognosisROC CurveBiomarkers, TumorMicroRNAsbiomarkerbreast cancercytologyimmune microenvironmentlymphocyte‐predominant breast cancermicroRNAmolecular diagnosticsreverse‐transcriptase quantitative polymerase chain reaction (RT‐qPCR)tumor‐infiltrating lymphocytes

Identifiers

PMID42137987
PMCPMC13177170

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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.